1116 results about "Protection mechanism" patented technology

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The invention relates to the technical field of weak current intelligent terminal control, in particular to a hierarchical response management control system for a weak current intelligent terminal. The system comprises a state sensing module, a prediction optimization module, a task scheduling module, a fusing control module and an execution feedback module. The state sensing module collects a power supply state, task information and environmental parameters, and constructs a multi-dimensional operation scene model; the prediction optimization module adopts an exponential weighted moving average algorithm to predict a task peak value, and judges a key event probability through an index mutation rule; the task scheduling module generates a hierarchical response sequence and a power supply strategy based on a reinforcement learning algorithm; the fusing control module executes task delay, edge autonomy and key task guarantee mechanisms when resources are abnormal or communication is interrupted; and the execution feedback module collects an operation result and updates the model to realize scheduling closed-loop optimization. According to the invention, the response efficiency and operation toughness of the weak current system in a complex scene are improved.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

The invention discloses a control method and device for heat dissipation equipment, and relates to the technical field of computers, and the method comprises the steps: employing a load dynamic perception and prediction model cooperative control technology, building a dynamic correlation model of a load and heat dissipation, and achieving the mode upgrade of a heat dissipation system from lagging regulation to advanced response. A control instruction is generated by predicting a load change trend in real time, and a dual-mode protection mechanism is combined, so that energy efficiency loss caused by traditional temperature control delay is avoided, system stability and energy efficiency performance are optimized, an efficient and energy-saving active heat dissipation solution is provided for high-load equipment, and the service life of the system is prolonged. The problems of low control efficiency of the heat dissipation equipment and energy efficiency loss caused by delayed adjustment in related technologies are solved, and the technical effects of improving the control efficiency of the heat dissipation equipment and reducing energy consumption are achieved.

The invention discloses a server BMC dynamic security authentication and firmware protection method and system based on a hardware root of trust, relates to the technical field of server remote management security, and discloses the server BMC dynamic security authentication and firmware protection method and system based on the hardware root of trust, which realize dynamic authentication by generating a device fingerprint through a security chip. According to the method, a multi-layer protection mechanism is constructed in combination with fragment encryption, differential hash check and memory integrity verification, and meanwhile, the data credibility is enhanced by using a block chain storage key hash value, so that the problems of static authentication risk, insufficient firmware tampering detection and missing of a trust chain in a traditional scheme are effectively solved, and the security protection capability of a BMC system can be improved.

The invention discloses a federated learning-driven cross-domain supply chain elastic inventory optimization system and a method thereof, and aims at realizing inventory data collaboration among same-level enterprises or regional nodes through transverse federated learning and ensuring data security by adopting a self-adaptive differential privacy protection mechanism. The method comprises the steps of constructing a transverse federated learning network, locally performing data preprocessing, adding differential privacy noise, iteratively training a global model based on federated deep reinforcement learning, generating a transverse inventory allocation and replenishment decision, and performing model adaptive adjustment in real time based on key performance indicators. According to the method, multi-target balance is considered, inventory configuration is dynamically optimized through a multi-target reward function, the inventory turnover rate is remarkably increased, the inventory holding cost is reduced, the service level is improved, and the method is suitable for various scenes such as retail chain, manufacturing industry distributed storage and cross-regional logistics distribution; and global optimal inventory configuration is realized on the premise of ensuring data privacy.

The invention relates to the technical field of cores, and discloses a method and a system for realizing USB flash disk equipment interaction based on a flash memory encryption technology, which comprises the following steps of: dividing an encryptable data block of a flash memory controller, and determining a dynamic entropy weight of the encryptable data block by utilizing an equipment interaction instruction and a random noise characteristic signal; generating a self-adaptive key generation sequence capable of encrypting data blocks through an address allocation mode and a dynamic entropy weight of a flash memory controller; calculating a side channel leakage risk index of the flash memory controller, and setting a security level label of an encryptable data block in combination with a self-adaptive key generation sequence; the method comprises the following steps: setting a differentiated security protection mechanism of an encipherable data block by constructing an access control matrix of the encipherable data block and an encryption risk area of a flash memory controller; and generating a secure interaction scheme of the USB flash disk equipment based on the self-adaptive key generation sequence in combination with the differential security protection mechanism and the access control matrix. According to the invention, the interaction safety and reliability of the USB flash disk equipment can be improved.

