142 results about "Model migration" patented technology

The invention relates to the field of equipment state monitoring in industrial Internet of Things, and discloses an equipment state monitoring, analysis and evaluation method and system based on big data, and the method comprises the steps: carrying out the adaptation of a multi-source heterogeneous data protocol, and carrying out the cleaning of a dynamic mask, and generating a standardized data stream; constructing a dynamic hypergraph of an embedded constraint equation based on physical topology; combining incremental tensor decomposition with manifold constraint to update a core tensor; abnormal association is positioned based on singular value distribution and a hyperedge propagation algorithm; cross-equipment model migration is realized through topological optimal transmission and knowledge distillation, and a target equipment evaluation model is generated; the system comprises a data preprocessing module, a hypergraph modeling module, a tensor analysis module, a state evaluation module, a transfer learning module and a dynamic tuning module. According to the method, through multi-source data dynamic cleaning, physical constraint hypergraph modeling, incremental tensor decomposition and manifold constraint, and in combination with an abnormal positioning closed loop and cross-equipment topology migration, equipment state monitoring and rapid model adaptation are realized.

The invention relates to the field of information automation management, and particularly discloses a water electrolysis efficiency dynamic scheduling method and system based on reinforcement learning. According to the method, a state space is constructed through multi-parameter dynamic perception, an improved depth deterministic strategy gradient (DDPG) algorithm is used for learning, a strategy is optimized by means of a self-adaptive exploration strategy and a multi-time scale learning mechanism, model migration and rapid starting are achieved, real-time feedback and closed-loop control are constructed, the water electrolysis efficiency is improved, and energy consumption is reduced. The method comprises a reward function and an exploration rate adjustment formula, so that the strategy optimization direction is accurate, and the exploration process is reasonable. The system comprises a data acquisition module, a processing module, a reinforcement learning module, a control execution module and a feedback monitoring module, and all the modules work cooperatively to improve the system performance.

The invention discloses a spatio-temporal cross-scale dimension reduction characterization method, system and device for a multi-modal physiological signal. The method comprises the steps of multi-modal signal time sequence alignment, sliding variable scale segmentation, single-modal spatio-temporal feature pre-training, cross-modal coupling alignment and model migration application. In the single-mode spatial-temporal feature pre-training, single-mode signal fragments under different time processes or spatial arrangements are reconstructed through a codec, and multi-level spatial-temporal modeling and analysis of a signal source are realized; in cross-modal coupling alignment, spatial-temporal characteristics of physiological signals of other modals are reconstructed through a codec, and a coupling relationship among different modals of a physiological system is learned; the model after cross-modal coupling alignment can be migrated to a specific task for fine tuning, and the comprehensive characterization capability of physiological signals in a few modals, specified space or limited time collected by a backward device on a physiological system is enhanced. According to the method, through bottom-layer dimension reduction and migration, effective characterization of the whole complex physiological state by the easily obtained signals is achieved.

The invention discloses a bearing variable working condition fault diagnosis method fusing model migration and feature migration learning, and the method comprises the steps: processing bearing vibration signals of a source domain and a target domain through wavelet transform, and extracting a time-frequency diagram; expanding the two-dimensional time-frequency graph data set by using DCGAN, and balancing the number of the two-dimensional time-frequency graph data set; then, model parameter migration is adopted, AlexNet network parameters pre-trained in a source domain are migrated, a migrated AlexNet network is constructed, and depth features are extracted; then, a domain adaptation method based on improved migration joint matching is provided, multiple strategies are fused, and a low-dimensional feature space with small distribution difference and good discrimination performance is obtained; and finally, on the basis of a labeled source domain feature data training model after domain adaptation, realizing identification and classification of unlabeled target domain feature data. The method is ideal in diagnosis performance and high in accuracy under variable working conditions and data imbalance, domain data distribution difference can be reduced by improving the migration joint matching method, and feature discrimination performance and fault diagnosis accuracy are improved.

