16results about How to "Protect data privacy" patented technology

The federal knowledge distillation method and application for rubber tire production parameter sharing divide the federal learning process into preheating and distillation two parts, the preheating part adopts the FedAvg federal training strategy, the distillation part utilizes the knowledge generated by each client, carries out the binned formation grouping knowledge, carries out the aggregation after the distribution to each client for the knowledge distillation in the server side.The invention proposes a new federal knowledge distillation method, through the method in the invention, the distributed training of the production line prediction model can be realized, and the model prediction accuracy of the server side is also higher than that of ordinary training.The invention can be used for the distributed training of the rubber tire production line key parameter prediction model, to improve the training efficiency, and in the case of not sharing data, the training result generated by the data can be shared, and the privacy can be well protected.

This invention provides a training method, localization method, and system for an intelligent agent localization model based on dialogue and vision collaboration. The training method is applied to a federated learning system and includes: a server initializing a global model and distributing it to a client; the client receiving model parameters, training the model using local dialogue commands and visual data, generating multimodal representations, and then inputting them into two structurally identical but parameter-independent localization branches for parallel processing, outputting localization representations and prediction results, and executing navigation decisions based on the localization results; constructing a total loss function that includes a loss term constraining the consistency of the bi-branch representations and constraining the consistency of navigation state changes, and updating the local model; the client evaluating reliability based on representation consistency and navigation stability, uploading the model update and reliability to the server, and the server using this data to weighted aggregate and update the global model, iterating until convergence. This invention can suppress noisy dialogue interference and improve localization accuracy, navigation stability, and model generalization ability in federated learning scenarios.

This invention relates to the field of industrial fault diagnosis technology and discloses a federated incremental mechanical fault diagnosis method in a dynamic client environment. The method includes: constructing a federated learning system composed of a cloud server and a dynamic client, configuring a shallow neural network, a sample dataset, a fault diagnosis model, and a local memory; training the fault diagnosis model on the client and storing prototype samples corresponding to historical fault categories in the local memory; applying perturbation to the prototype samples, inputting them into the shallow neural network to calculate gradient vectors, and uploading these vectors, along with model parameters and network parameters, from the client to the cloud server; performing gradient inversion iterations on the cloud server to construct a validation set to evaluate model performance, and calculating the aggregate weights of each model based on the validation loss, thereby constructing a global diagnostic model and distributing it to the client to achieve real-time fault diagnosis of mechanical equipment. This invention achieves collaborative, continuous, and high-precision mechanical fault diagnosis across distributed dynamic clients.

The invention provides a power plant thermal mechanical equipment lubricating oil quality on-line monitoring method and system, and the method comprises the steps: collecting a plurality of parameters of power plant thermal mechanical equipment lubricating oil, transmitting the parameters to a data processing and analysis module, carrying out the comprehensive analysis and evaluation of the parameters through a multi-sensor data fusion strategy based on deep learning, and carrying out the monitoring of the quality of the lubricating oil. Comprising the following steps: constructing an oil product state analysis model, extracting oil product health features, and generating oil product state early warning; constructing a predictive maintenance decision model, and generating an optimization maintenance strategy; constructing a multi-device collaborative monitoring framework, and realizing model sharing and collaborative learning among a plurality of sensors; and based on the result after comprehensive analysis, oil state early warning and equipment maintenance suggestions are generated, and real-time monitoring results and decision support are provided for users through a user interface and a remote monitoring module. And deep fusion analysis is carried out on multiple parameters of the oil product, and online intelligent evaluation and prediction are carried out, so that the problems of one-sided monitoring and slow alarm are effectively solved.

This invention relates to the field of IoT device identification and machine learning technology, and proposes an IoT device identification method and system based on incremental federated learning. The method includes: Step S1, each gateway node preprocesses and extracts features from network traffic to generate local feature vectors; Step S2, a single-layer self-organizing incremental learning neural network is used to dynamically cluster the feature vectors, and prototype data is selected to construct a local prototype set; Step S3, each node uploads the prototype data to a central server, which aggregates and distributes the global prototype set; Step S4, each node combines local data and global prototype data to perform local incremental training on the global model and uploads the model parameters; Step S5, the server uses a federated averaging algorithm to aggregate the model parameters, generate a new global model, and distributes it, repeating S4 to S5 until convergence; Step S6, if identification fails when a new device connects, the local clustering model is updated and a new round of training is triggered, enabling the global model to acquire the ability to identify new devices. The system includes: multiple gateway nodes (including data preprocessing, feature extraction, incremental clustering, and local training modules) and a central server (including data aggregation, model aggregation, and model distribution modules). Through the above scheme, the present invention achieves collaborative device identification that dynamically adapts to new device access, avoids catastrophic forgetting, and protects data privacy.

