231 results about "Co-training" patented technology

The invention provides an intelligent task alarm rule self-learning method and system based on support priority, and relates to the technical field of intellectualization, and the method comprises the steps: constructing a bidirectional feature extraction network to collect task operation data, and combining historical alarm data to generate a task exception association model; and carrying out migration mapping on the model and a business scene knowledge base by utilizing a migration learning algorithm, generating a task priority quantitative index, and constructing a self-adaptive priority scoring system. And training an alarm rule generator based on the model and a scoring system, generating a multi-level alarm strategy tree, and realizing differential alarm. And the incremental learning engine monitors a task state in real time, dynamically adjusts an alarm response strategy and feeds back data to optimize a rule. A federal learning mechanism is adopted to cooperatively train a model, and the generalization ability and adaptability of alarm rules are improved, so that more accurate and efficient task alarm is realized, the false alarm rate is reduced, and the operation and maintenance efficiency is improved.

The invention provides a private weight self-adaptive heterogeneous data federated cooperative training method and system in the technical field of federated learning and privacy computing, and the method comprises the steps: S1, enabling each client to carry out the differential privacy operation on a local data set based on a private weight, and obtaining a desensitized data set, encoding the desensitized data set through a heterogeneous data encoding model; s2, performing semantic alignment on each coding vector through a contrast learning model to obtain an aligned vector set; s3, training a local model through the alignment vector set, generating a local gradient, extracting local model parameters, and uploading the privacy weight, the local gradient and local difference parameters to a server; and S4, the server trains the global model based on the local difference parameter and the global gradient, extracts the global model parameter and issues the global model parameter to each client for training. The method has the advantages that the compatibility, the flexibility and the efficiency of heterogeneous data federation cooperative training are greatly improved.

The invention relates to the technical field of computer deep learning, and relates to an equipment fault diagnosis network model training method based on multi-source domain knowledge joint migration, and the method comprises the steps: constructing a training sample set, a verification set and a test set, and training a pre-constructed equipment fault diagnosis network model through the training sample set, optimizing parameters of the equipment fault diagnosis network model to obtain a trained equipment fault diagnosis network model; verifying the trained equipment fault diagnosis network model by adopting a verification set, evaluating the diagnosis performance of the trained equipment fault diagnosis network model, and selecting an optimal equipment fault diagnosis network model; and testing by adopting the test set optimal equipment fault diagnosis network model, and evaluating the performance of the optimal equipment fault diagnosis network model. According to the method, multi-source-domain cooperative training is carried out on the model by utilizing a collected multi-source-domain data set which is composed of a plurality of labeled source-domain data sets and unlabeled source-domain data sets and contains a small number of labels, domain invariant features in fault samples are learned, and shared diagnosis knowledge is migrated to a target domain from labeled data and unlabeled data together; therefore, the trained equipment fault diagnosis network model can complete a cross-domain fault diagnosis task without obtaining a target sample in advance.

The invention belongs to the field of electric power and the technical field of artificial intelligence, and particularly relates to an electric power field operation safety detection method and system based on a hybrid expert model. According to the method, the visual language model and the YOLO target detection model are combined, and efficient and accurate electric power field operation safety detection is realized under the condition of few samples through a hybrid expert model architecture. In the training stage, through multi-model cooperative training, an expert model set for different scenes and targets is constructed. In the detection stage, a two-stage detection mechanism is adopted, YOLO is firstly used for rapid preliminary detection, then VLM is used for rechecking a low-confidence target, and finally detection results are integrated to ensure the accuracy of detection. The method aims to effectively cope with complex environments and potential safety hazards in electric power field operation, safety detection efficiency and accuracy are remarkably improved, and compared with a single model architecture, the method shows wider application scenes and higher detection quality in an electric power field operation scene.

