24 results about "Dynamic security assessment" patented technology

The invention discloses an access control equipment data management method and system based on multi-source fusion. The method comprises the following steps: collecting a multi-source access control data stream in real time; based on a preset feature extraction rule set, extracting a multi-modal biological feature vector, a voucher legality identifier, an abnormal behavior probability value and an equipment health degree index, and inputting the multi-modal biological feature vector, the voucher legality identifier, the abnormal behavior probability value and the equipment health degree index into a dynamic security assessment matrix generation model to generate a real-time security assessment matrix; matching the dimension safety score of the real-time safety evaluation matrix with a preset threshold strategy library, and dynamically generating an access control strategy instruction set; and issuing the access control strategy instruction set to the target access control equipment execution terminal. The method has the following advantages and effects: the fault tolerance bottleneck of a single-dimensional decision chain is broken through, and the system misjudgment rate is reduced by at least one order of magnitude on the premise of ensuring the security by establishing a dynamic coupling mechanism of the multi-source data stream.

The invention discloses a movable formwork construction monitoring method and system based on finite element simulation, and relates to the field of bridge construction monitoring and structure health monitoring. The problems that in traditional movable formwork construction monitoring, a model is fixed, early warning lags behind, and it is difficult to dynamically reflect the real state of a structure are solved. According to the method, a digital structure module containing adjustable parameters is established, and working condition simulation is carried out to identify a theoretical high-risk area and optimize sensor layout; performing pre-simulation before each process to output a predicted value, synchronously acquiring actual measurement data during construction, and calculating a standardized deviation sequence; dynamic safety evaluation and early warning are carried out based on time domain feature extraction and multi-index fusion; reverse calibration and online updating are carried out on model parameters through Bayesian variational inference by utilizing historical deviation data, so that closed loop of simulation, monitoring, early warning and model optimization is realized, and the safety monitoring precision and real-time performance in the construction process of the movable formwork are remarkably improved.

The invention provides a non-probabilistic time-varying reliability analysis method based on ellipsoid correlation modeling. The method comprises the following steps: carrying out interval modeling on uncertainty input parameters; constructing a multi-dimensional ellipsoid uncertain domain based on correlation information among parameters; a system time-varying performance function is established, and an interval process of output response changing along with time is obtained through uncertainty propagation solution; identifying the maximum value point of the upper boundary and the minimum value point of the lower boundary in the interval process as reliability key points; respectively calculating non-probability reliability indexes based on the ellipsoid model at the moments of the two key points; and taking the smaller value of the two indexes as the non-probability time-varying reliability index of the system in the target time interval. According to the method, the problem that the evaluation result is conservative due to the fact that variable correlation is ignored in a traditional non-probability method is effectively solved, time-varying evolution of system performance can be accurately quantified, and an accurate and practical dynamic safety evaluation tool is provided for a complex engineering system with scarce data.

METHOD FOR DYNAMIC SECURITY ASSESSMENT OF STABILITY OF A POWER SYSTEM AND SYSTEM THEREOF The present disclosure relates to a computer-implemented method and system for providing a dynamic security assessment of stability of a power system comprising: receiving input data comprising an operational data record comprising OP-characterizing variables of the power system OP; applying the model to the OP-characterizing variables to obtain a stability classification. It is also disclosed a computer program product and a system configured to carry out the method herein disclosed.

The invention provides an electric power facility dynamic safety evaluation system based on blasting vibration propagation characteristics, which relates to the field of electric digital data processing and comprises a sensing and data acquisition module, a physical modeling and parameter library module, a real-time evaluation and risk inference module and a feedback execution and self-adaption module. The sensing and data acquisition module is used for converting on-site physical signals into high-quality data streams, the physical modeling and parameter library module is used for providing and building physical modeling and managing parameter information, and the real-time evaluation and risk inference module maps sensing data into system states and risk indexes through observation-model assimilation. The feedback execution and self-adaption module is responsible for driving decision execution, collecting feedback and continuously improving a system model; the system can monitor the influence of blasting vibration on electric power facilities in real time, accurately evaluate the structural damage degree and the system operation risk, and provide a scientific basis for safety protection and emergency decision-making of the electric power facilities.