The invention discloses an implicit gradient optimization-based large language model prison break attack resisting method, which is characterized in that continuous gradient optimization on resistance tokens is realized through a Gumbel-Softmax technology, calculation cost is reduced in combination with a two-stage proxy model screening mechanism, and semantic concealment is kept by adopting a dynamic regularization strategy. The system comprises a gradient optimization module, an agent screening module and a migration enhancement module, and can effectively improve the attack success rate and the cross-model migration capability of resistance prompts. The technical problems that a traditional attack resisting method is low in efficiency and poor in concealment are solved, the attack cost is reduced while the large model attack success rate is increased, the API calling frequency is effectively reduced, and the method is suitable for the field of large language model security testing.

The invention relates to the technical field of model transfer learning, and discloses a model adaptive optimization method based on transfer learning. The method comprises the steps that source domain model structure parameters and target domain task initial data distribution are obtained, the feature mapping relation of all levels of a source domain model is extracted, and a cross-domain feature migration reference topological framework is generated; dividing a migratable feature layer and a to-be-reconstructed feature layer according to a target domain data distribution difference, and dynamically adjusting a migration priority in combination with a sample distribution density; freezing and unfreezing the transferable feature layer layer by layer based on the priority, synchronously constructing a local feature reconstructor, and optimizing domain offset through iterative feature alignment; collecting a feature reconstruction error and a migration feature retention degree in each iteration, and calculating a dynamic balance coefficient to adjust a freezing proportion and reconstruction intensity; and fusing the two types of features through a global model integrator, and generating mixed feature representation to drive end-to-end training of a target domain task.

The invention discloses a large model migration optimization system driven by cross-modal feature alignment, particularly relates to the field of simulated confrontation, and is used for solving the problem of high-dimensional data storage and calculation efficiency of multi-source information processing in an adversarial environment. A closed loop is formed with multi-scale time agglomeration, context entropy pruning and combined change trend dynamic control, so that cross-modal high-dimensional information is balanced between compression storage and time sequence integrity, the weight in the mapping stage is adaptively adjusted according to the situation, and the residual error is corrected at any time; the long-term memory is continuously consolidated through incremental drilling and drift monitoring, and disaster forgetting is avoided; reasoning and scheduling real-time write-back performance indexes, and driving self-evolution of the structure; according to the method, the dual burden of video memory and delay caused by dimension explosion is inhibited, the migration alignment precision is kept, the strategy sequence is ensured to keep high-consistency and high-confidence output in the complex situation, meanwhile, the computing power demand is linearly reduced, and the resource utilization rate is remarkably improved.

The invention provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

The invention relates to the technical field of information security identification, and particularly discloses a multi-modal data artificial intelligence security classification identification method in combination with transfer learning, which comprises the following steps of: in a process of constructing a multi-modal behavior data set, acquiring file access frequency and network connection frequency in real time; fluctuation characteristic values are extracted through fast Fourier transform and a K-means clustering algorithm and used for evaluating internal threat and external attack risks faced by the system, the two types of characteristics are fused into a comprehensive characteristic vector, the comprehensive characteristic vector serves as input to construct a transfer learning model based on a support vector machine, intelligent recognition of the safety state of a target object is achieved, and the safety of the target object is improved. In order to improve the recognition accuracy, a proportional-integral-differential control mechanism is introduced into the system, the sampling period and the detection granularity are dynamically adjusted according to recognition errors, a closed-loop feedback optimization structure is formed, effective integration of multi-source heterogeneous behavior data, cross-scene migration of the model and online self-adaptive adjustment are achieved, and the recognition accuracy is improved. And the intelligent level of the security identification system is improved.

The invention relates to the technical field of aviation large model optimization, provides a multi-model migration optimization method and system for an aviation large model, and aims to solve the problem that the stability and consistency of knowledge migration are difficult to maintain in a complex aerodynamic environment due to dependence on a static feature space in the prior art. The method comprises the following steps: constructing a flight mechanics feature library based on flight mechanics parameters and control response data of a source aircraft model and a target aircraft model; executing coupling simulation to generate a compensation parameter set; establishing a model knowledge migration channel, reconstructing the knowledge migration channel according to the compensation parameter set, and generating a target migration channel and a migration weight regulation and control function; and adjusting the target migration channel and monitoring the distribution offset of the aerodynamic characteristic knowledge component, updating the aerodynamic difference characteristics, and iteratively executing the operation from coupling simulation to adjustment of the transmission state register until the distribution offset enters a steady-state operation interval. According to the method, high-precision knowledge migration from a wide-body aircraft to a narrow-body aircraft is realized, and the aviation airworthiness safety standard is met.

