113 results about "Security layer" patented technology

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention provides a fusion type secure storage system, electronic equipment, a data management method and a computer readable storage medium. The system comprises a hardware security layer, an encryption processing layer, a verification error correction layer, a data stream processing layer and an intelligent management layer which are in communication connection, the hardware security layer provides a physical trusted root for the system and executes a security policy; the encryption processing layer is used for carrying out encryption processing on data based on the security service provided by the physical trusted root; the verification and error correction layer is used for carrying out integrity verification and self-adaptive error correction coding on the encrypted data; the data stream processing layer is used for executing a near data calculation task on the data subjected to verification and error correction; and the intelligent management layer collects real-time state data from the hardware security layer, the encryption processing layer, the check error correction layer and the data stream processing layer, and performs dynamic strategy configuration and global optimization decision, thereby improving the active calculation, dynamic adaptation and intelligent optimization capabilities of the system.

The invention provides a large language model safety protection defense method and device based on dynamic regulation and control, and belongs to the field of artificial intelligence safety protection. The large language model security protection defense method comprises the following steps: constructing a non-security data set and a security data set jail break prompt data set, calculating a gradient average value of parameters of each layer of a large language model during back propagation of various data sets, calculating cosine similarity among gradients, determining a non-security layer, namely a layer most sensitive to non-security content, and performing security protection defense on the non-security layer. Therefore, the subsequent regulation and control are more accurate and effective; in the aspect of dynamic regulation and control of a specified non-security layer, a joint loss function is established to optimize and train a security offset vector, and the security offset vector is applied to a hidden state of the non-security layer in a big language model reasoning process to carry out intervention, so that output of a big language model is dynamically regulated and controlled; therefore, the robustness of the large language model is improved.

The invention discloses an intelligent RAG enhanced large model auxiliary writing system, and relates to the technical field of artificial intelligence auxiliary writing. In order to overcome the defects of an existing writing auxiliary tool, the adopted scheme comprises an interface and user layer for receiving a writing demand and redisplaying a generation result; the service layer is used for deploying three types of intelligent agents including writing, interactive writing enhancement and checking; the platform layer is used for carrying out industry adaptation analysis, mixed retrieval and RAG enhancement on writing demands and then sending the writing demands to the large model layer; three intelligent agents of writing, interactive writing enhancement and checking are called in sequence based on the output of the large model layer, and then a text is redisplayed to the interface and user layer after fact accuracy verification is completed through a tense knowledge graph; the data layer is used for providing a multi-type data storage system; the large model layer outputs a paragraph-level text and a reference index based on a writing large model, the paragraph-level text is returned to the platform layer, and reference index information is fed back to the interpretability and security layer for tracing; and the infrastructure layer is used for providing bottom layer resource support for each layer.

The invention discloses an engineering vehicle driver behavior analysis system based on federal learning. The system comprises a multi-source sensing layer which forms vehicle-road-person collaborative all-weather scenarized data input; the edge calculation layer is used for capturing a complex space relationship of a driving scene, embedding a lightweight cross-modal attention module, calculating an attention weight, and processing long-sequence driving operation data by adopting a Mamba state space model to capture a long-term dependency relationship of a driver state; the federated learning layer adopts a pFedMe algorithm to realize a driver customized model, during local training, the model learns local data distribution characteristics through a Moreau Envelopes regularization item, the cloud aggregates distribution of adaptive local data of each driver, models a federated learning process as a meta-learning problem, and performs training updating; a privacy and security layer; and differential privacy dynamic adjustment, Paillier improved homomorphic encryption and a Hyperledger Fabric block chain are adopted to prevent a log from being tampered.

