243 results about "Feature model" patented technology

The embodiment of the invention provides a cross-platform compatibility automatic testing method, which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, collecting chip platform information to construct a testing environment database, constructing a testing case feature model according to the instruction set features and the testing environment database, designing a test case generator based on the test case feature model, and generating a test task scheduling scheme based on the test execution framework; inputting the abnormal behavior characteristics and the performance baseline threshold value into a test result analyzer, constructing a scoring dimension containing functional integrity, a performance standard-reaching rate and a stability index, and mapping the abnormal behavior characteristics and the performance baseline threshold value to the scoring dimension to generate a test result scoring matrix, calculating a weighted average value of the test result scoring matrix and the optimized weight coefficient as a compatibility level, generating a test report based on the compatibility level, and executing regression verification; according to the invention, the comprehensiveness and accuracy of the test can be improved, and more efficient compatibility evaluation is realized.

The invention provides a power transformation equipment digital twinborn part time-varying consistency self-adaptive maintaining method and system, and relates to the technical field of power grids, and the method comprises the steps: collecting the real-time operation data of power transformation equipment, constructing a physical model, generating a digital twinborn model, and obtaining the initial state parameters of parts through deep learning training. And establishing a time-varying feature vector matrix based on the initial state parameters, performing real-time updating by using an adaptive Kalman filtering algorithm to obtain a dynamic feature model, and calculating a state deviation value of the digital twin component and the physical equipment. And when the deviation value exceeds a preset threshold value, triggering a self-adaptive consistency keeping mechanism, generating a state compensation parameter through deep reinforcement learning, applying the state compensation parameter to a digital twin model, and realizing dynamic synchronization of the digital twin component and the physical equipment. According to the invention, the state consistency of the digital twinning component and the physical equipment can be adaptively maintained, and the precision and reliability of digital twinning are improved.

The invention relates to the technical field of robots, and discloses a robot nondestructive testing method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-modal sensing data and motion state parameters; constructing a three-dimensional material feature model and analyzing to generate an initial defect feature evaluation model; loading material attributes and detection precision constraints to generate a comprehensive model, and combining motion parameter simulation to obtain path regulation and control data; training the dynamic defect prediction model to obtain a detection path correction model and generating initial detection parameters; obtaining dynamic correction simulation information based on the initial parameters and the like; constructing a multi-objective optimization model, and optimizing to obtain an optimal detection path parameter; and generating a safety detection probability by combining real-time model reasoning, and adjusting a detection strategy to realize dynamic path matching. The system comprises a data acquisition module, a feature modeling and analysis module, a comprehensive evaluation and simulation module and the like. The method improves the detection precision, efficiency and adaptability, and is suitable for nondestructive detection of complex targets.

The invention discloses a PLC controller fault detection system, and relates to the technical field of industrial control equipment fault detection.The system is characterized in that PLC operation environment data is acquired through a multi-physical-quantity holographic acquisition module, and after the PLC operation environment data is cleaned and subjected to feature fusion through a data preprocessing module, a fault model is constructed through a multi-physical-quantity fusion model module; the self-adaptive threshold value judgment module dynamically calculates and judges a threshold value and evaluates a state; the fault traceability analysis module constructs a propagation path diagram based on a model and historical cases, and realizes accurate traceability of a fault source and a propagation process; according to the invention, multi-physical-quantity holographic acquisition and feature fusion algorithms are integrated, multi-dimensional parameters are monitored synchronously, a comprehensive feature model is constructed, the fault identification precision is improved, and misjudgment is avoided; dynamic optimization is achieved through self-adaptive threshold judgment, the early warning accuracy is improved, meanwhile, accurate traceability is achieved through an element fault propagation algorithm, a path is optimized in combination with historical cases, the downtime is shortened through full-process intelligent support, and the maintenance cost is reduced.

