315 results about "Active security" patented technology

The invention provides an adaptive interference suppression carrier communication system based on a complex electromagnetic environment, relates to the technical field of communication systems, and solves the problems that an existing communication system is poor in adaptability, weak in anti-interference capability and lack of collaboration and safety in the complex electromagnetic environment. The system comprises an environment perception module used for deeply perceiving an environment and identifying known and unknown interferences by using AI; the cognitive decision engine is used for generating a multi-domain control instruction by utilizing AI according to environment, internal, cooperation and safety states; the self-adaptive transceiving control module is used for executing cross-multi-domain parameter self-adaptive adjustment; the distributed collaborative module is used for realizing information sharing among nodes, federated learning and collaborative resource management; and the endogenous security enhancement module is used for executing active security mechanisms such as dynamic heterogeneous redundancy, mimicry defense and physical layer authentication. The invention further discloses a corresponding communication method. According to the method, the robustness, adaptability, efficiency and safety of a communication system in a complex electromagnetic environment can be remarkably improved.

The invention relates to an encrypted traffic detection method based on multi-dimensional feature parallel fusion, and belongs to the technical field of network security. According to the method, when a multi-dimensional feature parallel fusion framework is constructed, the limitation of traditional statistical features on dynamic evolution characterization of encryption behaviors and the dependency of graph neural network topology modeling on computing resources are fully considered; through collaborative optimization of a Markov chain dynamic quantization protocol interaction state transition rule and a lightweight graph attention hierarchical compression mechanism, a detection model gives consideration to deep feature perception capability and efficient reasoning capability at the same time; based on the classification decision realized by the fusion mechanism, the recognition robustness and real-time defense efficiency of the encrypted malicious traffic are remarkably improved, and the active security protection level of the network is effectively enhanced.

The invention relates to a single battery early-stage thermal runaway early warning method based on operation parameter fusion and isolated forest algorithm optimization, and the method achieves the closed loop of early perception, precise recognition and real-time early warning of the thermal runaway of a battery through the dual-engine drive of LSTM time sequence feature enhancement and isolated forest dynamic optimization. The hysteresis limitation of traditional BMS threshold value alarm is broken through, and an early warning node is advanced before the thermal runaway chain reaction is started; the problem of abnormal monomer positioning in a large-scale battery cluster is solved, and high-efficiency detection of large-order-of-magnitude monomer / cabinet hierarchy is realized; an active safety protection system of the energy storage power station is constructed, and a landing technical support is provided for intelligent operation and maintenance.

The invention relates to the technical field of information data processing, particularly provides an active security defense method based on a network traffic time mode and data volume analysis, and solves the problem of poor security and reliability of network security defense. The method comprises the following steps: a network traffic capturing tool is used for acquiring a time mode and a data volume of network traffic corresponding to a network node; obtaining a model data set containing a time pattern and a data volume attack pattern, and segmenting the model data set to obtain a training set, a test set and a verification set; training the active security defense model by using the training set to obtain a trained active security defense model; and laying the active security defense model to a regional position, establishing communication with specific equipment, analyzing network traffic, obtaining an evaluation result of potential network attacks, and if the network attacks exist, adopting security protection measures for protection. According to the invention, the identification and defense efficiency of various network attacks can be improved, and the security and stability of the network are ensured.

The invention relates to the technical field of network security, in particular to a network security active security defense method and system based on vulnerability utilization, and the method comprises the steps: collecting multi-modal data through a sensing module deployed in a network, and constructing a prediction model of a high-risk region and an attack path in combination with a global network situation map; according to the path prediction data and the risk region division result, dynamically allocating defense resources; by adjusting the deployment density of trapping nodes and feedback chain logic, a trapping chain is formed to guide an attack behavior, the operation characteristic data of an attacker is captured in real time, and a defense strategy is dynamically adjusted. Through the above process, the method achieves the precision of resource distribution, the optimization of the synergistic effect of trapping nodes, and the real-time guidance and interference of attack behaviors, greatly improves the defense efficiency and flexibility of a network, reduces the security threats of high-risk nodes, and effectively protects the core assets of the network.

