7255 results about "Video streaming" patented technology

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

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 discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

The invention relates to the technical field of video behavior recognition, and discloses a behavior recognition and early warning system for a real-time video stream. The system comprises a spatio-temporal feature modeling module, a behavior fragment extraction module, an anomaly propagation modeling module, a risk area positioning module and an early warning strategy generation module. The spatial-temporal feature modeling module builds a dynamic model based on historical data, captures a skeleton key point three-dimensional coordinate sequence, a motion optical flow vector field and a micro-expression intensity spectrum, and outputs a theoretical behavior mode vector; the behavior fragment extraction module generates a multi-modal difference feature tensor through cross-modal difference analysis; the exception propagation modeling module generates an exception propagation path risk probability distribution cloud picture in combination with spatial constraint and trajectory information; the risk area positioning module identifies a high-risk area and marks a boundary; and the early warning strategy generation module dynamically configures monitoring parameters, starts high-frame-rate micro-expression capture for a high-risk area, and performs a track disturbance test on an adjacent area.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention relates to the technical field of airport safety monitoring, and discloses an airport video data real-time analysis system. The system comprises a video stream spatial-temporal feature modeling module, a behavior trajectory map construction module, an abnormal region association analysis module, a risk level semantic judgment module and a situation structure visualization module. According to the method, multi-scale spatial-temporal feature analysis is carried out on an airport monitoring video stream, a multi-dimensional behavior trajectory map is established, abnormal behavior region association is analyzed, risk level semantics are judged, and finally an airport global risk situation thermodynamic distribution map is generated. According to the system, the whole process processing from video data acquisition to risk situation visualization is realized, the abnormal behavior area can be accurately identified, the risk level and category are clear, comprehensive and visual situation information is provided for airport safety management, and the intelligent level of airport safety management is improved.

A video gateway system at a worksite is coupled to multiple cameras on a network, and backs-up video streams generated by the cameras to a backend cloud backup video storage system and a frontend (cache) video storage system. The video gateway system generates an aggregated video asset from a plurality of streams of video from the multiple cameras, and generates metadata and a backup report associated with the video asset. The video asset, metadata, and backup report are stored on the backend cloud backup video storage system in an file system, and are also stored on the frontend (cache) video storage system such that for at least a period of time, the video asset and the associated the metadata and backup report are stored on both the cloud backup video storage system, facilitating quick access and retrieval to the stored video for retrieval for streaming, activity detection, and other uses.

The invention discloses a short video intelligent editing method and system based on multi-modal analysis, and relates to the technical field of video editing. The method is used for improving editing efficiency and visual experience and comprises the following steps: extracting lip motion features of a character, visual saliency features of a commodity and a voice emotion intensity value from a target short video stream to form multi-modal time sequence data; afterwards, the voice stream is recorded, a product keyword timestamp is extracted, the alignment degree is calculated through dynamic time warping in combination with a visual saliency peak value, and a preliminary editing point set is generated through weighted evaluation in combination with an emotional intensity value; constructing an editing decision optimization model based on deep reinforcement learning, taking the multi-modal features as state input, adjusting the retention probability of editing points through a joint reward function, and selecting an optimal transition mode; and the lip movement and voice synchronization error before and after the editing point and the emotional and visual continuity of the transition section are analyzed, the discontinuous region is smoothed, and the edited finished product is output, so that precise short video intelligent editing is realized.

The invention discloses a video analysis-based multi-scene operator violation behavior identification method and system, and belongs to the technical field of intelligent operation safety monitoring and artificial intelligence identification, and the method comprises the steps: collecting a real-time video stream of a multi-scene operation site; recognizing a continuous action time sequence in the real-time video stream by using an action recognition depth model; constructing the continuous action time sequence into an action behavior sequence; the action behavior sequence is constructed into a directed behavior graph with time, space and action labels, the directed behavior graph is compared with a directed behavior graph corresponding to the standard action behavior sequence, and illegal behaviors are recognized; and carrying out multi-mode early warning on the identified illegal behaviors. According to the method, the bottleneck that the traditional image recognition technology is weak in action sequence semantic understanding and poor in environmental adaptability is broken through, and accurate recognition and real-time early warning of illegal behaviors in multi-scene operation are achieved.