The invention discloses a mobile game scene interaction intelligent optimization method based on a generative adversarial network, and aims to improve the game experience of players. Comprising the following steps: S1, collecting behavior data and interaction information of a player in a mobile game, and generating a preliminary behavior feature model; s2, constructing a generative adversarial network model; s3, generating diversified game scene interaction examples according to the trained generator; s4, evaluating and optimizing the generated scene interaction example by using a discriminator; s5, real-time feedback of the player is received, and the behavior feature model of the player is updated; s6, dynamically adjusting a generation strategy based on the updated behavior feature model, and optimizing a scene interaction effect; s7, environment data are integrated, scene interaction is optimized in real time, and the immersion of players is enhanced; and S8, providing user-defined scene interaction options for the players, and allowing the players to adjust according to personal preferences. The interactive experience and game fun of players can be effectively improved.

The invention discloses a workflow-based multi-modal water conservancy large model decision support method, which comprises the following steps of: S1, preprocessing multi-modal input data based on a local model, and converting a data format; s2, constructing a modular workflow supporting parallel processing through a specified DSL (Digital Subscriber Line) framework; s3, constructing a feature model suitable for the water conservancy field through the multi-modal data subjected to synchronous acquisition and processing, and performing field adaptation and fine adjustment on the feature model; s4, on the basis of the feature model subjected to domain adaptation and fine adjustment, a specified workflow is constructed, and multi-modal task resources are dynamically scheduled; and S5, combining a local knowledge base with networking data by using a dynamic fusion algorithm to realize dual-channel knowledge fusion for executing the multi-modal task in the S4 in parallel. According to the invention, real-time acquisition and parallel processing of multi-modal data can be realized, and the stability of a local knowledge base and the real-time performance of networking data are dynamically balanced through a dual-channel knowledge fusion mechanism.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention discloses a reservoir parameter prediction method based on an improved Transform. The reservoir parameter prediction method comprises the following steps: collecting logging data and corresponding rock core lithologic data; preprocessing the logging data through a rock core homing method; selecting an input logging characteristic curve through Pearson linear correlation analysis, and carrying out standardization processing on the input logging characteristic curve; the method comprises the following steps of: improving a Transform by using a TransBlock multi-head attention mechanism and a Real-LN residual structure, and performing feature extraction on a training sample by using a TransBlock algorithm obtained after improvement to form a TransBlock feature model; and obtaining a reservoir parameter prediction result from the to-be-detected data through the TransBlock feature model. According to the method, the problems of less data and insufficient generalization ability in reservoir parameter prediction are solved by improving Transformer, the prediction performance and generalization adaptation ability of the model are improved through limited logging data and hidden correlation between the logging data, and the accuracy of reservoir parameter prediction is remarkably improved.

The invention discloses a commodity evaluation monitoring method and system based on multi-dimensional data, and relates to the technical field of evaluation monitoring, and the method comprises the steps: collecting the multi-dimensional data, and carrying out the preprocessing of the multi-dimensional data; performing feature extraction on the preprocessed multi-dimensional data, and fusing the extracted features into a comprehensive feature vector; based on the comprehensive feature vector, through a space-time convolutional neural network, identifying and evaluating the space-time distribution of the hot spots; based on the spatial and temporal distribution of the evaluation hotspots, identifying areas and time periods of the evaluation hotspots, and evaluating the trend of the hotspots according to an identification result; and performing dynamic early warning according to the trend of predicting and evaluating the hot spots, and generating an evaluation report. According to the method, spatial-temporal feature modeling is carried out on data through STCNN, and the distribution conditions of evaluation hotspots in different time and spaces are identified. Based on the spatio-temporal characteristics, the area and time period of the hot spot can be accurately positioned and evaluated, and the diffusion trend of the hot spot can be predicted and evaluated.

The invention relates to the technical field of deep learning and computer vision, and discloses a histogram interactive attention-based pavement crack segmentation network processing method and system, so as to enhance the edge detail fidelity and improve the crack segmentation precision. The method comprises the steps of image preprocessing, up-sampling, down-sampling, feature fusion and image reconstruction processing. Wherein global feature modeling in the intensity sub-boxes and among the sub-boxes is realized by constructing a histogram interactive attention module (HIA); a double-branch detail enhancement feedforward module (DDEF) is introduced to enhance spatial detail and high-frequency edge information expression; meanwhile, a Fourier jump enhancement module (FFSM) is adopted to jointly refine jump connection features in a spatial domain and a frequency domain. Through the synergistic effect of the modules, the network can realize continuous recovery and structural consistency modeling of a crack boundary in a complex pavement environment, so that the accuracy and the stability of a segmentation result are remarkably improved.

