464 results about "Dynamic time warping" patented technology

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention provides a digital human interaction control method and device fusing emotion semantics and logical reasoning and a storage medium. The method comprises the following steps: analyzing multi-modal input data of a user, constructing emotion-semantics joint representation, and generating a logic decision path; and through a cognitive fusion module, emotion-semantic representation and a logic decision path are fused, and an interaction response adapting to emotion and logic consistency is generated. The system optimizes an emotion semantic model and a logical reasoning rule on line according to user feedback and interaction history, and real-time interaction of emotion dynamic and logical rules is achieved. The system can dynamically adjust the logic decision path based on the multi-mode emotional state of the user, and improves the naturalness and situation adaptability of interaction. A dynamic time warping algorithm and a factorization machine are introduced to process a multi-modal feature fusion problem, and the accuracy and robustness of emotional state recognition are improved. The online optimization mechanism enables the model and the rule to be evolved continuously, and reasoning errors are corrected automatically through user feedback, so that error circulation is avoided.

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

The invention relates to the technical field of intrusion detection, in particular to a network threat detection method and system, and the method comprises the following steps: building a threat path logic diagram through collecting field dependency items, action trigger timestamp items and action propagation hop count items of an attack behavior chain, and matching field dependency items among nodes based on a graph theory algorithm to obtain a threat path logic diagram; and detecting a mutual exclusion logic field combination, and generating a logic diagram structure with a connecting edge and a mutual exclusion mark. In the method, a threat path logic diagram is constructed by fusing field dependence, action timestamps and propagation hops, graph theory identification field mutual exclusion combination enhances cross-protocol attack chain analysis, and hidden Markov modeling state transition probability verifies time sequence continuity and path length. And performing dynamic time warping alignment on forward and reverse instruction sequences to extract semantic offset, overlapping rate and time sequence entropy, and performing non-linear score classification based on an isolated forest to detect an adversarial sample, topological structure analysis, time sequence verification, instruction alignment and non-linear classification to cooperatively identify a composite attack with field mutual exclusion and time sequence confusion.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent diagnosis system and method for a comprehensive energy station, and the system comprises a dynamic trajectory capture module, a lag response analysis module, a mismatch behavior diagnosis module and a fault chain positioning module. According to the method, a dynamic time warping algorithm is adopted to process sliding window time sequence data and calculate track slope difference, capture precision of instantaneous disturbance characteristics is enhanced, a grey correlation model is utilized to analyze track matching degree and set a dynamic lag threshold value, and a continuous non-convergence condition trigger mechanism is combined to improve hidden fault identification sensitivity. Multi-parameter range calculation and direction consistency check integrate start-stop frequency and load rate change trend, reduce misjudgment risk caused by single parameter fluctuation, construct a time sequence alignment axis and calculate delay response joint probability distribution, realize time-space association of historical data and real-time monitoring, accurately locate a coupling relationship of a multi-source fault chain, and improve the reliability of the multi-source fault chain. The abnormal state early warning capability is enhanced, and the fault positioning efficiency is optimized.

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention discloses a multi-modal image automatic labeling system and method, and relates to the technical field of image data processing. According to the multi-modal image automatic labeling system and method, time sequence alignment is carried out on video streams and laser radar point cloud data through an asymmetric dynamic time warping algorithm, semantic and geometric features are extracted, and the elastic coefficient of the algorithm is dynamically adjusted. And combining a modal perception attention mechanism, dynamically allocating fusion weights of the video stream and the laser radar according to the features, generating a cross-modal joint feature vector, and outputting a preliminary labeling result. And generating an annotation robustness index by calculating the prediction entropy and the three-dimensional intersection-to-union ratio confidence of the target detection frame, and iteratively optimizing the annotation result. And mapping the cross-modal features and the labeling result into a space-time correlation map, and displaying the three-dimensional positioning, motion trail and modal contribution degree thermodynamic diagram of the target in real time. The problems of time alignment, feature fusion and labeling robustness are effectively solved, and a high-precision and interpretable automatic labeling solution is provided.

