989 results about "Security controls" patented technology

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

The invention relates to the technical field of machine control, and discloses a chemical safety inspection robot control system and method based on sensor fusion, and the system comprises a data fusion module, a strategy construction module, a strategy analysis module, a real-time monitoring module, an optimization instruction module, and an event determination module. Original data of the chemical environment is fused into standardized data, and structural features are analyzed to identify risk factors; constructing a security situation map, mapping the security situation map to the robot, and determining a control strategy; when the robot begins to inspect, analyzing the control strategy to control the movement adjustment path action and the path safety inspection action of the robot; coding the movement adjustment path action and the path safety inspection action into a safety control instruction; optimizing the safety control instruction to obtain an optimized control instruction; when the robot completes inspection, an environment safety abstract and an abnormal event record are obtained; the control accuracy of the chemical safety inspection robot can be improved.

The invention discloses an intelligent manufacturing workstation safety control system oriented to man-machine cooperation, and belongs to the technical field of intelligent manufacturing and man-machine cooperation. The system comprises a multi-modal sensor array, a personnel state sensing module, a robot state monitoring module, a dynamic safety area adaptive calculation module, an intention prediction and risk assessment module, a cooperative control decision module and an execution feedback module. The dynamic safety area self-adaptive calculation module constructs geometric representation of a man-machine interaction space based on a topological manifold theory, and calculates a dynamic safety boundary in real time; the intention prediction and risk assessment module is fused with multi-source information to predict a future trajectory of a person and assess a collision risk; the cooperative control decision module performs multi-objective optimization with safety and efficiency as double objectives to generate an optimal adjustment strategy, the system realizes prospective safety control based on intention prediction, the cooperative efficiency is improved by 25%-45% on the premise of guaranteeing safety, and the working safety accident rate is reduced to be close to zero.

The invention relates to the technical field of intelligent monitoring of engineering hoisting, in particular to an intelligent monitoring system for an overweight segmented hoisting process based on BIM (Building Information Modeling) and force feedback, which comprises a data synchronization module, a state fusion module, a BIM matching module, a track optimization module and a simulation monitoring module. The system obtains a hoisting state through multi-source sensing data, matches a BIM fine model, and generates a theoretical motion track. And the trajectory optimization module generates a force feedback deviation field by comparing expected force feedback data with actual force feedback data, reversely corrects a theoretical trajectory, and realizes dynamic path optimization based on a mechanical state. And the simulation monitoring module drives virtual simulation according to the optimized trajectory, updates the posture of the BIM model in real time, dynamically calculates an interference area, performs risk assessment, and automatically generates and issues a control instruction. According to the scheme, the response precision and the active safety control capability of the hoisting process to the physical state change are improved.

The invention provides a cloud AI data leakage risk prediction and control method and system based on a flow map, and relates to the technical field of cloud computing data security. Based on the collected multi-source log data, constructing a time series data flow knowledge graph; inputting the knowledge graph into a space-time diagram risk prediction model, and obtaining a dynamic risk score of an entity and a predicted potential data leakage path by fusing a space-time diagram neural network and risk conduction simulation; according to the dynamic risk score and the potential data leakage path, gradient dynamic security management and control measures are generated and executed, and the measures comprise differentiated access control actions triggered according to the risk level; and generating a visual audit report of the data access link based on the risk prediction result and the security management and control process. Through a time sequence graph reflecting dynamic flow of data and utilizing STGNN to carry out risk conduction modeling and prediction, the active defense capability of cloud AI data security is improved, the accuracy of risk identification is effectively improved, and service interference is effectively reduced.

The invention discloses a hydraulic arm safety control method and device based on parallel learning and a high-order CBF, and the method comprises the steps: building a kinetic equation of a hydraulic mechanical arm through a Lagrange method, integrating unmodeled dynamics, structural parameter change and external interference into an uncertain item, and describing the generalized uncertainty of the uncertain item in a linear parameterization form; for the problem of insufficient excitation in a complex environment task, a parallel learning mechanism is introduced, and historical data and real-time data are combined to realize parameter identification. For high relative order safety constraints (such as obstacle distance constraints and joint limiting constraints) in a task space, an obstacle function family is constructed. A dynamic error buffer function is introduced, and a high-order adaptive control barrier function condition is designed. The high-order self-adaptive control obstacle function constraint is embedded into a real-time quadratic programming solving problem, input obtained through optimization solving can keep the track precision, meanwhile, a joint instruction is automatically corrected to prevent constraint failure, and minimum intervention type safety control is achieved.

