116results about How to "Guarantee data security" patented technology

The invention discloses a hierarchical multi-agent game confrontation and collaborative decision-making algorithm based on federated learning, and the algorithm comprises the following steps: setting a federated learning framework containing a plurality of agents, enabling the agents to be located in different regions, and carrying out the training of a local model through the data of the regions; in the federated learning framework, deploying a layered reinforcement learning model in each intelligent body; performing confrontation training on a plurality of agents in a game environment by using a hierarchical reinforcement learning model; on the basis of game confrontation, a collaborative decision algorithm is introduced to coordinate behaviors of multiple agents; and when a preset training round is reached or a specific condition is satisfied, uploading local model parameters to a central server, and sharing the model parameters through a federal learning framework to carry out global optimization. According to the method, efficient collaboration and strategy optimization among multiple agents can be realized on the premise that privacy is protected, and high adaptability and robustness are kept in a complex and changeable environment.

The invention relates to the technical field of data security, and discloses a data security risk assessment method based on model construction, and the method comprises the steps: firstly collecting historical data security events and related system configuration, network architecture and other multi-dimensional data, and carrying out the preprocessing to form a standardized data set; and then, constructing a data security risk prediction model, and predicting future possible data security events and potential influences thereof in combination with multi-dimensional factors. And obtaining a risk prediction result by obtaining data such as a system state and network flow in real time and inputting the data into the prediction model. Meanwhile, a risk influence assessment framework is established, the severity, occurrence frequency and business influence of the risk are comprehensively considered, and a risk comprehensive score is calculated. And finally, sorting and classifying the risks according to the scores, and formulating and implementing corresponding risk relief measures and emergency response plans for high-risk and medium-risk data assets. According to the invention, the accuracy and timeliness of data security risk assessment are improved.

The invention discloses a wind power master control system based on a safe and credible software and hardware platform, which belongs to the technical field of wind power generation and comprises a data acquisition module, a data analysis module, a monitoring result judgment module, an abnormal unit optimization control module and a data backup module. According to the invention, the data acquisition module acquires the operation monitoring parameters of the wind turbine generator in real time, and the operation monitoring parameters are processed by the data analysis module to obtain the operation monitoring evaluation value, so that the operation state of the wind turbine generator is accurately judged, and accurate monitoring and timely early warning of the wind turbine generator are realized; the problem that in the prior art, a wind power master control system is insufficient in safety performance and reliability is effectively solved, and the safe operation level and the fault handling capacity of a wind power plant are improved.

The invention discloses an intelligent security and protection management system based on 5G, AI and UWB positioning technologies. The intelligent security and protection management system comprises an image video acquisition device, an environmental factor acquisition device, a UWB positioning module, a 5G communication network module and an intelligent security and protection management platform, the intelligent security and protection management platform comprises an AI video identification subsystem, a cockpit subsystem, a virtual electronic fence subsystem, an operation risk security management and control subsystem, an operation ticket visa subsystem, a video linkage subsystem and a query statistics subsystem. The intelligent security and protection management system based on the 5G, AI and UWB positioning technologies has the advantages of high-precision positioning technology, intelligent video analysis, highly integrated system architecture, flexible expansibility and customizability, safe and reliable data transmission and storage and the like.

The invention is suitable for the technical field of escalator monitoring, and provides an escalator health assessment method and device based on deep learning and terminal equipment. The method comprises the steps that escalator operation data are collected through a sensor, and the escalator operation data comprise escalator vibration signals, escalator temperature, escalator operation noise and escalator operation current; local features used for representing the health state of the escalator are extracted from the escalator operation data through a convolutional neural network, the local features of various escalator operation data are fused through an attention mechanism, and global health state representation of the escalator is obtained; and an escalator domain knowledge graph is constructed, when the global health state characterization shows that the escalator state is abnormal, the reason of the escalator state abnormality is reasoned based on the relationship between entities in the escalator domain knowledge graph, and the escalator health condition is evaluated according to the reason of the escalator state abnormality. The escalator operation data can be comprehensively analyzed, the health state of the escalator is effectively evaluated, and the safety, reliability and operation efficiency of the escalator are improved.

