Artificial intelligence video processing method based on digital twinning

Abstract

The invention discloses an artificial intelligence video processing method based on digital twinning, and relates to the technical field of intelligent video analysis. According to the method, the problems of time inconsistency and frame missing of the multi-source video data are solved through a timestamp alignment mechanism and a time interpolation method, and the integrity and synchronism of the data are ensured; a multi-source integration technology and a three-dimensional convolutional network are used for fusing multi-view video data, a high-precision virtual model is constructed, dynamic scene mapping is optimized through real-time parameter updating and Kalman filtering, and the precision and the real-time performance of the model are improved; furthermore, by means of a long-short term memory network and an attention mechanism, a target track is accurately predicted, behavior pattern classification is achieved, reliable support is provided for intelligent decision making in a complex dynamic environment, the efficiency and accuracy of multi-source video data processing are effectively improved, and the adaptability and the intelligent level of the system in a dynamic scene are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent video analysis, and specifically to an artificial intelligence video processing method based on digital twin. Background Art

[0002] The field of video data processing and intelligent analysis plays a crucial role in modern society. It is widely applied in key scenarios such as smart cities, industrial monitoring, and autonomous driving, directly affecting the improvement of social efficiency and security. With the rapid development of sensor technology and computing power, achieving precise perception and understanding of the real world through video data has become an indispensable research direction. However, current solutions still expose significant limitations in practical applications. Existing video analysis methods mostly rely on single data sources or static models, making it difficult to adapt to complex and changing real-world scenarios, especially lacking in real-time performance and accuracy in dynamic environments. In addition, there is a gap between traditional methods in data processing and scene understanding, resulting in the lack of integrity and depth in analysis results.

[0003] The core challenge in this field lies in how to effectively integrate multi-source video data and achieve dynamic synchronization with the real-world scenario, while enhancing the understanding ability of artificial intelligence algorithms for complex content. Specifically, there is a technical gap between the all-round acquisition of video data and the real-time mapping of virtual models. The problems of data synchronization delay and distortion limit the accuracy of scene reconstruction. At the same time, the performance of deep learning algorithms in object recognition and behavior prediction highly depends on the quality and quantity of labeled data, and the generalization ability is insufficient when facing unseen new scenarios. These technical factors have not been fully resolved, leading to difficulties in achieving efficient target trajectory prediction and behavior analysis in dynamic environments, and thus affecting the reliability and practicality of intelligent processing. Summary of the Invention

[0004] The purpose of the present invention is to provide an artificial intelligence video processing method based on digital twin, which solves data synchronization and integrity problems through timestamp alignment and interpolation method, constructs a high-precision virtual model by using multi-source integration and three-dimensional convolutional network, and combines LSTM and attention mechanism to achieve accurate trajectory prediction and behavior classification, so as to provide efficient data processing and intelligent decision-making support for complex dynamic scenarios.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The present application provides an artificial intelligence video processing method based on digital twin, including the following steps:

[0007] S1. Obtain multi-source video data from multiple sensors, align the time series of each data stream through a preset timestamp alignment mechanism, and fill in the missing frames by using time interpolation method to obtain a synchronized multi-source video data set;

[0008] S2. Based on the synchronized multi-source video dataset, fuse video frames from different perspectives through multi-source integration technology, calculate the feature weights of each frame by the weighted average method, and obtain a unified multi-perspective feature representation;

[0009] S3. Based on the unified multi-perspective feature representation, construct a high-precision virtual model, extract spatio-temporal features using a 3D convolutional network, dynamically update the scene through real-time parameter mapping, and determine the initial state of the virtual model; then detect the data synchronization delay performance;

[0010] S4. Through the optimized dynamic virtual model, obtain the spatio-temporal coordinate sequence of the target object in the scene, analyze the coordinate change trend using a long short-term memory network, and judge the prediction result of the target trajectory;

[0011] S5. According to the prediction result of the target trajectory, apply a deep learning algorithm to extract the behavior features of the target object, enhance the key frame weights through an attention mechanism, and obtain the behavior pattern classification output; when the dynamic environment change exceeds the preset adaptation range, retrieve similar scene features from the pre-established scene library, adjust the parameters of the deep learning algorithm, and determine the final behavior analysis result;

[0012] S6. Through the final behavior analysis result, update the processing strategy of the multi-source video data in combination with the real-time synchronization ability, and optimize the integration technology parameters using an online learning method to obtain the feature representation input for the next cycle.

