596results about How to "Improve data security" patented technology

The invention relates to the technical field of ore conveying, and discloses an intelligent mine ore conveying management system, which comprises a deep reinforcement learning scheduling module used for realizing optimal scheduling through a deep reinforcement learning algorithm based on an ore conveying equipment state, a conveying path condition and ore queue information; the digital twinning system is used for creating a digital twinning model of the conveying equipment and carrying out multi-physics field simulation, real-time data synchronization and state prediction so as to manage the full life cycle of the equipment; and the edge calculation module is used for carrying out data acquisition, real-time processing and lightweight AI reasoning on a mine site so as to realize equipment anomaly detection and early warning. By introducing deep reinforcement learning scheduling, digital twinning, edge computing and block chain data management technologies, adaptive optimization scheduling, accurate state monitoring and fault prediction of the ore conveying system are realized, an efficient safety protection and intelligent user interface is provided, and the operation efficiency, reliability and safety of the system are improved.

The invention discloses a data real-time analysis and decision-making system based on an edge intelligent Internet of Things, belongs to the technical field of Internet of Things, and aims to solve the technical problems of insufficient real-time performance, high delay, large bandwidth occupation and high dependence on a central server in data processing of Internet of Things equipment. Comprising an edge device layer, an edge computing node layer, a cloud edge collaboration module and a central server layer, each edge computing node is used for taking data after local analysis processing as input, performing prediction analysis through a local model, making a decision through a decision generation mechanism based on an analysis result, and performing cloud edge collaboration. Generating decision optimization through an adaptive decision optimization algorithm, and performing data exchange with the central server layer through a cloud edge collaboration module; and the central server layer is used for carrying out model updating based on the real situation and the analysis result corresponding to the analyzed and processed data uploaded by each edge computing node, and returning the updated model parameters to the edge computing node layer.

The invention discloses an internet data security protection method and system based on an intelligent algorithm, and relates to the technical field of security protection. During system operation, user behavior data are collected in real time through log recording, a sensor and an API interface, the real-time operation behavior of a user is monitored, the interaction mode and transaction behavior of the user on a platform are analyzed, and the security of the user is improved. Calculating and acquiring a user behavior safety coefficient Yhxw, a data transmission safety coefficient Sjcs and a risk protection response coefficient Fxfh, identifying potential fraudulent behaviors, performing dynamic risk assessment by combining user historical behaviors, a transaction amount, a geographic position and data of payment equipment, performing threshold comparison to obtain an assessment scheme, and during login and transaction, combining multiple identity verification means to obtain a risk protection result; according to the method, user identity verification is carried out, security of login behaviors is dynamically monitored, network traffic and platform logs are analyzed, abnormal access and malicious requests or DDOS attacks are detected in real time, and abnormal behaviors or attacks are automatically identified and prevented.

The invention relates to the technical field of airport baggage management, in particular to an airport baggage tracking system and method based on a block chain, and the method comprises the steps: baggage information collection and identification: collecting basic baggage information through an intelligent tag, encrypting the basic baggage information to generate a unique identifier, and recording the unique identifier in the block chain; luggage state updating: collecting state change information by using the Internet of Things equipment, and writing the state change information into the block chain after verification of the smart contract; anomaly detection and prediction: simulating and analyzing a luggage state by means of a generative adversarial network, and triggering an alarm and updating the block chain if an anomaly is found; path optimization: adopting a reinforcement learning algorithm to optimize a transportation path according to a real-time state and historical data and recording the transportation path; and performing anomaly analysis and response, analyzing path anomaly based on the graph convolutional network, and processing and recording according to a strategy. The data security and transparency are guaranteed by using the block chain technology, the luggage management efficiency is improved by combining various advanced algorithms, the abnormal risk is reduced, the service experience is optimized, and the method is suitable for the field of airport luggage management.

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.

