14results about How to "Guaranteed data privacy" patented technology

This invention discloses an intelligent empowerment system for automotive after-sales service and its implementation method. The system includes a case collection module, a case review module, a knowledge sharing module, an intelligent recommendation module, a mutual assistance and communication module, a backend data analysis module, and a business empowerment module. Dealer repair personnel can submit standardized fault cases to the platform. After review, a case database is formed. The system calculates recommendation weights based on user profiles, case quality, and interaction data, achieving personalized distribution and precise matching of cases. Users can browse cases, seek help, and engage in interactive learning within the platform, forming a technical mutual assistance community. The backend data analysis module structurally extracts the collected cases and interaction information, generating standardized technical intelligence, and pushes it to business systems such as technical support, warranty management, spare parts planning, and training management, building a two-way empowerment mechanism between OEMs and dealers, and achieving closed-loop management from fault cases to business optimization.

This invention provides a network attack detection method and system for vehicle-to-grid (V2G) interaction. It includes: a local client capturing raw traffic data and extracting key features; then receiving the latest global model parameters, detecting the raw traffic data and encoding the attack detection results into metadata tags, while simultaneously using the key features to train the global model parameters to calculate the quantization gradient; extracting feature vectors from the raw traffic data and combining them with the metadata tags to perform attack prediction and obtain the attack prediction probability; the local client executing defensive actions based on reinforcement proxy learning on the attack prediction probability; the local client uploading the quantization gradient and metadata tags to the server via a secure sparse aggregation protocol; the server reconstructing the global gradient to update the global model parameters via a key exchange protocol and updating the attention weights of the global model using the metadata tags; the latest global model outputting the attack probability of V2G interaction, determining whether to issue an alarm and execute defensive actions.

The invention provides an intelligent breeding behavior analysis system and method based on edge calculation and AI vision, relates to the technical field of intelligent breeding and computer vision, and solves the technical problems of low efficiency, poor behavior recognition accuracy and abnormal early warning lag caused by dependence on manual and cloud processing in existing breeding management. The system comprises an edge end device which is used for collecting a video stream in real time and carrying out target detection and key point identification on the video stream through a lightweight deep learning model to obtain a preliminary behavior classification result; the fog end server is used for gathering a plurality of preliminary behavior classification results, performing complex behavior recognition, behavior time sequence analysis and abnormal behavior detection, obtaining an abnormal behavior result and triggering real-time early warning; and the cloud platform is used for receiving and storing all data of the farm, performing long-term data mining, multi-source data association analysis and model training and updating, and generating decision support information. The method is used in the intelligent breeding process.

The invention discloses a virtual power plant aggregation operation cost scheduling method based on an input convex neural network, and the method comprises the steps: constructing a feature coding model based on a deep neural network, carrying out the feature extraction of the output data of a distributed power supply in a virtual power plant, and obtaining the coding feature data; constructing an equivalent transformation model of the input convex neural network, and solving to obtain a multi-period aggregation operation cost agent model; and taking the obtained multi-period aggregation operation cost agent model as an operation cost function of the virtual power plant, constructing a multi-period random economic optimization scheduling model of the power distribution network, solving the model, obtaining an optimal aggregation power scheduling instruction of the virtual power plant, and issuing the optimal aggregation power scheduling instruction to execute scheduling. According to the method, implicit feature coding is utilized to guarantee data privacy, a nonlinear relation is accurately fitted by inputting the convex neural network, mathematical convexity is strictly guaranteed, the difficulty of non-convex solution is overcome, and it is guaranteed that a multi-period scheduling scheme has global economic optimality.

The invention provides a data sharing method of electronic equipment and a related device. The method comprises the following steps: receiving a click operation on a virtual icon corresponding to a memory data viewing function in a main interface of first electronic equipment; the first electronic device is controlled to start a camera and scan a target entity photo to obtain target authority verification data, and the target entity photo is associated with target data content uploaded to the cloud server by the second electronic device; sending a permission verification request to a cloud server, wherein the permission verification request comprises target permission verification data and equipment information of the first electronic equipment; receiving target data content associated with the target entity photo sent by the cloud server, wherein the target data content is sent after the cloud server confirms that the first electronic equipment has the permission of looking up the target data content based on the permission verification request; and controlling the first electronic equipment to output the target data content. According to the invention, data sharing of the electronic equipment is realized on the premise of ensuring data privacy.

The application discloses a large language model driven intelligent financial field database interaction method, and relates to the field of databases, and the method comprises the following steps: S1, constructing a language model and training; S2, recognizing and analyzing user input information by using the language model; S3, the language model selects a database interaction tool according to the recognition result, and queries the database; S4, the database interaction tool returns the query result, and saves the query data to an intermediate file; S5, steps S3-S4 are repeatedly executed based on an action chain until all database interaction tools finish querying, and the query result is output through the language model; S6, the output variable is replaced by using the query data in the intermediate file, and the result is returned to the user. The application reduces the threshold of user and database interaction, and realizes the innovation of user interaction.

The present application relates to a kind of ocean ranching intelligent decision system based on self game, comprising: multi-modal data perception module, for collecting multi-modal data and constructing space-time correlation feature atlas by graph neural network;Large model decision hub module, for mapping multi-modal data to unified semantic space by cross-modal alignment technology, and variable correlation reasoning based on injected marine ecological causal diagram;Dynamic game optimization module, for taking ecological protection target and economic benefit target as game parties, and searching Pareto optimal solution;Virtual-real linkage execution module, for driving ocean ranching digital twin and physical equipment to perform corresponding operation according to decision instruction, and realizing dynamic updating of model parameters by federated learning.The present application can improve the yield of cultivation, reduce ecological disaster loss, while supporting carbon sink asset digitalization certification, providing ocean ranching with intelligent decision support with ecological adaptability and economic robustness.