The invention belongs to the technical field of robot learning, and particularly discloses a robot continuous imitation learning method and system based on a double diffusion model. Comprising the following steps: generating old task data in a submerged space by utilizing a pre-trained latent diffusion model, generating a model through guidance of a task identifier in combination with conditional diffusion, generating an image and state data related to an old task, modeling to construct a diffusion model, realizing multi-modal mapping from a state to an action, and generating a diversified action sequence; high-quality images and state data are generated through a pre-trained variational auto-encoder and a conditional diffusion model, and the consistency of the generated data in time and the accuracy of global semantics are ensured through time sequence continuity constraint and semantic consistency constraint; and performing feedback optimization training on the latent diffusion model. According to the method, the loss weight is dynamically adjusted, and the model is ensured to show good generalization ability and stability in a complex task in combination with collaborative training of an imitation learning strategy and a generator.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

The invention discloses an accurate electric power theft detection method based on multi-modal Mamba. The method comprises the following steps: S1, carrying out adaptive multi-scale data preprocessing, and carrying out denoising and normalization processing on input power time sequence data by adopting an adaptive score standardization method and a local abnormal factor technology so as to improve data quality; s2, a multi-mode Mama feature extraction layer is adopted; s3, a federated learning training layer: constructing a secure federated learning framework, realizing cooperative training among a plurality of power supply areas, and introducing a differential privacy mechanism at the same time to protect privacy of model parameters and training data; s4, an intelligent confrontation defense layer; s5, an adaptive classification decision system; s6, a lightweight optimization strategy; efficient and safe cooperative detection is realized through innovative fusion of a multi-modal Mamba model and a federated learning framework; through an intelligent confrontation system and lightweight optimization, the robustness and practicability of the model are improved; according to the method, the detection accuracy, the calculation efficiency, the privacy protection and the like are remarkably improved.

The invention provides a large and small model cooperative training method and device for a multi-modal large language model. The method comprises the following steps: inputting multi-modal samples such as images, videos and audios into a multi-modal shared encoder to generate a multi-modal token; inputting the text sample into a text encoder to obtain a text token; multi-modal tokens of different modals are combined with text tokens, a pre-trained small multi-modal large language model is used for forward distillation of a pre-trained large multi-modal large language model, a projection layer is trained to obtain a trained projection layer, and the projection layer is connected between the small multi-modal large language model and the large multi-modal large language model; and utilizing the trained projection layer and the pre-trained large-scale multi-modal large language model to reversely distill the small-scale multi-modal large language model, thereby obtaining the trained large-scale and small-scale multi-modal large language models at the same time in the training process. Not only is the cross-modal alignment capability of the large-scale multi-modal large language model improved, but also the performance of the small-scale multi-modal large language model is enhanced through knowledge distillation.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention relates to the technical field of intelligent dialogues, and discloses a dialogue marketing strategy optimization method based on artificial intelligence, and the method comprises the following steps: collecting the voice, text, facial expression and physiological signals of a user in real time through a multi-modal perception assembly of a terminal device, constructing a dynamic emotion map, and extracting a multi-modal feature vector; a cross-modal information processing module is utilized to align the multi-modal data through a comparative learning algorithm, and causal relationship description of user behaviors and strategies and strategy risk scores are generated; cooperatively training a global strategy model through differential privacy and homomorphic encryption technologies; combining the dynamic emotion map and a causal model to generate an emotion adaptive dialogue script, and optimizing strategy selection through a reinforcement learning algorithm; and dynamically updating the emotion map, the risk score and the global model through a closed-loop feedback mechanism to form a real-time optimized strategy generation system. According to the invention, the practicability of artificial intelligence to real-time services can be improved.

The invention relates to a porous structure cross-domain reverse design method based on property constraint cooperative training, and the method comprises the steps: taking mechanical property information and random noise as the input of a trained reverse generation model, obtaining a coding vector, and obtaining reconstructed porous structure data through a trained variational auto-encoder model. According to the method, in the training process of the variational auto-encoder, mechanical property prediction cooperative training is directly carried out on feature vector distribution in an encoding space, the feature distribution of the encoding space is improved, and a more direct relation between the encoding space and the property is established. By increasing the constraint of the forward relationship, in the training process of the reverse generation model, the establishment of'property-coding space 'is easier, and compared with a common reverse design scheme, the method can improve the accuracy of the prediction structure and support the accurate cross-domain reverse design of the porous structure. According to the method, the characteristic that cross-domain structures are different in distribution is fully considered, and cross-domain reverse design with high accuracy is achieved.