The invention relates to a data set distribution difference-oriented electronic medical record data representation learning method and system, and the method comprises the steps: pre-training a source domain teacher model through a source domain data set of an existing disease, and extracting the health state representation information of a source domain disease patient; training a domain invariant feature extractor as a transition model, modeling general features through an adversarial training strategy, and establishing an independent extraction channel for private features to realize feature alignment among different domains; and migrating parameters of the transition model to a target domain emerging disease prediction model, and performing fine tuning in combination with target domain data to realize accurate prediction of emerging diseases. Through domain invariant feature extraction and model migration optimization, the generalization ability and prediction precision of the model on different data sets are significantly improved, clinical results of patients can be accurately predicted, accurate support is provided for medical decision, and a reliable foundation is laid for the cross-domain prediction problem in medical data analysis.

The invention relates to the technical field of sensitive target recognition and adversarial attack in remote sensing images, in particular to an adversarial patch generation method of a remote sensing image detection model based on particle swarm optimization, which comprises the following steps: firstly, generating an initial aggressive patch by adopting a particle swarm optimization algorithm, and introducing a cosine annealing mechanism to dynamically adjust inertia weight and improve search diversity; then, an AdamW optimizer is used for local refined updating, an L2 regular term is added to prevent overfitting, the patch generalization ability is enhanced, the whole process is combined with a multi-stage dynamic weight mechanism and a time sequence sensing strategy, attack loss, non-printability loss and smoothness loss are subjected to self-adaptive weighting, and the patch generalization ability is improved. And the balance of attack intensity, concealment and physical implementability is realized. The adversarial patch generated by the method can significantly reduce the performance of a target detection model in digital simulation, and has good cross-model migration ability and physical deployment potential.

The invention discloses a comprehensive traffic low-altitude global intelligent sensing method based on autonomous evolution, and belongs to the crossing field of intelligent traffic and computer vision. The method comprises the following steps: collecting multi-source sensing data and extracting a structured causal variable; a dynamic causal graph is constructed, and attribution analysis of perception errors is realized through intervention learning; realizing cross-scene model migration and few-sample self-adaption based on the knowledge graph; carrying out adaptive reasoning by adopting an environment-aware dynamic graph neural network; and realizing a causal-driven autonomous evolution closed loop based on multi-source feedback. According to the method, the crossing of the perception system from'perception-optimization 'to'understanding-evolution' in a low-altitude complex traffic environment is realized, the perception precision, the scene adaptability and the system interpretability are remarkably improved, and the manual operation and maintenance cost is reduced.

The invention provides an automatic operation and maintenance method and system suitable for a closed system. The technical problem that a traditional operation and maintenance scheme is difficult to apply due to network isolation and sample scarcity in closed environments such as finance and energy is solved. The method comprises the following steps: generating a unique and traceable migration identifier for all operation and maintenance data, models and reasoning results through a data and migration management module; quantitatively calculating a transferability score between the source domain and the target domain through a transferability evaluation module; an optimal model migration strategy is dynamically selected according to the mobility score, and efficient self-adaption of the model is achieved; closed-loop operation and maintenance are executed through a multi-agent cooperation system comprising detection, diagnosis and repair agents, and self-learning and self-optimization of the system are realized through small sample active learning and a knowledge base evolution mechanism. According to the method, rapid construction, continuous evolution and whole-process traceability of the artificial intelligence operation and maintenance capability in the closed system are realized, and the method has remarkable innovativeness and industrial application value.