The invention discloses a vehicle dynamic state assessment method and system fusing multi-sensing information, and relates to the technical field of vehicle state assessment, and the method comprises the steps: obtaining vehicle CAN data and intelligent driving system data, carrying out the independent analysis and fusion analysis, carrying out the safety response decomposition based on the analysis data relation, and obtaining a safety impact factor; time sequence pattern recognition is carried out based on the safety influence factors, and a time sequence trend relation is established; performing state time sequence evaluation through the time sequence trend relation to obtain dynamic risk evaluation information; and mapping the vehicle state according to the dynamic risk evaluation information to generate a vehicle dynamic evaluation result. The technical problems that in the prior art, vehicle multi-source information is difficult to fuse, the dynamic risk evolution trend of the vehicle cannot be captured through static evaluation, and consequently the accuracy and the foresight of vehicle state evaluation are insufficient are solved, and the technical effects that dynamic safety evaluation of the vehicle state is achieved, and the accuracy and the foresight of state evaluation are improved are achieved.

The application discloses a kind of dynamic security evaluation methods, device, medium and product for underwater system, it is related to underwater system dynamic evaluation technical field, including: three-level evaluation trigger mechanism, gradually refine security risk perception accuracy, propose the real-time index weight of whole process of security risk perception, dynamic adjustment method of evaluation time window, and if evaluation result does not converge, there is design based on post-evaluation closed-loop feedback mechanism, risk disposal result is evaluated again, guarantee that evaluation mechanism responds to the optimization adaptive of risk situation, proposed multi-level perception trigger containing three-level evaluation trigger, different levels of security risk grading determination is realized to underwater system, and it has feedback reprocessing capability to risk, proposed dynamic evaluation adjustment containing abnormal index coupling potential risk and dynamic reevaluation based on risk degree information, has a certain predictive processing capability to risk, and weight and time window will be dynamically adjusted according to risk condition.

The present application relates to the technical field of hoisting construction dynamic safety evaluation, and specifically discloses a hoisting construction dynamic safety evaluation method, which acquires hoisting system operation parameters in real time through a multi-modal sensor array, generates a hoisting dynamic parameter set, and the hoisting dynamic parameter set contains pressure distribution data of each hoisting point, hoist attitude angle data and load space position data; based on the hoisting dynamic parameter set, force system dynamic balance analysis is performed, and the force system imbalance degree of the crane is obtained through analysis; when the force system imbalance degree of the crane is greater than a set threshold, an imbalance active correction mechanism is triggered, and an electro-hydraulic proportional valve is used to adjust and correct the imbalance of the crane. The safety and efficiency of hoisting operation can be significantly improved. The automatic imbalance correction mechanism not only reduces the risk of accidents, but also improves the flexibility and response speed of operation, providing strong technical support for modern hoisting operations.

The invention provides a method and system for enhancing the power grid disturbance resistance of a phase modifier, and relates to the technical field of power grid disturbance resistance of the phase modifier, and the method comprises the steps: obtaining multi-source operation data and a disturbance historical sample library of a new energy station in a target power grid region, and generating a standardized multi-source operation data set containing a disturbance traceability mark; outputting a power grid disturbance characteristic quantity and a disturbance pre-judgment result through multi-modal characteristic fusion processing of transient impedance identification, frequency domain oscillation component extraction and multi-source characteristic weight distribution; a layered self-adaptive control strategy set of the phase modifier is generated by utilizing multi-kernel function dynamic weighting, fault scene training iteration and defense strategy matching optimization based dynamic safety evaluation model processing of a multi-kernel support vector machine; and millisecond-level response and running state dynamic correction when disturbance occurs are realized, and the power grid disturbance resistance of the phase modifier is enhanced. According to the method, the adaptability and defense precision of the phase modifier to disturbance of different risk levels are effectively improved.