The invention discloses a safety control method and system for an emerging control main body, and belongs to the technical field of power system active control, and the method comprises the steps: carrying out the bidirectional data interaction through a master station AGC and a field station AGC, and carrying out the bidirectional data interaction based on a bidirectional data interaction result and a cooperative anti-error parameter; whether the emerging control main body is in a controllable state or not is judged in real time through collaborative security locking logic; if the control main body is in the controllable state, active measurement data of the emerging control main body are obtained, and jump detection and multi-source checking are carried out on the active measurement data; after verification is passed, a pre-control instruction is generated based on the control strategy, and reasonability verification and control effect simulation verification are carried out; and after the control effect simulation verification is passed, the pre-control instruction is taken as a final control instruction to be issued to the emerging control main body for execution. The method can greatly improve the safety control capability of AGC on an emerging control main body, guarantees the frequency operation safety of a power grid, and improves the safe and reliable power supply capability of the power grid.

The invention relates to the technical field of data security, in particular to a business data security access method based on an intelligent strategy. The method comprises the following steps: constructing a context-aware intelligent permission template library; collecting multi-dimensional context information in real time and normalizing the multi-dimensional context information into feature vectors; authority granting is automatically decided through weighted similarity matching; creating an activated session object and executing dynamic constraint; and continuously monitoring the session risk and dynamically adjusting the authority. According to the method, the tracking identifier is embedded in the data and the data propagation path is monitored, so that the security control on the derivative data is realized, and the problem that the traditional method only controls the original data access and ignores the data diffusion risk is solved. And meanwhile, the session object and the dynamic constraint execution point are activated, so that the whole-process refined management and control of the permission life cycle are realized, and the requirements of service agility and security are balanced.

The invention relates to an intelligent control method, system and device for an industrial robot and a medium. The method comprises the steps that high-dimensional coding is conducted on the environment and the body state of the robot to generate a unified hidden state vector; generating a task-driven original control instruction through a deep reinforcement learning network based on the hidden state vector, predicting the safety risk of the instruction through a safety network in a prospective manner, and outputting safety measurement and gradient data; when the risk is predicted to exist, constructing and solving an optimization model taking minimization intervention as a target, and calculating to obtain a safety correction value; fusing the correction value and the original instruction to generate a final safety control instruction and executing the final safety control instruction; and updating the security prediction network online by using the executed real security feedback. The method solves the problems that in existing deep learning control, the decision risk is unpredictable in physical deployment, and a traditional safety protection mechanism extensively interrupts tasks, and the intelligent decision-making ability of the robot is reserved to the maximum extent while absolute safety is guaranteed.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention discloses a discharge power safety control method and system for a new energy power station. The control method comprises the following steps: step 1, collecting multi-dimensional data in real time; 2, constructing a safety evaluation model fusing the discharge power, the equipment operation state and the power grid parameters by using the multi-dimensional data; 3, implementing different preliminary power regulation strategies based on an evaluation result of the safety evaluation model; 4, evaluating the stability of the power grid in real time; 5, performing dynamic optimization adjustment on the discharge power of the new energy power station by using an intelligent optimization algorithm; the system comprises a data acquisition module, a data processing and safety evaluation module, a power regulation execution module, a power grid stability monitoring module and a control decision module. The method has the beneficial effects that the safety risk of the discharge power can be evaluated more accurately through multi-dimensional data fusion, construction of the safety evaluation model and dynamic adjustment of the weight of each data dimension.

The invention relates to the technical field of artificial intelligence and man-machine interaction, and discloses an AI multi-mode everything identification child interaction star key system, which comprises a multi-mode perception module, a data storage module, a self-adaptive strategy decision module, a situation arrangement and retrieval module, a content generation and safety control module, a multi-mode expression execution module and an asynchronous capability evaluation module. The system maps visual features to a semantic space through an attention mechanism to analyze a multi-modal anaphora intention, determines a cognitive age stage based on a user dynamic portrait matrix and matches an interaction strategy, generates reply content by using a large language model, and dynamically adjusts voice and action output parameters according to a real-time emotion vector. And finally updating the portrait by using a moving average algorithm. The problems that in the prior art, visual semantic alignment is lacked, and the content difficulty cannot follow dynamic evolution of the user ability are solved, and accurate intention understanding and personalized growth interaction are achieved.

The invention discloses a forklift safety control system and a forklift. The forklift safety control system comprises a sensor which is installed on a pallet fork of a forklift and used for detecting the distance between the pallet fork and an obstacle; the wireless communication device is mounted on the pallet fork, comprises a first wireless communication module connected with the sensor and is used for receiving a distance signal detected by the sensor; and the safety monitoring device is mounted in a cab of the forklift. The safety monitoring device comprises a second wireless communication module which communicates with the first wireless communication module through a wireless network and is used for receiving a distance signal sent by the first wireless communication module; the control module is connected with the second wireless communication module and is used for controlling the alarm according to the distance signal received by the second wireless communication module; and the alarm is connected with the control module. When the distance detected by the sensor is smaller than the preset safety distance, the control module controls the alarm to give an alarm to remind a forklift operator. The use safety of the forklift is improved.

