548 results about "Physical system" patented technology

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

The invention discloses a fast training type unmanned aerial vehicle reinforcement learning strategy method suitable for a microcontroller, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: constructing a standard four-rotor dynamic model containing the first-order delay characteristic of a motor, and simulating a real physical system; an asymmetric actor-commentator architecture is adopted to ensure the robustness and mobility of the strategy; a reward function coefficient is dynamically adjusted based on a course learning mechanism, and network parameters are optimized by adopting an offline strategy algorithm; performing network training and process optimization; and carrying out lightweight processing and deployment on the trained strategy network, landing the strategy in simulation to a real hardware platform with limited resources, jointly realizing rapid training, reliable migration and lightweight deployment, and realizing high-frequency autonomous flight control of the unmanned aerial vehicle on an embedded platform. According to the method, the high-performance reinforcement learning control strategy can be stably and efficiently operated on the microcontroller with limited resources, and the hardware cost and the power consumption of the intelligent unmanned aerial vehicle are greatly reduced.

The invention discloses a BIM-based hydropower station full life cycle design, construction, operation and maintenance integrated control system, and the system comprises an intelligent sensing layer which integrates 5G + Beidou positioning, a LoRa gateway and a sensor, collects multi-source heterogeneous data in real time, and transmits the multi-source heterogeneous data to a digital twinborn layer after the multi-source heterogeneous data is filtered by an edge node; a digital twinborn layer: constructing a parameterized model library through laser point cloud and BIM automatic registration, integrating a geological parameter dynamic correction algorithm, mapping a construction period stress field in real time, and updating model parameters based on sensing data self-evolution; the intelligent decision-making layer performs equipment fault prediction by using an LSTM neural network, optimizes multi-machine load distribution in combination with an improved PSO algorithm, and automatically adjusts a start-stop strategy when the load fluctuates; and the security execution layer is used for triggering equipment operation after virtual twinborn deduction verification through a block chain evidence storage instruction, realizing virtual-real dual verification in combination with an industrial firewall, and finally feeding back a running state to the sensing layer to calibrate and update a model, and supporting intelligent decision.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

The invention relates to the technical field of power system monitoring, in particular to a power system parameter dynamic verification method, system and device based on digital twinning and a storage medium. Constructing a digital twinborn model of the power system, establishing a state mapping relation between a physical system and a digital model, realizing bidirectional dynamic mapping between the physical system and the digital model through a digital mapping mechanism, and obtaining system operation characteristics; constructing a parameter verification model based on the operation characteristics, obtaining a verification objective function by fusing physical constraints and data driving, and dynamically adjusting parameter values to generate parameter correction values by evaluating model errors in real time by adopting an adaptive parameter correction algorithm; based on a dynamic optimization mechanism of multi-objective optimization, an optimization strategy is adaptively adjusted according to the running state, and the verification process is continuously optimized; and performing multi-dimensional evaluation on the verification result through the verification precision evaluation system to generate a parameter verification result. The technical problems that a traditional method is inaccurate in modeling, low in response speed and poor in adaptability are effectively solved.

The invention provides a weak supervision video anomaly detection method and system based on potential energy field damping dynamics. The method comprises the following steps: inputting a video into an anomaly detection model to obtain an original video feature sequence; calculating fluctuation potential energy by using the original video feature sequence and further obtaining a global inertia proxy vector; calculating interaction potential energy through the global inertia proxy vector and the original video feature vector; nonlinear self-adaptive damping force is generated through interaction potential energy; applying a nonlinear adaptive damping force to the original video feature vector to obtain a purified dynamic feature vector; constructing a loss function by using the purified dynamic feature vector, and training the model to obtain an optimized model; and inputting the video into the optimized model to obtain a final frame-level anomaly detection result. According to the method, anomaly detection is reconstructed from a classification problem to a signal decoupling and energy dissipation problem in a physical system, and the physical nature of a depolarization mechanism is explained from the theoretical level.

The invention discloses a power supply and distribution control system based on a virtual power plant, and relates to the technical field of power distribution systems. The system comprises five core modules: a digital twin simulation control closed loop module constructs a complete closed loop of data acquisition, simulation calculation, control execution and model iteration, and realizes real-time synchronization of a physical system and a digital mirror image and accurate issuing of a control instruction; the multi-agent source-load-storage optimization module improves the source-load-storage collaborative scheduling efficiency through distributed agent collection and reinforcement learning; the multi-scene energy storage adaptive control module generates a scene-based energy storage strategy based on meteorological, load and electricity price multi-dimensional perception and model prediction control; and the multi-type energy storage ratio linkage module realizes multi-type energy storage cooperative operation based on an energy storage characteristic database and scenarized weight optimization. All the modules work cooperatively, the system response speed, the control precision and the power supply stability are effectively improved, and the system is suitable for industrial parks and other complex power supply and distribution scenes.