The invention belongs to the field of energy consumption optimization, and particularly relates to a two-stage electrolytic copper foil electrodeposition energy consumption optimization method based on federal learning. The method comprises the steps that each client side collects multi-dimensional technological parameters in the electrolytic copper foil electro-deposition process, and technological parameter vectors are constructed; constructing a federated optimization framework, training a local energy consumption predictor by each client in a local autonomous optimization stage, and calling an intelligent optimization algorithm for optimization; in the federated collaborative optimization stage, each client updates a local energy consumption predictor based on the structure parameters of the global energy consumption predictor generated by the server, and calls an intelligent optimization algorithm to perform collaborative optimization; and based on the latest local energy consumption predictor, carrying out backtracking evaluation on the optimal process parameter vector set, and screening out the process parameter vector which has the lowest predicted energy consumption and is finally applied to production control. According to the method, flexible support for various intelligent optimizers is realized, and generalization, stability and reliability of the model in a real industrial scene are improved.

The invention discloses an indoor heating and ventilation distributed control method for buildings in a high-speed service area, and the method comprises the steps: collecting environment data, personnel data, equipment data and personnel comfort feelings at corresponding moments of all functional areas, constructing an initial data set, and carrying out the preprocessing, thereby obtaining a first training set; dividing the first training set into a plurality of second training data sets according to the functional area type; an initial reinforcement learning model is built on the central server, the heating and ventilation equipment control parameters serve as output, and pre-training of the initial reinforcement learning model is completed through the first training set; copying a plurality of trained initial reinforcement learning models, and performing fine tuning on the trained initial reinforcement learning models by using the second training data set to obtain a target model of each functional area; compressing the target model and deploying the compressed target model to an edge computing node of each functional region; and collecting data of each functional area in real time, inputting the data into the target model to obtain control parameters of each heating and ventilation device, and sending the control parameters to the heating and ventilation device control module for execution.

The application discloses a kind of queue data sharing storage evidence auditing method and system based on blockchain, for queue data sharing storage evidence method and system, design storage evidence logic, clear description complex belonging relationship change in the whole process of queue data sharing, including the belonging relationship of agency and queue, agency and research project, research project and queue, research project and analysis, analysis and queue, etc., ensure the reliability of queue data sharing process, while reducing storage overhead including queue storage evidence, variable standardization storage evidence, research project storage evidence and analysis storage evidence;For queue data sharing auditing method and system, each data flow process in queue is audited efficiently, by establishing local link information database and research object persistence MPT, realize the credibility of audit result while protecting research object data privacy.

The application discloses a kind of edge cloud cooperation model test time self-adapting method, device and storage medium, belong to edge computing technical field.The method includes: each edge device executes model inference operation, obtains and uploads statistical information and logic output to cloud end;Cloud end carries out joint estimation to the information uploaded by edge device;Cloud end generates pseudo-sample according to each edge device joint statistical information data;Cloud end optimizes model parameter according to pseudo-sample, and model parameter obtained by optimization is issued to edge device;Edge device updates the model of local according to the model parameter issued by cloud end, and carries out subsequent sample inference operation.The application cooperates between cloud end and edge device to carry out model self-adapting, to avoid introducing additional computing cost on edge device;In addition, an innovative cloud end model self-adapting scheme is introduced, a model self-adapting mode that is completely unnecessary to transmit original data on edge device is realized, and data privacy is effectively protected.

The application provides a TEE resource arrangement method and system in multi-access edge computing, a device and a storage medium, relates to the technical field of cloud computing, and the method comprises the following steps: an MEO receives TEE capability information of an MEC host reported by a VIM managed by the MEC host; the MEO selects a VIM and an associated MEPM managed by the MEC host with TEE capability according to the received TEE capability information and based on TEE capability requirements on the user side, initiates an MEC APP instantiation request to the MEPM, and allocates resources in the MEC host with TEE capability, so as to realize TEE resource arrangement of the MEO; the MEC host performs remote verification on a TEE instance after the TEE capability is turned on; and a third-party application verifies a TEE application instance environment. The technical scheme provided by the application enables edge services to deploy a service processing function or module with high requirements for data and code privacy protection in infrastructure resources supporting TEE when deploying APP.