The invention discloses an electronic archive data security system and method, and relates to the technical field of electronic document management. The electronic archive data preservation method comprises the following steps: acquiring a plurality of storage files to be stored and file attribute data of each storage file in real time; analyzing the risk level value of each storage file according to the file attribute data of the storage files, and marking the risk level value; classifying and storing the plurality of marked storage files into an electronic archive database; after a plurality of storage files are stored in a classified mode, time period access data of each storage file are obtained in real time, and real-time analysis is carried out in combination with risk level values. File attribute data are obtained in real time, and risk level evaluation is carried out on the file attribute data, so that the files can be marked and stored in a classified mode in real time; according to the method, the high-risk file is ensured to accept stricter protection measures, the risk assessment index is updated in real time, and the encryption strength is dynamically adjusted according to the risk assessment index, so that the protection capability on sensitive information is remarkably improved.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention discloses a wireless communication network data secure transmission method and system, and relates to the technical field of secure transmission. During system operation, agricultural monitoring data are collected in real time through various sensor devices arranged in an agricultural environment, encryption processing is performed by using a symmetric encryption algorithm, and a dynamic key length adjustment mechanism is supported; a random number generator is integrated, unified management is performed on key generation, distribution, updating and destruction in a system, a timestamp or a random serial number is embedded into each data packet, the uniqueness of the timestamp or the serial number is verified at a receiving end, so that replay attacks are resisted, and the data are comprehensively analyzed and evaluated according to a set algorithm, so that the replay attack is prevented. The method comprises the following steps: acquiring a security data transmission performance index SDTP through calculation, generating a monitoring report, detecting abnormal conditions in a data transmission process, including channel quality reduction, data loss or verification failure, and providing a visual monitoring result.

The invention discloses a federal learning method and device, a medium and a product, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring parameters and private watermark parameters of initial local business models of a plurality of federated learning clients; performing matrix processing on the private watermark parameters through a predefined smart contract to obtain a private watermark vector; embedding the private watermark vectors into parameters of the corresponding initial local business model, and performing aggregation by applying a federated average algorithm to obtain parameters of an initial global business model; verifying by applying a consensus mechanism of the block chain to obtain a verification result; when the verification result is correct and the initial global service model converges, obtaining a global service model; the global service model is an initial global service model. According to the method, under the characteristic that the block chain network cannot be tampered, record traceability of service model operation is achieved, watermark embedding is verifiable, and robustness of a traditional federated learning framework is enhanced.

The invention belongs to the technical field of automobile information safety fuzzy testing, discloses an automobile information safety fuzzy testing method based on a large model, and develops optimization application research of LLMs in an automobile software fuzzy testing scene by utilizing the advantages of an artificial intelligence technology in the aspects of data processing, natural language interaction, generalization ability and the like. The concealment and effectiveness of the test set generated by the fuzz test are optimized, and the fuzz test process efficiency is improved. Through the implementation of the invention, related key technologies are applied to the fuzz test of the automobile, and other related products and enterprises in China are radiated by writing technical standards, so that the fuzz test system can be used as an application demonstration and sample plate of the fuzz test of the intelligent driving automobile. According to market analysis, fuzzy test calculation is carried out on 100 thousand automobiles every year according to the method, and the data safety and the system safety are improved.

The invention provides an intelligent dialogue method based on user permission separation and a related device, and the method comprises the steps: analyzing a reply content output request from terminal equipment, and obtaining an initial question description and basic identity information of a target user; determining permission description according to the basic identity information of the target user; updating the initial problem description according to the permission description to obtain a target problem description; querying according to the target problem description to obtain a target data set; and inputting the target question description, the permission description, the target data set and the cue word template into the vertical field large model, and outputting target reply content. Therefore, the intelligent dialogue system is endowed with user permission recognition and separation capabilities, so that the actual questioning content of the user can be accurately speculated, the correct answer can be output, and the data security and the reply accuracy are improved.