[0013] Furthermore, align the time series of each data stream through a preset timestamp alignment mechanism, and use time interpolation to fill in the missing frames to obtain a synchronized multi-source video dataset, specifically including:

[0014] Collect multi-source video data from multiple sensors, and each video stream collected by the sensor has its own timestamp information; start the preset timestamp alignment mechanism, and then determine a unified time benchmark as the reference time point for all data streams;

[0015] Analyze the timestamps in each video data stream one by one, calculate the deviation value between each timestamp and the reference time, and adjust the time series of each data stream to be consistent with the reference time according to the deviation value through translation and scaling mathematical transformations;

[0016] After completing the timestamp alignment, perform processing using time interpolation, analyze several adjacent frames before and after the missing frame, and extract key features, including the movement direction, speed, shape change of the object, and the color and brightness information of the corresponding pixels at the adjacent frame positions;

[0017] Based on the pixel-level features, using linear interpolation or bilinear interpolation algorithms, calculate the pixel values of the missing frames according to the pixel information of adjacent frames; based on the angular features of object motion, predict the position and state of the object at the moment of the missing frame through a motion model, construct a complete missing frame image, and then integrate the processed multi-source video data into a synchronized data set.

[0018] Furthermore, for the synchronized multi-source video data sets from different sources, use multi-source integration technology to fuse video frames from different perspectives, calculate the feature weights of each frame by the weighted average method, and obtain a unified multi-perspective feature representation, specifically including:

[0019] Fuse the sensor and video frames from different perspectives, identify the features in each video frame, and through matching the feature points of the same object in different perspectives, splice and integrate each frame spatially reasonably to initially construct a comprehensive image of multi-perspective information;

[0020] Adopt the weighted average method to calculate the feature weights of each frame. First, extract the detailed features of the color, texture, and edges of the object in each frame. For each feature dimension, analyze its changes in different perspective frames and the contribution degree to the overall scene description;

[0021] Through such analysis and assignment of each feature dimension, calculate a set of comprehensive weights for each frame, and perform weighted summation on the features of each frame according to the comprehensive weights to organically fuse the features of video frames from different perspectives, thereby obtaining a unified multi-perspective feature representation;

[0022] When the data synchronization delay exceeds the preset threshold, predict the current frame position through the Kalman filtering algorithm and adjust the model state to obtain an optimized dynamic virtual model.

[0023] Furthermore, construct a high-precision virtual model according to the unified multi-perspective feature representation, specifically including:

[0024] Utilize the rich information in the unified multi-perspective feature representation to build a virtual model architecture corresponding to the real scene;

[0025] Then use a three-dimensional convolutional network to extract spatio-temporal features. Organize the unified multi-perspective feature data reasonably according to the time series and spatial dimensions and input it into the three-dimensional convolutional network. The convolutional kernels in the network slide on the three-dimensional scales of time and space to perform convolutional operations on the data;

[0026] Through the combined operations of convolutional layers, pooling layers, and activation functions, automatically extract the spatio-temporal features of the data; map the dynamic scene by updating the parameters in real time. As the real scene changes continuously, the multi-source video data is also continuously updated, and the model will obtain new data in real time to re-analyze and evaluate the previously extracted spatio-temporal features;

[0027] According to the differences between the new data and the existing model parameters, the backpropagation algorithm optimization method is used to dynamically adjust the parameters in the model, and then the data synchronization delay performance is detected.

[0028] Further, after detecting the data synchronization delay performance, when the data synchronization delay exceeds the preset threshold, the Kalman filter algorithm is used to predict the current frame position to obtain a preliminary prediction result. According to the preliminary prediction result, the change trend of the current frame position is obtained and smoothed by the linear interpolation method to obtain the smoothed position data;

[0029] Based on the smoothed position data, the key parameters in the model state are adjusted to obtain an updated state description, and then the real-time offset of the dynamic virtual model is calculated to obtain an offset adjustment value;

[0030] Based on the offset adjustment value, the coordinates of the dynamic virtual model are corrected to obtain corrected model data, and then the prediction output of the Kalman filter is fused to obtain an optimized dynamic virtual model.

[0031] Further, the spatio-temporal coordinate sequence of the target object in the scene is obtained, and the long short-term memory network is used to analyze the coordinate change trend to judge the prediction result of the target trajectory, specifically including:

[0032] Extract the data information related to the target object from the optimized dynamic virtual model, locate the representation of the target object in the virtual model through the data extraction algorithm, and obtain the coordinate values of the target object in the three-dimensional space at different time points from it. Arrange the coordinate values in chronological order to obtain the spatio-temporal coordinate sequence of the target object;

[0033] Input the obtained spatio-temporal coordinate sequence into the long short-term memory network to analyze the coordinate change trend to obtain a trend description. According to the coordinate change trend, calculate the moving direction of the target object to obtain a direction vector;

[0034] Based on the direction vector, the state parameters in the dynamic virtual model are adjusted to obtain an updated model state, and then the trend description of the long short-term memory network is fused to obtain a trajectory prediction output;

[0035] Judge according to the trajectory prediction output and the actual coordinate sequence to obtain a stable prediction result. Through the stable prediction result, update the position of the target object in the scene analysis to obtain the final coordinate distribution.

