291 results about "Dynamic time warping" patented technology

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 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 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 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 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 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 classroom multi-modal data processing method and system based on deep learning, and aims to solve the composite technical problem of poor classroom multi-modal data fusion analysis effect caused by global asynchronism of a data source, a nonlinear sequential relationship between modals and lack of perception for teaching semantics in the prior art. The method comprises the following steps: acquiring a classroom multi-modal data stream; innovative hierarchical cross-modal time alignment processing is carried out, coarse-grained global alignment is carried out firstly to correct initial time migration between devices, then fine-grained local alignment is carried out, and the core of the processing is that an innovative dynamic time warping algorithm is adopted. Higher alignment weights are given to semantic key segments such as teacher explanation key points and teacher-student interaction, and nonlinear alignment conforming to the teaching rhythm is achieved; and finally, the aligned features are sent to a fusion network to generate a unified fusion feature vector.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a service-oriented robot-oriented multi-modal emotion recognition method and system, and the method comprises the steps: collecting audio and video stream data of emotion changes of a user, and separating visual and voice data; extracting visual and voice emotion features through a pre-training model, and calculating prediction probability distribution of each mode; constructing a bimodal confidence quantitative model based on the distribution to obtain each modal confidence; and fusing the features by adopting a sectional type dynamic weight distribution strategy so as to identify the emotional state of the user. Visual and voice modes are fused, feature alignment is realized in combination with dynamic time warping, spatial optimization performance is shared and expressed through a confidence model, a dynamic weight strategy and a cross-modal time sequence cooperation module, and the method has high recognition accuracy, high robustness and real-time processing capacity in a complex environment and is suitable for various service scenes.

The invention belongs to the technical field of seismic exploration, and discloses a pre-stack gather optimization method based on multi-dimensional constraint dynamic time warping, which remarkably improves the space continuity and stability of a time shift field. According to the method, the time shift field which is smoother and more continuous in a three-dimensional space can be generated, and the common abnormal jump and unreasonable jitter of the time shift field in a conventional DTW method are effectively inhibited. This better conforms to the gradient characteristics of the geologic structure. Mismatching and processing illusion are effectively avoided, the adaptability of a complex structure area is improved, the global optimization capacity of the DTW algorithm is enhanced through multi-dimensional constraint, and the phenomenon that different horizons or effective waves are wrongly matched with interference waves due to local waveform complexity or noise interference is reduced. Therefore, common processing illusions such as event dislocation and local distortion in a conventional DTW method can be remarkably reduced.

The invention provides a machine tool thermal error prediction compensation method, device and equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of temperature data and thermal deformation data of different machine tools, and dividing the data into a source domain data set and a target domain data set; a dynamic time warping algorithm is adopted to construct an adjacent matrix between temperature sensors, a time-space diagram neural network model is constructed based on the adjacent matrix, and a source domain data set and a target domain data set are input into the model for cross-domain training to obtain a temperature data completion model; and missing temperature sensor data is input into the completion model for data completion, and thermal error prediction is performed based on the data after completion, so that high-precision thermal error prediction under the condition of temperature sensor data missing is realized. According to the method, the space-time diagram neural network is combined with a cross-domain training technology, so that accurate complementation of sensor data, cross-machine-tool-environment model adaptability and stable thermal error prediction precision are realized.

The invention discloses a dance movement recognition method, device and equipment based on dynamic time warping and a computer readable storage medium. The method comprises the steps of obtaining a user skeleton point sequence corresponding to a user dance movement and a standard skeleton point sequence corresponding to a standard dance movement; for each pair of frames in the user skeleton point sequence and the standard skeleton point sequence, multi-dimensional differences including joint point position differences, angle differences and motion amplitude differences are calculated; a double-layer dynamic weighting mechanism is applied to perform weighted fusion on the multi-dimensional difference values, and local cost between two frames is calculated; constructing a cost matrix based on local cost, and determining an optimal regular path and a dance motion similarity score by applying a path constraint dynamic time warping algorithm; and on the basis of the dance motion similarity score and the optimal regular path, generating an identification report including an overall score, a subitem score and visual correction information. The method has the advantages of being high in dance movement recognition precision, high in specialty and the like.

Embodiments include systems and methods for adaptive monitoring of operational technology networks. In some embodiments, the method includes collecting multi-modal time series data from a plurality of wireless sensor nodes deployed near at least one operational technology asset, aligning and fusing the time series data using dynamic time warping, extracting at least one feature and at least one dependency from the fused time series data, generating, based on the extracted feature and dependency, a real-time anomaly score using a trained machine learning model, determining, based on the real-time anomaly score, at least one anomaly regarding the operational technology asset, and presenting a visualization of the anomaly at an interactive user interface.