The invention discloses a remote equipment management and control method based on a network security protocol, and relates to the technical field of Internet of Things and industrial Internet, and the method comprises the following steps: S1, a management and control platform receives a management and control instruction for target remote equipment, and extracts instruction feature information of the management and control instruction, the instruction feature information at least comprises an instruction type, an operation object and a risk level identifier. According to the application, linkage analysis is carried out on the instruction features, the equipment state and the network situation through the dynamic security assessment engine, the crossing of risk assessment from static pre-judgment to real-time dynamic study and judgment is realized, potential risks are accurately revealed, an adaptive strategy execution chain is dynamically generated according to the risk level, and the risk assessment efficiency is improved. Security control is converted from single instruction execution into closed-loop reinforcement covering the whole process of beforehand, during and after beforehand, the controllability and security of instruction execution are remarkably improved, and the system continuously optimizes an evaluation model and a strategy library by collecting and analyzing historical data and utilizing machine learning to form a self-evolved intelligent security closed loop.

The application discloses a kind of based on finite element simulation mobile mould frame construction monitoring method and system, it is related to bridge construction monitoring and structural health monitoring field;Solve the problem of model fixed in traditional mobile mould frame construction monitoring, early warning lag, difficult to dynamically reflect the real state of structure.The present application establishes digital structure module including calibratable parameter, carries out working condition simulation to identify theoretical high-risk area and optimizes sensor layout;Carry out preposition simulation output predicted value before each process, synchronously collect measured data and calculate standardized deviation sequence during construction;Based on time domain feature extraction and multi-index fusion, dynamic safety evaluation and early warning are carried out;Using historical deviation data, the model parameters are calibrated and updated online by bayesian variational inference, realize the closed loop of simulation, monitoring, early warning, model optimization, significantly improve the safety monitoring precision and real-time of mobile mould frame construction process.

The invention relates to the technical field of industrial automation and hoisting transportation, in particular to a carbon block marshalling placement method based on multi-modal perception and dynamic safety assessment, which comprises the following steps: establishing a three-dimensional model of a furnace chamber; carbon block marshalling position information is collected, and a clamping mechanism is controlled to move and clamp; contour information and clamping force information of the carbon block marshalling are obtained, a carbon block marshalling three-dimensional model is established, and a safety envelope body of the carbon block marshalling in the current posture is calculated; calculating a dynamic safety margin when the carbon blocks are grouped and placed into a target furnace chamber in a current clamping state; constructing a dynamic virtual channel with a self-adaptive section size; planning a collision-free movement track for the clamping mechanism based on the dynamic virtual channel constraint; and the clamping mechanism is controlled to move according to the planned collision-free movement track, and the carbon blocks are grouped and placed at the target position in the furnace chamber. The problem of collision caused by irregular shapes and uncertain postures when the carbon blocks are grouped and put into a furnace chamber is solved, and damage such as edge breakage and corner falling of the carbon blocks is greatly reduced.

The application provides a method and system for enhancing the anti-power grid disturbance capability of a phase modifier, relates to the technical field of phase modifier anti-power grid disturbance, and comprises the following steps: acquiring new energy station multi-source operation data and a disturbance history sample library in a target power grid region, and generating a standardized multi-source operation data set containing a disturbance tracing mark; through multi-modal feature fusion processing of transient impedance identification, frequency domain oscillation component extraction and multi-source feature weight distribution, outputting power grid disturbance feature quantities and disturbance prediction results; using a multi-kernel function dynamic weighting, fault scene training iteration and defense strategy matching optimization based on a multi-kernel support vector machine dynamic safety evaluation model processing, generating a phase modifier layered adaptive control strategy set; achieving millisecond-level response and operation state dynamic correction when a disturbance occurs, and completing the enhancement of the anti-power grid disturbance capability of the phase modifier. The application effectively improves the adaptability and defense precision of the phase modifier to disturbances of different risk levels.

The application discloses a power operation safety evaluation system based on big data, which comprises a safety evaluation data acquisition module, a multi-dimensional safety feature selection module, a dynamic safety evaluation model construction module and an intelligent safety evaluation module. The application relates to the technical field of power safety data processing, and specifically relates to a power operation safety evaluation system based on big data. The application innovatively introduces a self-encoder model, accurately quantifies the nonlinear correlation between features by combining cosine similarity, determines an optimal sparse correlation graph through an automatic threshold adjustment algorithm, generates feature clusters by algorithm clustering, automatically identifies the optimal clustering structure, filters out double-optimal core features in terms of applicability and centrality, significantly reduces redundant features, enhances the output result accuracy of the evaluation model, and constructs a dynamic safety evaluation model based on the multi-view construction of feature clusters and the fusion of a double-cycle safety evaluation unit, thereby significantly improving the accuracy and stability of the power operation safety evaluation model.

