61 results about "Fusion frame" patented technology

The invention relates to the technical field of computers, in particular to a server running state monitoring method and system and a medium. The method comprises the steps of obtaining time sequence operation and maintenance data of a system service; the method comprises the following steps: mapping an unstructured log text into a low-dimensional dense text vector by adopting an embedded learning method, splicing the text vector and a standardized numerical vector of a structured index to obtain a unified high-dimensional feature vector, and forming a cross-time vector database; constructing an AI model used for time sequence prediction, anomaly detection and cascade reasoning of classification decision based on a multi-model fusion architecture; training the AI model based on the vector database; and inputting the real-time feature vector into the trained AI model, and outputting to obtain a decision result of the system service operation state. Uniform expression of cross-modal features is effectively realized, cascade model architecture design is cooperated, the dynamic adjustment capability of the model and the accuracy of composite fault detection are improved, and rapid decision-making of fault types is realized.

The invention discloses a remote sensing water body extraction method and device based on a U-Net and Transform fusion architecture, and relates to the technical field of remote sensing image intelligent processing and artificial intelligence, and the method comprises the steps: firstly obtaining a multispectral remote sensing image and knowledge-based product data, and carrying out the blocking, so as to obtain a multispectral image block and a space attention layer block; a U-Net encoder extracts spatial features, a Transform module fuses the spatial and temporal features and focuses on a water body area, and a decoder outputs a probability graph and processes the probability graph to generate a binary mask graph. And finally, splicing the mask graphs to generate a month-by-month data set. According to the method, multi-source data fusion and space-time modeling can be realized, the small water body and boundary identification precision is improved, large-range efficient coverage is realized under high resolution, and the requirements of high precision and high timeliness are met.

The invention discloses a deep counterfeit video detection method based on multi-dimensional feature collaborative modeling, which belongs to the field of computer vision and comprises the following steps of: preprocessing a complete face image by using a face recognition technology to obtain an image of a face local area; respectively extracting frequency domain features and spatial domain features of the face local region; carrying out adaptive weight modulation and nonlinear fusion on the extracted spatial domain features and frequency domain features by adopting a position sensing double-domain fusion module; the contribution degree of the local region in the local-global feature fusion process is adjusted, and a final global fusion feature is obtained; and the loss of each task is automatically weighted and balanced, and the prediction classification of the forged video is realized. According to the method, a multi-dimensional feature fusion framework of local and global and spatial and frequency domains is adopted, so that the detection precision of the deeply-forged video is effectively improved.

The invention discloses a video monitoring and AI linkage intelligent alarm verification system, and particularly relates to the technical field of video analysis, which comprises the following steps: carrying out dual anomaly preliminary screening by using a flow field structure entropy and a signal track singular value ratio, eliminating environmental noise interference, and constructing a multi-mode normal state baseline; generating a multi-modal event report containing a spatio-temporal context, uploading the multi-modal event report to a cloud, inputting the multi-modal event report into a physical perception cross attention network, configuring a physical embedding vector into a query vector and configuring a visual embedding vector into a key vector and a value vector through an asymmetric feature fusion architecture, and performing multi-modal event report analysis; actively guiding the attention weight distribution of the model on the video picture by using the change trend of the physical parameters, and outputting a confidence score based on the weighted fusion feature; performing closed-loop parameter correction on the baseline model by utilizing online incremental learning based on a verification result; the problems of high false alarm rate caused by lack of physical logic constraints and poor anti-interference capability in a complex environment in traditional monitoring are effectively solved.

The invention relates to a knowledge tracking model fusing dynamic edge weight and forgetting gating, and aims to solve the problems that an existing knowledge tracking model cannot effectively model dynamic association of knowledge points and neglects a memory recession effect. According to the method, by constructing a time sequence diagram neural network, based on a learnable time attenuation factor and an attention mechanism, the edge weight between knowledge points is dynamically adjusted, and self-adaptive modeling of the incidence relation evolving along with time and learning behaviors is achieved. Furthermore, according to the Ebbinghaus forgetting curve theory, personalized forgetting rate parameters are defined, and the generated memory intensity is embedded into the LSTM unit to serve as a gating signal, so that the influence of knowledge decline on the cognitive state of the student is explicitly simulated. The invention also provides a hierarchical fusion architecture, which combines the time sequence behavior sequence and the map structure information, and improves the prediction precision and generalization ability of the model. According to the method, chain state degradation caused by knowledge forgetting can be accurately identified, and a theoretical basis and decision support are provided for intelligent recommendation of personalized review paths in an adaptive learning system.