[0036] Further, according to the prediction result of the target trajectory, a deep learning algorithm is applied to extract the behavioral features of the target object, specifically including: using the prediction result of the target trajectory as the input, processing the trajectory data through a deep learning model; extracting local features through a convolutional layer, capturing long-term dependencies in the time series using a recurrent layer, and performing a non-linear transformation in combination with an activation function to extract the behavioral features of the target object.

[0037] Further, the weights of key frames are enhanced through an attention mechanism to obtain the output of behavioral pattern classification, specifically including:

[0038] Performing feature mapping on the extracted behavioral features to form a feature matrix, initializing the attention weight matrix, and calculating the attention score for each frame through a dot product attention calculation function in combination with a learnable query vector;

[0039] Then, normalizing through the Softmax function to make the scores become the relative weights of the frames, thereby weighted-fusing the original feature matrix to highlight the key frame information, and then constructing a classification model, and training the model with the comprehensive feature vector processed by the attention mechanism and the corresponding behavioral pattern label data;

[0040] After training is completed, the new behavioral feature data is processed through the same attention mechanism to obtain a comprehensive feature vector and input it into the trained model. The model outputs a probability distribution vector, and the category with the highest probability is selected as the final output of behavioral pattern classification.

[0041] Further, based on the final behavioral analysis result, combined with the real-time synchronization ability, the processing strategy of multi-source video data is updated, and an online learning method is used to optimize and integrate technical parameters to obtain the feature representation input for the next cycle, specifically including:

[0042] Processing multi-source video data through the behavioral analysis result combined with the real-time synchronization technology to obtain an adjusted processing strategy, and using an online learning method to optimize and integrate the parameters for the adjusted processing strategy to obtain an optimized parameter set;

[0043] Processing video data through the optimized parameter set to obtain a preliminary distribution of the feature representation. When the deviation between the preliminary distribution of the feature representation and the data obtained by the synchronization technology exceeds a preset threshold, an adjustment method is used to iteratively optimize the parameter set to obtain a stable feature representation;

[0044] Updating the dynamic update strategy through the stable feature representation combined with the periodic input to obtain the feature data for the next cycle, and using the dynamic update strategy to process multi-source video data to judge the trend change of the behavioral analysis to obtain the final analysis output;

[0045] Adjusting the processing strategy through the final analysis output combined with the real-time synchronization technology to obtain an updated video data processing flow.

[0046] The beneficial effects of the present invention are as follows:

[0047] Through the preset timestamp alignment mechanism and time interpolation method, the inconsistency and missing frame problems of multi-source video data in the time series are solved; the time series of each data stream is adjusted to a unified benchmark by the timestamp alignment mechanism, and accurate time correspondence is achieved through translation and scaling mathematical transformations; the time interpolation method is used to fill in the missing frames, and the content of the missing frames is predicted by combining pixel features and motion models; not only the time difference is eliminated, but also the integrity of the video data is restored, providing a high-quality data basis for subsequent multi-view feature fusion and behavior analysis, and ensuring the accuracy and reliability of data processing;

[0048] Through the multi-source integration technology and 3D convolutional network, the complexity problems of multi-view video data fusion and dynamic scene mapping are solved. The multi-source integration technology is used to fuse video frames from different perspectives, and the feature weights of each frame are calculated by the weighted average method to obtain a unified multi-view feature representation; the 3D convolutional network is used to extract spatio-temporal features, and the model state is dynamically adjusted by combining real-time parameter updates and backpropagation algorithms to ensure that the virtual model can accurately map dynamic scenes; when data synchronization delay is detected, the model state is optimized by the Kalman filtering algorithm, which not only improves the accuracy and real-time performance of the virtual model, but also provides technical support for the high-precision simulation of complex dynamic scenes, enhancing the adaptability and robustness of the system;

[0049] Through the long short-term memory network (LSTM) and attention mechanism, the accuracy problems of target trajectory prediction and behavior pattern classification are solved. The LSTM network analyzes the spatio-temporal coordinate sequence of the target object, captures long-term dependencies, predicts the target trajectory and adjusts the model state; the attention mechanism is used to enhance the weights of key frames, highlight important feature information, and extract behavior features and classify them by combining deep learning models; when the prediction result deviates greatly from the actual data, the parameters are iteratively optimized to ensure the stability and accuracy of trajectory prediction and behavior classification. It not only improves the accuracy of target trajectory prediction and behavior pattern classification, but also provides a reliable basis for intelligent decision-making in complex dynamic environments, enhancing the intelligence level and practicality of the system. Brief Description of the Drawings

[0050] For better understanding and implementation, the technical solutions of the present application will be described in detail below with reference to the drawings.