The invention relates to the field of finance, and discloses an AI-based investment project feasibility intelligent analysis and decision support system, which comprises a multi-modal data fusion engine used for extracting a feature vector of an unstructured text through a BERT variant model, carrying out joint coding on the feature vector and a structured data feature, and outputting fused multi-modal data; the dynamic knowledge graph construction module is connected with the multi-modal data fusion engine; the deep reinforcement learning decision module comprises a feasibility prediction network and a risk assessment network which are cooperatively trained; an interpretable AI module; and a closed loop feedback module. By means of a multi-modal data fusion engine and a deep reinforcement learning decision module, automatic processing and intelligent analysis of data are achieved, and the analysis time of a single project is greatly shortened to 8 minutes. The high-efficiency processing speed enables the investment institution to quickly respond to the market change, timely grasp the investment opportunity, and occupy the decision-making precedence in the financial market which changes instantaneously.

The invention discloses a cue word optimization training method and system based on multi-modal large model supervision. The method comprises the following steps: screening and optimizing a plurality of initial cue words by adopting a simple multi-modal large model, a complex multi-modal large model and a first text large model to obtain a simple optimal cue word and a complex optimal cue word; inputting the simple optimal cue word, the training set and the manual calibration problem into a simple multi-modal large model for training to obtain a simple total loss value; judging whether the total simple loss value is within a preset threshold range, and if yes, ending training; and otherwise, repeating the optimization of the process until the simple total loss value is within the preset threshold range, stopping training, and taking the simple optimal cue word obtained by the last update as the target optimal cue word. According to the method, manual intervention is reduced through cooperative training of a complex large model and a simple large model; manual calibration data and the reasoning process of a complex large model are combined, and the cue word accuracy is improved through double supervision.

The invention discloses a cross-modal large model construction method and system based on track spatio-temporal characteristics, and belongs to the crossing field of artificial intelligence and dynamic spatio-temporal data processing, and the method comprises the steps: carrying out the sliding sampling and spatial distribution difference judgment through the semantic dynamic segmentation of multi-scale track spatio-temporal data, and generating spatio-temporal data blocks with consistent semantics; designing a space-time encoder of a hybrid architecture, extracting track time sequence association and spatial features, and unifying dimensions; constructing a text space-time fusion mechanism, and dynamically adapting cross-modal features by means of an anchor interface and gating fusion; a staged instruction fine tuning strategy is adopted, semantic alignment of space-time and text features is optimized firstly, then model top-layer parameters are trained cooperatively, complex scene adaptation is enhanced in combination with instruction difficulty progression and hard sample mining, and space-time constraint regular terms are introduced to guarantee output rationality. According to the method, high-precision cross-modal reasoning capability is provided for scenes such as track analysis and track prediction.

The invention relates to a hyper-dimensional calculation model cooperative training method and device based on federated learning, and belongs to the field of distributed machine learning and data privacy protection. The device comprises a central server and M distributed client devices which communicate through network connection, the method comprises the following steps: S1, initializing parameters; s2, broadcasting parameters; s3, local super-dimensional calculation model parameters are updated; s4, the similarity between the two is calculated, and the local training data volume is counted; s5, uploading to a server; s6, calculating parameters of the global super-dimensional calculation model based on a dynamic weighted aggregation method; s7, judging whether convergence is carried out or the maximum training round number is reached; if yes, training is ended, and global super-dimensional calculation model parameters are output; otherwise, returning to the step S3; and S8, the user executes the classification task and outputs a prediction classification result. According to the method, the training efficiency and the global model performance of federal learning under a super-dimensional calculation model can be improved on the premise of ensuring data privacy and security.