The invention discloses a data management system based on a monitoring video stream, belongs to the field of data analysis, and aims to solve the problems of camera connection acquisition, image quality optimization, pedestrian related information analysis and data storage retrieval and backup in video monitoring. The system comprises a data acquisition module, a target detection module and a data storage module. The data acquisition module is used for identifying and connecting a video monitoring camera, performing video stream data acquisition, adjusting an acquisition frame rate by estimating a data size, and evaluating and optimizing image quality to obtain a detection image data set; the target detection module is used for carrying out feature extraction, classification and marking on pedestrians in the detection image data set, recording pedestrian trajectory information, carrying out real-time tracking and updating, identifying a behavior mode according to the trajectory information, dividing a gathering area and obtaining the pedestrian flow; and the data storage module is used for classifying and labeling the data, constructing an index structure to guarantee efficient retrieval, dividing data grades according to multi-dimensional features, and dynamically adjusting a backup time interval.

The invention discloses a network security early warning method and system based on big data, and relates to the technical field of network security, and the main scheme is that multiple types of data including network traffic, user behaviors, system logs and hardware state data are collected, and then index calculation is carried out based on the data; a network attack risk index, a user data exception risk index, a system security risk index and a hardware fault risk index are calculated, a network security comprehensive risk index is further calculated comprehensively, finally, a network security early warning threshold value is preset, the comprehensive index is compared with the preset network security early warning threshold value, and whether an early warning mechanism is triggered or not is judged according to a comparison result. And generating corresponding early warning information.

The invention provides an innovative integrated system which aims at monitoring the workload of medical staff and providing psychological support for patients at the same time. The system collects physiological and behavioral data of medical staff and physiological and psychological state information of a patient in real time through a multi-modal data acquisition technology. And by utilizing an advanced machine learning algorithm, the system can intelligently evaluate the workload of the medical personnel and predict the overwork risk. Meanwhile, according to individual requirements of the patient, the system generates customized psychological support schemes including emotion recognition, psychological education, social support and professional consultation. In addition, the system has the real-time feedback and intervention functions, and it is ensured that medical workers and patients get attention and help in time. The intelligent user interface design improves the user experience, and strict data security and privacy protection measures guarantee the security of user information. According to the design of the system, the efficiency and quality of medical care work are improved, the overwork risk of medical staff is reduced, comprehensive psychological care is provided for patients, the psychological welfare of the patients is enhanced, and overall rehabilitation of the patients is promoted.

The invention relates to the technical field of block chains, and particularly discloses a block chain supervision method and system based on NLP and rule fusion, and the method comprises the following steps: S1, an initialization stage: introducing an intelligent detection node and a supervision node into a block chain, and enabling the intelligent detection node and the supervision node to jointly participate in the transaction endorsement of the block chain; s2, in a supervision stage, the intelligent detection node and the supervision node inspect transaction contents in the current block and the historical block to obtain an inspection result, and the inspection result is reported to the decision node; and S3, in a decision-making stage, a decision-making node synthesizes the examination results of the intelligent detection node and the supervision node to generate a supervision conclusion, and carries out corresponding processing on illegal blocks.

The invention provides a fatigue driving adaptive detection method, which comprises the following steps: step S101, acquiring facial features of a driver during driving, and calculating a fatigue index of the driver according to the facial features of the driver and a fatigue monitoring calculation method; s102, when the calculated fatigue index exceeds a set fatigue index normal threshold value, a fatigue driving signal is sent to the vehicle; s103, if the driver does not have the fatigue driving behavior, re-judging the type to which the eyes of the driver belong; step S104, modifying parameters in a fatigue monitoring calculation method according to the type of the eyes of the driver, and performing adaptive adjustment on the fatigue monitoring calculation method; and S105, circulating the above steps by using the adjusted fatigue monitoring calculation method until the monitoring is normal. According to the invention, the problem of false alarm of the fatigue monitoring system caused by small eyes of the driver is solved, and the accuracy and adaptability of fatigue monitoring are improved.