[0051] Figure 1 It is a schematic flow chart of the artificial intelligence video processing method based on digital twin provided by the present application;

[0052] Figure 2 It is a schematic flow chart of obtaining a synchronized multi-source video data set by the artificial intelligence video processing method based on digital twin provided by the present application;

[0053] Figure 3 This is a schematic flowchart for the method of judging the prediction result of the target trajectory in the artificial intelligence video processing method based on digital twin provided by this application. Specific embodiments

[0054] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the exemplary embodiments will be described in detail herein, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0055] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0056] The following will elaborate in detail on the specific embodiments, features, and their effects of the present invention in conjunction with the drawings and preferred embodiments.

[0057] Please refer to Figures 1 - 3 , this embodiment provides an artificial intelligence video processing method based on digital twin, including the following steps:

[0058] S1. Obtain multi-source video data from multiple sensors, align the time series of each data stream through a preset timestamp alignment mechanism, and fill in the missing frames using time interpolation method to obtain a synchronized multi-source video dataset;

[0059] Further, aligning the time series of each data stream through a preset timestamp alignment mechanism and filling in the missing frames using time interpolation method to obtain a synchronized multi-source video dataset specifically includes:

[0060] S11. Collect multi-source video data from multiple sensors, and each video stream collected by the sensor carries its own timestamp information; start the preset timestamp alignment mechanism, and then determine a unified time reference as the reference time point for all data streams;

[0061] S12. Analyze the timestamps in each video data stream one by one, calculate the deviation value between each timestamp and the reference time, and adjust the time series of each data stream to be consistent with the reference time according to the deviation value through translation and scaling mathematical transformations, so as to achieve accurate time correspondence;

[0062] S13. After completing the timestamp alignment, for the missing frame problem that may occur in some video data due to reasons such as sensor failures and transmission packet losses, the time interpolation method is used for processing. Deeply analyze several adjacent frames before and after the missing frame, and extract key features, including the movement direction, speed, shape change of the object, and the color and brightness information of the pixels at the corresponding positions in adjacent frames.

[0063] S14. Based on the features at the pixel level, use the linear interpolation or bilinear interpolation algorithm to calculate the pixel values of the missing frames according to the pixel information of adjacent frames; based on the angular features of object movement, predict the position and state of the object at the moment of the missing frame through a motion model, and construct a complete missing frame image. Through these two methods, the missing frames can be effectively filled, and the integrity of the video data can be restored.

[0064] After timestamp alignment and missing frame processing, the time differences and data missing problems of each data stream are solved, and the processed multi-source video data is integrated into a synchronized data set.

[0065] S2. According to the synchronized multi-source video data set, fuse the video frames from different perspectives through multi-source integration technology, calculate the feature weights of each frame by the weighted average method, and obtain a unified multi-perspective feature representation.

[0066] Furthermore, use multi-source integration technology to fuse the video frames from different sensors and different perspectives, identify the features in each video frame, and through matching the feature points of the same object in different perspectives, splice and integrate each frame spatially reasonably, and initially construct a comprehensive image covering multi-perspective information.

[0067] Subsequently, in order to highlight the importance of video frames from different perspectives for the overall feature representation, the weighted average method is used to calculate the feature weights of each frame. First, extract the detailed features such as the color, texture, and edges of the objects in each frame. For each feature dimension, analyze its changes in the frames from different perspectives and its contribution to the overall scene description. For example, if a frame from a certain perspective can clearly show the key detailed texture of an object, while this texture feature is relatively blurred in other perspectives, then the weight of this frame in the texture feature dimension will be relatively high; through such analysis and assignment for each feature dimension, a set of comprehensive weights is calculated for each frame. Finally, according to the comprehensive weights, the features of each frame are weighted and summed, and the features of video frames from different perspectives are organically fused, so as to obtain a unified multi-perspective feature representation, which synthesizes the advantages of multi-source video data.

[0068] S3. According to the unified multi-perspective feature representation, construct a high-precision virtual model, use a three-dimensional convolutional network to extract spatio-temporal features, dynamically update the parameter mapping to map the dynamic scene, and determine the initial state of the virtual model; then detect the data synchronization delay performance.

[0069] When the data synchronization delay exceeds a preset threshold, the current frame position is predicted through the Kalman filtering algorithm, and the model state is adjusted to obtain an optimized dynamic virtual model;

[0070] Furthermore, based on the unified multi-view feature representation, a high-precision virtual model is constructed, specifically including:

[0071] Utilize the rich information in the unified multi-view feature representation to build a virtual model architecture corresponding to the real scene. The design of the model structure needs to fully consider the ability to carry the complex spatial and temporal dimension information contained in multi-source video data to ensure the accurate simulation of the real scene;

[0072] Then, a three-dimensional convolutional network is used to extract spatio-temporal features. The unified multi-view feature data is reasonably organized according to the time series and spatial dimensions and then input into the three-dimensional convolutional network. The convolutional kernels in the network slide on the three-dimensional scales of time and space to perform convolutional operations on the data;