The invention discloses a lightweight spliced bridge vibration monitoring system for cooperative work performance intelligent perception, and belongs to the technical field of bridge structure health monitoring, and the lightweight spliced bridge vibration monitoring system comprises a lightweight targeted sensing subsystem which comprises a plurality of perception assemblies arranged in a targeted manner; the intelligent edge acquisition and transmission subsystem has an independent addressing function and a time synchronization function and is used for preprocessing and lightweight processing; the cloud edge collaborative storage management subsystem performs hierarchical storage, security control and efficient retrieval on the data to form monitoring data; and the cooperative performance perception early warning subsystem realizes state evaluation and graded early warning of the cooperative work performance of the spliced bridge through multi-index fusion. By adopting the lightweight spliced bridge vibration monitoring system for cooperative work performance intelligent perception, the problems of insufficient lightweight and targeting performance, low data processing, transmission and storage efficiency, and inaccurate cooperative performance evaluation, damage positioning and early warning of traditional spliced bridge vibration monitoring are solved.

The embodiment of the invention relates to the technical field of robot moving trajectory planning, and provides a robot moving trajectory optimization control method and device, electronic equipment and a medium, and the method comprises the steps: obtaining visual observation data and a natural language task instruction when a robot executes a current moving trajectory; joint reasoning is carried out on the visual observation data and the natural language task instruction, a discrete action sequence is generated, smooth optimization is carried out, and a first continuous running track is generated; the robot is controlled to be converted from the current moving track to execute the first continuous moving track, and the visual observation data and the natural language task instruction of the robot executing the first continuous moving track continue to be obtained; and smooth transition processing is carried out on the newly generated second continuous moving track and the first continuous moving track, and the robot is controlled to be transited to the second continuous moving track from the first continuous moving track. Therefore, smooth trajectory generation meeting dynamic constraints, continuous trajectory transition under dynamic instructions and safety control in an abnormal state are realized.

The invention discloses a thermal management hierarchical linkage safety control method for an energy storage loop, and relates to the technical field of safety control of an electric energy storage system, and the method comprises the steps: an energy storage loop control system continuously and synchronously monitors a multi-dimensional operation state parameter set of an energy storage battery plug-in box in a battery module in the energy storage loop, the method comprises the following steps: constructing an operation feature vector of an energy storage battery subrack, performing correlation analysis processing on the operation feature vector to obtain abnormal state judgment information, performing abnormal state evolution analysis on the operation feature vector according to the abnormal state judgment information to obtain a thermal runaway risk identifier, and performing hierarchical coordination control on a battery module. And after the hierarchical coordination control is executed, continuously monitoring the operation feature vector, generating an out-of-control signal of the battery module, then executing electrical isolation processing on an energy storage loop corresponding to the battery module, and executing directional immersion processing according to the active fire-fighting logic after the electrical isolation processing is executed. The thermal runaway diffusion probability is effectively reduced, and the overall operation safety of the energy storage system is improved.

The invention discloses a control method of an electricity utilization information acquisition terminal with security situation awareness, and relates to the technical field of electricity utilization information acquisition. Multi-source state evaluation, command risk division, rule decision and execution feedback are no longer independent functions through operation modes, joint security state levels, control quotas, command risk levels, command quota state marks and mutual reference between local execution consistency indexes and collaborative domain execution consistency indexes. A technical system with a close coupling relationship is formed in the electricity utilization information acquisition terminal, so that the method is not only suitable for resident courts, special transformer users and important user scenes, but also capable of automatically updating operation mode definitions, safety state division thresholds, control quota parameters and safety control rules through version comparison after main station side strategy update messages are issued. And the terminal security control capability is continuously evolved along with operation experience and strategy adjustment, and a strong synergistic effect is embodied.

The invention relates to the technical field of electric digital data processing and energy storage system safety control, in particular to an optical storage and charging integrated station energy storage system. Comprising a data acquisition module which is used for acquiring a state feature vector of an energy storage system, planned scheduling power of an upper-layer energy management system, an external power grid interference index, and a real-time safety state and an economic value index of a battery management system in real time; the risk assessment module is used for generating a predictive risk score; the decision threshold module is used for calculating a dynamic intervention threshold; the arbitration decision module is used for generating a negative sign; the instruction correction module is used for determining a derating factor in response to the override mark and generating a correction scheduling instruction; or when the negative sign is not generated, outputting the planned scheduling power as a corrected scheduling instruction. According to the invention, early warning and active prevention of the potential thermal runaway risk are realized, and the intrinsic safety level and the operation reliability of the system are obviously improved.