The invention provides an axial plunger pump fault diagnosis method suitable for marking sample scarcity conditions, and belongs to the technical field of fault diagnosis, aiming at performance degradation caused by marking sample scarcity in axial plunger pump fault diagnosis of a deep learning model. According to the method, the dynamic pressure simulation model is constructed under the condition that the marked samples are scarce, and unmarked samples can be supplemented by generating high-quality simulation samples; a model migration framework and a semi-supervised multi-adversarial pre-adaptation strategy are constructed, and the generalization ability of the model in a target domain task is improved. Experimental results show that high-quality simulation samples are generated, and the plunger pump fault sample recognition rate under the condition that marked samples are scarce is increased.

The present application relates to a model migration method in the field of artificial intelligence, including: obtaining sample data of a target task, wherein the sample data includes multiple image samples; evaluating N pre-trained models based on the sample data to obtain N evaluation values, which are used to characterize the degree of fit between the pre-trained model and the target task, where N≥2; determining K pre-trained models from the N pre-trained models based on the N evaluation values, wherein the K pre-trained models are models corresponding to the first K evaluation values after sorting the N evaluation values from large to small, where 1≤K≤N; processing the sample data based on the K pre-trained models to obtain a target model for processing the target task, wherein the target model includes K pre-trained models. In this way, a pre-trained model suitable for the current task can be quickly found from a model library composed of a large number of pre-trained models, and the capabilities of multiple optimal pre-trained models can be integrated to jointly solve the current task, effectively improving the distribution-outside generalization capability of the pre-trained model.

The invention relates to the technical field of spectral data processing, and discloses a Raman spectrum model migration transfer method based on a standard substance, which comprises the following steps: S1, acquiring Raman spectrums under different devices as training data; s2, carrying out beam alignment; s3, performing point-by-point soft weighting on the spectrum after beam alignment by adopting a multi-scale parallel gating convolution strategy to obtain an enhanced feature map; s4, windowing segmentation is carried out based on the enhanced feature map, and intra-segment point-by-point gain is carried out; s5, superposing the gained segments, and carrying out normalization by using window energy to obtain local equalization output; s6, calculating a loss function according to local equalization, and optimizing model parameters; aiming at the common bottlenecks of baseline noise inconsistency, peak position offset, noise interference and the like in the existing Raman spectrum cross-device analysis process, the method is not limited to traditional piecewise linear migration, and self-adaptive spectrum migration is realized through the convolutional neural network, so that the spectrum consistency and comparability between different devices are improved.

The invention provides a corresponding method for solid engine digital twin modeling and design optimization aiming at the problems of lack of an integrated parametric modeling framework, weak proxy model mapping capability, lack of a cross-working-condition / cross-model migration mechanism and the like in the background technology. The invention provides a digital twin modeling and optimizing method for a solid engine. The method comprises the following steps: step 1, setting parameters and inputting a model; 2, parametric modeling and simulation calculation are carried out; 3, configuring input and output of the proxy model; 4, extracting hidden variable features; 5, training a mixed structure agent model; step 6, parameter optimization based on a calibration mechanism; and 7, optimizing a result and analyzing.

The invention discloses a cross-tax finance and taxation inspection method and device and a medium, and the method comprises the steps: obtaining multi-modal data according to a preset business path, carrying out the evaluation calculation of the multi-modal data, carrying out the processing of the multi-modal data according to a preset multi-modal cross verification mechanism, and carrying out the evaluation calculation of the multi-modal data; constructing a finance and taxation knowledge graph according to the set of entities and relationships, and determining priori knowledge according to the finance and taxation knowledge graph so as to determine a heterogeneous graph risk pre-training model in combination with the priori knowledge and a preset attention network; training the heterogeneous graph risk pre-training model according to a pre-sampled sample, and performing feature space alignment and model migration on the trained model to obtain a cross-tax migration model; and performing multi-tax risk collaborative identification through the cross-tax migration model to determine a risk point, performing motivation analysis to determine a motivation probability of the risk point, and performing finance and tax inspection decision according to the motivation probability so as to determine a finance and tax inspection report.