The invention discloses a water pump station operation adjusting device and method, and relates to the technical field of pump station operation adjusting, a first prediction result is output through operation data of a water pump station, and whether the first prediction result needs to be corrected or not is judged based on environment characteristics, equipment state data and water quality data to obtain inlet and outlet valve adjusting parameters; constructing a water pump station digital model, receiving a simulation regulation feedback result, and optimizing inlet and outlet valve regulation parameters by utilizing the simulation regulation feedback result; an inlet and outlet valve adjusting stability threshold value is obtained, relation feedback verification is conducted through the inlet and outlet valve adjusting stability threshold value, and whether a protection mechanism is triggered or not is judged through the simulation adjusting feedback result and the relation feedback verification result; and when the protection mechanism is triggered, the dynamic current stabilizer is started, and the current stabilization parameters of the dynamic current stabilizer are adjusted. Intelligent accurate control, active protection and energy efficiency optimization are realized, the system stability is improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

The invention relates to the technical field of computers, and discloses a server cluster security control method combined with multi-level encryption strategy joint scheduling, which comprises the following steps: S1, carrying out sensitivity evaluation on calculation tasks and data in a cluster, and classifying the data into low-sensitivity data, medium-sensitivity data and high-sensitivity data according to a preset rule; s2, encrypting the low-sensitivity data by adopting a first encryption algorithm, encrypting the medium-sensitivity data by adopting a second encryption algorithm, and encrypting the high-sensitivity data by adopting a homomorphic encryption algorithm; and S3, based on a joint scheduling mechanism of reinforcement learning, calculating task requirements and data sensitivity information by acquiring the load of the cluster in real time. Through a multi-level encryption strategy, dynamic resource scheduling based on deep Q learning and a security protection mechanism combining a zero-trust architecture and deep learning, the problems that cluster resource utilization efficiency is low, data security is insufficient and security threats cannot be responded in real time are solved.

The invention provides a text recognition method based on a language model, and the method comprises the steps: obtaining a to-be-detected text data flow in a target application program, including user input, interface display and network transmission text; preprocessing the text data stream to generate a multi-dimensional semantic vector sequence; performing feature modulation on the semantic vector sequence through a dynamic weight distribution module to generate a modulation feature matrix; inputting a logic judgment engine to execute multi-stage condition combination analysis, and generating risk probability distribution; demodulating the risk probability distribution to generate an interpretable risk feature vector; executing cross validation based on the risk feature vector and a preset threshold set, and generating a final risk judgment result; and when a high risk is determined, triggering a multi-layer protection mechanism, including content replacement, session interruption and safety alarm. The accuracy and interpretability of the system can be improved, and the safety and user experience of the target application program are guaranteed.

The embodiment of the invention provides a meteorological data processing method and device based on an edge protection gateway algorithm. The method is applied to the technical field of data processing, and comprises the following steps: acquiring meteorological data by utilizing an edge computing node, and transmitting, verifying and filtering through a mesh network to obtain an initial data set; establishing a protection mechanism for encrypted storage, and generating a hash value in combination with the block chain; establishing a standardized model for noise filtering and data restoration; constructing a fusion model to extract features; establishing a federated learning framework for prediction; and establishing a verification mechanism, and generating decision suggestions. According to the invention, by deploying a data processing and security protection mechanism at the edge node, efficient processing and security protection of meteorological data are realized, and the real-time performance and reliability of data processing are improved.