A method includes detecting a plurality of first malicious activities associated with a first attack. The method further includes detecting a plurality of second malicious activities associated with a second attack. The method further includes predicting, based on the plurality of first malicious activities associated with the first attack relating to the computing resources of the first entity, a future malicious activity associated with the second attack relating to the computing resources of the second entity. The method further includes generating an alert for the predicted future malicious activity associated with the second attack relating to the computing resources of the second entity. The alert includes one or more attributes of a previous alert generated for one of the plurality of first malicious activities associated with the first attack relating to the computing resources of the first entity.

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 belongs to the technical field of security federated learning, and particularly discloses a verifiable active security aggregation method and system for federated learning, and the method comprises the following steps: a client uses a local data set to train and generate a local model update, and sends the model update to an aggregation server through homomorphic encryption, and meanwhile, verification information is attached; the server calculates and sends an encryption aggregation model update and verification value to each client, and the client decrypts to obtain a partial aggregation result after verification, encrypts the partial aggregation result by using a shared secret with other users and sends the encrypted aggregation result to the server to be spread to the corresponding client; and the client carries out decryption to obtain a sufficient number of partial decryption results, and then carries out aggregation to obtain final global model update. According to the scheme provided by the invention, the privacy of model updating is protected, and the verifiability of an aggregation result under active security is provided.

The invention relates to a safety protection method based on dynamic monitoring of circulating flow of heat source equipment. The method comprises the following steps: collecting pipeline pressure data, circulating water flow data and circulating water temperature data in a circulating pipeline of heat source equipment through a multi-source sensor; inputting the circulation pipeline data into a preset heat source equipment state model to perform working condition evaluation so as to generate an equipment working condition report; performing fault feature extraction on the equipment working condition report, and identifying an abnormal operation mode according to the extracted fault features; in response to the identified abnormal operation mode, determining a fault type matched with the abnormal operation mode according to a preset fault type library; and based on the determined fault type, generating a corresponding control instruction by using a preset risk-instruction mapping rule. According to the invention, real-time monitoring of the operation state of the heat source equipment, automatic identification of abnormal modes and active safety protection control are realized.

The invention relates to the field of industrial safety monitoring, provides a real-time risk identification and active safety control method and system for industrial operating personnel in order to improve the accuracy and efficiency of a safety monitoring technology, and realizes real-time acquisition and edge preprocessing of human-machine-environment data by integrating a physiological / environmental / equipment multi-mode sensor. An FPN network is utilized to fuse spatial-temporal characteristics, Temporal-PC causal inference and a GBDT integration algorithm are combined, an interpretable risk propagation model is constructed, risk indexes are calculated, three-level early warning is divided, and hierarchical intervention is executed for different risk levels, including local early warning, environment regulation and control, emergency evacuation and multi-department emergency linkage. Through LSTM trend prediction and reinforcement learning dynamic parameter adjustment, closed-loop optimization of an intervention strategy is realized, the problems of information splitting, response lag and the like of a traditional monitoring system are solved, the risk identification precision and emergency disposal efficiency of a chemical high-risk scene are remarkably improved, and the method is suitable for operation safety control in high-temperature, high-pressure, toxic and harmful environments.

The invention provides an active safety control method for an unmanned vehicle in an unstructured environment. The method comprises the following steps: acquiring millimeter wave radar detection data and laser radar detection data in a driving process of the unmanned vehicle, and preprocessing the millimeter wave radar detection data and the laser radar detection data; carrying out target detection and matching based on the preprocessed millimeter wave radar detection data and laser radar detection data, and judging whether a target obstacle exists or not; if the target obstacle exists, the relative speed of the target obstacle and the unmanned vehicle and a maneuvering driving path area of the unmanned vehicle are obtained, and a risk area is delimited based on the relative speed and a safe distance threshold value in the maneuvering driving path area; obstacle channel filtering is carried out on the risk area to obtain obstacle information in the risk area; and if the target obstacle exists in the risk area, emergency braking is carried out. The problem that in the prior art, an active safety control method of the unmanned vehicle in a non-structural environment is prone to causing false detection, and consequently practicability is poor is solved.