The invention provides a software and hardware integrated intelligent terminal power transaction system and method, relates to the technical field of power market transaction, and solves the technical problems of data potential safety hazards and insufficient real-time performance caused by dependence on an external platform in a power transaction process in the prior art. The system specifically comprises a hardware architecture and a software function module, the hardware architecture comprises a processor, a redundant power supply, a storage device, a network interface, a security encryption chip and a display interface; the software function module comprises a core function layer, an interface layer and a security layer; wherein the core function layer is used for being responsible for processing and controlling the whole flow of power transaction; the interface layer is used for carrying out standardized and ordered external data interaction; and the security layer is used for guaranteeing the security of the terminal from multiple dimensions of communication, transaction, data and operation and maintenance. The method is used for electricity market transaction.

The invention relates to the technical field of intelligent robot control, in particular to a small robot dynamic balance control system based on DDPG, which comprises a sensor and a preprocessing module, provides accurate and reliable state data through denoising and standardization processing, and enhances the accuracy of subsequent processing; the ESO observer module estimates the system state and the total disturbance in real time, and the adaptive capacity and the anti-interference performance to the complex environment are improved; the information fusion and time sequence alignment module ensures information synchronization and accuracy through fusion processing and multi-dimensional state vector construction, meanwhile, the timeliness of a control decision is ensured, and the overall control effect is improved; the DDPG strategy network and the security layer module are combined with strategy optimization and multi-layer security constraints, continuous and safe control actions are output, and the stability and security of control are improved; and the actuator interface module realizes control instruction transmission, forms closed-loop feedback, adjusts a control strategy in real time, and improves the dynamic balance capability and the overall stability of the system.

A QR Code Verification Engine provides a multi-layered security framework for generating, validating, and authenticating QR codes while preventing tampering, fraud, and unauthorized access. The system embeds a hidden security layer within the QR code using steganographic encoding or invisible watermarking techniques, ensuring detection of any modifications. The hidden layer is encrypted using asymmetric cryptography, allowing only an authorized verification system to extract and validate it. An AI-powered tamper detection module analyzes QR codes for anomalies, while cryptographic hash verification ensures integrity. The system employs biometric authentication, push notification approvals, and contextual security measures to enhance user verification. Dynamic QR codes with expiration rules prevent replay attacks. Secure offline verification allows authentication without network connectivity. The system integrates with financial platforms, web security tools, and real-time fraud detection mechanisms, ensuring a highly secure and scalable QR code validation framework for transactions, identity verification, and access control applications.

The invention discloses a vehicle-mounted system safety detection and risk assessment system, which belongs to the technical field of vehicle-mounted network safety, and comprises a vehicle-mounted terminal environment sensing module used for collecting multi-level environment information of a vehicle-mounted system, including system layer information, network layer information and safety layer information; the vehicle-mounted configuration file and communication white list detection module is connected to the vehicle-mounted terminal environment sensing module and is used for establishing and maintaining a safety white list library according to the collected system information, comparing the current configuration file, the dynamic library, the port and the certificate state of the system with the safety white list library through periodic scanning, and sending the comparison result to the vehicle-mounted terminal environment sensing module; detecting configuration abnormity and file tampering events; a vehicle-mounted process and control task analysis module; a communication abnormity detection and code identification module; and a safety evaluation and risk feedback module. According to the invention, all-around monitoring and intelligent analysis of vehicle-mounted system configuration, process behaviors and network communication can be realized, and hidden backdoors, abnormal processes and malicious communication can be effectively identified.

The invention discloses a multi-AUV safe path planning method based on deep reinforcement learning. According to the method, a deep reinforcement learning algorithm model based on an MATD3 method is adopted, a policy network and a value network are updated by adopting security constraints, the security of policy learning is improved, the expected reward revenue is maximized under the condition that the expected security cost constraints are met, a frequent minimum and maximum optimization process is avoided by adopting first-order penalty optimization, and the security of policy learning is improved. Meanwhile, safety correction based on a safety layer is added in the training process to guarantee safety in the early stage of training, and exploratory and safety balance is brought to strategy optimization of reinforcement learning through safety constraint and safety correction. According to the multi-AUV path planning method, the time cooperation constraint and the space cooperation constraint of multi-AUV path planning are comprehensively considered, the centralized training and decentralized decision-making architecture is applied to multi-AUV path planning, the path planning method capable of ensuring cooperation safety is provided for a multi-AUV system, and the safety and the reliability of path planning are improved.