The invention relates to the technical field of electric bicycles, and discloses a data-driven electric bicycle battery fire hazard detection and protection method, which comprises the following steps: S1, multi-modal data acquisition and fusion, S2, data preprocessing and feature extraction, S3, data-driven intelligent detection: classifying normal and abnormal states through a supervised learning model, S4, hierarchical protection strategy, and S5, data-driven intelligent detection. S5, cloud data optimization and model continuous learning; S6, model data updating; S7, intelligent linkage and long-term optimization; S8, cloud risk data acquisition; and low, middle and high three-level protection mechanisms are adopted. Through dynamic risk assessment, charging and discharging power is limited in a medium risk, a charger is automatically disconnected, a user is prompted to stop operation, active protection is triggered in a high risk, and sound-light alarm and remote notification are triggered at the same time, so that accidents are prevented from further spreading.

The invention discloses a digital twinning-based thermal heat supply system defense cooperative protection method and system, particularly relates to the technical field of industrial control system safety, and is used for solving the problems of hidden attack missing detection and response delay caused by splitting of energy flow and data flow in an existing protection mechanism. Physical-network behavior collaborative verification is realized by constructing a digital twinborn model, and precise protection is realized by quantifying coupling failure characteristics of energy flow and instruction flow. The method specifically comprises the following steps: collecting production data, industrial control logs and network traffic; constructing a digital twin model to simulate expected behaviors of equipment, communication and physical states; comparing and detecting abnormal data in real time; extracting a thermodynamic potential gradient characteristic quantity and a path integral characteristic quantity from the abnormal data; calculating a covariant difference value of the two characteristic quantities, and judging an external attack when the covariant difference value exceeds a threshold value; when an attack is triggered, a micro-isolation gateway is deployed, dynamic access control is implemented based on an equipment fingerprint, a user behavior sequence and an environment fingerprint, and an attack chain is blocked.

The invention relates to a large model data protection method and device based on a trusted environment, equipment and a medium. In the scheme, after a server side obtains original large model data, the original large model data needs to be encrypted and signed, and then the original large model data and an encrypted information table are issued to a target application side; when the target application end calls the large model data, performing exception check according to a credible strategy; if the check is passed, performing signature verification on each encrypted data slice; if the signature verification succeeds, decrypting the encrypted slice data by using the encrypted information table to obtain original large model data; wherein the server side and the target application side are both in a trusted environment. It can be seen that the trusted environment and the key system are combined, and a dual protection mechanism for large model data is achieved; moreover, before the large model data is called, the operation environment of the target application end needs to be subjected to exception check, so that an attacker is prevented from acquiring or tampering the sensitive data, and all-around data protection is provided for the large model data.

According to the intelligent rolling construction method and system for the concrete face rockfill dam and the computer readable storage medium, self-adaptive adjustment of the rolling speed and frequency is achieved through three-dimensional digital elevation model generation, optimal rolling path planning, rolling parameter dynamic optimization model and real-time compaction degree prediction. The system integrates satellite positioning, BIM model verification, cloud data management and an abnormal protection mechanism, can correct path deviation in real time, generates a pressure supplementing instruction and stores construction data, provides an intelligent solution for high-precision and high-reliability rock-fill dam construction, and is suitable for popularization and application. The technical problems that in traditional rolling construction, manual experience is relied on, parameters are static, monitoring lags behind, and the rework rate is high can be solved.

The invention relates to the technical field of the Internet of Things, and discloses a wireless sensor network environment monitoring system and a safety protection method based on the Internet of Things, and the system method comprises the steps: collecting sensing node environment data to generate a multi-dimensional feature matrix, and constructing a safety protection strategy set; setting threat parameter types, and establishing a security threat association model by using historical data; extracting real-time attack features and flow parameters, and generating a dynamic protection instruction set and an optimization scheme in combination with the model; and reconstructing a network node control mechanism to generate a target protection scheme, and dynamically and synchronously calibrating an instruction time sequence. The system comprises a node data acquisition module, a security policy generation module, a threat parameter analysis module, a threat modeling module, a risk assessment module, a protection mechanism reconstruction module and a time sequence synchronization module. Through multi-dimensional feature analysis, dynamic threat modeling, adaptive risk assessment and time sequence encryption synchronization, the security protection capability of the wireless sensor network in a complex environment is improved, and the method is suitable for security protection requirements in the field of Internet of Things environment monitoring.