The invention relates to an automobile windshield shelter removing method and system and an automobile, and belongs to the technical field of automobile active safety. The method comprises the steps that the surface of the windshield is monitored through a vehicle-mounted camera; when a shielding object is identified, calculating a view shielding coefficient based on the area, the position, the light transmittance and the type of the shielding object, and determining an initial influence level according to the view shielding coefficient; corresponding cleaning strategies are executed according to the influence levels, windscreen wiper cleaning is started at the low level, pneumatic suction generated by vehicle intersection is used for cleaning at the middle level, and negative-pressure airflow cleaning is induced through dynamic opening of a skylight at the high level; if the shielding is not removed, the influence level is increased step by step, and circulation execution is carried out until the shielding is removed or automatic parking is triggered. According to the method, grading, closed-loop and non-contact intelligent removal of different types of sheltering objects is achieved, and invalid or even harmful operation of a traditional windscreen wiper in a dry sheltering scene is effectively avoided.

The invention relates to the technical field of data transmission, and discloses a gas meter information secure transmission method based on response function compensation, which comprises the following steps: collecting multi-source data and network signal quality information of a gas meter; according to a real-time network environment and physical conditions, data transmission parameters are dynamically adjusted through a response function compensation algorithm, when data transmission in a static time slot fails, a dynamic retransmission time slot is automatically allocated for retransmission, a retransmission frequency threshold value is set, and packet loss caused by channel errors or collision is compensated through a retransmission response function; a multi-layer encryption mechanism is adopted to encrypt transmission data, an optimal transmission channel is intelligently selected according to network conditions, adaptive hybrid communication is performed through star flash short-distance communication or Wi-Fi wide-area transmission, and a response compensation mechanism of multi-dimensional environmental parameters is established based on a signal transmission model; transmitting the encrypted data to a monitoring management platform through a secure channel; therefore, safety early warning decision is realized, and dynamic accurate transmission and active safety protection are realized.

The invention relates to the field of Internet of Things security, and discloses an IoT access control system with an active security capability, and the system comprises an Internet of Things security gateway, a network infrastructure and an external IoT service system: a port authentication module of the gateway executes the binding verification of an MAC white list and a dynamic IP-MAC; the traffic monitoring module loads a protocol feature sequence template based on a service type, and verifies a device traffic behavior in a time window; the equipment verification interface module is linked with the service system to verify the equipment registration state; and the active defense module triggers precise blocking and network repairing operation according to the verification result. According to the application, organic fusion of equipment identity credibility verification, service behavior compliance detection and attack instant disposal is realized, attacks such as MAC counterfeiting, protocol tampering and service layer counterfeiting are effectively defended, and meanwhile, through adaptive flow modeling and elastic strategy adaptation, the method is compatible with multiple service scenes such as intelligent buildings and industrial Internet of Things, and the service security is improved. And the active defense capability of the IoT network is remarkably improved.

The invention belongs to the technical field of automobile seats, and provides a zero-gravity seat protection method and system based on collision risk prejudgment and application, and the method comprises the steps: monitoring collision risk information and vehicle state information in real time, carrying out the fusion processing of the collision risk information and the vehicle state information, carrying out the collision risk prejudgment, and generating an early warning signal when a judgment result meets any preset collision triggering condition; multiple zero-gravity seat active safety protection actions are executed in a millisecond level in parallel or under condition triggering, so that a seat backrest is reset to a safety protection posture from a zero-gravity posture through a synergistic effect, cooperative fixing of the posture of a passenger is synchronously completed, and the zero-gravity seat active safety protection actions are completed within 200 milliseconds; and when it is detected that the angle of the seat backrest reaches the target angle, the dual self-locking function is triggered, and locking of the seat backrest is completed. According to the invention, millisecond man-machine cooperation zero-gravity seat active safety protection can be carried out based on prospective collision risk pre-judgment, and the method has the advantages of low cost, high reliability and low false triggering rate.