The invention relates to the technical field of energy system optimization, in particular to a multi-source heterogeneous energy data fusion method and system based on a neural network, and the method specifically comprises the steps: collecting and preprocessing structured, semi-structured and unstructured multi-source heterogeneous energy data, carrying out the feature convergence of the energy data based on neural routing driving, forming a comprehensive feature vector, and carrying out the fusion of the multi-source heterogeneous energy data. The method comprises the following steps: firstly obtaining a comprehensive feature vector, then obtaining a collaborative feature vector generated by collaborative feature learning developed by each subsystem in a distributed system, and finally fusing the collaborative feature vector and the comprehensive feature vector based on a deep fusion network to form a centralized and distributed fusion architecture, thereby realizing accurate fusion of centralized and distributed multi-source heterogeneous energy data. The problems that existing energy data sources are wide, formats are diversified, fusion difficulty is increased dramatically, compatibility and collaboration dilemma is caused remarkably by different service system architecture differences, and an existing fusion algorithm is difficult to consider precision and efficiency at the same time can be solved.

The invention discloses a small target detection method and device based on a neural network, a server and a storage medium, and belongs to the technical field of target detection. Comprising the following steps: performing convolution and MDPEM module processing on primary features to obtain high-resolution detail features; performing convolution and MDPEM module processing on the high-resolution detail features to obtain medium-resolution balance features; the medium-resolution balance features are processed through a multi-scale wavelet down-sampling layer and an MDPEM module, and low-resolution semantic features are obtained; performing AIFI processing on the low-resolution semantic features to obtain attention weighted semantic features; inputting the attention weighted semantic features, the medium-resolution balance features and the high-resolution detail features into a fusion architecture to obtain deep fusion features; and inputting the deep fusion feature into a decoder to obtain a small target detection result. Through multi-scale feature enhancement and fusion architecture, accurate detection of a small target is realized.

The invention provides a non-technical line loss state evaluation method based on multi-scale spatial-temporal feature fusion in a low-voltage distribution network environment, and relates to the technical field of electric power big data analysis and intelligent operation and maintenance of a distribution network. According to the invention, a fusion architecture based on a multi-scale time convolution network and a long short-term memory network is constructed; extracting multi-scale spatio-temporal characteristics of a user from instantaneous electricity utilization abrupt change to a periodic load rule through an MSTBlock unit; designing a cluster balance constraint mechanism to ensure that rare and key non-technical line loss abnormal early warning signals are not covered by mass normal power utilization data; according to the data scale, adaptively selecting a graph segmentation or spectral clustering integration strategy to output a clustering label, and mapping the clustering label into a user power consumption behavior evolution track; according to the method, the power utilization abnormal level can be identified from the original load signal with random fluctuation interference, and the troubleshooting priority is calculated in combination with the transformer area correlation analysis, so that the accuracy and interpretability of the non-technical line loss unsupervised evaluation decision of the power distribution network are remarkably improved.

The invention relates to the technical field of numerical calculation and data analysis, in particular to a data interpolation and fitting analysis system which comprises a data processing module, a depth generation interpolation module, an uncertainty quantification module, an interpretability optimization module, a multi-source heterogeneous data processing module and a hybrid calculation acceleration module. In the prior art, a traditional interpolation method is easy to generate over-fitting or under-fitting in a complex data distribution and high noise scene, and a single deep learning model is insufficient in generalization ability in a small sample or data sparse region; through the dynamic fusion architecture of the depth generation interpolation module, the advantages of a traditional numerical method and deep learning are combined, the weight is automatically adjusted based on data characteristics, the prediction error under noise data is remarkably reduced, the adaptability to complex distribution is improved, and the stability of a data sparse region is enhanced; the problems of insufficient precision and weak generalization ability of a single model of a traditional method are effectively solved.

The invention discloses a method and a system for realizing intelligent control on the basis of a VLM (Visual Language Model) + Actionality fusion architecture. The method aims at solving the problems that an existing end-to-end vision-language-action (VLA) model is scarce in data, high in hardware coupling degree, poor in controllability, weak in compatibility and the like. The core thought is to decouple high-level semantic understanding and bottom-level action execution, finish scene understanding and task planning through a VLM module, map high-level intentions into standardized API calling through an intention analysis and action calling module, execute specific actions through an Action module library packaging a traditional algorithm, and dispatch and monitor through an action execution engine. A large amount of end-to-end training data is not needed, the research and development cost is reduced, the system modularization, expandability and hardware universality are improved, the high delay problem is avoided, meanwhile, the controllability and safety of the control process are guaranteed, and the method is suitable for various intelligent scenes with bodies such as industrial manufacturing, medical assistance and family service.