The invention discloses a dynamic coupling modeling method and system for an electric power information physical system, and belongs to the technical field of electric power dispatching automation, and the method comprises the steps: analyzing an AGC control process business data flow, and constructing a workflow engine; performing node classification, generating an information node model and an information branch model of the information system, and constructing a rule engine; constructing an information system network matrix model based on the rule engine, and establishing a preset service strategy based on the information system network matrix model; constructing an information physical coupling model of the AGC control process; and according to the AGC actual service, loading a preset service strategy established by the information system network matrix model based on the synergistic effect of the workflow engine and the rule engine, and updating the information physical coupling model. According to the method, a basic model is provided for cross-level closed-loop simulation verification in the AGC process, the problems of unclear information physical dynamic coupling mechanism, insufficient service logic adaptive mapping and model iteration self-updating are solved, and a guarantee is provided for safe and stable operation of a large power grid.

Provided are computer-implemented methods and quantum computing systems for preparing computational states representing quantum states of a physical system, including performing a computational evolution of the state and then determining physical properties of the system using the time-evolved computational state. Hamiltonian dynamics of observables are computed on a quantum computer to provide information about the physical system represented by the Hamiltonian. Low energy electronic structure states of a physical system are prepared using adiabatic evolution. The energy of an equilibrium quantum state of a physical system is determined using a time-evolution operator. The energy of an eigenstate of a physical system is indirectly determined by evaluating expectation values of a time-evolution operator averaged across multiple shots for randomly-generated quantum circuits. Example methods enable calculation of the energy of an evolved computational state with chemical accuracy, due to avoiding discretization errors, with a smaller circuit depth than known alternatives.

The invention discloses an industrial control safety target range construction method and system based on a digital twinborn technology. The method comprises the following steps: constructing a digital twinborn simulation environment for an actual industrial scene, wherein the digital twinborn simulation environment comprises physical system modeling, communication network modeling and co-simulation modeling; defining an interactive interface between the simulation environment and an external tool, including selecting a communication mode according to a data exchange demand, defining a logic communication channel based on the selected communication mode and standardizing a data exchange format; converting an attack instruction initiated by an external tool into a simulation operation instruction in the simulation environment through the interactive interface, and feeding back a physical state change caused by the simulation operation instruction to the external tool; and an automatic target range resetting mechanism is established, and the state of the simulation environment is backed up and recovered through a snapshot mechanism. According to the invention, the management efficiency of the industrial control safety target range is improved, and the test reliability is improved.

The invention relates to the technical field of safe operation of a power system, in particular to a power distribution network information physical system fault collaborative recovery method, which comprises the following steps of: acquiring basic operation data of a communication layer and a physical layer, and respectively constructing fault recovery models of the communication layer and the physical layer; the communication layer takes optimal link performance as a target and applies constraints such as link capacity and connectivity, the physical layer takes multiple parameters such as load recovery as an optimization target and applies constraints such as network topology and power flow, and a double-layer optimization model, an upper layer optimization communication recovery strategy and a lower layer optimization physical recovery strategy are constructed for concurrent faults; and solving by using a double-layer optimization objective function to realize power distribution network fault collaborative recovery. According to the method, the interaction between the communication layer and the physical layer is comprehensively considered, so that the fault point can be more accurately positioned, the fault area can be quickly identified, the optimal recovery strategy is formulated, the power supply reliability is improved, and the healthy development of the smart power grid is promoted.

The invention provides a delay compensation control method for a double-vibrator wave energy conversion device, which comprises the following steps of: performing numerical modeling and hydrodynamic analysis on the double-vibrator wave energy conversion device, establishing a time domain motion equation of the double-vibrator wave energy conversion device, and introducing a time delay function into the time domain motion equation to simulate control signal transmission delay; simulating the execution delay of the brake by using a partial differential equation; establishing a state-space equation of the device, replacing a convolution term of the time-domain motion equation with the state-space equation, and calculating to obtain a motion state of the device; a Hamiltonian function is defined to convert a constrained optimization problem into an unconstrained optimization problem, and the Hamiltonian function is solved to obtain an optimal control criterion considering control delay so as to realize maximization of energy capture under the optimal control criterion. According to the method, the operation characteristics of a physical system are truly reflected by introducing control delay, so that the locking control method is effectively implemented in an actual physical device, and the energy capture efficiency of a wave energy conversion device and the reliability of system operation are effectively improved.