The invention discloses a motor fault detection method and system based on voiceprint recognition. According to the method, an annular microphone array is adopted to collect motor sound signals in a non-contact mode, a three-channel time-frequency data set is constructed through empirical mode decomposition (EMD) and a Mel-frequency cepstral coefficient (MFCC), fault diagnosis is carried out in combination with a CNN + ResNet network, and dynamic time warping (DTW) and CNN fusion matching is supported. The system comprises a preprocessing module, a fault template library and a matching algorithm, integrates wavelet denoising and multi-beam acquisition technologies, covers a frequency band of 50Hz-20kHz, can display a fault type and trend analysis in real time, and triggers secondary verification when the confidence coefficient is insufficient. According to the scheme, the anti-interference capability is improved through array signal processing, model parameters are optimized in combination with transfer learning, non-contact detection is achieved, the real-time performance and accuracy of fault diagnosis are remarkably improved, and the method is suitable for industrial motor health monitoring.

The invention relates to the technical field of safety monitoring, in particular to a building bearing capacity detection system based on the Internet of Things, which comprises a strain load module, an offset gradient module, a rhythm risk module, a threshold judgment module and a map reconstruction module. According to the method, the maximum strain excursion and direction reversal characteristics of adjacent time windows are analyzed and captured through the dynamic frequency excursion gradient, the response sensitivity to microcosmic bearing capacity fluctuation is improved, a strain jump rhythm map is constructed, sudden change time point time difference, amplitude difference and direction continuity parameters are extracted, and an abnormal load section is accurately positioned. The method comprises the following steps: identifying sudden strain jump events, performing cross-level rhythm map multi-dimensional comparison by applying a dynamic time warping algorithm, screening synchronous mutation nodes to generate a risk set, performing density clustering sorting based on direction consistency parameters, reconstructing a security level topological map, and improving visual spatial relevance and dynamic evolution characteristics of a structural instability path.

The invention relates to the technical field of fault detection, in particular to a heating and ventilation system fault positioning system and method based on big data, and the system comprises a multi-source sensing module, a disturbance feature module, a path modeling module, a frequency spectrum matching module and a fault positioning module. In the method, a real-time disturbance sequence is constructed through time window segmentation and parameter offset calculation, separation of an active response chain and an abnormal propagation path is realized through a directional joint state vector and a topological relation table, and time asynchronism of multi-device signal transmission is eliminated by adopting a dynamic time warping algorithm. In combination with a real-time parameter bidirectional verification mechanism of a frequency domain main frequency band energy mark, a valve opening degree and a pump rotating speed, the problem of path confusion in a multi-node parameter coupling scene of a traditional method is solved, the tracing efficiency of concurrent faults in a complex pipe network system is improved, the adaptability limitation of a single-dimensional threshold mechanism to equipment performance degradation is overcome, and the method is suitable for a complex pipe network system. And the error positioning probability caused by signal delay superposition is reduced.

The invention discloses a VEM-Token beat capture and alignment model construction method, which is a deepening innovation that a vocal music file is segmented into VEM-Token lexical elements by adopting music beats based on a VEM-Token vocal music emotion multi-modal model method. The core of the method is to establish a rhythm model, a rhythm capture model and a rhythm alignment model of a vocal music file, the rhythm model separates singing sound, accompaniment sound and emotional fluctuation from a sample vocal music file through multiple filters and captures a start point and an end point of a rhythm in a frequency spectrum format file, and the rhythm alignment model performs rhythm alignment on the vocal music file through a start point fine tuning model and an end point fine tuning model. And the user imitation file and the sample file are enabled to complete beat alignment. Models including a rhythm basic model, harmonic impact, joint learning, harmonic frequency layering, dynamic time warping and the like are adopted to capture rhythms, and models including the basic model, starting point fine tuning, terminal point fine tuning, whole-course alignment verification, a rhythm editor, rhythm free playing, repeated alignment, a communication interface protocol and the like are adopted to construct. Therefore, the method is suitable for accessing an Agent music agent and an AI music application.

