936 results about "Event recognition" patented technology

The invention discloses a security event log real-time monitoring and analyzing system and method for a power grid communication network, and relates to the technical field of network security management. The causal relationship graph building module is used for building a causal relationship graph of the target power grid communication network; the multi-dimensional correlation analysis module is used for collecting and analyzing multi-source heterogeneous log data in real time; the abnormal security event identification module is used for identifying an abnormal security event according to the causal relationship graph and the multi-dimensional correlation analysis result; and the attack chain tracking response module is used for tracking the attack chain. According to the method, the technical problem that the existing power grid communication network security monitoring lacks tracking of abnormal event evolution from the time dimension and cannot accurately identify and track a multi-stage attack chain is solved, and the effects of dynamically tracking the evolution process of the abnormal event and identifying a potential attack chain by establishing a time causal chain graph are achieved. And the detection precision and the response speed of the attack behavior are improved.

The embodiment of the invention relates to the technical field of video processing, and particularly discloses a video timestamp event identification and reasoning method based on a multi-modal large model. According to the embodiment of the invention, an input video is processed, and visual information and text information are extracted; performing interaction and fusion on the visual information and the text information to generate a video event semantic representation; and performing event identification on the semantic representation of the video event to obtain an event timestamp mark and event logical reasoning. According to the method, deep fusion and semantic association understanding of multi-modal information such as vision and texts can be realized, the problem that a traditional method is insufficient in semantic consistency and feature expression ability among modals is solved, and deep fusion of multi-modal features and accurate understanding of semantic association are realized; and the frame-level time information can be used as a visual code and a text feature to perform visual text feature alignment, and then video-level modeling is performed, so that the problem that a traditional model cannot directly capture an accurate time frame when a visual event occurs is solved.

The invention relates to the technical field of data processing, and discloses a data processing method and system for a smart city and a storage medium. The method comprises the following steps: acquiring city multi-source data through a distributed sensor network, and performing data cleaning and standardization processing to obtain standardized city data; establishing a domain concept system for semantic mapping to obtain semantic association data; analyzing the spatial-temporal characteristic and regional interaction relationship to obtain dynamic characteristic data; constructing a multi-level abnormal feature library for event recognition to obtain abnormal mode data; carrying out distributed optimization decision making to obtain management decision data; and establishing an evaluation system for strategy optimization to obtain a city management scheme. According to the method, efficient processing and intelligent analysis of the city data are realized, specifically, through a distributed computing architecture and a multi-level data processing method, the problems of low data processing efficiency, insufficient data association analysis, low computing resource utilization rate and the like are solved, and thus the overall performance of smart city data processing is improved.

The invention relates to the technical field of big data, and discloses a space-time big data abnormal event detection method and device and a storage medium. The method comprises the following steps: performing timestamp unification and space coordinate standardization processing on urban space-time big data to obtain a standardized space-time data set; feature vector cosine similarity between each data point in the standardized spatio-temporal data set and other data points is calculated to obtain a target similarity value, and an initial neighborhood data set is selected; performing space-time density distribution calculation on the initial neighborhood data set to obtain a core neighborhood data set; performing time sequence feature and spatial distribution feature extraction on the core neighborhood data set to obtain a fusion feature weight matrix; and inputting the fusion feature weight matrix into a support vector machine classifier to perform abnormal event recognition to obtain an abnormal event detection result, thereby improving the classification capability of high-dimensional nonlinear features, and simultaneously realizing all-directional feature description and detection of abnormal events.