The invention discloses an intelligent manufacturing real-time decision-making method and system based on digital twinning, and relates to the technical field of intelligent manufacturing. Comprising the following steps: S1, collecting multi-source operation data in a physical production system in real time; s2, dynamically constructing and updating a digital twinborn model of the physical production system based on the multi-source operation data acquired in real time; and S3, based on the digital twin model, monitoring the operation state of the physical production system in real time. According to the method, the multi-source operation data of the physical production system are collected in real time, and the digital twin model is dynamically updated, so that no lag deviation between the model and the physical system is ensured; when a dynamic disturbance event is identified, event-driven real-time simulation and predictive analysis are started immediately, delay caused by traditional batch processing is avoided, and disturbance influence can be accurately evaluated in combination with historical data and expert experience; and then an optimal decision scheme is generated and screened in real time through a multi-objective optimization algorithm.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention discloses an electric power information physical system risk identification method and system in extreme weather, and the method comprises the steps: introducing the influence of extreme weather into an information-energy flow model of an electric power information physical system, and building an information-energy-extreme weather unified model; n-2 fault scanning is carried out on a power-communication coupling line of the power information physical system to obtain a fault scene set, risks of the power information physical system in all fault scenes in the fault scene set are analyzed and recognized based on an information-energy-extreme weather unified model, and a risk recognition result is obtained; therefore, the influence of extreme weather is considered in the information-energy flow model, the information-physical interaction process can be accurately described, traditional line / element N-1 fault scanning is expanded into power-communication coupling line N-2 fault scanning, a more robust and practical fault scene set can be obtained, the risk identification result is more accurate, the calculation speed is high, and the method is suitable for popularization and application. Therefore, the accuracy of risk identification is improved, and the lower complexity is ensured.

This document presents one or more advantageous approaches for Reinforcement Learning (RL) powered management of one or more transmission parameters, such as transmit power and diversity, for maximizing the application-layer reliability and availability of a Cyber-Physical System (CPS) with a minimized level of radio / power resource consumption. Example mathematical models are also disclosed and are useful for transforming high-level “intents” (e.g., KPIs that are applicable to industrial automation and control systems) into low-level orchestration objectives that drive the RL-based control. These objectives are subsequently employed in the definition of an RL-powered “orchestrator,” which may comprise an appropriately configured network node or other computing platform associated with the wireless communication network used to provide inter-device communications for a CPS comprising a population of devices. Further, the disclosure details example communication—e.g., observations and corresponding control signaling—between the orchestrator and the environment being managed.

Disclosed in the present invention is a vertical handover method for a sky-air-ground integrated cyber-physical system. The method comprises: a base station acquiring state spaces sent by mobile devices; in the base station, using a constrained proximal policy optimization algorithm to obtain gradient values of vertical handover policy networks and gradient values of value networks, and sending the gradient values to a central learner; determining in the central learner a first average gradient value and a second average gradient value, determining updated parameters of the vertical handover policy networks and updated parameters of the value networks, and sending the updated parameters to the mobile devices; and in the mobile devices, when the cumulative value of all reward values is greater than or equal to a target value and the cumulative value of the reward values is stable, enabling finally obtained updated vertical handover policy networks to output final policies. The vertical handover method for a sky-air-ground integrated cyber-physical system provided in the present invention can be directly used for the vertical handover of a sky-air-ground integrated cyber-physical system.

The invention belongs to the field of energy storage system evaluation, and particularly provides a compressed air energy storage system state evaluation method based on digital twinning, in the method, a multi-physics field coupling digital twinning model is constructed based on a compressed air energy storage system, and a time sequence modeling module based on an LSTM-Transformer hybrid architecture is built in the digital twinning model; based on the calibrated digital twinborn model, state evaluation is carried out on the compressed air energy storage system in combination with a preset evaluation dimension, simulation deduction data of the digital twinborn model and real-time operation data of a physical system are fused in the evaluation process, and an evaluation result is obtained and output; and based on an evaluation result, triggering an uncertainty quantification result based on an LSTM-Transform architecture to carry out graded state early warning. According to the method, the multi-physics field coupling digital twinborn model is constructed and dynamically calibrated, simulation deduction, real-time data evaluation and graded early warning based on uncertainty quantification are fused, and the compressed air energy storage system state evaluation precision, real-time performance and early warning credibility are improved.