The invention discloses an intelligent networking distributed storage interaction system based on multi-cabin cooperation and a method thereof, and belongs to the technical field of camping intelligent cabin interaction, and the method comprises the following steps: taking each intelligent cabin as an independent node, initializing and accessing an intelligent networking distributed system, and enabling the nodes of the intelligent cabins to establish initial connection with other nodes in the system; each intelligent cabin collects user data, environment data and equipment state data through a built-in sensor and stores the collected data in a local storage unit in a classified mode. According to the method, the data synchronization among the nodes of the intelligent cabin is realized through a distributed database consistency algorithm, the data consistency under the condition of network delay or interruption is ensured, the data conflict and damage are prevented, and the system reliability is improved. Meanwhile, resource allocation is dynamically optimized based on an intelligent scheduling algorithm of machine learning, different load requirements are met, resources are saved, and the system efficiency and the intelligent level are improved.

The invention particularly relates to a data governance method and system based on a large model. According to the data governance method based on the large model, data are automatically collected, and the data are preliminarily cleaned and preprocessed; carrying out deep analysis, mining and prediction on the data by utilizing the large model, and carrying out data conversion and formatting according to business requirements; a data quality standard, a data security policy and a data management process are formulated and executed according to service requirements, so that the accuracy, integrity and security of data are ensured; the data quality and the data flow are monitored in real time, and if a data quality problem is found, the data quality problem is fed back to the large model so that the large model can solve the data quality problem in time. According to the data governance method and system based on the large model, intelligentization and automation of data governance are achieved, the burden of manual operation is relieved, the data governance efficiency is improved, the data security and compliance are optimized, intelligent decision support is achieved, and the data value mining capacity is improved.

The invention provides a safety protection method and device for an intelligent networked automobile, and the method comprises the steps: detecting a vehicle control abnormal instruction in a communication process between a target vehicle and a cloud end based on a vehicle control behavior characterization model and an abnormal behavior classification learning model; and based on the malicious traffic identification model, identifying hidden malignant traffic in the process of communication between the target vehicle and the cloud, through a deep reinforcement learning confrontation defense mode, a decision control model is trained according to a vehicle control abnormal instruction and hidden malignant flow, and the decision control model is used for carrying out safety protection on the intelligent network connection vehicle. Therefore, high-precision and high-reliability detection of attacks such as vehicle control instruction tampering attacks and strongly hidden malicious traffic under the intelligent network connection vehicle-cloud cooperation scene is realized, the attack concealment and attack efficiency are effectively improved, the robustness and generalization of the model are enhanced, and the method is suitable for the intelligent network connection vehicle-cloud cooperation scene. And the communication safety, the data safety and the driving safety of the intelligent networked automobile ecological system are improved.

The embodiment of the invention discloses an intelligent privacy protection method and device for a database and a medium, belongs to the technical field of data privacy protection, and solves the problems that an existing privacy protection method is inaccurate in sensitive information identification and classification and poor in privacy data protection effect. Comprising the following steps: analyzing to-be-identified data through a preset sensitive information identification model to obtain reference sensitive information; performing secondary verification on the reference sensitive information, and feeding back a verification result to a preset sensitive information identification model so as to optimize the sensitive information identification model to obtain a target sensitive information identification model; based on a target sensitive information identification model, identifying data in the production environment to obtain target sensitive information; carrying out desensitization processing on the target sensitive information, and carrying out statistical analysis on the data after desensitization processing; the statistical analysis result is verified, and data access record analysis is carried out under the condition that verification is passed, so that database privacy protection is achieved.

The embodiment of the invention discloses illness state detection equipment based on a multi-modal image, an illness state detection method and a medium. A specific embodiment of the method comprises the following steps: acquiring a patient historical medical record information set and current patient physical state information; sensitive data desensitization processing is conducted on the historical medical record information set of the patient, and a desensitized historical medical record information set of the patient is obtained; inputting the current patient physical state information and the desensitized patient historical medical record information set into an illness state detection model to obtain current illness state information; performing causal relationship extraction on the desensitized patient historical medical record information set to obtain a disease condition time sequence causal diagram; inputting the disease time sequence causal diagram, the desensitized patient historical medical record information set and the current disease information into a disease prediction model to obtain disease development prediction information; and performing illness state grade evaluation on the illness state development prediction information, and sending illness state early warning information. According to the embodiment, the accuracy of illness state detection and prediction can be improved, and the accuracy of early warning information sending is improved.