The invention relates to a substation operation risk identification method based on a multi-view video and high-precision positioning. Acquiring video data of a working site through a plurality of cameras with fixed visual angles and mobile video acquisition equipment; a high-precision positioning system is used for obtaining three-dimensional space coordinates of operators and equipment in real time; establishing a three-dimensional digital twinborn model of the substation equipment, and performing dynamic scene reconstruction based on the multi-view video stream to generate a real-time three-dimensional scene of the operation site; fusing the positioning data and the three-dimensional scene by adopting a space-time fusion algorithm to generate a dynamic digital portrait of the operator; and carrying out real-time analysis on behaviors and positions of operators by using a risk assessment algorithm based on a preset risk rule, calculating to obtain a risk assessment value, and setting a feedback mechanism to continuously optimize positioning and scene reconstruction precision. According to the invention, efficient, accurate and real-time identification and early warning of the operation risk of the transformer substation are realized, and the safety management level of an operation site is effectively improved.

The invention relates to the technical field of cluster video stream processing, and discloses a video stream real-time coding and decoding transmission method under a cluster. The method comprises the following steps: firstly, acquiring video stream coding parameter text data, link state time sequence data and equipment performance index data of multiple nodes of a target cluster to form a transmission link data set; semantic analysis is carried out on the coding parameter text data to obtain a coding semantic feature vector, dynamic fluctuation features are extracted from the link state time sequence data to obtain a link fluctuation feature vector, and cross-modal fusion is carried out to generate a fusion transmission feature set; generating an abnormal association degree score set by using a pre-trained multi-layer sensing network model, and obtaining an abnormal source node and an equipment defect type by combining root cause tracing according to the abnormal association degree score set; and finally, generating a dynamic optimization strategy and feeding back to the transmission control system to trigger parameter calibration. According to the method, the abnormal root cause can be accurately traced, the transmission parameters are optimized, and the cluster video stream transmission quality is improved.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention relates to the field of breeding industry, and discloses a pig behavior-based pig health condition analysis method and system, and the method comprises the steps: carrying out the comprehensive monitoring of pig behaviors, capturing the gait, feeding mode, excretion behavior and activity range of a pig in real time based on a behavior feature extraction algorithm, and obtaining a behavior state video stream sequence; multi-dimensional time sequence correlation analysis is carried out on the behavior state video stream sequence, and historical behavior data, pig weight changes and physiological parameters are combined; based on the dynamic time sequence feature vector, dynamically identifying a change track of pig behaviors by applying a self-adaptive behavior identification algorithm; performing correlation analysis on the detected abnormal behavior pattern and the potential health risk of the pig, fusing the environmental factors, group behavior data and health history of the pig, and identifying a potential health problem; and based on a risk early warning result, automatically adjusting environmental parameters and feeding management strategies, and providing intervention measure suggestions. The pig health management system has the advantage of improving the efficiency and accuracy of pig health management.

The invention discloses a self-adaptive illegal parking detection method, a detection system and a storage medium. The self-adaptive illegal parking detection method comprises the following steps: initializing the system; each video frame in an input video stream is processed according to the following steps: vehicle detection; performing regional filtration; performing multi-target tracking; vehicle state calculation: traversing each tracked vehicle, and updating the information of the tracked vehicle in the vehicle information object; multi-dimensional illegal parking judgment and alarm: according to a calculation result in the parking duration calculation step, executing the following judgment logics: traversing all static vehicles, determining a scene, obtaining a dynamic threshold value, triggering condition judgment and triggering an action; result visualization and output are carried out; and cleaning resources. By introducing the multi-dimensional dynamic judgment logic, the technical scheme of the invention can identify illegal parking behaviors more intelligently and more accurately, and the practicability and reliability of the system are significantly improved.

The invention discloses a campus face recognition abnormal behavior monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting the video stream data of a person in a campus in real time, and recognizing the behavior characteristics of the person in a video frame in real time through employing a lightweight target detection algorithm; extracting facial features of the personnel by using a pre-trained face recognition model, and recognizing identity information of the personnel; generating a personnel behavior semantic description vector with a spatio-temporal context based on the behavior characteristics and the identity information; inputting the personnel behavior semantic description vector into a dynamic early warning threshold engine, and outputting an abnormal behavior probability and risk level evaluation result; and triggering an early warning information pushing mechanism according to the abnormal behavior probability and the risk level evaluation result, and sending early warning information to a corresponding responsible person terminal. By using the embodiment of the invention, the accurate and automatic association of the abnormal behavior and the personnel identity can be realized, and the early warning accuracy, the handling response speed and the system adaptive ability of campus safety monitoring are improved.

The invention relates to the field of data processing, and provides a 5G-based video call information processing method and system, and the method comprises the steps: continuously obtaining a real-time video frame sequence and 5G network environment perception data in a video call scene, carrying out the multi-dimensional state mapping processing of the 5G network environment perception data, constructing a network transmission adaption model, and carrying out the real-time video frame sequence and 5G network environment perception data. Generating a video coding control instruction based on the network transmission adaptation model, performing content-aware coding conversion on the real-time video frame sequence, and outputting a coding optimization stream; in the transmission process of the coding optimization stream, link state fluctuation information is obtained through a 5G network feedback channel, transmission strategy dynamic calibration is performed on the coding optimization stream according to the link state fluctuation information, and a calibration transmission stream is obtained; and carrying out decoding time sequence alignment processing on the calibration transport stream, generating a visual call output sequence which is synchronous with the time of the original video stream unit, and pushing the visual call output sequence to a receiving end presentation device.