The invention discloses a modularized extensible Internet of Things multi-scene dynamic regulation and control system and a regulation and control method, and belongs to the field of intelligent regulation and control. The invention discloses a modularized extensible Internet of Things multi-scene dynamic regulation and control system and method. The system comprises a data acquisition unit, a cloud analysis unit and a regulation and control execution unit. According to the invention, the problem of single scene in the prior art is solved, the efficiency and reliability of data transmission are ensured by collecting multi-scene data of the Internet of Things in real time and automatically selecting the optimal communication protocol and link according to the network condition, cloud analysis is combined, and according to the preset scene feature model, the data is subjected to multi-scene data transmission. According to the invention, the scene type can be automatically identified and the corresponding regulation and control rule can be called, the accurate and real-time regulation and control of the Internet of Things equipment can be realized according to the regulation and control rule, the operation efficiency of the equipment and the overall performance of the system can be effectively improved, the regulation and control process can be evaluated and analyzed through feedback information, the regulation and control rule can be automatically adjusted, and a continuously optimized closed-loop control system can be formed.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

An AGV robot vision acquisition and avoidance control method based on deep thinking relates to the field of non-electrical variable control or adjustment systems, and comprises the following steps: acquiring real-time motion parameters of an automatic guided vehicle, calculating a key path point set and constructing feature vectors; collecting a visual image and depth data, extracting obstacle features through grid division, and generating a three-dimensional feature model; mapping the path points to a three-dimensional model to generate a dynamic scene sequence, and calculating a distance value with a dynamic target to determine a risk level; analyzing the scene by using a large language model, generating an obstacle avoidance scheme containing an obstacle avoidance track, speed and steering, and storing the obstacle avoidance scheme in a scheme library; current scene features are obtained, candidate schemes are selected through similarity matching, and the optimal scheme is selected for execution through safety scoring. By implementing the method, the obstacle avoidance success rate of the automated guided vehicle can be improved.

The invention discloses a deep learning-based mechanical multi-joint mechanical property prediction method, which comprises the following steps of: performing fine model simulation of a plurality of joints of SPR, FDS and welding spots, creating a plurality of fine models of mechanical connection joints, and realizing data construction; collecting layout feature information of the CAE fine model or the experimental material sheet; performing feature selection on the joint database based on a random forest method; a strength feature model is constructed based on a convolutional neural network, feature information data selected through a random forest method is used as a training set, corresponding joint strength feature information is used as a label, and the strength feature model of multiple joints is trained; a plurality of convolutional neural network models are integrated, predicted multi-joint strength feature information, model evaluation and model application are comprehensively obtained, historical data and simulation results are used as training data, and the deep learning model can efficiently predict mechanical properties when the number of joints and positions change.

The invention provides a writing track optimization system based on machine learning, and relates to the technical field of machine learning and artificial intelligence, and the system comprises a data collection module which synchronously collects inertial measurement and visual track data, and generates standardized writing data through dynamic alignment and coordinate unification; the feature extraction and fusion module is used for extracting spatio-temporal features and fusing multi-source features through a cross-modal attention mechanism; the model building module is used for building a double-branch multi-task collaborative model, classifying branches to predict character categories, regression branches to generate a space-time coordinate sequence, and designing a multi-objective optimization function fusing cross entropy loss, trajectory distance error and shape constraint loss; and the trajectory generation and recognition module is based on a two-stage training strategy of dynamic division, optimizes the overall shape similarity of the trajectory in the first training stage, enhances the distance precision of trajectory points in the second training stage, and realizes high-precision trajectory reconstruction and character recognition. According to the method, the problem of writing jitter is effectively solved, and the track smoothness and the recognition robustness are improved.

The invention discloses a digital AI intelligent analysis system and method based on big data, and relates to the technical field of financial data protection systems. Comprising a data arrangement module which is responsible for collecting original data and carrying out cleaning and normalized data arrangement operation; the feature engineering module is used for automatically extracting key features of data through a data mining technology; and the model training module is used for performing model training by applying a machine learning algorithm. According to the method, key features are automatically extracted by adopting an advanced data mining technology based on a feature engineering module, and a feature set is optimized through methods such as recursive feature elimination; model training time is shortened, the performance of the model is improved, the model training module supports multiple machine learning algorithms and allows a user-defined model, and the performance of the model is further improved through the hyper-parameter tuning function; in addition, the model storage and management unit simplifies the deployment and iteration process of the model.