The invention discloses a digital signature method and device, a verification method and device, equipment, a storage medium and a system. The digital signature method is applied to a sending end, and comprises the following steps: acquiring an additional value of original message data; connecting the additional value to the original message data to generate to-be-signed data; performing a first signature operation and a second signature operation based on the to-be-signed data to correspondingly obtain first signature data and second signature data; and connecting the first signature data and the second signature data in series to obtain final signature data. According to the digital signature method, the logic association of the signature data is realized by introducing the additional value, the signature uniqueness and tamper resistance are improved, quantum computing attacks can be effectively resisted through double signatures of the first signature operation and the second signature operation, the tamper resistance of the digital signature is enhanced, the data security is enhanced, and the security of the digital signature is improved. And the method has good compatibility with a traditional password system.

The invention provides a document question-answering system and method based on a large model, and relates to the technical field of artificial intelligence, and the system comprises a document segmentation module which is used for segmenting a document uploaded by a user according to a paragraph structure to obtain a plurality of paragraph nodes; and the answer generation module is connected with the document segmentation module and is used for performing similarity matching on a query question proposed by a user and each paragraph node and sending the paragraph node with the highest similarity into a local large language model for processing so as to display answer information associated with the query question to the user. The method has the beneficial effects that the document is segmented according to the paragraph structure, so that the related content is effectively prevented from being split into different paragraph nodes, the context continuity is ensured, and the accuracy of a search result is improved; and the answer information is generated by adopting a local large language model, so that data leakage and privacy risks are avoided, and the data security is enhanced while the user privacy is effectively protected.

The invention discloses a video stream non-perception encryption and decryption method based on an IP (Internet Protocol). The method comprises the following steps: acquiring a plaintext data stream, splitting the plaintext data stream into data packets, transmitting the data packets by using a UDP (User Datagram Protocol), introducing a forward error correction code, recombining the transmitted data packets, and generating an original plaintext video stream; carrying out network flow feature extraction on the plaintext data flow to obtain network flow features, carrying out load optimization to obtain a first network flow feature, carrying out encryption by using an encryption algorithm, and attaching an authentication tag to the end of the encrypted first network flow feature to obtain a second authentication tag; embedding the encrypted first network traffic feature into a data packet of the original plaintext video stream to obtain an encrypted data packet; the authentication tag is recalculated and compared with the second authentication tag, and the encrypted data packet is decrypted by using the corresponding key to obtain the original plaintext video stream after recombination, so that network attack can be effectively prevented, and the security of video transmission is guaranteed.

The invention provides a cloud edge collaborative anomaly detection method, system and equipment for a virtual power plant, and is applied to the technical field of data mining. The method comprises the following steps: performing anomaly detection on real-time operation data of each distributed resource in a virtual power plant based on a global anomaly detection model obtained from a cloud server; sending the detection result to monitoring equipment associated with each distributed resource corresponding to the edge end equipment; the global anomaly detection model is obtained by the cloud server through fusion processing according to local anomaly detection models trained by the edge end devices respectively. The problem that abnormal data generated in the data transmission process of the virtual power plant system affects the accuracy and reliability of energy management and decision making is solved.

The invention discloses a quantum key encryption communication method and system based on a virtual private network, relates to the technical field of data encryption transmission, and solves the technical problems that the existing quantum key online distribution is covered by an OKD network, the key providing capability and the key generation rate are far lower than the data transmission speed of a modern network, and the performance bottleneck exists. According to the method, the data of the virtual private network is transmitted and encrypted by taking online distribution of the quantum key as a main part and pre-charging as an auxiliary part; the data is encrypted by the online distributed quantum key or the pre-charged quantum key, so that the data security can be improved, and the performance bottleneck of an online distribution mode can be avoided; according to the application, when the key supply gap is made up in the pre-charging mode, the quantum key surplus in the key demand gap before the time range corresponding to the key supply gap and closest to the key supply gap is preferentially selected, and when pre-charging is carried out through the selected quantum key, due to the fact that the time from generation to use is short, the leakage risk can be greatly reduced.