The invention discloses a power distribution network planning method and system considering distributed energy uncertainty, and relates to the technical field of power grid planning, and the method comprises the steps: collecting distributed energy node data, compensating space-time migration in combination with meteorological data, and carrying out the time sequence alignment through a dynamic time warping algorithm; constructing an improved Wasserstein generative adversarial network to generate a conventional scene, and injecting Gaussian noise through potential spatial disturbance to generate an extreme scene deviating from training distribution; inputting the mixed scene set into a mixed integer nonlinear programming model, and adopting a graph neural network to establish a topology-power flow agent model to accelerate solution; updating line impedance parameters through a Kalman filter, and collecting and checking actual output; and decomposing the corrected planning scheme into cloud global optimization and edge local control, and carrying out cloud-edge collaboration. According to the method, the adaptability of a power distribution network planning scheme in a complex and uncertain environment is improved by combining spatial-temporal feature alignment, adversarial network scene enhancement, a graph neural network and cloud edge collaborative optimization.

The invention belongs to the technical field of data processing, relates to an aircraft multi-mode instruction conflict resolution method and system, and aims to solve the problems of high conflict detection time delay and priority strategy staticization of a traditional method. The method comprises the steps that after a multi-mode instruction is received, semantic feature extraction and time sequence alignment are completed through a three-layer attention mechanism and a dynamic time warping algorithm, and a structured instruction set is generated; evaluating the priority of each instruction by combining a four-dimensional dynamic weight system with reinforcement learning; inputting the instruction set with the priority into a six-tuple model to identify a conflict type and an instruction set; and a three-level progressive arbitration method is adopted to resolve conflicts, an optimal execution sequence is generated, and the four-dimensional dynamic weight system is fed back and optimized according to an execution result. According to the method, the real-time performance and the dynamic adaptability of multi-mode instruction conflict resolution are remarkably improved, and reliable guarantee is provided for aviation instruction interaction safety.

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 cloud mobile phone end-to-end performance tracking method and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining client touch event data and network event data, and generating a global unique identifier based on a preset Hash algorithm; obtaining server resource event data; performing timestamp calibration on the client touch event data and the server resource event data based on a hardware-level clock synchronization and software compensation algorithm to generate a synchronous timestamp; based on the synchronization timestamp, aligning the event sequences of the client and the server through a dynamic time warping algorithm to generate an aligned event sequence; performing causal probability calculation on the aligned event sequence based on a Bayesian network model, and determining a causal relationship weight between resource events; and performing root cause matching according to the causal relationship weight and a preset abnormal mode library, generating a root cause list with priority ranking, and triggering execution of a self-healing strategy.

The invention discloses a financial deep counterfeiting real-time detection and defense method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal data in a financial transaction scene, and carrying out the desensitization of an edge end; performing dynamic time sequence alignment on the multi-modal data, and calculating a synchronous error of lip motion and voice by adopting a dynamic time warping algorithm; inputting the features into a dynamic risk modeling layer, and generating dynamic risk features in combination with the updated risk feature library; analyzing the features through a double-flow GAN detector, and outputting a forgery probability; and a detection result is input into a compliance verification layer, the supervision file is analyzed through a legal BERT, a structured rule is generated, and real-time transaction interception and block chain log recording are executed. The method protects user privacy and data security, combines the risk feature library updated in real time, and has high flexibility and adaptability. According to the design of the double-flow GAN detector, image and video stream information is fully utilized, and the accuracy and reliability of detection are further improved.

The invention relates to multi-mode signal processing, and provides a signal processing method of a computer system. The method comprises the following steps: acquiring different modal signal data by using a plurality of sensors and performing timestamp synchronization; a depth time sequence alignment network and a multi-layer dynamic time warping algorithm are utilized to ensure signal time consistency; multi-modal signal features are extracted and fused through a multi-stream depth feature extraction network and a cross-modal attention mechanism; the system adopts a dynamic convolution kernel generation and meta-learning technology to carry out adaptive signal processing; noise suppression and signal optimization are carried out by using a multi-mode noise reduction auto-encoder and a generative adversarial network, and parameters are optimized and processed through closed-loop feedback control and reinforcement learning strategies. According to the method, synchronization, fusion, adaptive processing, noise suppression and real-time feedback control of multi-mode signals are realized, and the method has the advantages of high precision, high robustness and real-time performance.