The invention relates to the field of mechanical design and manufacturing, and provides a three-dimensional model processing method, a training and application method of a system, equipment and a medium, and a target boundary representation three-dimensional model is obtained based on a preset complex manufacturing feature model and a boundary representation three-dimensional model. The complex manufacturing feature model is added to the boundary representation three-dimensional model containing the simple structure manufacturing feature model, so that the boundary representation three-dimensional model containing the simple structure manufacturing feature model and the complex structure manufacturing feature model is synthesized. According to the three-dimensional model processing scheme provided by the invention, the complex manufacturing feature model is added in the boundary representation three-dimensional model, so that when the boundary representation three-dimensional model obtained by the processing scheme is used for training an image classification system, the image classification efficiency is improved; the generalization ability of the image classification system and the classification accuracy of the manufacturing feature model in the boundary representation three-dimensional model can be improved, so that the production efficiency and quality of process products are improved.

The invention relates to the technical field of digital employees, and discloses a digital employee creation method and system based on artificial intelligence, and the method comprises the steps: obtaining and analyzing post data, generating a digital employee portrait and a dynamic knowledge base for creating digital employees based on the post data, and creating the digital employees based on the digital employee portrait and the dynamic knowledge base. The system corresponds to the method. According to the method, the digital employee portrait including the skill map and the character feature model is generated through the post data, and the digital employee can dynamically adjust the skill weight based on the parameters in the character feature model by combining real-time updating of the dynamic knowledge base, so that deep cooperation of the skill and the character is realized; when a digital employee processes an instruction, the digital employee accurately calls a core skill and an associated skill, adapts to a personalized scene through character features, and meanwhile, depends on dynamic knowledge updating, the autonomous response and sustainable evolution ability of the digital employee to a complex business scene is enhanced, and the intelligent level and the actual business adaptation degree of the digital employee are effectively improved.

The invention provides an itinerary generation method and system based on a large language model. The itinerary generation method comprises the steps of obtaining a user social portrait, wherein the user social portrait is fused with at least one of historical activity information, geographic position information and social information of a user; performing multi-dimensional feature modeling on the interest points in the area where the user is located to obtain interest point embedding; the social portraits and the interest points of the users are embedded and converted into natural language descriptions, and the natural language descriptions and real-time context information are fused to generate context prompts; reasoning an activity intention of the user based on context prompt through a large language model, generating a natural language travel description, and then generating a structured activity plan by using the natural language travel description; according to the structured activity plan, candidate interest points are matched in a preset range in an area where the user is located, and recommended interest points are determined in the candidate interest points; and performing path optimization on the recommended interest points to generate a trip.

The invention discloses a humanoid robot path planning system based on an obstacle avoidance algorithm, and the system comprises the steps: S1, obtaining multi-source environment data, and generating a multi-dimensional dynamic environment representation; s2, realizing real-time avoidance of dynamic obstacles through local path re-planning; s3, a path multi-objective optimization module is communicated with a self-adaptive path adjustment module, and an optimal path with global adaptability is generated through a reinforcement learning mechanism and an iterative optimization process; s4, a multi-modal data fusion module which constructs a unified environment feature model through a feature extraction and fusion algorithm; s5, based on an initial path generated by a path multi-objective optimization module, performing dynamic correction on the path by using a nonlinear programming technology, and updating spatial distribution of path points in real time; and S6, quickly searching the global path space through a topological optimization algorithm, and determining a global optimal path. The method has the advantages of strong real-time performance, high adaptability, strong path optimization capability and accurate environment perception.