The invention discloses a body intelligent execution and training method and device based on an edge-end cooperation large model, and the method comprises the steps: transmitting a voice instruction to a robot body, converting the voice instruction into a text instruction through the robot body, and transmitting the text instruction, machine vision feedback and a visual image to an edge side unit; a multi-modal large model is arranged in the edge side unit; reasoning the text instruction, the machine vision feedback and the vision image by the multi-modal large model, obtaining the intention of the text instruction after reasoning, generating a task decomposition instruction according to the intention, and sending the task decomposition instruction to the robot body; the robot body calls a chassis moving API and an atomic skill library API according to the task decomposition instruction to complete a target task; and after the target task is completed, the robot body feeds back process algorithm data and an execution effect to the edge side unit, and the edge side unit carries out post-training upgrading according to the feedback. According to the method, the large model reasoning time delay and cost can be effectively reduced, the load of the cloud server is reduced, and the data security and privacy are improved.

The invention discloses an anti-DDoS attack data security protection system for a big data platform, and belongs to the technical field of data security. Historical network traffic features are learned by using a DDoS attack detection model constructed by a deep learning algorithm, so that a rule for identifying DDoS attacks can be constructed, DDoS attack detection is preliminarily realized, and the security of the DDoS attacks is improved. Meanwhile, an improved intelligent optimization algorithm is adopted to train a DDoS attack detection model, a better parameter optimization effect can be achieved, the DDoS attack detection accuracy is finally improved, DDoS attack relieving operation is executed according to a DDoS attack detection result, and the data security of a big data platform can be effectively improved.

According to the media stream encryption transmission method and the media stream transmission system provided by the invention, encryption identification information can be encrypted by adopting a quantum key, and a ciphertext token is obtained. The playing device can send the ciphertext token to the streaming media server, so that the streaming media server can obtain the encryption identification information based on the ciphertext token, encrypt the to-be-transmitted media stream data according to the encryption identification information to obtain the encrypted media stream data, and transmit the encrypted media stream data to the playing device. The playing device can obtain the to-be-transmitted media stream data based on the encrypted media stream data, and performs video playing according to the to-be-transmitted media stream data. Therefore, the leakage risk of the encrypted identification information and the media stream data in IP network transmission can be reduced, and the data security of media stream transmission can be enhanced.

The invention relates to the technical field of elevator safety, and discloses an elevator intelligent inspection method which comprises the following steps: acquiring operation data, state data and fault data of an elevator through a sensor to obtain an elevator data set, and analyzing the data in the elevator data set to generate service data; a maintenance task of the elevator is obtained, a function library of the inspection robot is formed for the maintenance task, a health state analysis result of the elevator is obtained according to the service data, and an inspection task of the elevator is determined; the inspection task of the elevator is issued to the inspection robot, task parameters and task conditions are integrated to obtain task data, the task data are analyzed, and the time required for completing each task is predicted; in combination with the elevator position and the action track of the inspection robot, the inspection route of each inspection robot is planned and decomposed into individual tasks to be issued to the inspection robot, and an inspection report is output through inspection of the inspection robot; the efficiency, performance and effectiveness of elevator inspection are improved, and on-demand inspection is achieved.

