107 results about "Model migration" patented technology

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 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 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 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.

The application provides an automated operation method and system suitable for a closed system. It aims to solve the technical problem that traditional operation schemes are difficult to apply due to network isolation and sample scarcity in closed environments such as finance and energy. The method includes: generating a unique and traceable migration identifier for all operation data, models and inference results through a data and migration governance module; quantitatively calculating the transferability score between the source domain and the target domain through a transferability evaluation module; dynamically selecting the optimal model migration strategy according to the transferability score to achieve efficient self-adaptation of the model; through a multi-agent collaboration system containing detection, diagnosis and repair agents, a closed-loop operation is performed, and the system is self-learning and self-optimizing through small sample active learning and knowledge base evolution mechanism. The application realizes the rapid construction, continuous evolution and whole-process traceability of artificial intelligence operation capability in a closed system, and has significant innovation and industrial application value.

The present invention discloses a large-scale model-based acne grading model migration method, which is applied to the intersection of computer science and medicine. Existing deep learning-based acne grading models are usually trained on data collected in a specific hospital. Therefore, when the model is migrated to a new hospital or other region, the offset of the data distribution usually leads to a decrease in effectiveness. The present invention first collects acne grading data from multiple different hospitals; then designs an acne grading model and selects one hospital data as source data for training and testing; secondly, the datasets of other hospitals are used as the target domain (i.e., the target hospital), and these data are all unlabeled. The large-scale model is adjusted on the target hospital dataset to make it suitable for the acne grading task; then, the acne grading model is adjusted on the target hospital dataset with the help of the output of the large-scale model and the designed loss function to complete the model migration process; finally, the model is tested.

The embodiment of the invention provides an existing building safety assessment method introducing a transfer learning mechanism. The method is applied to the technical field of constructional engineering safety and comprises the steps of constructing a three-dimensional evaluation model and performing finite element simulation on stress distribution; synthesizing the damage image and the simulation data through a conditional generative adversarial network to form a training data set; utilizing a damage detection model with double-branch attention and an HPC module to identify damage and mapping the damage to a stress sensitive feature library; and the risk assessment model calculates a failure risk and a structure degradation trend index, and dynamically adjusts an early warning threshold. In this way, by fusing multi-source heterogeneous data processing, dynamic model migration and risk feature extraction mechanisms, comprehensive perception, accurate evaluation and dynamic early warning of the building safety state are achieved, the knowledge experience migration reuse problem is effectively solved, and the accuracy, timeliness, self-adaptability and generalization ability of evaluation are remarkably improved.

This invention discloses a method, system, device, storage medium, and product for deploying large-scale model inference. The method involves dividing the vocabulary of a large-scale model into blocks to obtain several plaintext blocks; generating an initialization vector based on the MAC address of the server to be deployed; randomly generating a first key; encrypting the plaintext blocks using a ciphertext block chaining mode based on the initialization vector and the first key to obtain several ciphertext blocks; encrypting the first key using a public key to obtain a second key; and sending the second key and the ciphertext blocks to the server to be deployed, enabling the server to decrypt the second key and the ciphertext blocks using its private key to obtain the vocabulary of the large-scale model and perform inference deployment. Using this invention, the security of large-scale models can be improved, effectively avoiding the leakage of source code during large-scale model migration and deployment, and preventing data from being tampered with or stolen during transmission.

The invention discloses a diffusion model migration method based on prediction residual guidance and a related device, and relates to the technical field of diffusion model knowledge migration, and the method comprises the steps: taking noise data as input at each denoising time step, and carrying out denoising on the noise data; determining a first prediction noise, a second prediction noise and a third prediction noise by using the basic model, the adaptive model and the target model respectively, calculating a deviation between the first prediction noise and the second prediction noise to obtain a prediction residual error, performing weighted summation on the third prediction noise and the prediction residual error to obtain a guide prediction noise, and outputting the guide prediction noise. And on the basis of the noise data and the guide prediction noise, noise data of the next denoising time step is calculated until the last denoising time step is reached, and an output result of the target model is obtained, and the output result is a picture, a video, a voice or a text. According to the method, the knowledge migration of the diffusion model can be completed on the premise of not accessing original training data and not training.

The application discloses a kind of considering preference cloud manufacturing system digital twin migration modeling method, based on the production relationship of cloud manufacturing system, the knowledge graph of cloud manufacturing system is constructed;The digital twin model set in the cloud model library of cloud manufacturing system is constructed;The digital twin model includes geometric model, behavior model, logic model and performance model;Based on the characteristics of cloud manufacturing digital twin model migration, the demand set of digital twin modeling of cloud manufacturing service demand side is constructed;According to the interaction and preference relationship between the modeling demand and the model to be selected, a recommendation algorithm is constructed to select the optimal model from the digital twin model library for migration.The migration modeling method considering preference provided by the application provides an efficient and accurate modeling method for the precise application of cloud manufacturing system digital twin, which has important value for the intelligent improvement of cloud manufacturing system.

The invention relates to the technical field of elevator intelligent operation and maintenance and fault prediction, and provides an elevator predictive fault early diagnosis method based on data driving. Constructing a physical constraint library containing dynamic constraints, time sequence continuity constraints and multi-sensor coupling rules; a conditional generative adversarial network with physical constraints is designed, the rule base is embedded into the training process in a loss function form, and a high-fidelity fault sample conforming to the elevator operation rule is generated; constructing a balanced data set after double screening of time sequence similarity and physical rules; on this basis, a meta-learning mechanism is introduced, a small sample fault diagnosis model is trained, and model migration can be rapidly completed only by using a very small number of real samples of the target elevator through the two stages of meta-training and meta-adaptation. Accurate predictive diagnosis of two typical early faults of elevator traction machine bearing micro-wear and portal crane belt slight slip is realized.

The invention discloses an adaptive conversion method and system of a large model on an AI platform, particularly relates to the technical field of AI large model transfer learning, and aims to obtain CPU / GPU utilization rate, memory bandwidth and network bandwidth of a target platform, divide tasks in combination with task characteristics, and generate a reasonable task allocation scheme according to platform node load. According to the method, through complexity control and resource allocation optimization in the task execution process, smooth execution of high-complexity tasks is ensured, and excessive resource occupation and task execution bottleneck are avoided. The system collects and compares feedback information of task operation in real time, discovers uneven resource allocation or result deviation in time, and adjusts and optimizes the information. According to the method, the adaptability and execution efficiency of the large model among different platforms can be improved, and efficient operation in different computing environments is ensured. The method not only optimizes the utilization rate of platform resources, but also improves the stability of task execution and the success rate of model migration, and has high practical application value.