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

The invention discloses a tightening process abnormal fluctuation analysis and early warning method and system, and the method comprises the steps: collecting original torque and angle signals of a tightening process in real time, carrying out the time synchronization processing, and carrying out the abnormal value filtering and feature extraction, thereby obtaining a feature data set; then comparing and analyzing the feature data set and a pre-generated standard tightening curve, and performing anomaly judgment through a trained anomaly recognition model based on a comparison result to generate an anomaly recognition result; and finally, determining an early warning level according to an abnormal recognition result and outputting corresponding early warning information. The system correspondingly comprises a data acquisition module, a feature extraction module, an abnormity identification module and an early warning output module. According to the method and system, the dynamic time warping algorithm and the long-short-term memory network model are adopted, early-stage tiny abnormal fluctuation in the tightening process can be effectively recognized, multi-stage early warning and preventive quality control are achieved, and the quality control level and production efficiency of the tightening process are remarkably improved.

The invention discloses a synchronous analysis method, device and equipment based on multi-person postures and dynamic time warping and a computer readable storage medium, and the method comprises the steps: carrying out the processing of a video stream containing a plurality of individuals, so as to detect and output a face bounding box of each individual in a scene; for each face bounding box, generating an independent skeleton point sequence uniquely corresponding to each individual; based on each independent skeleton point sequence, constructing an independent action sequence of a plurality of individuals; calculating and outputting an individual similarity score of each individual according to each independent action sequence and the corresponding standard action template; performing statistical variance calculation on the set of the similarity scores of all the individuals to obtain a group synchronism index representing group action synchronism; and generating an analysis report containing the evaluation result of the individual performance and the group synchronism. The method has the advantages that the key points of the individuals in the multi-target scene are accurately associated, and the collaboration of group actions is objectively analyzed.

The invention discloses an intelligent monitoring and early warning system and method for project progress and cost, and relates to the technical field of constructional engineering management. The streaming data fusion module adopts an Apache Flink engine and applies a dynamic time warping algorithm to carry out time alignment on multi-source asynchronous data to realize semantic unification; the space-time diagram neural network prediction module constructs a space-time association diagram, integrates global features such as weather and supply chain fluctuation, and adaptively learns an influence weight by using a graph attention mechanism; the self-adaptive early warning module adopts a Bayesian online learning framework, an early warning threshold value is dynamically adjusted according to a historical false alarm rate and a missing report rate, the method comprises the steps of data acquisition, fusion, mapping, joint prediction and self-adaptive early warning, the problem of data islands is solved, complex space-time association is accurately captured through a space-time diagram neural network, and real-time early warning is achieved. Joint prediction of progress and cost risk is realized, false report and missing report are reduced, and real-time performance, accuracy and decision-making efficiency of engineering management and control are improved.

The invention discloses a VR / AR martial art teaching method, device and equipment based on dynamic time warping and a computer readable storage medium. The method comprises the steps of obtaining a user skeleton point sequence corresponding to a user action and a standard action template; determining a specific weight coefficient of the joint for comparison processing; comparing the user skeleton point sequence with a standard action template based on a specific weight coefficient of a joint by applying a weighted dynamic time warping algorithm so as to generate an optimal warping path; obtaining a sequence matching score and a posture matching score according to the optimal regular path and the key force generation event sequence; inputting the sequence matching score, the posture matching score and the determined existing force application sequence error and / or action posture error into a language generation type model to generate a text feedback instruction; and based on the text feedback instruction, generating and displaying immersive teaching feedback in a VR or AR environment. The method has the advantages of multi-dimensional evaluation, immersive feedback and the like.

The invention discloses a charging pile data management method and system. The method comprises the steps of generating a session fingerprint and a sampling jitter plan at a charging pile end, and writing the session fingerprint and the sampling jitter plan into an under-chain hash structure; time alignment is realized through phase correlation and dynamic time warping based on multi-channel records; constructing an energy sequence and calculating an energy reconciliation distance to generate a session consistency label; according to the unified time axis and the rate rule, generating a microaccounting state machine and outputting an accounting deviation; a session consistency score is calculated by fusing multiple indexes, and diagnosis judgment is completed; and generating a compliance abstract, executing signature evidence storage, and providing a session result for the operation and maintenance interface and the settlement interface. Through multi-channel data acquisition and time alignment, energy reconciliation, accounting modeling and anomaly detection in a trusted execution environment, high-precision reconciliation and compliance intelligent evaluation of the charging pile session data are realized.

The invention discloses an emotional man-machine interaction method, device and equipment based on emotion recognition and a computer readable storage medium. The method comprises the steps that first modal action data, second modal facial expression data and third modal physiological parameter data of a user are synchronously collected; processing the first modal motion data and a preset standard emotion-motion template through a posture estimation and dynamic time warping algorithm to generate a motion evaluation score; inputting the action evaluation score, the second modal facial expression data and the third modal physiological parameter data into a preset multi-modal fusion model for processing to generate a quantized user emotion level; based on the quantized user emotion level, determining an interface adjustment parameter and an emotional feedback text; and applying the interface adjustment parameter and the emotional feedback text to the current human-computer interaction interface. The emotional man-machine interaction method based on emotion recognition has the advantages of being accurate in emotion recognition, intelligent in interaction, personalized and the like.