The invention discloses a power load prediction method and device based on multi-scale time-frequency collaborative learning, and the method comprises the following steps: S1, obtaining power load data; s2, establishing a power load prediction model which is used for performing multi-scale decomposition on the power load data to generate time sequence subsets of different scales; extracting a time feature and a frequency feature from each time sequence subset; performing time-frequency collaborative learning based on the time characteristics and the frequency characteristics; fusing the time feature and the frequency feature by adopting a double-layer progressive fusion architecture to obtain a fused feature; mapping the fusion feature into a load prediction value through a linear projection layer; and S3, training and verifying the power load prediction model based on the load data set, and predicting the power load by using the trained power load prediction model. The method has the technical effect of high multivariable dynamic modeling capture capability.

A multi-modal signal recognition and dialogue method based on a large language model comprises the steps that a signal coding module based on time sequence modeling is constructed, preprocessing and feature extraction are conducted on input I / Q signal data, and semantic alignment pre-training of signal features and modulation type text description is achieved through a contrast learning mechanism; designing a multi-modal fusion architecture, mapping signal features to a hidden space of a large language model by adopting a signal projector, and realizing deep fusion of the signal features and text features through special markers; constructing a dialogue generation module based on a pre-trained large language model, receiving the fused multi-modal input, and generating a natural language answer about signal analysis; performing feature alignment by training a double-layer MLP projector to realize end-to-end multi-modal signal understanding and dialogue ability; an intelligent question-answering system in a reasoning stage is constructed, natural language interaction between a user and the system is realized through a predefined professional prompt word template and a signal feature fusion mechanism, and multi-dimensional signal analysis query requirements are supported. According to the invention, the organic combination of signal understanding and natural language generation is realized, and the accuracy of signal identification and the user interaction experience are improved.

The invention discloses a digital human video rendering method and device and a medium, and relates to the crossing field of computer graphics and generative adversarial networks, and the method comprises the steps: constructing a generative adversarial model based on a generative adversarial network of a single fusion architecture; performing multi-modal preprocessing on the original audio and video data based on the digital human reference image; extracting dual-granularity speech features through a speech feature extraction module of a generative adversarial model, and fusing the dual-granularity speech features; determining a local deformation field of the digital human reference image in a UV parameterized space based on a reference key point corresponding to the digital human reference image and the fused speech features; sampling the identity texture of the digital human reference image to generate a target digital human face image; and verifying the digital human face image based on multiple scales through a discriminator of a generative adversarial model. Through dual-granularity speech feature fusion and generator multi-resolution injection, deep alignment of speech semantics and facial actions is realized.

The invention discloses a gas detection precision improving method based on a multi-algorithm fusion framework. A photonic crystal resonant cavity and a tunable band-pass filtering structure are integrated in a Fourier transform spectrometer light path; collecting a wide-spectrum light intensity signal, and constructing a spectral signal database in combination with the enhancement characteristic and the filtering characteristic of the resonant cavity; zero calibration and dynamic baseline deduction operation are carried out, and self-adaptive variational mode decomposition and wavelet transform are adopted to carry out signal denoising on the spectral signals; an environment compensation model is established, and an Arrhenius type correction factor is adopted to suppress water vapor cross interference; constructing an RLS and fuzzy control combined hybrid adaptive filter; separating aliasing spectral signals by adopting a non-negative matrix factorization algorithm; establishing a quantitative relation model; in the online concentration prediction process, the initial concentration value is subjected to recursive optimization by using a hybrid adaptive filter, the deviation is continuously corrected through an environment compensation model, and an accurate concentration value is output. The method can significantly improve the accuracy and stability of multi-gas detection.

The invention discloses a telescopic device deformation prediction and control system based on multi-modal fusion deep learning, and relates to the technical field of mechanical engineering. The system comprises a data acquisition module, a preprocessing module, a data fusion module, a deep learning prediction module, a control decision module and a feedback optimization module. The data acquisition module acquires multi-modal data by using a distributed sensor array; the preprocessing module processes the data to ensure the quality; the data fusion module fuses the data to generate comprehensive data; the deep learning prediction module outputs a predicted deformation value by means of an LSTM and CNN fusion architecture; the control decision module performs hierarchical control according to a comparison result of a predicted value and a safety threshold and comprises a fuzzy control unit optimization instruction; and the feedback optimization module optimizes model parameters and control strategies online by using a reinforcement learning algorithm, so that high-precision prediction and self-adaptive control of deformation of the telescopic device are realized, and the structural safety and the system intelligence level are improved.