The invention relates to the technical field of databases, in particular to an intelligent mapping and classifying method based on heterogeneous data sources, which comprises the following steps: collecting heterogeneous data streams through an API (Application Program Interface) gateway and converting the heterogeneous data streams into structured data packets; using a semantic topology engine to fuse BERT semantic extraction, a graph convolutional network and a dynamic time warping technology to generate a cross-source association graph; constructing a field type clustering center by adopting a meta-learning framework based on the atlas, generating an initial classification rule through mode compatibility measurement, and dynamically updating the rule by means of adversarial training; outputting a DSL configuration script in combination with a template engine and an AST compiling technology; and dynamically adjusting a graph convolution weight and classifier parameters by using a strategy gradient algorithm through a reinforcement learning agent, and establishing a mapping-classification-verification collaborative optimization mechanism. According to the method, cross-source data semantic association accuracy is improved, small sample adaptive classification is realized, and system robustness and efficiency are improved.

The invention provides an autism assessment system and method based on multi-modal time sequence data fusion, and relates to the technical field of children autism spectrum disorder assessment calculation processing. The invention innovatively provides a dual time sequence alignment method based on dynamic time warping (DTW) and LSTM prediction, the problem of time sequence dislocation of cross-modal data (heart rate / eye movement / limb movement) caused by acquisition frequency difference is solved, millisecond-level synchronization precision is realized, the technical problem of cross-modal data time sequence dislocation in an existing autism assessment system is solved, and the accuracy of time sequence alignment of the cross-modal data in the autism assessment system is improved. The small-range time deviation caused by acquisition equipment delay or physiological response difference is eliminated, and the robustness and reliability of the autism evaluation system are improved.

The invention discloses a substation equipment state intelligent sensing and early warning method based on multi-source data. The method comprises the steps that real-time operation data and historical abnormal state data of substation equipment are acquired; obtaining time sequence data based on all historical abnormal state data to form a reference template; comparing the real-time operation data of the substation equipment with the reference data based on a dynamic time warping algorithm, and calculating the similarity; judging whether the current running state is abnormal or not based on a similarity result, if yes, classifying an abnormal type, and analyzing and predicting a future running trend; and uploading the newly appearing exception to a database, and optimizing the algorithm. The method has the advantages that the real-time data and the historical abnormal data are combined, the operation state of the equipment is monitored in real time, the abnormity is accurately identified and classified based on the dynamic time warping algorithm, potential faults are predicted in advance, scientific basis is provided for equipment maintenance and management, and sudden faults are reduced.

The invention discloses an intelligent rehabilitation training evaluation method, device and equipment based on multi-modal information fusion and a computer readable storage medium, and the method comprises the steps: synchronously collecting user action video data and electromyographic signal data for a user performing rehabilitation training; aligning the user action video data and the electromyographic signal data based on the first timestamp and the second timestamp, and respectively generating a synchronous user posture feature sequence and a synchronous user electromyographic feature sequence; generating a comprehensive rehabilitation evaluation report by applying a feature layer fusion dynamic time warping algorithm; updating the personalized evaluation benchmark when the benchmark updating condition is met; when the stage promotion condition is met, determining that the user enters a new rehabilitation stage; on the basis of the comprehensive rehabilitation evaluation report and the new rehabilitation stage, targeted rehabilitation training guidance is generated and output. The method has the advantages of accurately identifying and deeply diagnosing compensatory actions and providing a personalized and intelligent adaptive rehabilitation process.