Systems and methods for securing agent communications in a multi-agent system including deploying an agent to communicate with external servers, instantiating a sidecar security service in communication with the agent and including at least one of a guardrails service, a security layer, an encryption module, and an integrity checker configured to validate resources tools using hash-based verification mechanisms. Communications including messages between the primary and the external servers are intercepted by the sidecar security service, which then performs at least one of filtering the one or more messages by the guardrails service, authenticating one or more server connections by the security layer, encrypting one or more outbound requests by the encryption module, or verifying an integrity of resources and tool definitions by the integrity checker.

The invention relates to the field of path planning, and discloses an unmanned surface vehicle track planning method and device and a medium, and the method comprises the steps: obtaining the real-time dynamic water area environment information of an unmanned surface vehicle; constructing a decision model of unmanned surface vessel track planning, and adopting an improved deep reinforcement learning SAC method based on strategy divergence to train and iteratively solve the decision model to obtain an optimal approximation strategy, namely a trained decision model; and inputting the real-time dynamic water area environment information into a trained decision model to obtain an optimal execution action of the unmanned surface vessel, and completing route planning. According to the method, a constraint perception adaptive entropy adjustment mechanism and a security layer verification module are innovatively introduced, so that the algorithm actively perceives and avoids risks in each stage of training and execution, the multi-target balance problem is successfully solved, and the USV autonomous operation capability and the navigation safety level are remarkably improved.

The invention provides a multi-virtual machine isolation operation system based on security level automatic scheduling, and relates to the technical field of cloud computing and virtualization. According to the system, an intelligent security level evaluation module is combined with multi-dimensional analysis to generate a task security level, a security-layered virtual machine resource pool realizes hierarchical cluster management and elastic scaling, an intelligent scheduling engine realizes task allocation based on security level matching and resource optimization, and a cross-level data exchange control module realizes fine-grained data management and control. The system solves the problems that an existing virtual machine management system is insufficient in security isolation, low in manual scheduling efficiency, poor in dynamic adaptability and weak in cross-domain data management and control, deep binding of the security level and resource allocation is achieved, and the resource utilization rate and the system robustness are improved. Performing subject term cloud computing; a virtualization technology; a security level; isolating the virtual machine; and automatic scheduling.

The application discloses a kind of multi-level quantitative evaluation methods for electronic product information clearing, verification and traceable credible closed-loop system, comprising S100, evaluation framework establishment and test environment preparation step: S110, define evaluation model, the evaluation model includes clearing effect layer (L1), verification credible layer (L2), trace and authentication layer (L3), system behavior and security layer (L4) and performance and efficiency layer (L5).The application has the advantages that: from qualitative to quantitative: a large number of quantitative indicators such as KL divergence, bit recovery rate, throughput are introduced, and the evaluation results are objective, accurate and comparable.The method carries out deep security testing: not only test function, but also take the system itself as attack target, carry out penetration testing and process behavior monitoring, can find deeply hidden design defects and security vulnerabilities, which cannot be achieved by traditional function testing.At the same time, the method can guide technology development, and provide decision basis for procurement and supervision;And high automation and repeatability.

A method for securely transmitting time-critical data within a communication system including local networks in which data are transmitted via switching, at least one network superimposed with respect to the local networks, in which data are transmitted via routing, and a gateway system for connecting the communication system to an unsecured external network, wherein network layer communication via the superimposed network is authorized only between authenticated system components, switches authenticate connected terminal devices and assign these to a physical or logical local network in accordance with a respective terminal device identity, security layer communication is authorized within the local networks implicitly based on an assignment of respective terminal devices to the same local network, communication at OSI layer 3-7 between terminal devices of different local networks or with terminal devices in the unsecured external network is authorized via zero trust proxies that are each assigned to a local network.