The invention relates to the technical field of artificial intelligence, in particular to a customer service reply method and system of a multi-modal fusion architecture and a storage medium, and the method comprises the following steps: obtaining initial input data of a user, preprocessing the initial input data, and generating a standard data source; performing decision compliance analysis on the standard data source to generate a decision judgment result; and generating a corresponding decision scheme according to the decision judgment result. According to the invention, through a space-semantic double-constraint graph network, the technical defects of fragmentation and low standardization degree of multi-modal data identification by traditional intelligent customer service are effectively solved, and the problem of insufficient adaptation of a general identification technology to a government affair format is overcome; in a semantic constraint dimension, spatial features and a government and enterprise professional semantic dictionary are deeply fused based on a node association mechanism of a graph network, conversion from multi-modal data to structured policy elements is automatically completed, and the recognition accuracy is improved by more than 40% compared with that of a traditional NLP technology.

The invention provides a training teaching video intelligent analysis and knowledge point automatic marking method based on multi-modal fusion, and the method comprises the steps: collecting multi-modal data in a training teaching process, and carrying out the time alignment processing; extracting feature information of the modal data, fusing the feature information through a cross-modal fusion architecture, and generating a unified teaching behavior representation vector; based on the teaching behavior representation vector, identifying operation steps in the practical teaching process through a sequence labeling model, and determining the category and the starting and ending time boundary of each operation step; matching the identified operation steps with a preset skill knowledge base, and generating a standardized knowledge point label containing knowledge point content, starting and ending timestamps and confidence information; and storing the standardized knowledge point labels into a database, and constructing a retrieval index. According to the invention, the unstructured teaching video is converted into a searchable, navigable and analyzable knowledge unit, and automatic identification and structured marking of practical teaching operation are realized.

The application discloses a video moire removal method based on selective time domain fusion. The method inputs the features spliced after frame alignment module output into the selective time domain fusion module. After the spliced features are segmented by the time domain channel fusion module, the convolution layer and the activation function are used for processing and then fusion, the spatial feature adaptive module is input to refine the fusion features, the time domain information of adjacent frames is effectively fused, and the useful features of the fusion frame are adaptively explored. Meanwhile, the selective time domain fusion module effectively guides the learning of the parameters of the shallow adjacent frame alignment through the spatial exploration of the deep fusion features, and avoids the system instability caused by the excessive feature offset. In addition, the moire removal sub-network used in the method effectively removes the moire of each scale in the fusion frame by embedding the multi-scale attention module into each level of the encoder of the encoder-decoder network.

The invention is applicable to the technical field of natural language processing, and provides a multi-granularity event detection method based on adaptive fusion and type perception, which comprises the following steps of: performing multi-granularity dynamic feature fusion, a type perception graph neural network and adaptive context fusion; key challenges such as data sparsity, type imbalance and complex context understanding in event detection in the professional field are effectively solved. The multi-granularity feature fusion mechanism successfully captures event clues in different language units, the type perception graph neural network effectively relieves the problem of insufficient representation of low-frequency event types, and the adaptive context fusion architecture significantly improves the modeling ability of the model for complex long-distance dependency relationships. A powerful end-to-end event detection solution is formed through the synergistic effect of the innovation points, and a new technical thought is provided for solving typical problems in text processing in the professional field.

The invention discloses a transient evaluation method based on SHAP, and relates to the field of transient stability of a power system, and the method comprises the steps: S1, constructing a prediction-interpretation synchronization framework: embedding a Shapley module supporting parallel calculation into a deep neural network, and achieving the synchronous execution of transient stability evaluation and feature attribution in forward propagation; s2, designing a spatio-temporal feature fusion architecture: adopting a time-space-feature three-dimensional tensor as input, constructing a deep SHAP network by referring to a convolutional neural network thought, and enhancing transient process dynamic feature modeling capability; and S3, establishing a quantitative decision support mechanism: based on the quantitative influence of the Shapley value analysis feature on the stability, realizing transparent explanation of an evaluation result, and providing a decision basis for beforehand prevention and control strategy making and post emergency control deployment. According to the method, the balance between the transparency and the accuracy of transient stability evaluation is realized, and a more accurate, quicker and interpretable evaluation result is provided for power grid dispatching.