The invention discloses a traffic large model construction and decision-making method and device based on multi-modal two-way map reasoning, and the method comprises the steps: constructing a multi-modal data set of a text, an image and a track, generating fusion features through spatial-temporal clustering and cross-modal Transform coding, carrying out the two-way map reasoning in combination with a traffic knowledge map, and carrying out the decision-making of the traffic large model. The method comprises the following steps: generating an embedded representation through a forward graph neural network, reversely mapping a decision scheme generated by a language model to a graph to verify consistency, outputting knowledge to enhance embedding, fusing multi-modal features and knowledge embedding by adopting an LoRA multi-task joint fine tuning technology, adapting to traffic field tasks, deploying a real-time inference engine, and carrying out real-time inference on the traffic field. And processing the dynamic data flow through an aging perception attention mechanism, and outputting traffic event identification, path planning and scene question and answer results in parallel. Compared with the prior art, the method has the advantages that the problems of insufficient multi-source heterogeneous data fusion, low knowledge utilization efficiency and poor real-time decision consistency can be solved, and the semantic understanding and decision accuracy of the traffic large model is effectively improved.

The invention discloses a photoelectric event identification optimization method and system for a single photoelectron detector, and the method comprises the steps: collecting environment noise data, carrying out the modeling of environment noise, and generating a noise processing model; real-time operation monitoring data of the target single photoelectron detector are acquired, noise processing is carried out, and time-frequency domain waveform decoupling is carried out after processing is completed; performing pulse accumulation detection through a time-frequency domain decoupling result, marking an accumulation event, and separating accumulated pulses by using a pre-trained pulse separation network to obtain pulse separation information; generating a floating judgment boundary parameter according to the pulse separation information, and carrying out photoelectric event judgment to obtain a photoelectric event judgment list; and obtaining a historical photoelectric event discrimination list of the target single photoelectron detector, extracting an abnormal discrimination event, analyzing whether maintenance is needed, generating a maintenance self-inspection report, and pushing the maintenance self-inspection report. The detector keeps high robustness and anti-interference capability in the whole life cycle, and the identification error rate improvement caused by aging change is avoided.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

The invention discloses a method for evaluating the ecological restoration effect of a micro-polluted river, particularly relates to the field of ecological restoration, and is used for solving the problem of accurate identification and evaluation of sudden pulse pollution events of the micro-polluted river. The data consistency and high resolution are ensured through a unified time and space reference axis, and the accuracy of event identification is guaranteed; a time sequence windowing comparison and deviation mapping inspection technology is adopted, random interference is effectively filtered, and an original fragment of a pulse pollution event is extracted; the impact force and diffusion characteristics of the pollution event are analyzed by applying an amplitude change and propagation dynamic quantification method, and accurate data support is provided for a risk distribution map; according to the method, a correlation mapping model of adaptive clustering and priority evaluation is combined, pollution events are scientifically classified and sorted, high-risk areas with high impact force and high diffusion potential are accurately identified, accurate calibration and deployment and control division are carried out according to a risk distribution map, and the pertinence and efficiency of treatment measures are optimized.

The invention discloses an urban traffic event semantic recognition method based on a knowledge graph, and relates to the technical field of intelligent traffic and artificial intelligence, and the method comprises the steps: obtaining the multi-modal traffic data of urban traffic, and constructing a knowledge graph model; preprocessing and feature extraction are carried out on the multi-modal traffic data, the extracted multi-modal features are mapped to entity nodes of a knowledge graph model, a fusion feature vector is generated, and semantic embedding coding is carried out on the fusion feature vector through a graph neural network; constructing an event inference rule base based on a semantic embedding coding result, and performing multi-layer inference calculation on the fusion feature vector by using a graph convolutional neural network to obtain a matching strength score of the candidate traffic event and a standard event mode in the knowledge graph; and in combination with the event space-time constraint condition and the historical event mode, outputting a traffic event recognition result, confidence evaluation and disposal suggestions. According to the invention, the accuracy and practicability of urban traffic event identification are improved.