[0073] Through the combined operations of convolutional layers, pooling layers, activation functions, etc., the spatio-temporal features such as the spatial shape, position of objects in the data, and the motion trajectories and dynamic behaviors changing over time are automatically extracted; for example, the convolutional kernels can capture the spatial position changes of objects at different times, as well as features such as the motion trends of objects between adjacent time frames;

[0074] During the operation of the model, the dynamic scene is mapped by real-time updating of parameters. As the real scene continuously changes, the multi-source video data is also continuously updated. The model will obtain new data in real time and re-analyze and evaluate the previously extracted spatio-temporal features. According to the differences between the new data and the existing model parameters, optimization methods such as the backpropagation algorithm are used to dynamically adjust the parameters in the model, such as the weights and biases of the convolutional kernels. In this way, it is ensured that the virtual model can accurately reflect the real-time changes of the real scene and always be synchronized with the dynamic scene. After a series of parameter updates, the model reaches a stable state, and at this time, the initial state of the virtual model is determined, which contains the accurate simulation information of the real scene based on the current multi-view feature representation.

[0075] Subsequently, data synchronization delay performance detection is carried out. During the operation of the virtual model, a dedicated monitoring module is set up to continuously record the time interval from the acquisition of multi-source video data to its input into the virtual model for processing, as well as the time lag between the model output result and the actual situation of the real scene. By statistically analyzing these time data, key indicators such as the average value, maximum value, and minimum value of the data synchronization delay are calculated. For example, if it is detected multiple times that the time interval from data acquisition to model processing is long, or the time lag between the model output result and the actual situation of the real scene exceeds the acceptable range, it indicates that there is a data synchronization delay problem.

[0076] Furthermore, after detecting the data synchronization delay performance, when the data synchronization delay exceeds the preset threshold, the Kalman filter algorithm is used to predict the current frame position to obtain a preliminary prediction result. According to the preliminary prediction result, the change trend of the current frame position is obtained and smoothed through linear interpolation to obtain the smoothed position data.

[0077] Based on the smoothed position data, key parameters in the model state are adjusted to obtain an updated state description, and then the real-time offset of the dynamic virtual model is calculated to obtain an offset adjustment value.

[0078] Based on the offset adjustment value, the coordinates of the dynamic virtual model are corrected to obtain corrected model data, and then the predicted output of the Kalman filter is fused to obtain an optimized dynamic virtual model.

[0079] S4. Through the optimized dynamic virtual model, the spatio-temporal coordinate sequence of the target object in the scene is obtained, and the long short-term memory network is used to analyze the coordinate change trend to judge the prediction result of the target trajectory.

[0080] Furthermore, obtaining the spatio-temporal coordinate sequence of the target object in the scene and using the long short-term memory network to analyze the coordinate change trend to judge the prediction result of the target trajectory specifically includes:

[0081] S41. Extract data information related to the target object from the optimized dynamic virtual model, locate the representation of the target object in the virtual model through a data extraction algorithm, and obtain the coordinate values of the target object in three-dimensional space at different time points from it. The coordinate values are arranged in chronological order to obtain the spatio-temporal coordinate sequence of the target object.

[0082] S42. Input the obtained spatio-temporal coordinate sequence into a Long Short-Term Memory network (LSTM) to analyze the trend of coordinate changes, obtain a trend description, calculate the moving direction of the target object based on the coordinate change trend, and obtain a direction vector. The LSTM network structure has unique advantages in processing time series data. Its internal memory units can effectively capture long-term dependencies. When processing the spatio-temporal coordinate sequence of the target object, the input gate, forget gate, and output gate in the network work together. The input gate determines which new coordinate information will be added to the memory unit; the forget gate controls which historical information in the memory unit needs to be retained or discarded; the output gate generates an output result based on the state of the memory unit. As the coordinate sequence data is input into the network in sequence, the LSTM network gradually learns the variation law of the coordinate values over time, such as whether the moving direction of the object is stable within a period of time and whether the speed changes significantly.

[0083] S43. Adjust the state parameters in the dynamic virtual model through the direction vector to obtain an updated model state, and then fuse the trend description of the Long Short-Term Memory network to obtain a trajectory prediction output.

[0084] S44. Make a judgment based on the trajectory prediction output and the actual coordinate sequence to obtain a stable prediction result, and update the position of the target object in the scene analysis through the stable prediction result to obtain the final coordinate distribution.

[0085] Among them, when the deviation between the trajectory prediction output and the actual coordinate sequence exceeds a preset threshold, a stable prediction result is obtained by iteratively adjusting the state parameters.