A method includes providing, for display on a first client device of a first participant of a plurality of participants of a virtual meeting, a user interface (UI) during the virtual meeting. The UI includes multiple regions each presenting a visual item corresponding to a video stream generated by a client device of a respective participant of the virtual meeting. The method includes detecting engagement of the first participant with a first visual item corresponding to a video stream generated by a second client device of a second participant of the virtual meeting. The method further includes generating one or more information items associated with the second participant. The method further includes causing the one or more information items to be presented within the UI on the first client device of the first participant during the virtual meeting.

An Internet of Things (IoT) or vehicle dash cam may store both a high-resolution and low-resolution video stream on a device. The video streams are selectively accessible by remote devices. Because of the relatively smaller storage requirements of low-resolution video files, retaining of additional video data on the vehicle device (beyond what would be possible with only high-resolution video) is possible. The user may be provided an option to adjust the amount of low-resolution and high-resolution video to store on the device. A combined media file may be generated by a device to include time-synced high-resolution video, low-resolution video, and / or metadata for a particular time period.

The invention discloses a blasting operation field behavior compliance identification method, system and device and a medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a multi-source video stream and environment monitoring data of a blasting operation site; identifying a current operation stage according to the multi-source video stream; identifying a scene risk factor according to the multi-source video stream and the environment monitoring data; inputting the multi-source video stream into a pre-trained deep learning model, and analyzing identity information, a behavior sequence, an equipment wearing state and position information of each operator; calling a compliance rule set matched with the current operation stage and the scene risk factor in a pre-constructed security rule library; based on the compliance rule set, performing compliance verification on the identity information, the behavior sequence, the equipment wearing state and the position information; and if the violation behavior exists through verification, generating a violation judgment result containing the violation type, the risk level, the associated personnel and the spatial region identifier. According to the invention, intelligent monitoring and violation identification of the whole process of blasting operation can be realized.

The invention discloses a multi-view child motion coordination ability evaluation system and method based on deep learning, belongs to the technical field of motion evaluation, and solves the problems that child motion evaluation in the prior art mainly depends on manual observation and simple physical testing, and multiple angles and key details of child actions are difficult to synchronously track. The method comprises the following steps: training to obtain a skeleton point detection model and an athletic ability evaluation model, acquiring real-time video streams of personnel entering a field based on an acquisition camera, identifying skeleton key points in a preprocessing data set by the skeleton point detection model, performing multi-person detection on a personnel matching result based on an athletic area division method, and evaluating the athletic ability of the personnel. The exercise ability evaluation model carries out quantitative analysis on multi-person detection results; according to the invention, visual identification and motion state detection technologies are combined, a front view angle and side view angle dual-camera layout is adopted, and a deep learning algorithm is matched, so that automatic children dynamic motion evaluation is realized. The action process can be completely captured, and the detection accuracy and efficiency are improved.

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention relates to the technical field of computer video processing, and discloses a low-delay video stream real-time processing method and device, and the method comprises the steps: obtaining original video stream data, and processing the original video stream data through employing a lightweight motion prediction method; processing the macro block data set and the predicted coding configuration parameter by adopting multi-thread assembly line coding to obtain a coded data block; establishing a data transmission mechanism to perform data flow control on the unified memory access interface; a heterogeneous task scheduling strategy is adopted to distribute task division results; a lightweight neural network is adopted to carry out parameter adaptive adjustment, and an optimized video stream processing result is obtained; according to the method, a zero-copy data transmission technology is adopted, and optimal configuration and efficient utilization of computing resources are achieved.

The invention discloses a digital human interaction method and device based on multi-modal sentiment analysis and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-modal data of a user in real time through a multi-source sensor device; the multi-modal data comprises face video stream data, voice audio stream data and text dialogue data; calling data analysis engines corresponding to different modalities, and extracting corresponding modal feature sequences; according to the current interaction scene, the modal feature sequence and the historical dialogue context features are fused, and a comprehensive emotion evaluation result is generated; outputting a corresponding multi-modal response data packet based on the modal feature sequence through an interactive response engine corresponding to a comprehensive emotion evaluation result; and executing the multi-modal response data packet. And after feature fusion is carried out in combination with the current interaction scene, the generated response can more accurately fit the current emotion demand and communication context of the user, so that the digital human can be more easily fused into various scenes needing emotion interaction.