The invention provides a cross-modal feature fusion method based on joint attention, which combines multi-modal data from different sources to predict the relative position of a target to be tracked, and comprises the following steps of: firstly, designing a cross-modal data representation network model for the data of different modals; generating a time sequence high-dimensional implicit code retaining key dependency information from the original data through a code conversion algorithm; secondly, a heterogeneous attention network unit is designed, and features of time sequence high-dimensional implicit codes corresponding to different modal data are extracted through two different forms of attention; and finally, through a cooperative training mechanism, information complementation among different modal data is utilized to generate the most effective feature representation for a time sequence prediction model, and accurate prediction of the moving position of the tracked target is realized.

The invention discloses an efficient federal collaborative learning framework for edge-cloud heterogeneous model co-modeling, and belongs to the technical field of federal learning. In order to solve the problems that traditional federated learning is low in one-way knowledge transmission efficiency, privacy protection and model performance are difficult to balance and only isomorphic model parameter aggregation is supported, mutual training among diversified models is realized by combining a cloud edge interactive handshake protocol (CEHP) and an information gain evaluation mechanism (IGVE); and an attention feature fusion mechanism (AFF) is designed to realize feature exchange between heterogeneous models of different targets, and meanwhile, an inverse gradient equalizer is designed to promote dynamic evolution of the models. By means of the multi-target strategy gradient algorithm, the relation between different targets is deeply mined and balanced, and the efficient federal collaborative learning framework is comprehensively and meticulously optimized. According to the method, generalization of the cloud side model and fine granularity of the edge side model are promoted, so that edge-cloud win-win cooperative training is realized.

The invention provides a big data integration and distribution unit component of a hierarchical architecture and a use method, relates to the technical field of data integration and processing, and solves the problems of real-time integration, unified processing and safe distribution of multi-source heterogeneous data. The device adopts a five-layer modular architecture; a data access layer supports multi-source data access and is buffered through Kafka; the cleaning conversion layer performs data cleaning and standardization by using a FlinkSQL, an ETL engine and a rule engine; the intelligent distribution layer judges a data distribution path in combination with a rule engine and a machine learning model; the storage calculation layer adopts a cold and hot separation storage strategy and supports parallel operation of Spark batch processing and Flink stream processing; and the service and security layer provides an interface through an API gateway, and guarantees data security in combination with AES-256 encryption and RBAC access control. Besides, dynamic resource scheduling is realized through Kubernetes, cross-domain model cooperative training is supported by utilizing a federated learning technology, the intelligent shunting decision-making capability is improved, various industrial protocols such as MQTT and Modbus are compatible, and VXLAN tunnel transmission is supported.

The invention belongs to the field of crossing of remote sensing visual questioning and answering and artificial intelligence, and particularly relates to a remote sensing visual questioning and answering method based on a large language model and a multi-level attention mechanism. Comprising the following steps: 1, constructing a MogaNet-based semantic segmentation network; 2, text coding is carried out through a large language model; 3, constructing a mixed attention guiding module; 4, constructing a bidirectional gating cross attention module; and 5, constructing and cooperatively training a remote sensing visual question-answer network. According to the MLaVQA framework provided by the invention, efficient and accurate visual question-answering processing of remote sensing images can be realized, when large-scale multi-modal remote sensing data is processed, complex semantic questions can be quickly answered, fine monitoring and intelligent management of earth resources are realized, the data utilization efficiency and decision support capability are remarkably improved, and the method is suitable for large-scale multi-modal remote sensing data processing. And technical support is provided for natural resource management, ecological protection and sustainable development.

The invention discloses a collaborative training method, medium and device based on an LLM large and small model, and the method comprises the steps: S1, obtaining a huge knowledge system through the combination of the language understanding capability and pre-training capability of a general large model, and carrying out the cold start of an AI service 0 sample needed by a business scene, and enabling the AI service 0 sample to be online; s2, performing a small amount of annotation on sample data generated by a business scene, performing fine tuning on the general large model to form a scene large model, performing continuous learning to enable the scene large model to have knowledge in the field, and quickly improving an algorithm effect; and S3, distilling knowledge in the field obtained by the scene large model into a plurality of small models, and fusing results of the plurality of small models by using a scoring mechanism to realize collaborative training of the large and small models. According to the method, any scene text service can be subjected to cold start online under the condition of limited hardware resources, large and small model cooperative training of large model knowledge can be obtained through a small amount of labeling, and the model learning ability and the working efficiency are greatly improved.