The invention discloses a construction method and system for a safety protection system of a hydropower station monitoring system, and relates to the technical field of information safety and network safety, and the construction method comprises the steps: constructing a multi-level and graded safety architecture, and enabling the safety measures of all levels to complement each other; real-time data is acquired from a network layer and a data layer for analysis, security threats are analyzed in real time by using an artificial intelligence technology, and a security policy is automatically adjusted; when the system is attacked or breaks down, the normal function is automatically repaired and recovered, and the system is combined with an AI dynamic security strategy; the AES encryption algorithm is adopted to encrypt data, and encryption guarantee is provided for security measures of all levels. According to the invention, the multi-level and graded security architecture is constructed, and the AI technology is combined to carry out real-time analysis and automatically adjust the security policy, so that mutual complementation and coordination of security measures of each level are realized. When attacks or faults occur, the system has automatic restoration and recovery functions, and continuous and stable operation and data security of the hydropower station monitoring system are guaranteed.

The embodiment of the invention provides a data processing method, a branch predictor and related equipment, the method is applied to the branch predictor, prediction data are stored in the branch predictor, and the prediction data comprise prediction data belonging to a host mode and prediction data belonging to a virtual machine mode; the method comprises the following steps: acquiring a data disabling command, wherein the data disabling command is sent out in a mode switching process of a host mode and a virtual machine mode; and based on the data disabling command, disabling prediction data belonging to a virtual machine mode in the branch predictor, and keeping prediction data belonging to a host mode in the branch predictor in an available state. According to the embodiment of the invention, the instruction prediction efficiency can be improved on the premise of ensuring the data security.

The invention belongs to the technical field of regional power consumption prediction, and aims to solve the problem that detailed power consumption data of each region can be directly exposed by an existing regional power consumption prediction method. The distributed calculation-based large-area power consumption prediction method comprises the following steps of: dividing a large area of which the power consumption is to be predicted into a plurality of small areas, and performing secondary division on the divided small areas into a plurality of areas; constructing a power consumption prediction model of each district, and updating the power consumption prediction model of the core district by using the encryption loss data and gradient data of the other districts as the power consumption prediction model of the small district to which the core district belongs; the core small area receives encryption model parameters of the other small areas and generates model gradients corresponding to the small areas; and constructing a power consumption prediction model of the whole large area through encrypted data transmission and model updating iteration between the core small area and the other small areas. According to the method, the leakage problem in data transmission can be avoided, and the prediction accuracy and safety are improved.

The invention discloses an unauthorized access control method and device, equipment and a medium, and the method comprises the following steps: receiving an API access request, the API access request comprising API information of a first API to be accessed and user information of a first user to access the first API; according to the initial API set and the API access request, judging whether the access of the first user to the first API belongs to authorized access or first unauthorized access; based on the authorized access of the first user to the first API, a data access request of the first user is received, and the data access request comprises data information of first data to be accessed; obtaining a first data type corresponding to the first API according to the user information of the first user and the data information of the first data; and according to the plug-in corresponding to the first data type, judging whether the access of the first user to the first data belongs to authorized access or second unauthorized access. According to the method, the operation of API background software and the data security can be effectively guaranteed by controlling the user to have the unauthorized access to the API and the corresponding data.

The application discloses a screen projection method, device and system, electronic equipment and a storage medium. The screen projection method comprises the following steps: receiving a broadcast signal; analyzing the broadcast signal to obtain screen projection information; judging whether to display a screen projection request prompt based on the screen projection information; if it is judged to display the screen projection request prompt, the screen projection request prompt is displayed; and in response to a confirmation operation of the user agreeing to the screen projection request prompt, a screen projection operation is performed. The application triggers the screen projection operation by the screen projection end, specifically, the broadcast signal sent by the screen projection end is received, and the screen projection operation is performed according to the screen projection information carried in the broadcast signal, so that the user does not need to search for the screen projection end on the screen projection end, the operation is convenient, the error rate of the screen projection connection is reduced, and the user experience is improved.