The invention relates to the technical field of data processing, and discloses a management method and system for software data assets and a computer readable medium. The method comprises the following steps: scanning transaction software to extract user behaviors and transaction information, and performing risk analysis to generate a data directory; calculating a risk coefficient through a compliance model to form a thermodynamic diagram; executing exception analysis and establishing a hierarchical protection mechanism; monitoring a data timeliness optimization storage strategy to form life cycle management; an anti-fraud algorithm is applied to generate risk control insights; and compliant cross-mechanism data exchange is realized based on insight data. According to the method, full-life-cycle intelligent management and cross-mechanism value collaborative mining of financial software data assets are realized on the premise of ensuring data security and privacy.

The invention discloses a bus module self-recovery method and device, equipment and a storage medium. According to the invention, the communication state of the bus module is monitored in real time, and the abnormal detection signal is identified; when an abnormal detection signal is identified, the fault type is automatically diagnosed; dynamically selecting and combining an optimal recovery strategy according to the fault type; executing a self-recovery operation corresponding to the recovery strategy, and verifying a recovery effect; if the recovery fails, automatically adjusting and executing the recovery strategy of the next priority according to the fault type and the failure frequency; isolating communication between the fault bus module and other normal modules through a bus arbitration mechanism; recording fault features and a recovery process, and generating historical data for subsequent fault prediction; and if recovery fails for multiple times, triggering a system-level security protection mechanism, and giving an alarm. According to the method, the fault type can be quickly and accurately identified, the optimal recovery strategy is selected in a targeted manner, and the system maintainability and safety of the bus module system are effectively improved.

The invention discloses a power data security transmission strategy dynamic optimization method based on gray measurement, which comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data in a power system, and generating a time sequence data set and a structure attribute data set; s2, performing privacy protection and noise addition on the time sequence data set and the structure attribute data set by applying a differential privacy mechanism; s3, performing evaluation by adopting a gray scale measurement and calculation method, calculating a gray scale weight and generating a transmission strategy; s4, according to the gray scale weight classification data, adjusting a transmission path and a time sequence; s5, dynamically adjusting the differential privacy noise intensity and the gray scale weight according to the network bandwidth, the transmission delay and the data requirement; and S6, continuously monitoring the transmission efficiency and the privacy effect, and adjusting a transmission strategy and a protection mechanism in real time according to feedback. According to the method, the dynamic collaborative optimization of privacy protection and scheduling efficiency of the power data in the transmission process is realized by fusing a differential privacy mechanism and a gray calculation method.

The invention relates to the technical field of computer network security, in particular to a computer data dynamic protection method and system based on fusion factors, and the method comprises the following steps: S1, collecting multi-source heterogeneous data; s2, generating a dynamic fusion factor; s3, performing real-time risk level evaluation; s4, generating a self-adaptive protection strategy; and S5, performing cross-level protection. According to the method, the attack detection accuracy and response speed are remarkably improved through multi-source heterogeneous data fusion and a cross-hierarchy collaborative protection mechanism, and meanwhile, the deployment cost is reduced while the protocol stack hierarchy coupling attack detection problem is solved by utilizing a dynamic weight adjustment and hardware acceleration technology; and the problems of a single-dimensional defense blind area and strategy lag in a traditional scheme are effectively solved.

The invention provides a countercurrent tracking control method of an energy storage system and a related device, and relates to the technical field of power system control. According to the countercurrent tracking control method and system for the energy storage system provided by the invention, the problems of low response speed and poor adaptability of a traditional method are solved by dynamically adjusting the output power of the energy storage converter and establishing a multi-stage protection mechanism, and the countercurrent tracking control method and system have the advantages of improving the safety and the operation efficiency of a power grid.