The invention discloses a method for improving code writing efficiency. The method comprises the following steps: S1, acquiring and preprocessing a user programming instruction sequence; s2, constructing a behavior feature model, and generating a semantic map sequence in combination with big data analysis; s3, analyzing a user code and a project structure, and extracting context features of the code; s4, based on the semantic map sequence and code context features, using Transform to predict a current position code sequence under grammar constraints, and generating candidate segments; s5, grading and sorting the candidate segments according to the context structure matching degree, the semantic similarity and the confidence coefficient of the behavior feature model; s6, presenting the candidate code snippet with the highest score to the user and recording the response behavior of the user; and S7, storing the candidate code snippets and user feedback data, and updating model parameters and generation logic in real time. According to the method, through behavior-driven modeling, context fusion prediction and a multi-factor dynamic scoring mechanism, the code writing efficiency and quality are remarkably improved.

The invention discloses an AR 6DoF tracking and large space identification method based on a WeChat applet, and the method comprises the following steps: S1, obtaining the position and attitude data of equipment, and constructing a data set; s2, constructing a virtual-real space coordinate system alignment standard, and preliminarily aligning a virtual object and a real scene; s3, point cloud data are collected and processed, PointNet + + is used for denoising and simplification, and a three-dimensional space feature model is constructed; s4, in combination with ICP and a global optimization alignment algorithm, realizing accurate docking of a virtual object and a real scene; s5, updating the data of the dynamic sensor, and keeping the real-time stability of the virtual object and the real scene; and S6, based on the updated alignment relationship, virtual object space repositioning is carried out, and synchronization is continued. According to the method, the ICP algorithm and the least square method global optimization alignment algorithm are combined, accurate alignment of virtual and real space coordinate systems is achieved, and the purpose of large space recognition is achieved.

The invention discloses a twin modeling method and device based on a geographic process, equipment and a medium. According to the method, firstly, a target object of the geographic process is constructed according to definition of twin objects in the geographic process, a graph topological structure is constructed according to the relation between the target object and geographic environment factors, and then the influence value of the geographic environment factors on the target object is extracted by utilizing a relation feature model; and finally, according to the influence value, carrying out quantitative reconstruction on the graph topological structure to obtain a geographical process evolution relation graph, and completing twin modeling of the geographical process. In the process, the target object is obtained by defining the twin object in the geographic process, then the relation feature model is used for mining the relation feature, namely the influence value, between the target object and the geographic environment factor, and finally the graph topological structure established by the target object and the geographic environment factor is subjected to quantitative reconstruction according to the influence value. Therefore, the geographical process influenced by various geographical environment elements can be coupled to realize twin modeling of the geographical process.

The invention discloses a shore power load prediction method and system based on a self-attention mechanism, and the method comprises the steps: building a high-precision load prediction model through combining a historical load time sequence, an environment feature and a ship feature as input features, and employing a multi-head self-attention mechanism of a Transform model; multi-modal feature representation is generated through feature embedding and position coding, a global dependency relationship between a time sequence and multi-modal features is captured by using a multi-head self-attention mechanism, deep features are further extracted in combination with a feedforward neural network, and an accurate load prediction result can be generated after the model is subjected to multiple rounds of training and verification. According to the method, the load prediction precision, the prediction efficiency and the robustness and adaptability of the model are improved, and energy optimization of a green port is supported.

The invention relates to the technical field of computers, and discloses a test case generation method, device and equipment and a storage medium, and the method comprises the steps: constructing a scene feature model based on the multi-source sensing data of a vehicle collected in a to-be-tested target scene, and matching a target source scene from a pre-constructed test template library based on the scene feature model; mapping test configuration information corresponding to the target source scene to the target scene to generate an initial test template adaptive to the target scene; and generating a target test case in combination with the scene feature model, the initial test template, the multi-modal interaction rule base and the target test case evaluation rule. The scene coverage rate, the scene adaptability and the test efficiency of the intelligent cabin test can be improved.

The invention discloses a multi-modal large language model training method based on indication data set optimization. The method comprises the following steps: S1, initial training: training a multi-modal large language model by using an initial multi-modal data set; s2, updating the indication data set; after the multi-modal data set is changed, inputting the multi-modal data set into a multi-modal dynamic diffusion network to extract dynamic characteristics, and then dynamically screening and adjusting an indication data set according to the importance and the change condition of the dynamic characteristics; s3, updating a feature fusion mechanism: extracting static features from the multi-modal data set by using a traditional multi-modal language feature model, and fusing the dynamic features and the static features to obtain fused features; s4, joint training: performing joint training on the multi-modal large language model according to the indication data set and the fusion features; and repeating the steps S2-S4 until the training is completed. According to the scheme, the trained model can have better generalization ability and performance.