The invention relates to the technical field of oil well fault monitoring, and discloses an oil well indicator diagram real-time fault prediction method and system. The method comprises the following steps: acquiring an oil well sensor data stream, buffering and checking data integrity through a sliding window, and aligning multi-channel sensor data by applying a dynamic time warping algorithm to generate a standardized data stream; extracting time domain features based on the data stream, and comparing the time domain features with a historical feature library after principal component analysis dimension reduction to generate a feature difference index; triggering a multi-level threshold strategy according to the difference index, collecting an incremental training data set, finely tuning the model by adopting an elastic weight preserving algorithm, and generating a hot switching ready model; after the model is loaded, a fault probability value is generated through GPU accelerated reasoning, and an early warning event with a timestamp is generated; and finally analyzing the message into an early warning protocol message edge for transmission, and dynamically optimizing system resources based on logs. According to the method, the delay problem of high-frequency data flow is effectively solved, and the fault prediction accuracy and the system response speed are remarkably improved.

The invention relates to the technical field of resource allocation, in particular to a data preset learning-based dynamic material allocation system for multiple stations, which comprises a topology modeling module, a demand clustering module, a strategy generation module and a decision optimization module. According to the method, laser ranging and dynamic time warping are combined, the work station layout is converted into a moving time consumption parameter, a space correlation coefficient is adjusted according to a difference value proportion, a topological relation matrix is updated in real time, DTW quantifies the mode similarity of a demand interval standard deviation and a usage variable coefficient, a multi-dimensional demand feature vector is constructed, and the prediction and actual consumption matching degree is improved; the method comprises the steps of establishing a response capability curve based on OEE data, dynamically associating residual capacity with demand characteristics through linear programming, realizing strategy pre-generation and capacity early warning, constructing a cooperation unit through a Hungary algorithm, matching material and equipment parameters through cosine similarity, triggering strategy reconstruction, synchronously updating topology, forming a closed-loop optimization system and enhancing the cross-station dynamic response capability.

The invention relates to the technical field of signal noise reduction and reconstruction, in particular to a microseismic signal noise reduction and reconstruction method and system based on multi-scale decomposition, and the method comprises the steps: S1, carrying out the multi-scale Shaplet decomposition of a noise-containing microseismic signal based on different sequence lengths, measuring the matching degree between each signal block and all candidate Shaplets through Euclidean distance, and obtaining the matching degree between each signal block and each candidate Shaplet; constructing a multi-scale feature matrix M; s2, based on the multi-scale feature matrix M, multi-scale weighted importance measurement is carried out through a time convolution network and an attention mechanism so as to evaluate the importance of each signal block in the noise reduction process; and S3, constructing a U-Net microseismic signal noise reduction model, designing a loss function in combination with the reconstruction error and dynamic time warping, and carrying out multiple iterative training on the U-Net microseismic signal noise reduction model until the error meets a preset requirement. According to the method, noise can be effectively identified and removed, and especially in data containing different noise, the method is beneficial to high-quality noise reduction of micro-seismic monitoring data.

The invention relates to the technical field of precision machining, in particular to a cutting force signal synchronous calibration method based on multi-sensor data fusion, which comprises the following steps of: 1, dynamically preprocessing acquired signals; step 2, constructing a reference time axis based on the spindle current signal, and realizing time domain alignment of multichannel signals by adopting a dynamic time warping algorithm; 3, establishing a feature incidence matrix containing a force-vibration-temperature coupling relation, and generating a fusion cutting force feature vector through frequency domain energy weight distribution; 4, a dynamic transfer function model is constructed, model parameters are updated in real time, and the fusion feature vector is mapped into a calibration cutting force signal; and 5, carrying out dynamic compensation on the calibration signal by adopting a nonlinear inverse compensation and residual modal screening strategy. According to the method, the accuracy of cutting force signals can be remarkably improved, errors and uncertainty in the machining process are reduced, and the production quality and efficiency are optimized.