[0086] Specifically, based on the combination of the optimized dynamic virtual model and the Long Short-Term Memory network (LSTM), the spatio-temporal coordinate sequence of the target object can be efficiently obtained, and its motion trend can be accurately analyzed, so as to achieve accurate prediction of the target trajectory. This process can not only capture the dynamic changes of the target object in three-dimensional space, but also effectively process the long-term dependencies in the time series through the memory units of the LSTM network, solving the problems of information loss or inaccurate prediction that may exist in traditional methods when dealing with complex motion trajectories. By adjusting the model state through the direction vector and combining the trend description to generate a trajectory prediction output, the system can update the position information of the target object in real time to ensure the stability and accuracy of the prediction result. In addition, when the prediction result deviates from the actual coordinate sequence beyond the preset threshold, the system corrects itself by iteratively adjusting the state parameters, further improving the reliability of the prediction.

[0087] S5. According to the prediction result of the target trajectory, apply a deep learning algorithm to extract the behavioral features of the target object, enhance the weights of key frames through an attention mechanism, and obtain the classification output of the behavior pattern; when the dynamic environment change exceeds the preset adaptation range, retrieve similar scene features from the pre-established scene library, adjust the parameters of the deep learning algorithm, and determine the final behavior analysis result.

[0088] Further, according to the prediction result of the target trajectory, applying a deep learning algorithm to extract the behavioral features of the target object specifically includes:

[0089] Take the prediction result of the target trajectory as the input, and process the trajectory data through a deep learning model (such as a recurrent neural network RNN, a long short-term memory network LSTM, or a convolutional neural network CNN); these models can capture the spatio-temporal features in the trajectory data, such as the position change, speed, acceleration, and motion pattern of the object, etc.

[0090] Extract local features through the convolutional layer, capture the long-term dependencies in the time series using the recurrent layer, and perform non-linear transformation in combination with the activation function, thereby extracting the behavioral features of the target object.

[0091] Further, enhance the weights of key frames through an attention mechanism to obtain the classification output of the behavior pattern, specifically including:

[0092] Map the extracted behavioral features to form a feature matrix, initialize the attention weight matrix, and calculate the attention score for each frame through attention calculation functions such as dot product in combination with a learnable query vector;

[0093] Then normalize through the Softmax function to make the scores become the relative weights of the frames, use this to weightedly fuse the original feature matrix, highlight the key frame information, and then construct a classification model such as a support vector machine, a multi-layer perceptron, or a convolutional neural network, and train the model with the comprehensive feature vector processed by the attention mechanism and the corresponding behavior pattern label data.

[0094] After training, input the new behavioral feature data processed by the same attention mechanism to obtain the comprehensive feature vector into the trained model. The model outputs a probability distribution vector, and select the category with the highest probability as the final behavior pattern classification output, so as to accurately judge the behavior pattern of the target object.

[0095] S6. Through the final behavior analysis result, update the processing strategy of the multi-source video data in combination with the real-time synchronization ability, and use the online learning method to optimize and integrate the technical parameters to obtain the feature representation input for the next cycle.

[0096] Furthermore, based on the final behavior analysis results, combined with the real-time synchronization capability, update the processing strategy for multi-source video data, and adopt an online learning method to optimize and integrate technical parameters to obtain the feature representation input for the next cycle, specifically including:

[0097] Process multi-source video data through the behavior analysis results combined with the real-time synchronization technology to obtain an adjusted processing strategy, and adopt an online learning method to optimize and integrate parameters for the adjusted processing strategy to obtain an optimized parameter set;

[0098] Process video data through the optimized parameter set to obtain a preliminary distribution of the feature representation. When the deviation between the preliminary distribution of the feature representation and the data obtained by the synchronization technology exceeds a preset threshold, adopt an adjustment method to iteratively optimize the parameter set to obtain a stable feature representation;

[0099] Update the dynamic update strategy through the stable feature representation combined with the periodic input to obtain the feature data for the next cycle, and adopt the dynamic update strategy to process multi-source video data to judge the trend change of the behavior analysis to obtain the final analysis output;

[0100] Adjust the processing strategy through the final analysis output combined with the real-time synchronization technology to obtain an updated video data processing flow.

[0101] The artificial intelligence video processing method based on digital twin in this embodiment first obtains multi-source video data from multiple sensors, and through the timestamp alignment mechanism and time interpolation method, obtains a synchronized multi-source video data set; then fuses video frames from different perspectives and calculates weights to obtain a unified multi-perspective feature representation; subsequently constructs a high-precision virtual model, extracts spatio-temporal features to determine the initial state, and optimizes the model if the data synchronization delay exceeds the threshold; uses the optimized model to obtain the spatio-temporal coordinate sequence of the target object, analyzes the coordinate change trend to judge the target trajectory prediction result; then extracts behavior features and classifies them according to the prediction result, and adjusts the algorithm parameters if the environmental change exceeds the range to obtain the final behavior analysis result; finally, combines this result and the real-time synchronization capability to update the processing strategy, optimize and integrate technical parameters, and provide the feature representation input for the next cycle, realizing comprehensive, accurate and intelligent processing of video data, effectively analyzing the target object's trajectory and behavior, and adapting to dynamic environmental changes.