The invention relates to the technical field of intelligent control, and discloses an optical lens flexible clamping identification control method and system, and the method comprises the steps: constructing a composite supporting structure, adjusting the rigidity of the composite supporting structure, selecting an optimal wave band to calculate the spectral reflectivity, introducing polarization features, constructing an anti-reflection defect detection algorithm, and correcting the spectral reflectivity; a modular clamping unit based on topological structure recognition is constructed, edge feature extraction and shape classification are performed on an optical lens, optimization force distribution is calculated, strategy weight is dynamically adjusted according to a task completion rate by calculating strategy entropy and introducing a loss function in a combined manner, the rigidity of the optical lens is calculated, and force control or position control is adopted in a differentiated manner; based on the rigidity of the lens, constructing a self-adaptive clamping structure, minimizing the deformation energy of the lens, and calculating and predicting the residual life; a three-level heterogeneous computing architecture is constructed based on a self-adaptive clamping structure, state synchronization of a physical system and a virtual mirror image is realized, and a defect detection model in an anti-reflection defect detection algorithm is cooperatively trained.

The invention discloses a false data injection attack detection method for an electric power information physical system, relates to the technical field of attack detection, and solves the problem that in the prior art, only the damage of FDIA to a measurement data time mode is considered, and the spatial correlation between measurement data is affected even if the FDIA is not considered. According to the method, an FDIA detection model structure based on correlation difference is designed, and the attention action range is constrained by embedding position information and introducing a correction factor. Besides, in combination with fine granularity and multi-scale characteristics of a measurement data sequence, topological correlation is introduced, correlation differences are defined, a correlation difference-based antagonism discriminant criterion reasoning method is designed, and the distinguishability of normal and attack measurement data is amplified through adversarial training, so that a discriminant criterion is obtained. Complete embodiments show that compared with an existing detection model, the provided model has higher detection accuracy and robustness, and is excellent in performance when facing injection attacks with different amplitudes.

The invention discloses an AGC information physical system reliability verification method and system, and relates to the technical field of power dispatching automation. The problems that an existing AGC system only adopts a static operation mode and an ideal communication condition in a simulation test, and coupling response of continuous operation and sudden disturbance of a power grid under a long time scale is difficult to reproduce truly are solved. The state evolution under event driving is realized by constructing three types of simulation event sources of a time sequence operation sample, a master station control instruction and external disturbance and setting a priority mechanism of external disturbance gt, master station instruction gt and time sequence propulsion; an uplink channel fault model is introduced to simulate on-off, delay and errors of measurement data, and a downlink channel fault model is introduced to simulate transmission abnormity of a control instruction; closed-loop simulation of frequency response and electrical quantity updating is realized by combining promotion of a rotor motion equation and dynamic load flow calculation; and finally, by analyzing indexes such as frequency deviation, tie line deviation and control response delay, comprehensive reliability evaluation of the AGC system in a complex information physical environment is completed.

The invention discloses a safety door intelligent integrated control method and system based on the Internet of Things, and the method comprises the steps: deploying distributed miniature edge computing nodes, and carrying out the collection and preprocessing of multi-source Internet of Things data; constructing a hierarchical multi-modal deep learning model, and performing high-order feature extraction and fusion on multi-source data; constructing a digital twin model of the safety door intelligent integrated control system, and establishing virtual mapping of a physical system; an adaptive strategy based on reinforcement learning is adopted, fusion features and digital twinborn prediction serve as state input, and an optimal control action dynamically adjusted according to real-time data is output; a distributed constraint optimization and utility transfer cooperative control model is built, each system in the safety door integrated control system is used as an intelligent agent, cooperative control is carried out, accurate simulation of a physical state and prediction of a future state are achieved, and the safety door integrated control system has the advantages of being simple in structure and convenient to use. And efficient cooperative control is realized based on a cooperative control model of distributed constraint optimization and utility transfer.