The present application belongs to the technical field of electrocardiosignal data processing, and discloses an electrocardio rhythm feature recognition data processing method, system, medium and program product, which comprises double verification of heartbeat quantity and ventricular premature beat proportion on acquired electrocardio feature data; generation of an instantaneous heart rate sequence, dynamic determination of a total number of bins and a heart rate step, mapping to obtain a symbol array and constructing a word sequence; calculation of Shannon information entropy and P wave disappearance probability; determination of a Shannon information entropy threshold and a P wave disappearance probability threshold, combined feature joint classification matching of the Shannon information entropy and the P wave disappearance probability, and output of a final electrocardio rhythm feature classification label result. The present application solves the problems of insufficient anti-interference ability and dependence on long-range signals in electrocardio data processing of portable devices, and realizes accurate identification of target irregular electrocardio rhythm features in complex environments without long-range signals on the basis of retaining the portability of the devices, thereby better adapting to instant data screening scenes of various terminal devices.

The application discloses a kind of machine learning model inference and forget request collaborative processing method, machine learning online service provider is updated model in real time in background based on forget request, and robustness detection is carried out to user inference request, service scheduling is carried out according to detection result, to realize faster response speed.The method comprises the following steps:1) sub-model training: split data set and train multiple sub-models with independent inference ability and online service;2) forget request processing: corresponding sub-model is updated in real time based on forget request received during service;3) inference request response: high robustness inference request is responded preferentially, and low robustness request is responded based on new model after model is completed updating.The application first proposes the collaborative processing method of multiple requests in the online service scene of machine learning, while guaranteeing privacy and correctness, higher response speed is realized, which is superior to existing method, with the advantages of strong universality, easy to deploy and the like.

The present invention discloses a road parking identification method and system of an integrated encryption chip PDA device. The method performs an encryption operation on first identification data of an image of a road parking position to form second identification data. The road parking identification data processing work is run in a virtual cache container, and a request for performing an encryption operation is received through an integrated encryption chip and decrypted and compared. After decrypting and comparing the second identification data, parking identification authority is allocated. The encryption transmission does not require additional software to implement, and encryption, classification, and secure data identification upload and other operations can be completed directly during identification. At the same time, the processing capacity of the encryption chip is amplified through container technology, so that all logic codes of business programs and algorithms are run in a protected secure container, thereby implementing the security of algorithms and codes and executable modules, and ensuring data security and financial security.

This application provides a hard disk isolation method, apparatus, electronic device, and storage medium. In this application, by first determining the pool state of the hard disk pool to which the hard disk to be detected belongs, then detecting whether the hard disk pool is in a steady state, and finally combining the isolation determination method based on fault redundancy type and the number of abnormal devices, the problem of low isolation efficiency caused by repeated isolation determinations triggered by reliance on PG status can be effectively avoided in existing methods. This improves isolation efficiency and minimizes the impact on hard disk-related services.

The invention belongs to the technical field of air-ground cooperative rescue, and discloses a mooring type unmanned aerial vehicle disaster relief system and a working method.The mooring type unmanned aerial vehicle disaster relief system comprises the steps that stable hovering in a complex environment is achieved through an unmanned aerial vehicle by means of a hovering module, releasing and recycling of a robot are accurately controlled through a winch mechanism, and a guiding bin and a UWB recycling positioning sensor are combined; it is ensured that the robot recovery alignment deviation does not exceed a preset threshold value, and the robot delivery and recovery problems are solved; the snakelike robot has excellent complex ruin gap adaptability by means of a multi-section joint structure and multi-sensor fusion, environment data and survivor information are accurately collected, and search and rescue accuracy is improved; the emergency module is used for realizing automatic return flight of the unmanned aerial vehicle, inertial navigation return of the robot or extreme power-saving rescue waiting for faults such as mooring disconnection and signal loss, and the data cache is used for automatically storing key task data, so that task continuity and data safety are guaranteed, and the search and rescue efficiency and reliability in extreme disaster scenes are greatly improved on the whole.