The invention discloses a robust federated learning method for processing heterogeneous noise and non-independent identically distributed data, and belongs to the technical field of federated learning. The method provides a robust learning framework of two-stage client quality perception. The method comprises the following steps of: 1, constructing a category-level loss vector and clustering by using a Gaussian mixture model, and accurately dividing a clean and noise client set; stage 2, performing differential training: performing standard training on the clean client; dual-network cooperative training, dynamic sample screening and exchange, and a self-distillation and entropy regularization mechanism are introduced to a noise client, so that robust learning is realized; in the global aggregation stage, a distance sensing weighting strategy is further adopted to dynamically suppress the influence of a noise client; according to the method, original data does not need to be shared, the robustness and generalization performance of the federated learning model in the coexistence environment of heterogeneous noise and non-independent identically distributed data can be effectively improved, and the method has wide application value in the fields of medical images, financial risk control and the like.

The invention belongs to the field of privacy calculation and data security, and particularly relates to a personalized federated map learning method and system based on depth map similarity learning, and the method comprises the steps: collecting local map data, and obtaining an unsupervised map encoder through transverse federated average learning; inputting local graph data into an unsupervised graph encoder, and calculating a dense vector; inputting the dense vector into a central server to calculate the similarity, and constructing a personalized model; performing federal iteration training on the participants according to the personalized model; the method is composed of two core parts, namely a personalized parameter aggregation mechanism based on depth map similarity learning and a participant selection mechanism based on uniform label distribution, an unsupervised map encoder is trained cooperatively, and low-dimensional dense vectors are generated based on local map data of participants. Therefore, the model actually obtained by the participant can adapt to the local data, so that the data actually participating in the training is distributed as uniformly as possible, the representation of the information is more comprehensive, and the universality and generalization of the model are improved.

The invention relates to an access control strategy automatic generation system and method based on multi-task learning, and the method comprises the steps: carrying out the word segmentation, cleaning and embedded vector conversion of an original access control text through a data preprocessing module, and constructing a normative input format; the feature sharing layer module is used for extracting deep semantic features of a text through multi-layer bidirectional coding and an attention mechanism and providing unified representation for downstream tasks; the access control statement identification module is used for judging whether each sentence in the text is an access control statement or not and realizing automatic identification of strategy related contents; and the attribute extraction and annotation module is used for annotating words in the access control statements and extracting subject, object and operation access control attributes. A word coding layer and a sentence coding layer are shared, local and global attention mechanisms are combined, key information of a text is extracted, the semantic understanding ability is enhanced, and cooperative training of statement recognition and attribute extraction is achieved; a conditional random field CRF structure is used for sequence labeling, and the structural rationality of attribute labels is ensured.

The invention relates to the technical field of intelligent coal mine safety production, and discloses an intelligent coal mine safety early warning method and system based on deep learning, and the method comprises the steps: constructing a difficulty evaluation function, and achieving the progressive learning from simple to complex; based on a difficulty assessment result, a model-independent meta-learning algorithm is realized, so that the model quickly adapts to new mining area characteristics; constructing a privacy protection federated learning framework by using the meta-learning model, and realizing multi-mining-area cooperative training; a continuous learning module is constructed, and original experience is reserved when new knowledge is learned; constructing a meta-knowledge evaluation module to realize cross-mining-area safety knowledge sharing; according to the invention, the security risk identification accuracy is improved; multi-mining-area cooperative training is realized on the premise of protecting data privacy; the method has continuous optimization capability and effectively solves the problem of model drift; and efficient sharing and migration of cross-mining-area safety knowledge are realized.