The invention discloses an intelligent operation and maintenance management system for a distributed photovoltaic project, and relates to the technical field of project operation and maintenance management. 18 types of sensing terminals are deployed, 5G + LoRa networking is adopted, millisecond-level snapshot is carried out when abnormity occurs, and data are uploaded after being processed by edge nodes; the digital twin operation and maintenance center constructs a 1: 1 holographic model, maps the equipment state in real time, and simulates the fault condition; the adaptive evolution fault diagnosis module fuses multi-source data to generate a fault tree; the dynamic scheduling module integrates resources to generate an order dispatching scheme and supports unmanned aerial vehicle inspection; the energy efficiency self-adaptive optimization module is linked with energy storage peak regulation and optimizes an MPPT strategy; and the active safety protection module quickly disposes and gives an alarm when detecting abnormity. The method improves the operation and maintenance efficiency and the power generation efficiency of the photovoltaic project, reduces the cost, guarantees the safety, excavates the comprehensive value, and is suitable for various distributed photovoltaic projects.

The invention relates to the technical field of industrial internet, in particular to a control system and method for stirrer production. Comprising an edge data preprocessing unit, a flexible production instruction distribution unit and a working condition diagnosis and protection control unit. By constructing an integrated control parameter mapping model, an industrial operation signal is converted into a collaborative sensing digital primitive; the production task is compiled into an intelligent production execution process cluster based on a resource scheduling algorithm, and equipment is driven to execute differentiated production; and performing situation assessment on the digital primitives by using the anomaly detection model, and generating a regulation and control instruction to execute process logic recombination and safety protection. According to the invention, the problems of low production information integration level and tight data in each link of a production control system are solved, and flexible scheduling and active security defense in the production process are realized.

The invention provides an emergency call system, method and device based on AI vision and a storage medium, and relates to the technical field of active safety of intelligent network connection automobiles. The system comprises a camera module, a DMS module, an OMS module, an AI vision algorithm module, an AI vision application module and an eCall application module which work cooperatively. Aiming at the three defects that a traditional emergency call system depends on collision triggering, data dimensions are deficient and active protection is absent, DMS and OMS are innovatively and deeply integrated with the emergency call system, a multi-modal fusion decision mechanism is realized in combination with a layered AI visual architecture, and through deep cooperation of camera shooting, DMS, OMS, AI visual algorithm, AI visual application and an eCall application module, the multi-modal fusion decision mechanism of the emergency call system is realized. A full-link intelligent safety closed loop of sensing, decision making, control and rescue is constructed, early recognition, active intervention and accurate rescue of sudden diseases of drivers, serious fatigue disability and emergency physiological events of passengers are achieved, and meanwhile the rescue quality of collision accidents is improved.

The invention relates to a switch cabinet with a built-in intelligent management and control system, through integration of a sensor network module, a data acquisition module, an intelligent processing module, a remote monitoring module and an active safety protection module, conversion from passive maintenance to active management and control is realized, and the switch cabinet is equipped with various sensors, so that the operation state of equipment can be monitored in real time; historical data are generated through timestamp records, and the intelligent processing module predicts the service life and the fault risk of the equipment in real time by applying a health management algorithm and a deep learning algorithm, identifies different fault modes and sends out an alarm signal; the active safety protection module comprises an electric power cut-off device, a temperature overrun protection unit and an emergency power supply unit, and can quickly cut off a power supply or start a cooling measure when abnormal parameters are detected, so that the equipment safety is ensured; and the intelligent level and the reliability of the switch cabinet are improved.