[0102] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to obtain equivalent embodiments with equivalent changes, but as long as the technical content of the present invention is not departed from, any brief modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An artificial intelligence video processing method based on digital twin, characterized in that: The steps are as follows: Obtain multi-source video data from multiple sensors, align the time series of each data stream through a preset timestamp alignment mechanism, and use time interpolation to fill in the missing frames to obtain a synchronized multi-source video dataset; Based on the synchronized multi-source video dataset, fuse video frames from different perspectives through multi-source integration technology, and calculate the feature weights of each frame by weighted average to obtain a unified multi-perspective feature representation; Based on the unified multi-perspective feature representation, construct a high-precision virtual model, use a three-dimensional convolutional network to extract spatio-temporal features, dynamically update the scene through real-time parameter mapping, and determine the initial state of the virtual model; then detect the data synchronization delay performance; Through the optimized dynamic virtual model, obtain the spatio-temporal coordinate sequence of the target object in the scene, use a long short-term memory network to analyze the coordinate change trend, and judge the prediction result of the target trajectory; Based on the prediction result of the target trajectory, apply a deep learning algorithm to extract the behavioral features of the target object, and enhance the key frame weights through an attention mechanism to obtain the output of behavioral pattern classification; When the dynamic environment change exceeds the preset adaptation range, retrieve similar scene features from the pre-established scene library, adjust the parameters of the deep learning algorithm, and determine the final behavioral analysis result; Based on the final behavioral analysis result, update the processing strategy of the multi-source video data in combination with the real-time synchronization ability, and use an online learning method to optimize the integration technology parameters to obtain the input of the feature representation for the next cycle.

2. The artificial intelligence video processing method based on digital twin according to claim 1, wherein: Align the time series of each data stream through a preset timestamp alignment mechanism, and use time interpolation to fill in the missing frames to obtain a synchronized multi-source video dataset. Specifically, it includes: Collect multi-source video data from multiple sensors, and each video stream collected by the sensor has its own timestamp information; start the preset timestamp alignment mechanism, and then determine a unified time reference as the reference time point for all data streams; Analyze the timestamps in each video data stream one by one, calculate the deviation value between each timestamp and the reference time, and adjust the time series of each data stream to be consistent with the reference time through translation and scaling mathematical transformations according to the deviation value; After completing the timestamp alignment, perform processing using time interpolation, analyze several adjacent frames before and after the missing frame, and extract key features, including the movement direction, speed, shape change of the object, and the color and brightness information of the corresponding pixels at the adjacent frame positions; Based on the features at the pixel level, use linear interpolation or bilinear interpolation algorithms to calculate the pixel values of the missing frames according to the pixel information of the adjacent frames; based on the angular features of the object movement, predict the position and state of the object at the moment of the missing frame through a motion model, construct a complete missing frame picture, and then integrate the processed multi-source video data into a synchronized dataset.

3. The artificial intelligence video processing method based on digital twin according to claim 1, characterized in that: Use multi-source integration technology to fuse video frames from different perspectives based on the synchronized multi-source video dataset, and calculate the feature weights of each frame by weighted average to obtain a unified multi-perspective feature representation. Specifically, it includes: Fuse the sensor and video frames from different perspectives, identify the features in each video frame, and reasonably splice and integrate each frame spatially by matching the feature points of the same object in different perspectives to preliminarily construct a comprehensive picture of multi-perspective information; Use the weighted average method to calculate the feature weights of each frame. First, extract the color, texture, and edge detailed features of the objects in each frame. For each feature dimension, analyze its changes in frames from different perspectives and its contribution to the overall scene description; Through such analysis and assignment of each feature dimension, calculate a set of comprehensive weights for each frame, and perform weighted summation on the features of each frame according to the comprehensive weights to organically fuse the features of video frames from different perspectives, thereby obtaining a unified multi-perspective feature representation; When the data synchronization delay exceeds the preset threshold, predict the current frame position through the Kalman filter algorithm, adjust the model state, and obtain an optimized dynamic virtual model.

4. The artificial intelligence video processing method based on digital twin according to claim 1, wherein: Construct a high-precision virtual model based on the unified multi-perspective feature representation, specifically including: Utilize the rich information in the unified multi-perspective feature representation to build a virtual model architecture corresponding to the real scene; Then use a three-dimensional convolutional network to extract spatio-temporal features. Reasonably organize the unified multi-perspective feature data in time series and spatial dimensions and input it into the three-dimensional convolutional network. The convolutional kernels in the network slide on the three-dimensional scales of time and space to perform convolutional operations on the data; Through the combined operations of convolutional layers, pooling layers, and activation functions, automatically extract the spatio-temporal features of the data; map the dynamic scene by updating the parameters in real time. As the real scene continuously changes and the multi-source video data is continuously updated, the model will obtain new data in real time and re-analyze and evaluate the previously extracted spatio-temporal features; According to the differences between the new data and the existing model parameters, use the backpropagation algorithm optimization method to dynamically adjust the parameters in the model, and then perform data synchronization delay performance detection.