The invention discloses a multi-modal heterogeneous medical equipment data fusion and decision support method and device, and aims to solve the problems that the fusion precision is low due to space-time semantic difference of medical equipment multi-modal heterogeneous data (equipment operation parameters, clinical records, fault signals and the like), equipment management decisions depend on experience, and standards are not uniform. According to the method, breakthrough is achieved through three-level data alignment of'time-space-semantics', hierarchical fusion of'data level-feature level-decision level ', three-level decision driven by a knowledge graph and dynamic feedback optimization: time alignment uses a dynamic time warping algorithm, space alignment depends on a unified data dictionary, and semantic alignment introduces an attention mechanism; the feature level fusion quantifies the feature support degree based on the D-S evidence theory; the decision-making layer constructs a'rule-case-prediction 'three-level system, and combines cosine similarity retrieval and information entropy quantification uncertainty. The method and device can support medical equipment maintenance, clinical diagnosis and treatment and other scenes, and the medical service standardization level and the equipment management efficiency are improved.

The invention discloses a mesh belt furnace multi-equipment collaborative material tracking and graphical monitoring method, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: carrying out the dynamic feature extraction of material block data based on a dynamic twin database, carrying out the analysis through combining a causal reasoning algorithm, and generating an enhanced data set with a causal label; on the basis of the enhanced data set with the causal label, when material tracking abnormity is detected, constructing a dynamic causal graph, calculating the contribution degree of equipment operation parameters to the abnormity through a Bayesian network, and generating a repair instruction set; and the repair instruction set is issued to the PLC to adjust the equipment operation parameters, the state change of the material blocks is monitored in real time, and when the deviation exceeds the tolerance range, the causal graph is triggered to update and a regulation and control instruction is generated to regulate and control the equipment operation parameters. According to the method, the causal network node weight is updated in real time by adopting the dynamic time warping algorithm, and accurate positioning of an abnormal source and intelligent generation of a repair strategy are realized.

The invention discloses a production processing data collaborative management system and method of a curtain wall single aluminum plate, and relates to the technical field of data collaborative management. According to the method, the production processing data and the quality detection data of the curtain wall single aluminum plate are collected, the dynamic time warping algorithm is adopted to analyze the time sequence data change between the working procedures, the time deviation and the performance fluctuation between the working procedures are recognized, then the working procedure coordination index is obtained, and the time synchronism and the efficiency matching degree between the working procedures are measured. And performing classification and regression analysis on the quality index of each processing procedure through a decision tree algorithm to obtain a quality stability index, and identifying and predicting quality fluctuation and abnormal modes in different procedures. And judging whether the sequence and priority of the production processes need to be adjusted or not through comprehensive analysis of the process coordination index and the quality stability index. And time inconsistency, efficiency bottleneck and quality fluctuation in the production process are identified, intelligent optimal scheduling of the production process is realized, and the collaborative management level of the production process is improved.

The invention discloses a sign language recognition system based on a dynamic time warping algorithm, and the system comprises a data collection module which is used for collecting gesture data in real time through a camera and a depth sensor; the feature extraction and time sequence conversion module is used for converting the gesture data into a time sequence; the dynamic time warping matching module is used for matching the input gesture with the gesture in the template library by utilizing a dynamic time warping algorithm; the matching result selection and template classification module is used for selecting the most matched template according to the similarity and determining the gesture category; the embedded platform and hardware acceleration module is used for executing a sign language recognition algorithm on the embedded platform and improving the recognition speed and real-time performance through hardware acceleration; and the character and voice output and real-time feedback optimization module is used for converting the recognition result into characters and voice output. Through hardware acceleration and data compression optimization, the sign language recognition precision, real-time performance and adaptability are improved, and the sign language recognition method is suitable for an embedded system with limited resources.

The invention relates to an intelligent video editing method fusing human face features and human voice features, which comprises the steps of input preprocessing, multi-modal analysis, fusion scoring and automatic editing, and adopts a multi-modal mode for analysis, so that the recall rate of key segments is improved, and the efficiency of editing is improved. A personalized editing strategy is supported, facial expression changes and voice emotion peak values are aligned through dynamic time warping, a CLIP-like structure is used for training a bimodal encoder, the human face and the voice are mapped to a unified vector space, the association weight of the human face and the voice is automatically learned, and the intelligent degree of editing is improved.