The application provides a soft and hard cooperative security AI NAS storage and calculation system and an access authentication method, and belongs to the technical field of network storage. The system comprises a hardware security layer, a lobster security core module, a compulsory authentication gateway, an encrypted storage engine, an intelligent access control module and a security audit and monitoring module. The hardware security layer comprises a lobster security chip and a hardware root of trust module. The lobster security chip is an independent security processor, and is internally provided with a true random number generator, an encryption acceleration engine and a security storage area. The hardware root of trust module is used for system startup verification and root key hardware protection. Through the soft and hard cooperative architecture and the compulsory authentication mechanism, the application realizes a complete trust chain from the hardware root of trust to the application layer, can effectively prevent ransomware attacks and data leakage risks, and improves data operation efficiency.

The invention discloses a data encryption method and device, equipment and a storage medium, and relates to the technical field of information security, and the method comprises the steps: receiving a data encryption request which is sent by a business application and carries a tenant identifier, an application identifier and to-be-encrypted data; querying an application key ciphertext corresponding to the application identifier and a tenant key ciphertext corresponding to the tenant identifier from a tenant security layer, and obtaining a platform master key from a platform security layer; decrypting the target tenant key ciphertext by using the platform master key in the tenant security layer to obtain a tenant key plaintext, and decrypting the application key ciphertext by using the tenant key plaintext to obtain an application key plaintext; and sending the application key plaintext and the to-be-encrypted data to a virtual cipher machine allocated to the current tenant in advance, encrypting the to-be-encrypted data based on the application key plaintext to obtain a data ciphertext, and sending the data ciphertext to the service application. According to the invention, the security of the data encryption process can be improved, and data leakage is avoided.

The invention provides a perception adaptive video stream code rate decision-making method and system based on a neural symbol reinforcement learning buffer area, and belongs to the technical field of computer network and multimedia transmission. According to the method, a neural symbol reinforcement learning agent is constructed, a comprehensive state vector is generated by collecting a playing state, video content information and user preference, and double decisions of code rate selection and buffer area adjustment are jointly output; security rules are formalized in a first-order logic mode, security exploration is achieved through a security monitor and a security layer strategy, and illegal risks such as underflow of a buffer area are avoided; the bandwidth prediction precision is improved by adopting HTTP / 3 stable connection sampling and a hybrid prediction model; and designing a multi-target reward function to balance user experience quality and buffer occupation optimization. According to the method, high-video-quality, low-lagging and low-delay transmission can be stably realized in a low-speed to 5G high-speed network, and unnecessary buffer accumulation can be remarkably reduced on the premise of ensuring smooth playing, so that end-to-end delay is reduced.

The invention relates to the technical field of machining process control, in particular to a plate connection parameter dynamic adjustment method based on multi-level rule judgment, which comprises the following steps: constructing a three-level rule judgment system including a material safety layer, a quality guarantee layer and a process optimization layer; taking a material characteristic set obtained based on finite element numerical simulation as process parameter input, and sequentially executing rule judgment according to the hierarchical priority sequence; material stress field distribution and thermal deformation data adjustment safety threshold values are calculated in real time; calling a multi-level rule tree corresponding to the material type, performing iterative operation from a leaf node to a root node of the multi-level rule tree to obtain final process parameters of the material, and performing systematic verification; and generating an equipment control instruction set from the verified process parameters according to a standard format, and outputting the equipment control instruction set to an execution end. According to the method, the processing safety, the quality stability and the process optimization effect can be remarkably improved, meanwhile, the equipment debugging cost is reduced, and the reliability and compatibility of process parameters are enhanced.

A method, apparatus, device, and readable storage medium for testing the security layer of an electronic control system are disclosed, relating to the field of electronic control technology for new energy vehicles. Specifically, the method includes an electronic control unit (ECU) receiving a test data packet from a HIL simulator via a hardware interface. This data packet includes scenario-based extreme operating condition parameters, encrypted interference signals, encrypted sensor signals corresponding to the hardware to be interacted with, and encrypted abnormal signals. The ECU performs integrity verification and signal threshold judgment on the test data packet to obtain a first result, and generates control commands based on the first result. The ECU encrypts and digitally signs the control commands based on a preset security key to obtain a target control command, which is then sent to the HIL simulator via a communication bus. The HIL simulator decrypts the target control command, timestamps it, and verifies the digital signature to obtain a second result, and executes the target control command based on the second result, thereby achieving security layer testing of the electronic control system. This application can improve the realism and anti-interference capability of security layer testing of electronic control systems.