The embodiment of the invention provides an end-cloud cooperative training method, system and device in the field of artificial intelligence, and can be used for stripping an embedding layer to terminal training in an end-cloud cooperative training process so as to improve the privacy security of end-side data. The method comprises the steps that a client uses training data as input of a representation layer to obtain a representation vector, the representation layer is used for obtaining a vector corresponding to the input data from a representation word list stored in the client, and the representation word list is stored in the client; the client sends the representation vector to the cloud platform, so that the cloud platform takes the representation vector as the input of a language model deployed at the cloud platform side to obtain an output feature; the client receives an output feature sent by the cloud platform, wherein the output feature is obtained by inputting a representation vector into a language model by the cloud platform; and the client calculates a loss value by using the output feature, updates the representation layer according to the loss value, and sends the loss value to the cloud platform, and the loss value is used for the cloud platform to update the language model.

The invention relates to a multi-agent reinforcement learning training method, system and device driven by an unreal engine and a medium. The method comprises the following steps: acquiring core attribute data of multiple agents, including real-time communication bandwidth, initial communication topology configuration, training stage progress, single agent computing power and cluster overall computing load state; analyzing the real-time communication bandwidth and the initial communication topology configuration to form an optimized topology configuration scheme; in combination with an optimized topology configuration scheme and a training stage progress, generating a cooperative training progress state by quantifying progress differences among intelligent agents; extracting load balance data according to the single agent computing capability and the cluster load state; integrating load balance data, a collaborative training progress state and an optimized topology configuration scheme, and constructing a multi-layer collaborative training framework; and integrated optimization is carried out to generate a cooperative training scheme for guiding multi-agent communication, training and resource allocation, so that the training efficiency is effectively improved, the resource load is balanced, and the cooperative synchronism is enhanced.

The invention discloses an artificial intelligence network optimization training system and method based on deep learning, and the system comprises a request analysis module which is used for determining a training task type and an initial model hyper-parameter, and obtaining an available edge device list; the parameter monitoring module is used for determining a model index monitoring threshold value, monitoring model indexes in the training process in real time and adjusting corresponding model hyper-parameters; the multi-target optimization module is used for initializing target weights and calculating a multi-target optimal solution set; the self-adaptive training module is used for generating a training strategy and optimizing and adjusting the training strategy; and the cooperative training module is used for allocating training tasks to the available edge devices and feeding back model indexes in the training process to the parameter detection module. According to the invention, the training mode is dynamically adjusted according to the specific parameters of the artificial intelligence network, multi-objective optimization is realized, distributed training of edge devices is supported, and the robustness of the model is improved, so that the training precision and efficiency are improved, and the application range is expanded.

The invention provides a marine image data identification method and system. The method comprises the following steps: step 1, inputting an original marine image; 2, a visual feature extraction module extracts hierarchical features through a multi-scale convolutional neural network; 3, pre-training an LLM semantic reasoning hierarchical attention mechanism semantic mapping multi-task cooperative training framework by the language model agent module; and 4, a dynamic strategy optimization module reinforces learning optimization strategies to adjust identification parameters in real time, and generates a preliminary identification result. And 5, when the user needs to optimize, combining user feedback, a data enhancement interactive learning mechanism and a semantic consistency data enhancement optimization model to realize model updating and parameter adjustment, and returning to the step 2 again. And when the user does not need optimization, outputting a final identification result of the ocean image. According to the invention, a visual coding technology is combined with the semantic understanding capability of LLM, and a recognition solution capable of adaptively processing a multi-modal ocean image is constructed.

The invention discloses an edge caching method for federal deep reinforcement learning based on semantic enhancement, and the method comprises the steps: obtaining request original data and a candidate content set, and carrying out the multi-modal feature extraction; calculating a cache utilization rate, request diversity and a dynamic semantic matching weight; calculating the semantic similarity between the request content and the candidate content, and performing adjustment in combination with a dynamic semantic matching weight to obtain a weighted semantic similarity; generating action probability distribution according to the state of the node, and fusing the action probability distribution with the semantic attention weight to obtain action preference distribution; action execution is selected according to the action preference distribution, and rewards are evaluated; and periodically executing federal cooperative training optimization, locally training a semantic perception strategy network by adopting an SAC algorithm, executing multi-factor dynamic weighted federal aggregation to update a global model, and distributing the global model to each node. According to the method, the edge cache hit rate and the semantic hit rate can be improved, the service response delay is reduced, and the robustness of heterogeneous nodes and a complex network environment is enhanced.