The application discloses a kind of group learning incentive methods based on blockchain, first the node in cluster is divided into network node, data node, computing node with incentive mechanism and other nodes, the incentive method based on blockchain and reputation mechanism is used to main node, it is supplemented with the reward and punishment mechanism based on game theory to calculate the possible reward and fine of each node, make the benefit of each node honest work maximum.The application can realize the virtuous cycle of group learning, rely on the reputation recorded by blockchain and game theory method to promote data node to provide real information;In the era of deep learning and machine learning, it can play an important role, such as between various financial institutions, this cannot make them trustingly carry out privacy protection data fusion, but the application can evaluate the credit between each institution, maintain the benefit balance of data demander and data provider, so that each party voluntarily joins the group learning process, and fully develops potential data value.

The invention belongs to the technical field of intelligent charging pile cooperative scheduling, and discloses a new energy automobile intelligent charging pile cooperative scheduling system based on the Internet. The system comprises a user charging demand prediction module, a virtual power grid demand aggregation module, a predicted charging demand charging scheduling module and an automatic cost settlement transfer module, user charging demands are predicted based on an overall prediction model, predicted charging demand reports are obtained, all the predicted charging demand reports are integrated, and the integrated charging demand reports are obtained. Based on the power grid state data, an optimization target report is generated, a reinforcement learning algorithm is created for each predicted charging demand, an optimal charging scheduling report is generated for each predicted charging demand, and automatic cost settlement and transfer are performed based on event data in the block chain. The method has the remarkable advantages that the user charging demand prediction capability is high, the global scheduling optimization effect in the region is good, and the vehicle-power bidirectional mode application effect is great.

The invention discloses a privacy auditing single-round federal learning method and device for biomedical data, and relates to the technical field of artificial intelligence and privacy protection, and the method comprises the steps: carrying out initialization, generating or receiving a CKKS public and private key pair and an ECDSA signature key pair by a medical institution, and initializing an auditing threshold by a central research institution; each medical institution trains a local model based on a local training set to output and obtain features, encrypts and signs the output features, and uploads the features to the central research institution; and the central research institution performs signature verification and ciphertext analysis so as to perform encrypted domain data auditing and verification, and realizes encrypted domain security aggregation and knowledge distillation in combination with threshold setting and self-adaptive updating. According to the application, end-to-end encryption transmission, uploaded data identity verification and model quality auditing in an encryption domain under biomedicine multi-center cooperation can be realized, and data privacy, model authenticity and result traceability are ensured.

The invention discloses a client and sample joint optimization method for federated learning of the industrial Internet of Things, which comprises the following steps: S1, constructing a cloud-side-end architecture, S2, initializing a cloud server, S3, executing importance perception data shuffling by terminal equipment, S4, screening clients participating in federated learning training by an edge server, and S4, carrying out data shuffling by the edge server. S7, the edge server executes synchronous aggregation and uploads the updated data to the cloud server; S8, the cloud server executes global aggregation and issues the updated data to the edge server for synchronization; S9, the steps S3-S8 are repeated, and S7, the selected client device executes grouping importance sampling; S7, local model training is executed and the updated data is uploaded to the edge server; and the model converges or reaches a preset training round. According to the method, the training efficiency, the convergence speed and the prediction precision can be remarkably improved in a resource constraint scene.

The invention discloses a tonsil postoperative diet management system and method, and relates to the technical field of medical care, and the system comprises a front end, a rear end, a database, an image recognition module and an intelligent interaction module. All the parts collaboratively realize the following functions: a data management function, a system management function, an authority management function, an automatic recipe recommendation function, an image recognition function and an interactive forum function. The personalized diet recipe can be automatically recommended according to the basic information, the medical history, the number of postoperative days and the tonsil mucosa recovery condition of the patient, meanwhile, an interaction platform between a doctor and the patient is built, and the problems that existing clinical diet guidance is general, individuation is lacked and doctor-patient communication is inconvenient are solved.

The present application relates to the technical field of transformer cooling, and discloses a transformer adaptive spraying cooling method, system, device and medium. The method comprises the following steps: based on the multi-dimensional working condition data of each main transformer, an oil temperature prediction value of the next moment of the corresponding main transformer is determined by using a machine learning model combining federated learning and gradient boosting machine; a first oil temperature deviation between the oil temperature prediction value and a preset target oil temperature and spraying energy consumption are used to construct a flow control target function corresponding to each main transformer, each flow control target function is solved according to the corresponding flow control constraint condition, and the optimal nozzle flow of the corresponding main transformer is obtained; and based on the optimal nozzle flow of each main transformer, the output power of the water pump of the corresponding main transformer is updated in real time, so that adaptive spraying cooling is performed on each main transformer. The present application realizes the precision, energy saving and safety of transformer spraying cooling.

The invention discloses an operation method and system of an intelligent make-up vending machine integrating AR make-up trying and skin analysis, and relates to the technical field of intelligent make-up equipment, and the operation method comprises the steps: synchronously collecting a face multispectral image, thermal imaging data and environment illumination data of a user through a multi-modal data collection module, and constructing an initial multi-dimensional skin state model; and generating a dynamic skin file by using a dynamic skin analysis engine, and generating a vivid AR trial makeup effect in combination with a light field reconstruction technology. The system captures emotion feedback data of a user, performs real-time association analysis, generates a personalized makeup product recommendation list based on fused data, and finally updates a dynamic skin analysis engine and an intelligent recommendation algorithm based on desensitization data to form a closed-loop intelligent service system. The reality sense of AR trial makeup and the personalized recommendation precision are remarkably improved, and the self-adaptive capability of the system is enhanced.