The invention relates to the field of three-dimensional scanning, and particularly discloses a fixed and handheld integrated laser scanning method and system, and the method comprises the steps: building a high-precision global coordinate system based on the mechanical and magnetic field reference of a detachable fixed module in a fixed mode, and obtaining an initial point cloud; in a hand-held mode, aligning hand-held data to the global coordinate system by identifying space-time cohesion features common in view with fixed scanning; by taking fixed data as an optimization anchor point, constructing a hierarchical factor graph model fused with an uncertainty weight, and carrying out constraint optimization on the aligned handheld data to correct an accumulative error of the handheld data; and finally, outputting a complete and consistent three-dimensional model through closed-loop verification and parameter self-adaption steps. The system comprises a scanning host, a detachable fixing module and a processing unit. According to the method and the system, the architecture error problem of mode switching is fundamentally solved through a standard unification and anchor point optimization fusion architecture, and high-precision and high-efficiency scanning of single equipment in various complex scenes is realized.

This invention belongs to the field of metal pressure processing and intelligent manufacturing, specifically relating to an intelligent prediction and process optimization method for the forming performance of hot-rolled titanium strip. The invention discloses an intelligent prediction and process optimization method for the forming performance of hot-rolled titanium strip, aiming to solve the problems of low prediction accuracy and lack of closed-loop optimization caused by the complex coupling of hot-rolling process parameters. The method includes: collecting multi-source heterogeneous data from hot rolling and constructing a multi-dimensional feature correlation matrix; establishing a prediction model based on a fusion architecture of deep convolutional neural networks and long short-term memory networks, extracting deep features of microstructure evolution and mechanical response; using the prediction model as the objective evaluation function, employing a non-dominated sorting genetic algorithm to perform collaborative optimization of process parameters throughout the entire process; and achieving real-time feedback correction and closed-loop control through an online learning mechanism. This invention achieves accurate performance prediction and global process optimization, improving prediction robustness and finished product qualification rate, and reducing production costs.

The invention relates to the technical field of brain-like intelligent driving, in particular to a brain-like intelligent driving heterogeneous fusion method, system and device and a medium, a collaborative computing basis is constructed through a fusion architecture, a brain-like pulse unit and a deep learning unit are integrated, and heterogeneous data interaction is achieved in cooperation with bidirectional signal conversion. A cross-modal distillation algorithm is proposed, knowledge from a source modal to a target modal is migrated through comparative learning, a meta-learning framework is introduced to optimize the migration process, few-sample task adaptation is supported, and multi-modal data support is enhanced; on the basis of a quantum enhancement calculation module, a neural pulse signal is coded by utilizing quantum state characteristics, parameter solution is converted into a Hamiltonian optimization problem in combination with a quantum optimization algorithm, and a classical-quantum hybrid calculation framework is formed; through event-driven calculation, long-distance dependence modeling and a quantum acceleration mechanism, cross-modal knowledge migration, adaptive task optimization and leap-type improvement of calculation efficiency are realized, and artificial intelligence technology upgrading is promoted.

Provided are a point cloud data playing method and packaging method, a device, and a storage medium. The point cloud data playing method comprises: acquiring a fusion frame of point cloud data and time information of the fusion frame, the time information of the fusion frame comprising the decoding time of the fusion frame; and decoding the fusion frame on the basis of the decoding time of the fusion frame, so as to play the point cloud data.

The invention relates to a ghost imaging neural differential analysis method under a selected plaintext attack condition in the technical field of computational imaging and optical security. The method comprises the following steps: firstly, designing a differentiated plaintext, and deducing a projection speckle by adopting differential calculation; then, constructing a dimensionality reduction simulation training set and an experimental test set based on the structural features of the projection speckles; then, constructing a one-dimensional signal noise reduction neural network model, and training the one-dimensional signal noise reduction neural network model by adopting the dimension reduction simulation training set; and finally, inputting the experimental test set into the trained one-dimensional signal noise reduction neural network model to obtain deciphered speckles, and based on the deciphered speckles, adopting ghost imaging correlation operation to obtain a cracked plaintext image. That is to say, based on the basic principle of ghost imaging encryption, the inherent linear property of the ghost imaging light path is utilized, and the fusion architecture of the differential attack and the neural network is used, so that the cracking of various ghost imaging encryption methods can be realized, and the universality and reliability of ghost imaging encryption analysis can be improved.