The application discloses a table question and answer task ability enhancement processing method of a large language model. For table data, a TABLE-UAM network including an encoding part and a decoding part is constructed; different table data sets are sequentially input into the TABLE-UAM network for two-stage training, the first stage is difference reconstruction training, and the second stage is combined with multiple large language models for training; the trained TABLE-UAM network and the large language model are spliced, and used for processing input question text and table data to output answers. The newly-built TABLE-UAM network structure can efficiently encode and feature extract table data, effectively perceive table row and column dependency features, multi-table dependency features and global relationship features; and the two-stage training method can improve the table analysis task ability, significantly enhance the cross-model migration ability, and has strong generalization and practical value.

The invention discloses a material mechanical property prediction method and device based on transfer learning and integrated learning and a medium, and belongs to the technical field of crossing of materials, mechanics and artificial intelligence. Aiming at the problems that a traditional material mechanical property test is long in period, high in sample requirement, high in cost and the like, rapid estimation of the material mechanical property of a material small-size sample is achieved through methods such as transfer learning and integrated learning, and the method specifically comprises the steps of preprocessing, modal enhancement, sample enhancement, model migration and combination generation. The method breaks through the limitation of a traditional mechanical test, realizes rapid prediction of the mechanical property of the material, can be widely applied to prototype iterative development and quality inspection of the material in the fields of aviation, automobiles and the like, and remarkably improves the design efficiency and detection efficiency of the material.

The invention belongs to the field of rectification, and particularly relates to a rectification product concentration online detection system which comprises a data acquisition module, the data acquisition module is connected with a data processing and fusion module, the data processing and fusion module is connected with a digital twinborn model module, the digital twinborn model module is connected with a model self-adaptive correction module, and the model self-adaptive correction module is connected with a digital twinborn model. The model adaptive correction module is connected with a working condition mode identification module, the working condition mode identification module is connected with a model migration loading module, the model migration loading module is connected with a multi-working condition model library management module, and the multi-working condition model library management module is connected with a sensor data module. According to the system, high-real-time and high-precision concentration detection is achieved, the strong self-adaption and working condition migration capacity is achieved, the robustness and reliability of the system are remarkably improved, and intelligent self-management and self-recovery of the whole life cycle are achieved.

The present invention discloses a multi-agent strategy model migration method and system with unchanged sub-task semantics. The present invention encodes the multi-agent task to be executed into an executable sub-task through an extensible sub-task encoder, and assigns the sub-task to each agent in executing the multi-agent task. Then, the adaptive action decoder uses the assigned sub-tasks and the observation data of the current agent to calculate the specific action of the agent's interaction with the environment; when the multi-agent task to be executed changes, the extensible sub-task encoder and the adaptive action decoder can ensure that the assigned sub-tasks have consistent and extensible semantics among various multi-agent tasks, and at the same time, the decomposed sub-tasks give the tasks independence, thereby realizing the model migration of the multi-agent strategy model among various multi-agent tasks. The present invention can realize the model migration of the multi-agent strategy model among various multi-agent tasks.

The invention discloses a model migration method, an optical fiber same route detection method, electronic equipment and a program product. The model migration method comprises the following steps: acquiring a training data set of a target area; the training data set comprises feature data and same routing state information of the optical fiber pairs in the corresponding areas; according to the training data set of the target area, training the second model to obtain a first model corresponding to the target area; the second model is obtained through training based on a training data set of a preset area; the first model is used for performing same-route detection on the to-be-detected optical fiber pair in the target area.

The invention relates to the technical field of automobile in-loop simulation, in particular to an automatic emergency braking in-loop simulation method and system, and the method comprises the steps: constructing a virtual simulation environment comprising a plurality of traffic scenes and a vehicle dynamics model; an AEB decision model is constructed based on a preset reinforcement learning algorithm, a state space of the AEB decision model at least comprises a vehicle state, a target object state, a driving area state, a conflict risk field and a traffic consciousness code, and an action space of the AEB decision model at least comprises an intention action and a control action; and AEB controller hardware is accessed to the virtual simulation environment, and real-time interaction and closed-loop training of the AEB controller and the virtual simulation environment are realized. The method can solve the problems of difficult model migration, poor adaptability, lack of online learning ability and the like in the prior art, and has good migration ability and adaptability.