The invention discloses a power grid control method and device based on a hybrid expert network and a medium, and belongs to the technical field of power grid optimization, and the method comprises the steps: obtaining power grid data which comprises node voltage, load demands, generator output and energy storage charging and discharging data; based on the power grid data, an intelligent scheduling model driven by multi-modal data is constructed, and the intelligent scheduling model comprises a plurality of expert sub-networks composed of a steady-state regulation and control model, a deep reinforcement learning model, a model prediction control model, a new energy consumption model and dynamic safety evaluation, and dynamically allocating expert weights through the gating network to realize self-adaptive decision making. Through the combination of the dynamic expert network and the lightweight gating mechanism, the multi-modal data of the power grid can be analyzed in real time, the optimal expert combination is activated in a self-adaptive manner, and dynamic spatial-temporal characteristic capture is realized. Compared with a traditional deep reinforcement learning method, the method is remarkably improved, and excellent self-adaptability and real-time performance are shown.

A systematic methodology is presented based on singular value decomposition (SVD) to store TS data and perform calculations using it to estimate post fault perturbed system trajectories. The obtained results are compared with corresponding non-linear dynamic simulation results. The proposed approach is tested and validated on the IEEE 39-bus as well as the WECC 179 bus system consisting of detailed generator, exciter, and governor models. In support, the size of data associated with TS analysis to effectively aid in DSA is reduced by up to a factor of 65, with error<0.01%. The scalability potential of the approach presented is promising to make TS analysis widespread for operation and planning of large-scale systems.

Provided is a system and method for updating dynamic power system models during each DSA analysis cycle. In one example, the method may include iteratively executing a dynamic security analysis (DSA) on a power grid based on a simulation of a dynamic power system model, receiving measurements from one or more sensors on the power grid, modifying one or more parameters of the dynamic power system model used in a previous iteration of the DSA based on the received measurements, simulating the modified dynamic power system model, determining a security value of the power grid during a current iteration of the DSA based on the simulated modified dynamic power system model, and storing the determined security value in memory.

The present application relates to the field of industrial automation and hoisting and transportation technology, and particularly relates to a carbon block grouping and placing method based on multi-modal perception and dynamic safety evaluation, comprising the following steps: establishing a furnace chamber three-dimensional model; collecting carbon block grouping position information, controlling the movement and clamping of a clamping mechanism; obtaining the contour information and clamping force information of the carbon block grouping, establishing a carbon block grouping three-dimensional model, and calculating the safety envelope of the carbon block grouping under the current posture; calculating the dynamic safety margin of placing the carbon block grouping into a target furnace chamber under the current clamping state; constructing a dynamic virtual channel with adaptive cross-sectional size; planning a collision-free motion trajectory for the clamping mechanism based on the dynamic virtual channel constraint; and controlling the clamping mechanism to move according to the planned collision-free motion trajectory, and placing the carbon block grouping at a target position in the furnace chamber. The present application solves the problem of bumping caused by irregular shape and uncertainty of posture when the carbon block grouping is placed into the furnace chamber, and greatly reduces the damage such as edge collapse and corner drop of the carbon block.

System and method ensure stability of power transmission system or power grid with mixed generation sources of renewable and non-renewable types while providing a best combination of stability services that minimizes both cost of generation and guaranteed reliability. An energy market management (EMM) module derives an energy market dispatch for a mix of generation sources including renewable and conventional types based on a steady state optimization of supplier cost and a power demand forecast. Dynamic Security Assessment (DSA) module performs a dynamic contingency assessment of stability for the power transmission system based on the energy market dispatch and sends feedback to the EMM module with results of the dynamic assessment of stability. EMM module clears the energy market dispatch on a condition that feedback indicates a stable dynamic assessment. For unstable assessment, data driven, model-based and / or rule-based algorithms provide recommendations on how EMM module can find stable market solutions.