The invention relates to the technical field of automobile starting, in particular to an AMT starting control method, system and device based on a residual network and a storage medium, and the method comprises the steps that the residual network is constructed, historical semaphores needed for clutch and engine control during automobile starting are collected, and clutch sliding friction work, clutch impact degree and engine torque are selected; constructing a data set as the input of the network by the three parameters through a binary converted data image; a self-attention mechanism is introduced into the network to extract different types of image features, importance of different features is dynamically learned by using a gating mechanism, and importance weight distribution is performed on the extracted features; training a network model by using the collected training sample set to obtain a mapping relationship between input and output; and finally, the output value of the network is the optimal starting control parameter after the network decision. According to the method, by introducing the fusion architecture of the residual network and the attention mechanism, the comprehensive performance of AMT starting control is effectively improved.

The invention relates to the technical field of visual language task processing, and discloses a visual language task processing method and device based on hierarchical optimal transmission and a medium. Firstly, an attack specific data set and a prompt specific data set are constructed, and diversified sub-models are constructed from the dimension of attack type confrontation; and text prompt is creatively introduced as a second dimension, so that the sub-models have complementarity in robust characteristics and a characteristic alignment mode. Secondly, a thought of directly fusing all sub-models is abandoned, and a two-stage fusion framework is designed: the sub-models with the same attack type and different prompts are fused firstly, and then fusion results of different attack types are subjected to secondary fusion, so that the semantic similarity between the models during each alignment is effectively ensured, and the fusion efficiency is improved; therefore, the accuracy of the fusion process based on the optimal transmission alignment method is improved. Therefore, the robustness of the visual language model is improved on the basis of ensuring the accuracy of processing the visual language task by the visual language model.

The application discloses an intelligent hierarchical caching method and system based on a super-fusion architecture, which comprises fusion architecture collection, initial caching hierarchy, three-way load prediction, caching resource scheduling and intelligent hierarchical caching. The application relates to the technical field of data caching intelligent hierarchy, and particularly discloses an intelligent hierarchical caching method and system based on a super-fusion architecture. The initial caching hierarchy method based on heat score calculation is adopted in the scheme, a more dynamic, fine-grained and controllable three-layer caching initialization structure is realized, and the cold and hot data misplacement rate and initial scheduling cost are effectively reduced. A time sequence alignment enhanced adaptive load three-channel prediction model is adopted, a time sequence alignment enhancement and neighborhood enhancement mechanism are introduced, a double-channel gate fusion backbone network of a time channel and a resource channel is combined, and three-way load prediction is performed. A reinforcement learning method based on risk-sensitive reward improvement is adopted for caching resource scheduling.

The invention relates to an active sonar target identification method based on multi-pulse accumulation and two-stage fusion. The method comprises the following steps: firstly, acquiring a multi-dimensional feature value of a target, performing multi-pulse (ping) accumulation on each feature to form a feature subset, and performing feature-level fusion through a fusion operator so as to extract stable feature representation with strong discriminability; on the basis, the probability that each fusion feature belongs to a real target and a non-real target is calculated based on prior distribution, and decision-level fusion is performed on probability evidences of all the features by adopting a D-S evidence theory, so that the credibility of a final recognition result is obtained. Through time sequence accumulation and a two-stage fusion architecture, multi-dimensional information and time sequence information of a target are effectively integrated, the recognition accuracy and decision robustness are remarkably improved, and meanwhile, the false alarm rate is greatly reduced.

The invention belongs to the technical field of redundant network verification, and particularly relates to a redundant network verification method fusing frame type dynamic mapping and adaptive optimization, which comprises the following steps: acquiring input combined frames, and judging whether the frame types of the input combined frames are the same or not; detecting whether the input combined frame has an error frame or not; when the frame types of the input combined frames are the same and error frames do not exist, the input combined frames enter a preset first mapping table for verification logic selection, and table items of the first mapping table are updated; otherwise, the input combined frame enters a preset second mapping table for verification logic selection, and table items of the second mapping table are updated; the first mapping table and the second mapping table are used for mapping the frame type and the function algorithm needing to be verified. By presetting mapping tables for different combination conditions, different frame type combinations and verification algorithms are in one-to-one correspondence, the complexity of search and update operation is low, and the method has high flexibility and expandability.