The invention discloses a student psychological risk perception method based on multiple modes, and relates to the technical field of emotion calculation and intelligent education. The method comprises the following steps: firstly, extracting a facial expression feature vector and a voice intonation feature vector respectively by using a convolutional neural network and Fourier transform through a collected video stream and an audio stream; then adaptive denoising processing is carried out on environmental interference, timestamp alignment and dynamic time warping are carried out on the denoised multi-modal data, time sequence synchronization is ensured, and corrected multi-modal sequence data are formed; then, dynamic emotion track features are extracted from the sequence data, a preliminary emotion state label is generated by comparing the dynamic emotion track features with a baseline threshold value, and the threshold value is adaptively updated in combination with historical data so as to improve the judgment accuracy; and finally, aggregating the emotional state labels of a plurality of students to generate a visual group emotional thermodynamic diagram so as to realize macroscopic perception of group psychological risks. The accuracy, robustness and visualization degree of student psychological state analysis are effectively improved, and an efficient technical means is provided for campus psychological early warning.

The invention relates to the technical field of toxic gas detection, and discloses a gas hazard prediction method fusing multi-sensor data, which comprises the following steps: step 1, collecting original environment data from various heterogeneous sensors in a target area; step 2, carrying out time alignment processing on the original environment data by adopting an improved dynamic time warping algorithm, so that multiple types of sensor data are aligned on a unified event timeline; and step 3, performing perception conflict detection based on the aligned data, judging whether the output of the plurality of groups of sensors has trend difference or not, when the difference exists, constructing a confrontation revenue function, and using each sensor as a strategy participant by using a game theory model. According to the technical scheme, the improved dynamic time warping algorithm is adopted to carry out event-level time alignment on multi-class heterogeneous sensor data, and the technical effect of realizing high-precision synchronous fusion of multi-source data in a gas rapid diffusion or sudden leakage scene is achieved.

The invention belongs to the technical field of intelligent dispatching of power systems, and particularly relates to a power distribution network historical case retrieval and recommendation method based on multi-modal data fusion and depth similarity matching. The method comprises the following steps: constructing a multi-modal case feature library, and storing historical case data including a topological structure, a load curve and a scheduling text; receiving current power distribution network fault or scheduling demand information; calculating the topological similarity between the current scene and the historical case by adopting a graph neural network; calculating load distribution similarity by applying a dynamic time warping algorithm; calculating the semantic similarity of the scheduling text based on a fine-tuned BERT model of the electric power major; dynamically adjusting each feature weight according to the real-time new energy permeability, and generating a comprehensive similarity score; candidate cases conforming to power grid safe operation conditions are screened through a physical constraint verification layer; and outputting the recommendation case and the applicability analysis report thereof.

The invention belongs to the technical field of power system load prediction, and discloses a load prediction method and system based on similar day screening and time sequence alignment decomposition. The method comprises the following steps: acquiring a date feature vector and a meteorological feature vector of a to-be-predicted day, and a historical comprehensive feature matrix; screening a plurality of historical days with the highest comprehensive similarity from the historical comprehensive feature matrix as similar days; performing time sequence alignment on the load sequences of the screened similar days; extracting a trend component, a periodic component and a residual component of the aligned load sequence; performing trend component prediction, periodic component prediction and residual component prediction; and performing fusion based on the trend component prediction value, the periodic component prediction value and the residual component prediction value to obtain a load prediction result of the to-be-predicted day. According to the method, a high-precision load prediction model is constructed through multi-feature weighted similar day screening, dynamic time warping time sequence alignment and a multi-resolution time sequence decomposition technology, and the load prediction precision in extreme weather is improved.

The invention discloses a power distribution network fault section positioning method based on an improved dynamic time bending algorithm. The method comprises the following steps: step 1, collecting transient zero-sequence voltage of a power distribution network model bus; step 2, collecting transient zero-sequence current data at two ends of each section by using a VMD algorithm; 3, forming time sequences corresponding to the two ends of each section; step 4, solving the distance by using an improved dynamic time warping (VDDTW) algorithm, and standardizing the VDDTW distance; and 5, setting a threshold value, and comparing the size of the standardized VDDTW distance to realize the judgment of a fault section.