This application provides a model migration method, device, storage medium, and program product, particularly relating to the field of artificial intelligence chip technology. The method includes: reading the original model under the original framework; extracting the original definitions of each module and the original configuration data structure of the original model; converting the original definitions of each module into target definitions under the target framework, and simultaneously converting the original configuration data structure into a target configuration data structure under the target framework. Next, based on the model structure definition template of the target framework and the target definitions of each module, the model structure definition under the target framework is automatically generated, achieving automated model structure conversion; finally, based on the model structure definition and target configuration data structure under the target framework, the target model under the target framework is obtained, thus automating model migration and effectively improving the efficiency of model migration.

The invention discloses a bidirectional time sequence feature enhanced adaptive transient stability evaluation method and system, and aims to construct a bidirectional time sequence feature enhanced evaluation model BTFE combined by a bidirectional long-short-term memory network and a Kolmogorov-Arnold network in order to overcome the defect of feature extraction capability of an existing deep learning model, and integrate bidirectional time sequence features in a transient process, so that the adaptive transient stability evaluation method and system based on bidirectional time sequence feature enhancement are obtained. The capturing capability of the model on a complex dynamic process is effectively improved; the model updating method based on the adaptive sample screening strategy is designed for the defect of adaptability of a transient stability evaluation model based on deep learning, samples required by model updating in different stages are adaptively screened out according to heterogeneity of a source domain and uncertainty of a target domain, and the labeling cost of the samples during model updating is reduced; based on a model migration strategy, the BTFE model is finely adjusted by using the screened samples, so that the adaptability of the model to the specific features of the target task is enhanced, and the model updating speed is increased.

The invention discloses a table question and answer task capability enhancement processing method of a large language model. Aiming at the table data, constructing a TABLE-UAM network comprising a coding part and a decoding part; the different table data sets are sequentially input into the TABLE-UAM network for two-stage training in sequence, the first stage is reconstruction difference training, and the second stage is combined with multiple large language model training; and splicing the trained TABLE-UAM network and the large language model for processing the input question text and table data and outputting an answer. According to the method, a newly established TABLE-UAM network structure can efficiently perform coding and feature extraction on table data, and the table row and column dependency feature, the multi-table dependency feature and the global relationship feature are effectively perceived; moreover, the two-stage training method can improve the table analysis task capability, remarkably enhances the cross-model migration capability, and is higher in generalization and practical value.

The invention discloses a motor cross-domain optimization agent model transfer learning method and system and a storage medium, and relates to the technical field of motor design, and the method comprises the following steps: S1, pre-training a source domain agent model based on a first data set; s2, performing rapid prediction in the new target domain parameter space, and screening out bridge design points in an intelligent sampling mode; s3, only performing high-fidelity simulation on the bridge design points, and constructing a target domain data set; s4, loading a source domain agent model, freezing a front-end layer, finely adjusting a rear-end layer by using a target domain data set, and migrating into a target domain agent model; and S5, replacing high-fidelity simulation with the target domain agent model, and finding out optimal design parameters of the target domain in combination with a global optimization algorithm. According to the agent model transfer learning method and system for motor cross-domain optimization and the storage medium, the number of expensive simulation times needed in a new design domain is greatly reduced, the calculation cost is remarkably reduced, and the optimization efficiency is improved.

The application relates to the field of artificial intelligence and hardware acceleration technology, in particular to a model migration method and system, an electronic device and a computer storage medium, the method comprising the following steps: determining a mapping operator and a replacement operator of a to-be-migrated model based on hardware characteristic description information of a target platform, wherein the to-be-migrated model is a model to be migrated to the target platform; obtaining a task type of a calculation task, determining a network structure based on the task type and the hardware characteristic description information, wherein the calculation task is a to-be-executed task input by the to-be-migrated model, and the network structure is used for adapting the to-be-migrated model to the target platform; and deploying the mapping operator and the replacement operator to the target platform through the network structure and the hardware characteristic description information, so as to obtain an executable model adapted to the target platform. The application solves the technical problem of insufficient operator adaptation caused by hardware architecture difference when a model is migrated to an NPU.