The invention discloses a fusion checking method and system for fixed value and state information of a relay protection device, and relates to the technical field of relay protection of a power system, and the method comprises the following steps: obtaining fixed value parameters of the relay protection device, and screening operation state variables based on protection criterion logic corresponding to the fixed value parameters; constructing a multi-dimensional operation state space based on the operation state variable, and determining a corresponding critical boundary; performing track mapping on the operation state to obtain an evolution path of the operation state along with time change; based on the evolution path, identifying a state combination interval, and marking the state combination interval causing protection maloperation or refusal as a constant value risk area; and based on the constant value risk area, outputting corresponding safety levels of the constant value in different operation state intervals. According to the method, the running state evolution path is constructed and the fixed value risk area is identified, so that the dynamic safety evaluation of the fixed value under the multi-state combination is realized, and the problem that the hidden failure interval cannot be identified and the risk boundary cannot be described in the prior art is solved.

This invention discloses a method and platform for single- and dual-channel EEG decoding and physical device control based on heterogeneous computing power, relating to the field of brain-computer interfaces. Addressing the shortcomings of existing systems, such as reliance on multi-channel acquisition, limited computing power, and lack of dynamic safety assessment in control, this invention trains a cross-paradigm neural decoding large-scale model based on a domestically produced NPU heterogeneous architecture. Under simplified single- and dual-channel input, it extracts weak EEG features by combining knowledge distillation and spatiotemporal attention mechanisms. The decoding intent confidence is compared with a dynamic threshold generated by an MLP network based on parameters such as risk level. Finally, it converts the decoded intent into machine instructions through a hierarchical instruction interface to control external devices (such as XR, wheelchairs, etc.). This invention overcomes the bottleneck of missing spatial information in low-channel environments, significantly reducing hardware costs while achieving calibration-free high-precision decoding and a low-latency, highly reliable physical collision avoidance control closed loop.

The invention provides a crane safety assessment method, system, equipment and medium, and relates to the technical field of cranes, the method comprises the following steps: obtaining a basic information array of a target crane, defining an assessment system including a first order and a second order, the first order being a key structure-direct degree of freedom, and the second order being a key structure-two-layer intermediate variable-indirect degree of freedom; according to the evaluation system, loading a sensing array on a target crane, carrying out sensing activation and monitoring along with the operation of the crane, and determining sensing data; and deploying an intelligent evaluator according to the evaluation system, cooperating with contract intervention, and executing automatic management and control under sensing monitoring and dynamic security evaluation based on sensing data, in which urgent control risk management is performed through contract intervention, and risk management and control under code element logic depth evaluation is performed through the intelligent evaluator. The technical problem that in the prior art, the safety management efficiency of the hoisting equipment is poor can be solved, and the technical effect of improving the safety management efficiency of the crane is achieved.

The invention discloses a distributed equipment cluster security assessment method and system based on adaptive target selection, an edge computing device and a medium, and the method comprises the steps: collecting the multi-dimensional state data of a distributed equipment cluster in real time, and calculating the threat index of each terminal through an attention mechanism and reinforcement learning; determining a target terminal based on the threat index, and determining a malicious action; simulating the operation logic of the target terminal through the lightweight GAN model to obtain a simulation prediction result, generating a disturbance signal based on the simulation prediction result, and injecting the disturbance signal into the target terminal to induce the target terminal to execute a malicious action; and obtaining a system performance difference before and after the distributed device cluster attacks, generating a reinforcement learning reward signal, and updating model parameters. According to the method, efficient, accurate and dynamic safety assessment of the distributed equipment cluster is realized through a closed-loop process of state acquisition, risk assessment, target determination, action generation, simulation prediction, disturbance injection and feedback optimization.

This invention provides an event-driven method and system for evaluating power system frequency security indicators, applicable to modern power grids containing a high proportion of renewable energy. The method first parameterizes the system's dynamic frequency trajectory using an open-loop decoupling strategy, then constructs an event-driven piecewise analytical model. This model automatically handles high-order dynamics and strictly nonlinear elements within frequency regulation units. By combining the parameterized frequency trajectory with an iterative solver, it accurately calculates key frequency security indicators after a fault, including the time of the frequency minimum, the rate of frequency change, and the quasi-steady-state frequency deviation. This method approximates detailed closed-loop time-domain simulation in terms of evaluation accuracy, while simultaneously improving computational efficiency by tens of times, providing a highly efficient "white-box" analysis tool for online dynamic security assessment and primary frequency regulation reserve optimization.