5. The method for processing artificial intelligence video based on digital twin according to claim 4, characterized in that: After performing data synchronization delay performance detection, when the data synchronization delay exceeds the preset threshold, predict the current frame position through the Kalman filter algorithm to obtain a preliminary prediction result. According to the preliminary prediction result, obtain the change trend of the current frame position, and perform smoothing processing through the linear interpolation method to obtain smoothed position data; Adjust the key parameters in the model state through the smoothed position data to obtain an updated state description, and then calculate the real-time offset of the dynamic virtual model to obtain an offset adjustment value; Correct the coordinates of the dynamic virtual model through the offset adjustment value to obtain corrected model data, and then fuse the predicted output of the Kalman filter to obtain an optimized dynamic virtual model.

6. The method for artificial intelligence video processing based on digital twin according to claim 1, characterized in that: Obtain the spatio-temporal coordinate sequence of the target object in the scene, and use a long short-term memory network to analyze the coordinate change trend and judge the prediction result of the target trajectory, specifically including: Extract the data information related to the target object from the optimized dynamic virtual model, locate the representation of the target object in the virtual model through the data extraction algorithm, and obtain the coordinate values of the target object in three-dimensional space at different time points from it. Arrange the coordinate values in chronological order to obtain the spatio-temporal coordinate sequence of the target object; Input the obtained spatio-temporal coordinate sequence into a long short-term memory network, analyze the coordinate change trend, obtain a trend description, calculate the moving direction of the target object according to the coordinate change trend, and obtain a direction vector; Adjust the state parameters in the dynamic virtual model through the direction vector to obtain an updated model state, and then fuse the trend description of the long short-term memory network to obtain a trajectory prediction output; Make a judgment based on the trajectory prediction output and the actual coordinate sequence to obtain a stable prediction result, and update the position of the target object in the scene analysis through the stable prediction result to obtain the final coordinate distribution.

7. The method for processing an artificial intelligence video based on digital twin according to claim 1, characterized in that: According to the prediction result of the target trajectory, apply a deep learning algorithm to extract the behavioral characteristics of the target object, specifically including: using the prediction result of the target trajectory as input, processing the trajectory data through a deep learning model; extracting local features through a convolutional layer, using a recurrent layer to capture long-term dependencies in the time series, and combining an activation function for non-linear transformation to extract the behavioral characteristics of the target object.

8. The method for artificial intelligence video processing based on digital twin according to claim 1, characterized in that: Enhance the weights of key frames through an attention mechanism to obtain a behavioral pattern classification output, specifically including: Perform feature mapping on the extracted behavioral characteristics to form a feature matrix, initialize the attention weight matrix, and calculate the attention score for each frame through a dot product attention calculation function combined with a learnable query vector; Then normalize through the Softmax function to make the scores the relative weights of the frames, weighted fuse the original feature matrix with this to highlight the key frame information, and then construct a classification model, training the model with the comprehensive feature vector processed by the attention mechanism and the corresponding behavioral pattern label data; After training is completed, input the new behavioral feature data processed by the same attention mechanism to obtain a comprehensive feature vector into the trained model. The model outputs a probability distribution vector, and select the category with the highest probability as the final behavioral pattern classification output.

9. The artificial intelligence video processing method based on digital twin according to claim 1, wherein: Through the final behavioral analysis result, update the processing strategy of multi-source video data in combination with the real-time synchronization ability, and adopt an online learning method to optimize and integrate technical parameters to obtain the feature representation input for the next cycle, specifically including: Process multi-source video data through the behavioral analysis result combined with real-time synchronization technology to obtain an adjusted processing strategy, and adopt an online learning method to optimize and integrate parameters for the adjusted processing strategy to obtain an optimized parameter set; Process video data through the optimized parameter set to obtain a preliminary distribution of feature representation. When the deviation between the preliminary distribution of feature representation and the data obtained by the synchronization technology exceeds a preset threshold, adopt an adjustment method to iteratively optimize the parameter set to obtain a stable feature representation; Update the dynamic update strategy through the stable feature representation combined with the periodic input to obtain the feature data for the next cycle, and adopt the dynamic update strategy to process multi-source video data to judge the trend change of the behavioral analysis to obtain the final analysis output; Adjust the processing strategy through the final analysis output combined with real-time synchronization technology to obtain an updated video data processing process.

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