The invention belongs to the technical field of meteorological safety, and discloses a circum-Bohai sea low-altitude meteorological safety engine artificial intelligence algorithm based on historical reanalysis and multi-mode data. The low-altitude meteorological safety engine artificial intelligence algorithm comprises the following modules: a meteorological variable reconstruction and extraction module, an extreme event identification module, a model training module, a mode fusion module and a safety engine construction module. The low-altitude meteorological safety engine artificial intelligence algorithm of the Bohai Sea has the following advantages: (1) data fusion and timeliness improvement are realized; (2) the low-altitude small-scale extreme weather identification precision is improved; (3) performing multi-dimensional meteorological risk assessment system and standardized grading; and (4) performing dynamic adaptive optimization and real-time adjustment.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The present invention relates to the technical field of electrical operation, and in particular to an automatic tracking system for safety events in an electrical operation process based on AI recognition, comprising a multimodal data acquisition module, a data preprocessing and fusion module, a safety event recognition module, an event tracking and positioning module, a risk assessment and early warning module, an automatic intervention module and a data storage module. The multimodal data of an operation site is acquired in real time by the multimodal data acquisition module, a dynamic feature set is generated by the data preprocessing and fusion module, safety events identify safety events in the electrical operation process, the event tracking and positioning module realizes dynamic positioning and trajectory tracking of the target, the risk assessment and early warning module assesses the risk level of the event and generates a multi-level early warning signal, and the automatic intervention module generates strategies such as equipment power-off, evacuation warning and operation suspension prompt according to the early warning signal and the positioning result; the present invention improves the real-time nature of electrical operation safety monitoring and the intelligent level of intervention.

The invention provides a visual early warning method and system for a network security event in the cross technical field of network security and machine learning, and the method comprises the steps: S1, creating a security event recognition model, and setting a loss function of the security event recognition model; s2, acquiring historical monitoring data to construct a data set; s3, the data set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are respectively utilized to train, verify and test the security event identification model; s4, performing knowledge distillation and deployment on the security event recognition model passing the test; s5, collecting real-time monitoring data, and inputting the data into the security event recognition model to obtain a security event recognition report; and S6, displaying the security event identification report and statistical data of the security event identification report and the real-time monitoring data in real time through a visual interface. The method has the advantage that the accuracy and the timeliness of early warning of the network security event are greatly improved.

The invention relates to the technical field of intelligent traffic monitoring, in particular to a vehicle identification and traffic incident detection method and system based on the view angle of an unmanned aerial vehicle, and the method comprises the steps: obtaining the aerial traffic video data of the unmanned aerial vehicle and the flight control data of the unmanned aerial vehicle; performing target identification on the image in the video data by adopting a target detection algorithm to obtain vehicle information; tracking the vehicle in the video data by using a target tracking algorithm, and mapping the identified vehicle to a unified coordinate system in combination with the flight control data to generate vehicle trajectory data; constructing a comprehensive road description model based on the vehicle trajectory data and the vehicle information; and forming a traffic incident recognition module based on the comprehensive road description model, and recognizing the input aerial video according to a preset traffic incident recognition rule. According to the invention, by optimizing the vehicle identification algorithm, constructing the road description model and designing the efficient traffic event identification module, the application efficiency of the unmanned aerial vehicle aerial video in the traffic monitoring field is effectively improved.

The invention provides a multi-dimensional data fusion system for operating truck risk rating, and relates to the technical field of data processing, and the system comprises a data collection module which is used for obtaining the dynamic operation data and historical static information of a target operating truck; the data preprocessing module is used for performing field alignment, abnormal value elimination and format standardization; the feature analysis module is used for extracting dynamic behavior features of vehicle operation and performing statistical calculation in a preset time window; the event identification and risk assignment module is used for identifying a single event or a combined event, assigning an event risk weight to the single event or the combined event, and determining a corresponding road condition risk level in combination with the vehicle position information; the multi-dimensional fusion module is used for fusing features and generating time serialized multi-dimensional risk feature vectors; the risk rating module is used for scoring the multi-dimensional risk feature vector and outputting a corresponding risk rating result; according to the invention, the accuracy of the multi-dimensional data fusion system is improved.