The invention relates to the technical field of data transmission, and discloses a USB interface management and control method and device for information security protection, and the method comprises the steps: recognizing a USB connection state, collecting the information of a to-be-connected device, and carrying out the identity verification; collecting a historical transmission task of the to-be-connected equipment, extracting a historical task index, and determining the available capacity of a transmission channel; a real-time transmission task is collected to determine real-time structure information and real-time content information, a transmission risk value is determined, and whether the available capacity of a transmission channel is adjusted or not is judged; when the adjustment is judged to be carried out, collecting a characteristic index, comparing the characteristic index with a historical adjustment scheme, and determining an adjustment coefficient according to a comparison result to adjust the available capacity of the transmission channel; and communicating the to-be-connected equipment with the workstation by using the adjusted available capacity of the transmission channel. According to the method, the information security protection capability is enhanced, and potential safety hazards and fault risks are reduced through resource scheduling and risk management.

The invention discloses a risk assessment method for enterprise cross-domain data. The method comprises the following steps: collecting original assessment data of an enterprise for data exchange and sharing among different departments; preprocessing the collected original evaluation data, wherein the preprocessing comprises the steps of carrying out standardization processing on numerical data and carrying out fuzzification processing on text description data; constructing a risk assessment model based on a Bayesian network, and determining a dependency relationship and a conditional probability between risk factors by introducing expert knowledge and historical data; calculating the posterior probability of each risk factor through a Bayesian network; an expert evaluation method is adopted to allocate a weight for each risk evaluation index to carry out risk-based scoring on each risk factor; and calculating a comprehensive risk score of the enterprise cross-domain data through a comprehensive risk scoring formula. According to the method, an enterprise can be helped to accurately evaluate and manage risks in a complex data environment, and the data security and compliance are improved.

The invention discloses a complete-cycle nursing simulation training system for a breast reconstruction patient, and relates to the technical field of nursing simulation training for the breast reconstruction patient, and the system comprises a collection and preparation module which is used for collecting breast reconstruction data and constructing a support vector machine (SVM) model to generate a rehabilitation scheme; the guidance acquisition module is used for guiding the patient to act by using AR, and the patient acquires a rehabilitation scheme; the rehabilitation module is used for collecting patient data to generate a rehabilitation scheme; the adjustment encryption module is used for collecting feedback of the patient for adjustment and performing security encryption on the transmitted data; and the visual storage module is used for displaying the rehabilitation scheme of the patient in real time and storing breast reconstruction data. By integrating automatic literature capture, natural language processing, the support vector machine, the AR technology and the intelligent wearable device, the whole-process intelligence from rehabilitation scheme generation to real-time action guidance and rehabilitation effect evaluation is realized, and the use convenience and data security of a patient are improved.

The invention provides a low-voltage communication demodulation platform, which relates to the technical field of communication and comprises a demodulation main system. The demodulation main system comprises a signal acquisition module, a data storage module, a clock management module, an interface module, a fault detection and recovery module, a protocol conversion module, a signal processing module, a frequency spectrum management module, a safety communication module, a power grid environment adaptation module and an intelligent demodulation module. The intelligent demodulation module realizes adaptive modulation identification and demodulation through a content convolutional neural network (CNN) and a support vector machine (SVM), the CNN is used for extracting features of carrier signals, and the SVM performs modulation mode classification; the intelligent demodulation module is combined with a Kalman filter through a generative adversarial network (GAN) to realize channel noise processing and enhancement, the GAN is used for generating a low-noise signal, and the Kalman filter further optimizes the signal quality.

According to the data backup and recovery method and device, the storage medium and the computer equipment provided by the invention, the health state of the Redis cluster associated with the target activity can be monitored in real time when the e-commerce platform holds the target activity; when the Redis cluster service is detected to be abnormal, data backup and snapshot can be performed on the Redis cluster through a preset backup and snapshot mechanism, so that the risk of data loss can be effectively reduced, and the availability and data security of the Redis cluster can be improved; then, a target data recovery strategy corresponding to the activity magnitude of the target activity can be determined, and the activity data stored in the Redis cluster is recovered by using the target data recovery strategy, so that the reliability and the stability of the Internet e-commerce activity system are remarkably improved, the service interruption time caused by the Redis cluster fault is shortened, and the user experience is improved. Therefore, the user experience and the enterprise operation efficiency are improved.