The invention provides a password device discovery method and a computer device, and relates to the technical field of password application. The method comprises the following steps: responding to an access event triggered by a software application on a current password device, obtaining device feature information of the current password device in multiple dimensions, generating device fingerprint information of the current password device according to the device feature information in multiple dimensions, and sending an access request to a server of the software application according to the device fingerprint information, the access request is used for enabling the server to detect the equipment fingerprint information by adopting an equipment fingerprint detection algorithm according to a preset equipment fingerprint database to obtain a fingerprint detection result of the current password equipment, receiving a first access response sent by the server, and sending the first access response to the server; the first access response is an access response triggered when the server determines that the current password device is the registered password device. Complicated and unique equipment fingerprint information is generated through multi-dimensional equipment feature information, and omnibearing and high-precision identification of the identity of the password equipment is realized.

This invention discloses a personalized AI avatar construction and group digital experience asset inheritance system based on a private data domain, relating to the fields of artificial intelligence and digital asset management. This invention uses a user-isolated private data domain as its operating platform to construct a personalized AI avatar that highly matches the user's personality, thinking, and behavioral habits for an individual user or corresponding group. Through continuous collection and structured processing of user interaction and decision-making data, it accumulates storable, iterative, and transferable digital experience assets within the group. The system supports the authorized integration and scheduling of external compliant experience assets under secure isolation, and employs a dual-drive engine of "general knowledge + personalized experience" for intelligent decision-making and scenario-based responses. Based on anticipating potential user needs, it outputs decision references and adaptive services, while relying on the private data domain to achieve end-to-end data isolation, encrypted storage, and controllable permissions. This invention effectively solves the technical problems of insufficient personalization in existing AI avatars, the inability to scalably inherit digital experience, and the single, passive interaction mode. It can be widely applied to scenarios such as personal intelligent assistants, family management, team collaboration, experience inheritance, and digital experience circulation, demonstrating strong technological innovation and rich application scenarios.

The invention discloses an oil field mechanical arm grabbing 3D visual guidance system based on deep learning. The system comprises an oil field mechanical arm loaded with a motion control system, and a data acquisition module, a data processing module, an initialization calibration module, a target detection module and a grabbing detection module which are in communication connection with the oil field mechanical arm. In oil field operation, when the system initially operates or moves, the initialization calibration module obtains parameter initialization. The data processing module calculates collected images in real time, after it is ensured that the drill rod is static, the images and point cloud data are transmitted to the target detection module to determine the position of an object and then serve as an initial posture estimation result to be transmitted to the grabbing detection module, and finally the result is transmitted to the mechanical arm motion control system for grabbing operation. In conclusion, efficient, intelligent and accurate recognition and grabbing of the drill rod are achieved, new possibility is provided for application of the oil field mechanical arm in complex tasks, and far-reaching influences are generated on oil field automation and intelligent production.

The application discloses a multi-source education data attribution analysis system and method based on a large model Function Call, and belongs to the technical field of computers. The system comprises: a multi-source data access module for constructing a unified education data warehouse; an intelligent semantic routing module for identity recognition and intent analysis; a function registration and execution module for triggering atomized analysis functions; and a comprehensive attribution analysis module for generating an attribution report in combination with a knowledge base. The method comprises the following steps: accessing multi-source education data and preprocessing, guiding the large model to identify the intent by using the prompt engineering, outputting a structured instruction to trigger the function call, and finally performing causal reasoning. The application realizes the deep fusion and accurate attribution of multi-source education data, reduces the interaction threshold, and provides a scientific basis for teaching decisions.

The application discloses a kind of mobile group perception data privacy protection face emotion recognition method, by the extraction of feature of inquiring user local computing power and encryption, matching is carried out on cloud by the way of data outsourcing, then the matching condition is obtained to recognize face expression data, it is related to including face feature vector provider, two cloud servers CP, CSP of providing strong computing power and storing encrypted data and multiple mobile devices for face emotion feature recognition.The application method uses homomorphic encryption technology, allows feature vector matching in ciphertext state, thereby improves the computing efficiency and data privacy protection level, reduces the risk of personal privacy leakage, and provides higher privacy protection degree, with high application flexibility, can adapt to the needs of different application scenarios, including medical care, social media analysis and personalized recommendation, etc., is worth promoting.