The invention relates to the technical field of electrochemical energy storage safety, particularly discloses a multistage thermal runaway protection and AI early warning method for an energy storage system, and aims to solve the problems that traditional early warning depends on macroscopic signal lag, single-mode misinformation is high, and a protection strategy is rigid. According to the method, thermal state data are collected through a multi-modal monitoring system, core features are extracted after the data are preprocessed, global feature vectors are generated through weighted fusion, a CNN-LSTM hybrid model is input to predict normal, slight thermal runaway risks, moderate thermal runaway risks, severe thermal runaway risks and the risk level of thermal runaway, and the risk level of thermal runaway is calculated. Dynamically triggering grading protection based on the risk grade; early early warning and accurate gradient intervention of thermal runaway are achieved, and the active safety protection capacity of the energy storage system is remarkably improved.

The invention relates to the technical field of switch cabinet state monitoring, in particular to an intelligent switch cabinet anti-explosion abnormity monitoring method and system, and the system comprises a data collection module, a data storage and management module, a correlation analysis module, a time sequence prediction module, a risk assessment and early warning module, a communication interface module and a man-machine interaction module. According to the invention, by setting a multi-source sensing and intelligent analysis integrated monitoring architecture, early prediction and accurate diagnosis of the fire blast risk of the switch cabinet are realized, the contribution weight of each inducement parameter to a fault result is quantified through a dynamic correlation analysis algorithm, and a time sequence prediction model is introduced to establish an advanced early warning mechanism of parameter change, so that the early warning of the fire blast risk of the switch cabinet is realized. According to the method, when direct fault indexes such as temperature and gas concentration do not exceed the standard, a potential fault development path can be recognized in advance by analyzing the change trend of incentive parameters such as current and voltage, so that an accident handling mode is converted from post-remedy to pre-prevention, and the active safety guarantee capability of operation of the switch cabinet is improved.

The invention discloses a battery active safety early warning method and a related device, and belongs to the technical field of battery safety. The method comprises the following steps: monitoring and collecting expansion force, voltage and temperature data of a battery in real time; inputting the expansion force, the voltage and the temperature data of the battery into a pre-established multi-factor comprehensive early warning model to obtain an early warning grade; and taking corresponding early warning and fire fighting measures according to the early warning level. The device comprises a data acquisition module, a main control unit, an alarm device and a fire fighting device, the data acquisition module comprises an expansive force sensor, a voltage sensor and a temperature sensor which are mounted on a battery; and the main control unit is used for judging the working state of the battery according to the expansion force, voltage and temperature data acquired by the data acquisition module and controlling the alarm device and the fire fighting device to work. According to the invention, timely early warning and measure taking under the abnormal state of the battery can be ensured, and the safety of the battery system is improved.

The invention relates to the technical field of data processing, in particular to a smart community management and control method based on multi-dimensional data monitoring, which comprises the following steps: collecting multi-dimensional data in a smart community, and sequentially caching, cleaning, integrating and compressing the multi-dimensional data to output optimized data, training an early warning model by using the optimization data to output an active safety model; monitoring and controlling the smart community through the active security model, giving an alarm when an abnormal condition occurs, and displaying alarm information on a visual interface; determining the management and control stability of the smart community based on the variance of the packet loss rate of the multi-dimensional data; if the management and control stability does not meet the requirement, adjusting the cache size of the multi-dimensional data, or determining the management and control reliability of the smart community based on the early warning missing report rate of the abnormal condition; and if the control reliability does not meet the requirement, adjusting the batch training size of the optimized data. The management and control stability of the smart community is improved.

The invention discloses an unmanned aerial vehicle nest full life cycle management and control method, system, product and device, and relates to the field of unmanned aerial vehicle nests, and the method comprises the steps: employing a bidirectional access mechanism to carry out the bidirectional validity verification of an unmanned aerial vehicle and a nest, and generating an access safety certificate; integrating multi-dimensional data through edge computing nodes, and constructing a visual relation model and a dynamic behavior monitoring model; carrying out real-time risk identification and early warning by adopting a multi-dimensional feature fusion anomaly detection algorithm; the digital twinning technology is utilized to fuse aircraft nest operation data, and predictive maintenance is achieved through a life prediction model; and safely erasing the sensitive data of the equipment and cancelling the voucher to form a management closed loop. Through a bidirectional access mechanism, multi-dimensional anomaly detection, predictive maintenance and safe decommissioning processing, full-link active safety protection from access, operation, maintenance to decommissioning is realized, the safety and reliability of a machine nest system are improved, and the system can be applied to complex scenes with strict safety requirements such as electric power inspection.