The invention discloses a cross-platform virtual-real fusion scene construction method and system based on AI space calculation, and relates to the technical field of artificial intelligence and space calculation, and the method comprises the steps: carrying out the multi-scale feature fusion based on a received cross-modal conversion instruction set, and generating an initial image sequence; carrying out implicit field coding on a target object by combining a three-dimensional reconstruction algorithm to obtain an initial parameterized model; performing space-time alignment on the multi-view video stream, loading a digital scene asset package in combination with physical sensing data and a preset spatial index structure, and establishing a bidirectional data channel between a virtual scene and a physical sensor; performing rendering and illumination parameter adjustment on the initial parameterized model to obtain an optimized parameter model; performing differential coding processing on the optimization parameter model to obtain a target virtual-real scene fusion model; and distributing the target virtual-real scene fusion model to a preset terminal. The invention provides a virtual-real fusion construction method for end-to-end collaborative optimization, which is suitable for cross-platform live broadcast or dynamic interaction scenes.

The invention discloses a multi-communication group fusion holding method and system for an intelligent door lock, and the method comprises the steps: building an independent multicast tree taking a feature vector as an identifier according to a communication group request initiated by each main control door lock; generating a multicast message containing a target communication group feature vector and a digital signature according to a merging request sent by the first master control door lock; according to feature vectors exchanged by the master control door locks of the two parties through a secure channel, a target master control door lock initiates feature interception registration, and negotiates and establishes a boundary feature tag rewriting rule; and according to the established forwarding path and the rewriting rule, the audio and video streams of the communication groups of the two parties are subjected to feature tag rewriting through the network boundary router, and then bidirectional intercommunication is realized. By utilizing the embodiment of the invention, the dynamic security isolation and seamless fusion of multiple communication groups can be realized, the network load and transmission delay are reduced, meanwhile, the terminal door lock can realize audio and video intercommunication without sensing the network address of the opposite end or changing the configuration, and the expansibility, the security and the user experience of the system are improved.

The invention relates to a knowledge-intensive visual question and answer automatic data generation method and device, and the method comprises the steps: constructing an original visual data set containing the professional knowledge of a target domain according to a static image, a video stream and multimedia content; extracting a representative frame sequence, converting the audio information into text information, and extracting character information in the static image to construct a structured visual instance database; according to the prompt text meeting the preset professional depth condition, establishing a three-level prompt system containing domain knowledge, an evaluation standard and a generation specification; generating a corresponding visual question and answer pair data set according to the dynamic cooperation of the main agent and the domain expert agent; generating a multi-agent quality evaluation system according to the quality evaluation result; and designing a difficulty grading mechanism according to the negative example sample. According to the method, the professionality, the accuracy and the diversity of the visual question and answer data are remarkably improved, and reliable data support is provided for training and evaluation of a multi-modal large model.

The invention discloses a server resource dynamic scheduling method for dealing with video stream high concurrent access, and particularly relates to the technical field of computer network and intelligent scheduling. A user access behavior data set is constructed; a deep learning model is adopted to train an access hot spot prediction model based on the time sequence features to predict a future access hot spot area and a peak trend; acquiring response delay, CPU / GPU occupancy rate and bandwidth load information of the heterogeneous server group, and generating a resource state multi-dimensional parameter set; carrying out joint modeling on the model and the parameter set, constructing a resource scheduling priority model by adopting a graph neural network, and generating an optimal scheduling path graph based on an A * improved algorithm; task transfer, instance elastic expansion, cache preheating and other scheduling operations are executed according to the model; according to the method, the resource utilization efficiency and the service quality of the video system in a high-concurrency scene can be improved, and the method has the advantages of being high in real-time performance, intelligent in scheduling and high in self-learning capability.

The invention provides an AI-based nuclear power station high-risk operation compliance real-time monitoring method and system, and the method comprises the steps: processing human body joint point coordinate data of an operator through employing an AI dynamic contour modeling algorithm, and generating a limb movement track sequence; scanning the preset operation area to generate a relative motion vector between the special operation tool for the nuclear power station and the operator; detecting an action deviation mode of the operator based on the limb movement track sequence, and generating a detection result; fusing the relative motion vector and multi-channel video stream frame synchronization data obtained by applying an AI video analysis algorithm to generate a space intrusion relation between the nuclear power station special operation tool and the nuclear power station high-risk equipment; and when the action deviation mode exists in the detection result and the space intrusion relation exceeds a preset safety threshold, generating an operation compliance signal. According to the invention, real-time dynamic evaluation of human motion compliance and tool equipment safety distance in the high-risk operation process of the nuclear power station is realized, and the safety monitoring precision and real-time early warning capability of high-risk operation are improved.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.