The present invention discloses a hierarchical encryption real-time normal height service method and system, comprising: step 1: hierarchically peeling a digital elevation benchmark model into k digital elevation benchmark sub-models, and encrypting the sub-models; step 2: storing the encrypted sub-models on multiple independent servers, respectively, and deploying a decryption algorithm and an elevation anomaly interpolation algorithm; step 3: deploying a data leakage prevention module on a broadcast server; step 4: the broadcast server receives a request for obtaining information content sent by a user terminal, and after verification, the broadcast server sends a request to a network RTK server to obtain differential data, and the server sends a request to obtain an elevation anomaly component; step 5: the broadcast server merges all the collected elevation anomaly components into an elevation anomaly, uses the elevation anomaly to correct the geodetic height in the received differential data to obtain a normal height, and broadcasts it to the user terminal. The present invention improves work efficiency and data security.

This invention provides a campus hardware and software integrated management method and system supporting remote operation via mobile devices. It collects status parameters and business data from heterogeneous devices in the campus teaching, security, and logistics domains, synchronizes them with a hybrid clock, and pushes them to a campus digital twin virtual model incorporating geographic information, achieving full-domain resource virtual-real mapping and 3D visualization. A sliding time window combined with a business weight correction mechanism is used to determine adaptive warning thresholds and cross-device linkage strategies based on device operating time and business scenarios. Mobile devices access the system via an end-to-end encrypted link, enabling remote hierarchical handling based on dynamic hierarchical permissions. Hierarchical warnings are generated when a single parameter exceeds limits or multi-dimensional correlation anomalies are detected. If remote handling is ineffective, maintenance work orders are automatically generated and resources are intelligently scheduled, forming a closed-loop management process. This solution breaks down campus hardware and software data silos, enabling deep mobile control and intelligent operation and maintenance, significantly improving campus management efficiency and emergency response capabilities.

The invention provides a comprehensive analysis and management system for a data platform. The comprehensive analysis and management system comprises a central data processing unit and a plurality of local data processing nodes, the local nodes collect data and send the data to the central unit, and the central unit comprises a data analysis module and a data mining module. The data analysis module constructs a data model training sample set, and a data model is obtained through training and used for predicting the future trend of local node data. And the data mining module classifies the data according to the data storage and access frequency and the future trend, and optimizes the storage and access positions. And the central unit sends the trained model and parameters to local nodes, and the local nodes predict future data trends based on the model and put forward optimization suggestions. And the central unit is also responsible for issuing the data classification result to the local nodes so as to provide data storage and access position suggestions. The access efficiency can be improved, and the storage cost can be reduced.

This application provides a component-centric isolation and sharing device and its operating method based on containerized encapsulation. The device includes a component encapsulation and registration center for building and registering container images; a containerized runtime scheduling engine for isolating environment instantiation of components and managing their lifecycle; a centralized access proxy gateway for verifying requests and routing them; and a unified configuration and policy management center for storing and managing various policies and providing query and update interfaces. The advantages of this application are: containerized encapsulation achieves environment decoupling and consistency, eliminating dependency conflicts; strong isolation policies ensure multi-tenant data security and system stability; centralized management enables fine-grained access control; intelligent elastic scaling optimizes resource utilization; and it supports centralized management of the entire component lifecycle, improving reusability, maintainability, and collaborative efficiency.

The application provides a three-dimensional rendering engine for multi-source design model integration of ocean petroleum engineering, relates to the field of computer graphics and three-dimensional visualization technology, and comprises four layers of technical architecture: a cloud rendering layer realizes high-performance graphics processing based on a Vulkan API; a cloud transmission layer is responsible for pixel stream transmission and instruction interaction; an integrated SDK layer encapsulates common interfaces; and an application docking layer realizes business development. A service-side rendering plus pixel stream pushing technical route is adopted, low-delay video transmission is realized through WebRTC, instruction interaction is realized in combination with an MQTT protocol, rendering calculation is concentrated on the service side, and the client only needs to process video stream decoding. The model browsing frame rate of the application is not lower than 20 frames per second, the first preloading time is less than 1 minute, and the system response time after preloading is less than 3 seconds, so that the smooth display problem of large-scale three-dimensional models on ordinary terminal devices is effectively solved, and high-fidelity three-dimensional visualization support is provided for the whole business process of ocean engineering.