The invention relates to an intelligent simulation model framework construction method. The method comprises the following steps: S1, completing model label design; s2, constructing a model system architecture specification; s3, based on the model label and the model system architecture specification, establishing a knowledge graph intelligent recommendation model; and S4, completing generation and development of a simulation model by using the knowledge graph intelligent recommendation model. According to the invention, through a label design method, a multi-dimensional and multi-feature model system label library is constructed, a model system knowledge graph is formed, and a label-based model system framework intelligent recommendation capability is provided, so that generation, development and reconstruction of a simulation model are realized; the problems that the model development learning cost is high, the development efficiency is poor, and the intelligent level is low are solved.

The invention discloses a frequency converter fault prediction method based on artificial intelligence. The method comprises the following steps: firstly, acquiring sensing data sets corresponding to a plurality of parameters of the frequency converter respectively, selecting a target parameter from the plurality of parameters, and further acquiring a plurality of target data sets corresponding to the target parameter in historical data; secondly, constructing and training an integrated learning model, calculating the correlation degree between any two data sets in each target data set of the target parameters through the model, and combining the correlation degree into a correlation array of the target data sets; then, analyzing and processing the correlation array corresponding to each target data set, calculating to obtain a plurality of representative vectors of each target data set, and constructing a spatial feature model based on the representative vectors; and finally, collecting a real-time data set of the target parameter in the current time period, calculating to obtain a real-time representative vector, and inputting the real-time representative vector into the spatial feature model to obtain a fault prediction result. The method can effectively improve the accuracy and efficiency of frequency converter fault prediction.

The invention discloses an evaluation method for a multi-modal large model, and the method comprises the following steps: S1, data collection: collecting multi-modal data from a multi-modal data source, including images, voices and / or texts, the data of each modal including features related to the performance of the model; s2, model training: training a multi-modal large model by using the collected multi-modal data; s3, performance evaluation: performing performance evaluation on the trained multi-modal large model; s4, model optimization: optimizing the multi-modal large model according to a performance evaluation result; and S5, result analysis and suggestion: carrying out result analysis on the optimized multi-modal large model, and providing a corresponding optimization suggestion. The scheme has the advantages of less manual participation, high efficiency and high accuracy, and is suitable for the evaluation requirements of various multi-modal large models.

An AI-powered system for automated, genre-specific, and context-dependent video summarization, consisting of: an input module configured to receive and store healthcare video data comprising a plurality of frames, the input module comprising a storage module configured to store the input video; a centralized processing unit comprising a plurality of modules implemented by an AI-assisted processor, a memory, and a graphics processing unit, wherein the memory stores instructions executed by the processor and the graphics processing unit, the centralized processing module comprising: a data preprocessing module configured to receive an input video having a plurality of frames and normalize the plurality of frames to generate preprocessed frames; a genre-specific complexity calculation module configured to quantify the perceptual complexity of each preprocessed frame using genre-specific metrics to generate frame-by-frame complexity values; a subshot-wise analysis module configured to aggregate the frame-wise complexity values using a sliding window approach to generate subshot complexity vectors; a time complexity graph module configured to model temporal relationships between sub-shots by creating a graph in which sub-shots are represented as nodes and temporal similarities between sub-shots are represented as edges; an adaptive thresholding module configured to dynamically adjust complexity thresholds based on local and global trends in the subshot complexity vectors; a complexity-based model selection module configured to classify video frames as complex or non-complex based on dynamically adjusted complexity thresholds and pass each frame to an appropriate neural network architecture; a spatial feature extraction module connected to the complexity-based model selection module and configured to extract spatial features from each frame using the corresponding neural network architecture and apply spatial pyramid pooling to the extracted features; a temporal feature modeling module configured to process the spatial features to capture temporal dependencies in the video; and a video summary generation module configured to predict frame importance values, select frames using diversity-aware optimization, and generate a summarized video while maintaining procedural accuracy and relevance; and an output module connected to the central processing unit and configured to display the aggregated video via a user interface, wherein the user interface also facilitates uploading of the input video.