This invention discloses an artificial intelligence-based method for constructing a cross-hotel guest social network, belonging to the field of network construction technology. This invention constructs a multi-layered, three-dimensional social profile network structure, capable of comprehensively capturing guests' behavioral patterns and emotional state changes in various functional areas of the hotel, improving matching accuracy and guest satisfaction with their social experience. Simultaneously, by constructing a cross-hotel social relationship network graph and introducing a node influence index evaluation mechanism, this invention enables the system to identify guests' potential value and connectivity in the social network, constructing a complete social network containing multi-level relationships, and achieving intuitive matching of cross-hotel guest behavioral patterns based on vectorized analysis technology of behavioral feature graphs.

This invention discloses a method for load prediction and optimized control of heating stations based on distributed machine learning, comprising: setting up corresponding edge computing devices near multiple heating stations, constructing feature vectors based on the load characteristics and time series characteristics of the heating stations; a cloud server initially sets up a global heat load prediction network model and distributes it to each edge computing device; the edge computing devices use local data to train the heat load prediction network model to obtain a local heat load prediction network model; the cloud server aggregates the local heat load prediction network models of multiple edge computing devices, updates the global heat load prediction network model, and distributes it to each edge computing device; while keeping the total flow rate of the heating system and the return water temperature of the secondary network constant, a secondary network supply water temperature prediction model is established, the setpoint of the secondary network supply water temperature is calculated, and the primary network regulating valve is adjusted to control the secondary heating network supply water temperature.

This application provides a wireless power transmission method, apparatus, device, and readable storage medium. This application allows for the continuous transmission of wireless power to various users, enabling them to train target models locally and transmit already trained target models. It can also determine whether the instantaneous channel state information of the wireless link is known, and adjust the wireless power resource allocation strategy for each user based on this information. If the instantaneous channel state information is known, a resource allocation strategy based on a deep Q-learning network and a greedy algorithm can be used to optimize the wireless power resource allocation strategy for each user. This effectively avoids the problem of users participating in federated learning being forced to interrupt federated learning due to excessive power consumption or long execution latency, thus preventing the upload of model parameters.

The invention provides a multi-modal agricultural knowledge graph construction method based on a large language model. The method comprises the following steps: firstly, carrying out standardized preprocessing on multi-modal data such as texts, images and sensors in the agricultural field; constructing an initial agricultural knowledge ontology by adopting a top-down and bottom-up combined mixed mode, and identifying a new concept based on a large language model to realize dynamic updating of the ontology; fusing the text entity, the image feature vector and the sensor time sequence feature into a multi-modal triple and storing the multi-modal triple into an image database; and finally, analyzing a user query intention by using a large language model, and performing multi-hop path reasoning in combination with a graph neural network to generate a structured result. The problems that in the prior art, multi-modal fusion is difficult, knowledge updating lags behind, and semantic reasoning capacity is weak are solved, the integrity, timeliness and intelligent decision support capacity of the knowledge graph are improved, lightweight deployment and a federated learning mechanism can be combined, an edge computing scene can be adapted, and the data privacy protection requirement is met.

This invention belongs to the field of AC / DC hybrid distribution network operation and control technology. A method for autonomous and mutual-assistance operation of AC / DC hybrid distribution networks based on flexible multi-state switches includes the following steps: Step S1: Calculate the comprehensive stability index of each node; Step S2: Monitor the comprehensive stability index in real time; Step S3: In the autonomous mode, the SOP within each zone adopts an AC / DC power decoupling control strategy based on dynamic virtual impedance; Step S4: When the power deficit of a zone exceeds a preset proportion of the zone's rated load and the duration exceeds a preset time threshold, the mutual-assistance mode is activated; Step S5: When a system fault occurs, a fault recovery strategy based on model predictive control is adopted; Step S6: Monitor the communication status between the global coordination unit and the control units of each zone in real time. This achieves safe, stable, efficient, and economical system operation, reduces communication dependence, and improves power supply reliability.