The invention discloses a pipeline intrusion event identification method based on a distributed optical fiber sound wave sensing system, and the method comprises the steps: collecting intrusion sound wave signals of different positions and types of pipelines in actual engineering, carrying out the data preprocessing, and constructing a pipeline intrusion event data set; a multi-scale prime number convolution network model based on dynamic convolution and global attention is constructed, features of different time scales and frequency components are extracted through a multi-scale prime number convolution module, and key intrusion features are highlighted and noise interference is suppressed in combination with dynamic convolution capable of adaptively correcting a convolution kernel and an enhanced global attention module. Outputting an event recognition result; according to the method, the problems that the processing capacity of long sequence signals is insufficient and engineering background noise covers target signal features in pipeline intrusion identification are solved, and the monitoring success rate and accuracy of pipeline intrusion events are improved.

The invention discloses a flying dust noise monitoring data intelligent analysis method based on deep learning, and the method comprises the steps: obtaining initial multi-source monitoring data, carrying out the noise reduction of flying dust data in the data through employing a wavelet threshold value, carrying out the noise reduction of non-environmental interference in the data through adaptive frequency band filtering, and carrying out the noise reduction of the non-environmental interference in the data; the method comprises the following steps: extracting multi-scale time sequence features by using a 1D-CNN (Convolutional Neural Network) to establish a flying dust branch, extracting long-range frequency spectrum dependence through a Transform encoder to establish a noise branch, and performing cross-modal feature interaction through an attention fusion mechanism to obtain a double-flow deep neural network model; inputting the noise reduction monitoring data into a model for identification, and outputting an event classification probability and a decision factor; and performing intelligent early warning according to an evaluation result. The recognition accuracy of complex environment events is effectively improved, and the recognition accuracy of construction dust raising events is improved.

The invention provides a power load prediction method and device, and belongs to the technical field of power load prediction.The method comprises the steps that current waveform data are obtained, and fundamental wave and harmonic components in the current waveform data are extracted; carrying out waveform spatial form geometric analysis to obtain a real-time load characteristic sequence, and then carrying out segmentation processing; the current effective value sequence of each time window is converted into a time-frequency domain energy distribution vector, and then a three-level feature library is constructed; constructing a three-dimensional tensor model through equipment start-stop event identification, inputting the three-dimensional tensor model into a multi-target optimizer to evolve feature weights, and filtering abnormal samples to obtain a feature cluster; performing random masking processing on the time sequence data of the feature cluster to generate a mask sequence, inputting the mask sequence into an encoder to reconstruct masking data, comparing, learning and judging abnormal output correction data, and inputting the corrected data into a prediction network to generate a feedback signal flow; and analyzing the feedback signal flow to update the prediction network weight. Based on the method, the invention also provides power load prediction equipment. According to the invention, the precision of power load prediction is obviously improved.

The invention relates to the technical field of optical cable monitoring, and discloses an optical cable early warning method based on passive sensing integration, and the method comprises the steps: carrying out the passive sensing signal collection of an optical cable disposed in a target region, so as to obtain a disturbance signal transmitted along the optical cable; performing preprocessing operation on the disturbance signal to obtain a normalized disturbance signal sequence; meanwhile, environment data are collected through a multi-parameter sensor network; preprocessing the disturbance signal and the environment data, constructing a multi-dimensional feature vector containing gas concentration sudden change features, temperature distribution abnormity and vibration frequency-amplitude, performing disturbance event identification based on the multi-dimensional feature vector, and determining corresponding disturbance event data; and combining the disturbance event data with the spatial distribution of the optical fiber sensing signal and the vibration wave propagation time difference, positioning the positioning result data corresponding to the occurrence of the abnormal event, generating an early warning instruction based on the positioning result data, and pushing the early warning instruction to the user terminal through the monitoring platform. According to the invention, the accuracy of optical cable safety monitoring can be improved.