The invention relates to a multi-modal emotion recognition fusion and communication method oriented to real-time human-computer interaction, and aims to solve the problems of asynchronous modal time sequence, non-uniform feature dimension, single fusion mode, high communication delay and the like of an existing emotion recognition system and improve the recognition precision and real-time performance of the system. According to the method, voice, images and physiological signals are synchronously collected through a multi-modal input module, feature alignment is achieved through time sequence interpolation, dynamic time warping and space coordinate mapping, double-domain feature fusion is conducted in combination with a time path network and a space path network, emotional state judgment is completed through a lightweight neural network, and the emotional state recognition accuracy is improved. And outputting six types of basic emotions and confidence coefficients. Meanwhile, a low-delay communication protocol based on UDP clipping extension realizes rapid feedback of emotion data, and in combination with modal priority scheduling, data compression and bandwidth sensing mechanisms, high-efficiency and low-delay transmission is ensured, and the real-time interaction requirement in a weak network environment is met. The method has the advantages of high accuracy, low power consumption, low time delay and flexible deployment, is suitable for various real-time interaction application scenes such as voice assistants, virtual customer service, emotion accompanying and telemedicine, and has wide application value and market prospect.

The invention discloses a secondary equipment hidden fault identification method and system based on transient state-steady state data space-time alignment. The method comprises the following steps: collecting transient state recording data and steady state SCADA (Supervisory Control And Data Acquisition) data; unified time service is carried out on transient state and steady state data, and a time mark error is corrected; performing Lagrange interpolation resampling on the transient data to generate a virtual sequence aligned with a time axis of the steady-state data; constructing a cost matrix for the resampled transient sequence and the resampled steady-state sequence, and backtracking a shortest path to obtain an aligned fusion sequence; inputting the fusion sequence into a multi-scale feature fusion network, and outputting a fault type and confidence; the system comprises a data acquisition module, a clock drift correction module, a resampling alignment module, a dynamic time warping module and a fault feature extraction module. According to the method, the fusion problem caused by sampling rate difference, clock drift and dimension difference of transient and steady data is solved, millisecond-level space-time alignment is realized, and the precision of data fusion is remarkably improved.

The invention relates to the technical field of government affair services, in particular to an NLP entity recognition method based on semantic extension, which comprises the following steps: collecting multi-source time sequence data and generating an entrepreneurship ecological data graph through a dynamic time warping algorithm; dynamic evolution of a term library is realized by using a generative adversarial network, and a term semantic association graph is constructed in combination with a graph convolutional network; a double-path attention mechanism is deployed in a Transform encoder, and global semantics and local attention guided by terms are fused through a gating loop unit; modeling fuzzy query analysis into a Markov decision process, and optimizing a semantic extension strategy by adopting a reinforcement learning agent; multi-granularity features are extracted through a feature pyramid network, and model parameters are optimized in combination with a bidirectional long-short-term memory network and an online learning feedback loop; and finally, calculating entity service association strength by using a graph attention network, and generating an interpretable report based on a Shapley value attribution algorithm. A closed-loop learning system is formed, and the business starting policy matching accuracy is improved.

The invention relates to the technical field of generators, and provides an intelligent speed regulation universal generator system, which is characterized in that a global sensing module performs fusion processing on static characteristic parameters and dynamic characteristic parameters of a prime mover and parameter data of a universal generator, and realizes unified representation of the state of the whole system in combination with sensing state diagram construction; the working condition prediction module realizes short-term, medium-term and long-term layered trend prediction based on a multi-branch structure of a long-short-term memory network and a graph attention network, and ensures that a speed regulation strategy adapts to working condition evolution in advance; the target optimization module constructs operation constraint conditions based on static characteristic parameters of a prime mover, generates an elastic constraint boundary through Bayesian reasoning, and effectively improves the operation efficiency by combining dynamic adjustment of a working condition evolution trend; the execution scheduling module verifies instruction time sequence matching through dynamic time warping, and adjusts execution gain in combination with dynamic characteristic parameters of a prime motor and a load change rate, so that the self-adaptive operation capability of a generator system under complex working conditions is remarkably enhanced.

The invention belongs to the technical field of pilot ability assessment, and particularly discloses a pilot ability assessment method based on dynamic time warping and hierarchical clustering, which comprises the following steps of: acquiring eye movement data of a tested pilot in a flight simulation task process, preprocessing the eye movement data, extracting behavior indexes of each stage of a flight task, and calculating a pilot ability assessment result; generating a fixation area number sequence according to the fixation point position; in the selection target evaluation stage, a dynamic time warping algorithm is used for carrying out nonlinear alignment on the gaze sequences of all the pilots, and an eye movement difference degree matrix between the pilots is generated; based on the difference degree matrix, adopting a hierarchical clustering algorithm to group the pilots; and outputting an ability evaluation result of the pilot according to the distribution of the pilot in the difference degree matrix and the deviation information of the pilot and the teacher watching sequence. According to the method, structured comparison and capability grade evaluation of complex cognitive behaviors can be realized, and an evaluation result has relatively high objectivity and interpretability.