The invention is suitable for the field of data transmission interaction, and provides a server data security interaction method and system, and the method comprises the following steps: continuously collecting multi-dimensional time sequence data when a mobile client interacts with a server, and carrying out the fusion to construct a behavior track trust model; acquiring a data point and additional data of the current session, calculating a deviation degree between the data point and the trajectory trust model, and integrating the additional data to perform weighted fusion to obtain a risk confidence score of the current session; dynamically selecting a corresponding verification mechanism from a verification strategy library according to the risk confidence score, and generating a verification strategy instruction; and receiving and executing the verification strategy instruction, recording verification process data of an operator, and feeding back the verification process data. According to the invention, continuous and non-interference active safety protection is carried out on the identity of the mobile client operator in the whole session life cycle.

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 provides a train active safety control method and device, and relates to the technical field of train control. The method comprises the following steps: the sensing module preprocesses image information and radar point cloud information to obtain coordinate system fusion data; the edge calculation unit performs target detection on the coordinate system fusion data according to a pre-trained target detection model to obtain security data information; the network central control unit calls an API function based on the selected target bus driving identifier to determine a target communication system, and generates and sends a braking control instruction according to the target communication system and the security data information; and the brake control system performs brake control on an execution mechanism according to the brake control instruction. The device executes the method. According to the method and the device provided by the embodiment of the invention, the real-time performance and the effectiveness of active safety control of the train can be improved.

The invention provides an access control remote online control system and method based on property end access confirmation, and the system replaces manual registration: paper registration / talkback confirmation is needed traditionally, and the system realizes non-contact electronic initiation; the cloud platform realizes active security upgrade: traditionally, the cloud platform depends on naked eye judgment of security personnel, and the system realizes intelligent risk early warning (such as abnormal wandering, multi-person following and the like) through AI behavior analysis; no tracing exists after door opening traditionally, but the system realizes double-link image retention during door opening and after door opening, so that the behavior tracing capability is enhanced; the system realizes full-link visualization: traditional process information is split, and the system realizes'request-approval-execution-recording 'full-process data penetration and real-time monitoring; according to the system, dynamic safety protection is realized, and a triple protection mechanism of geocoding binding, dynamic two-dimensional code (traditional static password is easy to leak) and encryption instruction transmission is adopted.

The invention relates to the technical field of vehicle safety, in particular to a verification method and device for an active hood with the active safety recognition capability, and the method comprises the steps: obtaining the type information of the active hood with the active safety recognition capability, and collecting a working condition signal and an impact signal when the active hood is verified; generating a verification system of the active hood according to the type information, the working condition signal and the impact signal; and generating a verification result when the active hood is verified by using the verification system. Therefore, the problem that in the related technology, the active safety identification capability of the vehicle is not fully considered, so that the product does not have the safety function authentication capability in the popularization and application process is solved.

The invention belongs to the technical field of energy storage battery safety fire fighting, and particularly relates to an energy storage battery active safety fire fighting system with aerogel, which comprises a sensing layer, a transmission layer, a platform layer and an execution layer, the sensing layer collects operation and environment data of the energy storage battery, and the transmission layer transmits the data to the platform layer; the platform layer comprises three modules: a data preprocessing module processes received data; the risk identification module evaluates the preprocessed data in real time according to a preset strategy by using two active identification algorithms to obtain a risk evaluation result; the fire-fighting strategy making module determines an optimal fire-fighting strategy accordingly; and the execution layer receives the strategy and carries out control and aerogel fire extinguishing operation on the energy storage battery. The problem that in the prior art, an energy storage battery lacks an effective technical means in the field of active fire fighting can be solved.