Systems and methods are described herein for updating a representation of a virtual payment card in response to a virtual transaction between a first device (e.g., a user device) and a second device (e.g., a merchant point-of-sale device). The systems and methods may be used to authenticate user identity with an additional layer of security and / or combat phishing attempts intended to dupe users into disclosing confidential profile information. In response to a completed transaction, the second device delivers interactivity data (e.g., animation data, card image data, contextual data, notification sound data, etc.) to the first device, which actuates a distinct representation (e.g., an animation, a graphic image, a notification sound, etc.) of the card image of the virtual payment card used in the completed transaction.

A method and system of enhancing cyber resilience performed by a computing system is disclosed. According to one embodiment of the present disclosure, the method may comprise a step of acquiring vulnerability data of a target system based on resilience feature data including a quantitative assessment vector and a non-quantitative assessment text of the target system, a step of inputting input data including the vulnerability data into an artificial intelligence model and generating an enhancement strategy for the target system based on output of the artificial intelligence model and a step of controlling a security layer of the target system based on the enhancement strategy.

The application relates to the technical field of neural rehabilitation and brain-computer interface, and discloses a brain-computer interface driven full-scene neural rehabilitation management cloud platform system, which comprises a perception layer, a processing layer, an application layer and a security layer. The perception layer collects basic signs and electroencephalogram data in a physical environment; the processing layer receives the data for pre-processing and denoising decoding, fuses multi-dimensional evaluation results to generate a dynamic rehabilitation scheme, adjusts control parameters in combination with a closed-loop feedback mechanism, and outputs control interaction instructions to external equipment through a general adaptation protocol; the application layer receives the instructions to render an interactive interface for hospital, community and family end users, and generates flow record data; and the security layer performs hierarchical protection based on the flow record data, and completes privacy calculation of a model by using block chain storage and federal learning. The application solves the problem of data fragmentation during cross-scene flow, improves the individual adaptability of a rehabilitation scheme, and guarantees the security of flow data.

The invention provides a security defense system and method for an educational training platform, a medium and a product. The system comprises a traffic protection layer used for processing network traffic to obtain first feature data; the container security layer is used for processing the network traffic to obtain second feature data; the privacy protection layer is used for processing the network traffic to obtain third feature data; the gating router is used for constructing feature vectors based on the three kinds of feature data, determining weights of n expert models according to the feature vectors, arranging the n expert models according to a descending order of the weights, and selecting the first k expert models to identify attack behavior information in the network traffic, k is greater than or equal to 1 and less than or equal to n, and k and n are positive integers; and the intelligent decision-making layer is used for generating a defense strategy based on the attack behavior information, evaluating the effect of the defense strategy to obtain effect information corresponding to each expert model, and feeding back the effect information to the gating router.

A system for providing credential activation layered security is disclosed. In particular, the system adds a layer of additional security at ingress and egress points of a location, such as a building. When a user attempts to check in at the location, the user may provide a proof of physical presence, a proof of digital presence, or a combination thereof, such as at a device at the location. In order to activate a credential for accessing physical and / or logical access control systems of the location, the system may authenticate the proof of physical presence, the proof of digital presence, or both. If the system authenticates the user, the user may be checked-in and the credential may be activated so that the user may access the physical and / or logical access control systems of the location so as to gain access to the ingress point or exit via the egress point.