The micro-service processing system provided by the embodiment of the invention comprises an event identification module which is used for defining a business domain event type and registering an event attribute structure; the event bus module constructs a distributed communication network containing a dual-channel message queue, and the event bus module implements partition routing according to event types; the event processing module is composed of an event producer and a consumer which are executed asynchronously, the event producer packages service actions into a standardized event data packet, and the consumer processes subscription events through a thread pool; the event storage module adopts a hierarchical log database for persistence of an event sequence and supports reestablishment of a service state according to a timestamp; and the security control module is used for implementing service authentication and event content encryption on a transmission layer. Through event bus dual-channel design, sender box transaction binding and hierarchical storage optimization, millisecond-level event processing delay is realized while the reliability of distributed transactions is ensured, and second-level state recovery can be realized based on a complete event log when a service fault occurs.

The invention relates to an agricultural machine safety control method and device and a computer readable storage medium. The method comprises the following steps: synchronously acquiring agricultural machine operation data through multiple sensors, extracting terrain, earth surface and motion features, constructing feature vectors, and identifying a geographic position category label of an agricultural machine by using a pre-training model; loading a corresponding exclusive safety control rule according to the label, wherein the rule comprises a specific sensor combination, a risk mapping matrix and a basic operation parameter; screening real-time data according to a rule to calculate a risk state vector, and linearly transforming the risk state vector into a control instruction correction through a mapping matrix; and finally, a control instruction is generated in combination with the basic parameters and is issued to an actuator, so that safety control based on environment category accurate perception and dynamic rule self-adaption is realized, and the operation safety, the environment adaptability and the operation efficiency of the agricultural machinery under complex terrains are effectively improved.

The invention discloses a power grid big data multi-dimensional risk perception method and system, and belongs to the technical field of power grid big data risk prediction.The power grid big data multi-dimensional risk perception method comprises the steps that multi-source perception data is collected, time-space synchronization and normalization processing are conducted, and a multi-dimensional feature representation matrix is constructed; constructing a feature coordinate system based on the matrix, mapping the feature coordinate system to a risk topology network, and generating a key node connection weight map; a multi-dimensional disturbance response entropy model is further constructed, and nonlinear situation modeling of power grid state disturbance is achieved; generating a risk energy distribution diagram by using the prediction model, extracting a high-risk path, and generating a microcosmic situation compensation curve in combination with a historical trajectory for adjusting an abnormal threshold; and finally, outputting a risk level evaluation result, and triggering a safety control and early warning response mechanism in a linkage manner. The method has the advantages of being high in multi-source data fusion degree, high in risk prediction accuracy and intelligent in response control, and the risk perception and active defense capability of a power grid in a complex environment is effectively improved.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

A system and method for performing cross-entity correlation of a service account in a cloud computing environment for applying a cybersecurity control is presented. The method includes detecting a terminal service principal in a cloud computing environment; detecting a hint which connects the terminal service principal to a root service principal; storing a representation of the terminal service principal, and the root service in a security database, wherein the security database includes a representation of the cloud computing environment; and applying a control on representation of the terminal service principal based at least on the root service principal.

The invention relates to a spacecraft collaborative formation safety control method and system based on an adjustable self-adaptive control barrier function. The method comprises the following steps: establishing a six-degree-of-freedom model for describing relative motion of a spacecraft formation; multiple safety key constraints which need to be met by the spacecraft formation are established, and the constraints are formalized into a zero-hyperlevel set function of a state variable; a disturbance observer is designed, and unknown lumped disturbance existing in the six-degree-of-freedom model is estimated and compensated online; a safety controller is constructed, the safety controller comprises an adjustable self-adaptive control barrier function used for generating a safety constraint boundary changing along with time, and parameters of the adjustable self-adaptive control barrier function are adjusted online through the auxiliary dynamic system; and constructing a quadratic programming problem, and solving to obtain an optimal safety control instruction which minimizes the deviation between the actual control input and the nominal control input. Compared with the prior art, the method has the advantages of improving the task success rate and safety of the spacecraft formation in a complex, dynamic and uncertain environment and the like.