Disclosed is a fraud-related gang incident recognition system and method based on intelligence sharing and graph calculation, and a related device. The solution mainly comprises an intelligence sharing platform and an incident model based on graph calculation. The intelligence sharing platform integrates a plurality of trained recognition models, establishes a model intelligence sharing system, and summarizes multivariate information of each model in real time. The incident model based on graph calculation is configured to perform data exchange with the intelligence sharing platform. The incident model based on graph calculation is configured to classify nodes based on a graph neural network, and perform analysis on same on the basis of economic characteristics and social characteristics, to construct a social network graph of the incident, further identify overlapping gang structures on the basis of the social network graph, and transmit the overlapping gang structures to the intelligence sharing platform for recognition processing. In the solution of the present invention, gang organizations in social networks can be effectively discovered and identified by combining a graph calculation method of node attributes and a network topology structure and by using an intelligence sharing platform.

The invention discloses a double-branch sound-vibration fusion event recognition and positioning method based on DAS and AI, and the method comprises the steps: synchronously collecting sound and vibration data, and constructing a sound recognition branch and a vibration positioning branch; the sound branches extract multi-scale joint features through time-frequency domain feature fusion, weights of CNN and BiLSTM are dynamically adjusted to realize adaptive fusion, and event types and confidence coefficients are output; and the vibration branch calculates an initial coordinate by using a four-point space-time weighted optimization method, performs position fine tuning in combination with an ASTCN network, and outputs a final event position. Performing probability distribution verification by fusing double branch results and adopting a dynamic likelihood ratio evaluation mechanism, and outputting a final judgment result; intelligent event classification is achieved through the voice recognition branch, high-precision event space-time positioning is achieved through the vibration positioning branch, intelligent cooperation and system optimization of multi-modal data are achieved through the fusion and verification module, and the bottlenecks of the traditional technology in the aspects of recognition precision, positioning errors and system robustness are effectively broken through.

A heavy traffic abnormal event detection and state discrimination system belongs to the field of intelligent traffic and comprises a data acquisition and labeling module, an image processing module, a traffic state analysis module, a state determination and abnormal event identification module and a transfer learning module. The data acquisition and labeling module comprises a data acquisition unit and a data labeling unit; the image processing module comprises an image enhancement unit and an image compression and reconstruction unit; the traffic state analysis module comprises a traffic bottleneck identification unit and a traffic flow wave analysis unit; the state judgment and abnormal event recognition module comprises a vehicle statistical unit, a congestion state recognition unit, a state transition analysis unit and a compressed sensing model establishment unit; and the transfer learning module comprises a transfer learning unit. According to the method, the false detection rate of the traffic flow abnormal events in bad weather can be reduced, so that the traffic abnormal events can be detected all day long, and the occurrence probability of the traffic events in bad weather conditions can be reduced.

The invention discloses an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention discloses a physical constraint fused multi-scale extreme gale event AI identification method and device and a medium, and relates to the technical field of atmospheric science and artificial intelligence. According to the method, a novel multi-scale extreme gale event identification model is constructed by integrating multi-scale meteorological data. The model effectively captures extreme gale features under different scales through a multi-scale feature extraction module, a feature fusion sharing module and a multi-task output module. In addition, a physical constraint loss design is introduced into the model to ensure that outputs such as wind speed and air pressure conform to meteorological dynamics rules. Through deep learning analysis of meteorological data and combination of physical mechanism understanding, different types of extreme gale events such as hurricane, thunderstorm gale and tornado can be effectively distinguished, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

The invention relates to the field of campus safety prevention and control, and discloses an AI campus intelligent prevention system based on intelligent perception and automatic response early warning, and the system comprises a data collection and perception module which is used for collecting multi-dimensional data in a campus in real time through a collection device; the event perception and behavior identification module and the multi-modal data fusion module are used for carrying out fusion analysis on data from different perception devices; the AI analysis and event research and judgment module, the event early warning and notification module, the emergency response and disposal module and the resource scheduling and management module are used for reasonable allocation and efficient use of resources; the event recording and data archiving module is used for carrying out detailed recording and archiving on each processed event; and a disaster emergency response and auxiliary system connection module. By constructing a video, audio and environment data three-in-one multi-source sensing system, comprehensive real-time monitoring of abnormal behaviors, sound and environment changes in a campus is realized, and the effects of improving event recognition accuracy and sensing coverage are achieved.