Provided are computer-implemented methods and quantum computing systems for preparing computational states representing quantum states of a physical system, including performing a computational evolution of the state and then determining physical properties of the system using the time-evolved computational state. A described computer-implemented method enables computing Hamiltonian dynamics of observables on a quantum computer to provide information about the physical system represented by the Hamiltonian. A described computer-implemented method prepares low energy electronic structure states of a physical system using adiabatic evolution. The method determines the energy of an equilibrium quantum state of a physical system using a time evolution operator that enables adiabatic evolution of a prepared electronic structure state on a quantum computing system. A described method involves indirectly determining the energy of an eigenstate of a physical system by evaluating the expectation values of a time-evolution operator averaged across multiple shots for randomly-generated quantum circuits.

The invention discloses a voiceprint feature extraction and positioning method for a pipeline leakage point, and the method specifically comprises the following steps: S1, a voiceprint feature extraction stage: collecting a pipeline sound signal through a distributed sensor, extracting and optimizing a multi-domain voiceprint feature after adaptive denoising, and relates to the technical field of pipeline leakage detection. According to the voiceprint feature extraction and positioning method for the pipeline leakage points, multiple leakage point signals of the same pipeline section can be effectively separated and positioned by improving a FastICA algorithm and a density peak value clustering algorithm, the positioning error can be controlled within 0.8 m, the positioning precision in a multi-leakage point scene is remarkably improved, and the positioning accuracy is improved. Based on an edge node-cooperative gateway-cloud center information physical system architecture, a distributed clock synchronization protocol is adopted, cooperative monitoring and simultaneous positioning of multiple branch pipelines are achieved, the response time is shortened to be within 3 seconds, and the problems of positioning conflict and response delay of the multiple branch pipelines in a traditional method are solved.

The invention relates to the technical field of intelligent power grids, in particular to a multi-dimensional dynamic elastic recognition method and system for key coupling nodes of a power distribution information physical system, and the system comprises a key node screening unit which constructs a unified CPDS model and preliminarily screens a candidate key node set based on structure controllability and structure observability; the fuzzy scene generation unit is used for constructing a dynamic fuzzy disaster scene set by utilizing a Markov chain Monte Carlo method and generating a set containing a large number of dynamic fuzzy disaster scenes; an elastic function construction unit; a node calculation output unit; according to the novel CPDS key coupling node accurate identification method, structure controllability, structure observability, dynamic fuzzy disaster scenes, reverse reinforcement learning and Shapley value comprehensive calculation are fused, so that the accuracy, fairness and adaptability of elastic analysis and decision making of a system are improved; and the rapid response and recovery capability of the power grid to disasters or attack disturbance is improved.

The invention provides a flexible distributed energy network multi-agent collaborative optimization method based on MARL, and the method comprises the steps: building a distributed energy network multi-agent interaction framework which comprises a local energy transaction model for distinguishing an intelligent soft switching path, a network cost model based on a power transmission distribution factor, and a power distribution network operator and user income model; a physical system operation constraint model is established and covers intelligent soft switch operation characteristics, network topology radial constraint, energy storage charging and discharging dynamic states, controllable distributed power supply and photovoltaic output limitation, translational load scheduling and DistFlow power flow constraint. And finally, constructing a collaborative optimization mechanism based on a multi-agent near-end strategy optimization algorithm, modeling the system into a partially observable Markov decision process, adopting a centralized training distributed execution framework, and guaranteeing transaction security and data privacy through block chain evidence storage, RSA encryption and an intelligent contract. The system economy and the operation flexibility are improved.

The disclosure includes a digital twin system. The digital twin allows for real time classification and ranking of data received from a distributed learning knowledge acquirer. The digital twin system ranks the data while the distributed learning knowledge acquirer is performing a drift evaluation. The distributed learning knowledge acquirer uses the ranked data as training data for discriminative AI models. The digital twin system offers more flexibility and precision in ranking the data. The digital twin system is a digital twin providing contextual active dynamic learning to the distributed learning knowledge acquirer's physical system. Digital twin system causes a model driven approach to allow for superior predictive capabilities by being able to examine large state spaces.

Disclosed herein are systems and methods for anomaly detection. A distributed physical state estimation system determines low-level state estimates covering respective sections of a cyber-physical system based on raw, high-performance measurement data. Low-level state estimates may be determined for a plurality of sections (substations) concurrently. An upper-level state estimate may be derived from the low-level state estimates. Anomalies pertaining to the system may be detected through analysis of the low-level and upper-level state estimates. The anomalies may be analyzed to determined whether the system is exhibiting behavior indicative of a fault, cyber-attack, and / or compromise.

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.