The invention belongs to the field of artificial intelligence, particularly relates to a multivariable self-optimization control coal mill agent operation method, and aims to solve the problems of variable coupling interference, parameter solidification lag and poor working condition adaptability in traditional control. According to the strategy, an attention mechanism-based LSTM dynamic model is constructed, and future states of six types of variables such as air volume and temperature are predicted; defining a weighted optimization function fusing powder making efficiency, energy consumption, wear and stability, and introducing a soft constraint to guarantee a safety boundary; an optimal control combination is searched on line every five seconds by adopting a depth deterministic strategy gradient algorithm, and the four-dimensional action space of an air door, a hot blast valve, a coal feeder and a grinding roller is covered; and triggering an incremental learning fine tuning model through residual feedback to prevent disastrous forgetting. According to the scheme, multivariable cooperation, online self-optimization and intelligent safety control under all working conditions are realized, and the dynamic response capability and the comprehensive operation efficiency of the coal mill are remarkably improved.

The invention discloses an active defense security control method for a hidden false data injection attack, and relates to the field of information physical system security control, in particular to an active defense security control method. The invention aims to solve the problem that the existing detection method can encrypt a transmitted signal, but is limited to detection of a specific type of attack, or dynamic coupling changing the configuration of a control system can increase the burden of a state estimator, and even causes the performance reduction of the system. The method comprises the following steps of: 1, acquiring output feedback gains of the information physical system under different switching signals; and 2, based on the output feedback control gain obtained in the step 1, performing active attack detection and positioning on the FDI attack, and when the attack is detected and positioned, performing active switching by a controller of the information physical system so as to construct an active security controller aiming at the FDI attack.

The invention relates to a cross-trust-domain data security convergence system, method and device and a medium, and belongs to the technical field of network security. The system comprises a quantum enabling resource layer, an intelligent security control layer and a security execution layer, the quantum enabling resource layer comprises a quantum entangled state pre-distribution network and a quantum random number source cluster; the intelligent security control layer comprises a dynamic trust evaluation engine and a policy arrangement center which are respectively used for carrying out dynamic trust evaluation on a cross-domain communication requester and generating a session security policy packet; and the security execution layer comprises a cross-domain intelligent security gateway and an authentication auditing block chain, and is used for performing identity authentication and key generation under the constraint of the session security policy packet, executing cross-trust-domain data security convergence based on the generated key, and recording to form a security narrative chain for auditing traceability and authentication. The system can effectively support large-scale, high-real-time and high-security cross-network and cross-domain data security convergence.

The invention provides an agent security control network training method, a control method, electronic equipment and a medium, and relates to the technical field of agents. The training method comprises the steps of obtaining a training sample set; inputting the training sample set into a pre-constructed heterogeneous graph control barrier function network, outputting barrier function values corresponding to the intelligent agents, and updating parameters of the heterogeneous graph control barrier function network based on a preset first loss function; inputting the training sample set into a task controller, and outputting an action sequence; correcting the action sequence through quadratic programming under the forward security constraint model to obtain a central security action sequence of each agent; taking the central security action sequence as a supervision label, and training the distributed control network together with the training sample set; and repeating the steps until a preset iteration termination condition is met. According to the invention, the intelligent agents can still output high-reliability safety control actions under the condition of only depending on local observation.

The invention discloses a mechanical arm dynamic tracking and obstacle avoidance system and method based on reinforcement learning. The mechanical arm dynamic tracking and obstacle avoidance system comprises a sensing module, a target motion prediction module, a reinforcement learning decision module, a safety control and constraint module, an execution module and a training and simulation module. According to the invention, a complete system including environment perception, target motion prediction, reinforcement learning decision and execution control is constructed, so that the mechanical arm can predictively track a moving target in an unknown or dynamic environment, and real-time obstacle avoidance is carried out on a static or dynamic obstacle; and the requirements of safety, robustness and real-time performance of an industrial site are met in the whole control process.

The invention discloses an aircraft complex terrain dynamic path planning method and system based on reinforcement learning, and the method comprises the steps: S1, carrying out the multi-source heterogeneous perception fusion, and generating a state vector through the multi-source perception fusion; s2, element reinforcement learning intelligent decision making: dynamically adjusting the weight of a reward function by utilizing an element reinforcement learning agent and outputting an action instruction; s3, a safety control instruction is generated, model prediction control is adopted to conduct rolling optimization on the instruction under the condition that dynamics and safety constraints are met, and a final control instruction is generated; and S4, online learning and strategy updating: storing interaction experience through an online learning mechanism, preferentially sampling high-complexity scene data, and finely adjusting decision network parameters. According to the method, the dynamic adaptive path planning of the aircraft in the complex terrain is realized, the defects of poor adaptability and dependence on prior data in the prior art are overcome, and the autonomy and safety of path planning are improved.