The invention discloses an enterprise-level AI system and method based on a software and hardware deep fusion seven-layer integrated framework, and belongs to the technical field of artificial intelligence. The system comprises a hardware layer, a computing power layer, a reasoning layer, a middle layer, a platform layer, an application layer and a security layer from bottom to top; in combination with unified computing power scheduling, local private large model reasoning, a self-developed multi-modal document analysis engine, deep integration of an enterprise IT system and full-stack security management and control, an enterprise-level AI infrastructure integrating data, computing power, algorithm, application and security is constructed; the method has the advantages that the AI resource utilization rate is remarkably increased by 40% or above, the unstructured document analysis accuracy rate is remarkably increased by 90% or above, the AI application deployment period is shortened by 60% or above, and the requirements for privatization, safety, controllability and efficient landing of high-supervision industries such as finance, government affairs and medical treatment are met.

This invention discloses a method for commercial encryption protection and multi-system security integration in a power demand-side management system. The method involves: constructing a business-driven layered commercial encryption protection strategy and configuring differentiated commercial encryption protection schemes; building a three-tiered integration architecture consisting of a core layer, an interface layer, and a security layer; and achieving deep integration of commercial encryption protection and the integration process. During multi-system data interaction, interface authentication, encrypted data transmission, data decryption and verification, and storage and log protection are completed sequentially, with commercial encryption technology embedded throughout to ensure security. This invention offers advantages such as compliant commercial encryption protection, standardized and efficient multi-system integration, end-to-end security protection coverage, and deep integration with business needs.

The invention relates to a block cipher algorithm architecture, an algorithm calling method and device and electronic equipment. The algorithm architecture comprises a quantum security layer which is used for taking a quantum true random number generator as a key entropy source and generating an unpredictable initialization vector and a dynamic key seed based on a quantum optical effect; the hybrid encryption layer is used for encrypting a session key by adopting a hybrid encryption mechanism of post quantum cryptography and SM4 and using a public key algorithm based on lattice cryptography, generating a master key through an anti-quantum algorithm, and generating a sub-key sequence in combination with an SM4 round key expansion algorithm; the core algorithm layer is used for optimizing a round function, dynamically generating an S-box replacement table based on quantum random numbers through an implicit coding technology of dynamic S-box generation and white-box protection, carrying out implicit coding on XOR and shift operations in the round function and injecting redundant noise data; and the protocol adaptation layer is used for integrating a dynamic defense protocol. The encryption security can be improved, and the algorithm efficiency can be remarkably improved.

This application discloses a method, apparatus, device, and storage medium for generating SCADA configuration screens based on generative AI, relating to the field of industrial control technology. The method includes: collecting natural language design requirements using an intent understanding module and the perception layer in an AI Agent cognitive architecture; obtaining state acquisition data and integrating it before inputting it to the cognitive layer; the inference engine and large language model in the cognitive layer decompose the configuration design task of the integrated data, generating a configuration design operation instruction set and inputting it to the execution layer; the execution layer processes the operation instruction set to obtain each component to be laid out in the configuration screen and its configuration data; the layout engine determines the component layout result in the configuration canvas; the variable binding engine binds and maps each component to the real-time data points of the SCADA system; and the target configuration screen is generated by combining the Shell command security module and the security layered architecture to improve the efficiency of generating SCADA configuration screens.

The invention provides a cross-end collaborative hierarchical encryption and differential data secure transmission method and system. Obtaining editing operation data generated by at least two collaborative terminals for the same design object, and generating a composite difference unit; determining a security level identifier of the composite differential unit; splitting the composite differential unit into a core segment, an association segment and a verification segment; generating a dynamic layer key for the core segment, the associated segment and the verification segment of the composite differential unit; a composite differential unit instead of a whole object is used as protection granularity; the security hierarchy is dynamic and can change along with the dependency relationship of the design object and the change of the terminal state; the same design change is split into a plurality of pieces and is transmitted in a staggered manner, so that the anti-replay and anti-correlation analysis capabilities are improved; the reversible disturbance mechanism enables the data seen by the intermediate link to be further distorted, and the authorized receiving end can still be accurately recovered; and the consistency verification and shadow state mechanism avoids direct disk falling of wrong recombination results, and the collaborative design reliability is improved.