The invention relates to a method and system for identifying a highway event by using a video large model, and the method comprises the steps: employing a three-stage processing architecture, firstly carrying out the real-time target detection and preliminary event judgment of a highway monitoring video stream through employing a YOLO algorithm, and generating an event candidate set; inputting the candidate events and the video clips thereof into a specially trained visual large model for deep semantic analysis and secondary reasoning; and finally, a reasoning result is rechecked through a rule engine, and false alarms are filtered by applying illusion suppression and a space-time association rule. According to the method, the real-time performance of traditional target detection and the deep reasoning capability of a visual large model are fused, so that the problems of high false alarm rate and high missing report rate of a traditional method are effectively solved, the accuracy and reliability of event identification in a complex traffic scene are remarkably improved, and meanwhile, the real-time processing capability of a system on multiple paths of high-definition video streams is ensured.

The invention relates to the technical field of fault diagnosis, in particular to an SMT production line equipment fault diagnosis method and system based on the Internet of Things, and the method comprises the following steps: obtaining multiple types of signals, aligning a time window, judging that the trend is consistent and fluctuation is synchronous to generate a sudden change event, recognizing an intersection point, extracting a key response position, and outputting an inflection point node number. And constructing a response time sequence and a propagation path, identifying abnormal nodes, and generating a fault traceability result. According to the invention, through time alignment and trend linkage identification of current, temperature and acceleration signals, fusion of trend direction judgment and fluctuation synchronization relation, a cross-equipment linkage sudden change identification mode is established, key response points are extracted through cross positioning of a jump terminal point and a vibration peak value, and a dynamic judgment mechanism of inflection point nodes is formed. In combination with a response sequential sequence and a chain path structure, local signal fluctuation analysis is converted into perception, and the judgment accuracy and traceability of abrupt change abnormity are effectively improved.

The invention provides an emergency event identification method based on a neural network model and computer equipment, and the method comprises the steps: obtaining a multi-source perception data set covering a target region, carrying out the feature coding processing of the multi-source perception data set, obtaining a multi-dimensional feature representation set of perception data units, and carrying out the recognition of an emergency event. Calling a pre-trained emergency event recognition neural network model to perform event recognition processing on the multi-dimensional feature representation set, generating an event recognition result of the sensing data unit, and determining the type of an emergency event existing in the target area and spatial-temporal feature information of the emergency event in the sensing data unit according to the event recognition result; and based on the emergency event type and the time-space feature information, generating an emergency early warning instruction containing an event positioning identifier, and sending the emergency early warning instruction to the target emergency response terminal to start a response operation. According to the invention, the comprehensiveness and accuracy of emergency event identification are improved, and the conversion efficiency of the identification result to the actual response is enhanced, so that the timely early warning of the emergency event is effectively supported.

The invention discloses a method, a system and equipment for voice interaction inside and outside a vehicle and a storage medium, and relates to the technical field of intelligent cabins and human-computer interaction. Identifying an interaction trigger type (in-vehicle active request, out-of-vehicle passive request, or system active trigger) based on the perceived data and / or the vehicle event; determining a corresponding response permission strategy according to the trigger type, and determining whether to allow the system to respond based on the response permission strategy; and if so, calling a large language model to generate a voice text, synthesizing voice by combining external perception characteristics, and broadcasting the voice through a loudspeaker outside the vehicle or a sound box in the vehicle. According to the scheme, three types of interaction intentions including the in-vehicle active request, the out-vehicle passive request or the system active triggering are recognized, the large language model is called to generate the scene-adaptive voice text, and personalized voice synthesis and broadcasting are performed in combination with the external perception characteristics, so that the response efficiency, the expression naturalness and the object adaptability of the in-vehicle and out-vehicle voice interaction are improved.