The invention discloses a multi-mode intelligent tension self-adaptive control method, and belongs to the technical field of tension control of wire rod processing devices. According to the method, an LSTM-Transform hybrid model is utilized to realize the collaborative optimization control of tension prediction, the rotating speed and the pitch; by means of an edge computing architecture and a Modbus-TCP real-time communication protocol, a data processing and control instruction generation period is compressed to be within 50ms, and the dynamic response capability of a system is improved; and meanwhile, a graded safety protection mechanism is designed, a robust control mode is switched to at a millisecond level when vibration energy exceeds a limit or the temperature is abnormal, and the MES system is linked to push a fault code, so that the control precision is remarkably improved, the material loss rate and the operation and maintenance complexity are reduced, and a high-reliability and self-adaptive intelligent solution is provided for high-speed wire rod processing equipment.

The invention belongs to the technical field of seismic equipment monitoring, and provides a seismic network communication fault positioning method based on multi-dimensional protocol diagnosis, which comprises physical layer IOT analysis, network layer protocol analysis, transport layer protocol analysis, application layer protocol analysis, a protection mechanism adaptive adjustment algorithm and a cross-layer collaborative analysis state mechanism. According to the method, multi-dimensional protocol fault analysis is realized, and the analysis accuracy is higher than that of a single-dimensional mode through analysis of application, transmission, network and physical four-layer protocols. Analysis is carried out through the sequence from the physical layer to the application layer, positioning can be completed after the problem is positioned on the upper layer, and high analysis efficiency is achieved. According to the method, the analysis process is completed mainly in a network detection mode, only physical layer analysis needs IOT hardware equipment to participate, the analysis can be achieved in a pure soft mode, and the method has the good cost advantage.

The invention discloses a self-adaptive system and method for end-side cloud dynamic task scheduling of a power distribution internet of things, and relates to the technical field of cloud computing regulation and control, and the method comprises the steps: collecting power distribution network environment data and worker operation data through a power distribution network equipment sensor connected through a gateway; carrying out preprocessing and feature extraction on the collected data to generate a standardized data stream; dynamically distributing calculation tasks to a local gateway or a cloud server according to a worker request type and an equipment load state; dynamically adjusting a resource allocation strategy based on the network bandwidth of the power distribution network, the computing power of the equipment and the task priority; monitoring data transmission safety and equipment state abnormity of the cloud computing environment of the power distribution network in real time, and triggering a protection mechanism; and displaying the data processing result, the task state and the security alarm information through a gateway interface. The method has the advantages that by dynamically distributing local and cloud computing resources, the task processing efficiency and the system adaptability under the power distribution network multi-device cooperation scene are remarkably improved.

The invention discloses a reconfigurable power distribution line and an overload protection method thereof, and the method comprises the steps: combining real-time load monitoring, load prediction and topology reconstruction, and dynamically adjusting a power distribution path to cope with load fluctuation and sudden faults. The method comprises the following steps: firstly, acquiring real-time load data of a distribution line by a system, and performing load prediction by adopting an ARIMA algorithm; when the load exceeds a set threshold value, an overload protection mechanism is started, and local load cutting off, load transferring or standby power supply starting are carried out. Through automatic topology reconstruction, a power distribution path is reconfigured when the system is overloaded or breaks down, and load balance and continuous power supply are ensured. The intelligent and flexible power distribution network management method provided by the invention can effectively improve the reliability of the power distribution network, avoid power interruption caused by overload or faults, and improve the emergency response and recovery capability of the power distribution network.

The invention relates to the technical field of unmanned aerial vehicle safety, in particular to an unmanned control system dynamic key trust chain construction method based on a Feiteng E2000TCM, and the method comprises the steps: generating a hardware root key which cannot be exported through the TCM of Feiteng E2000TCM, and enabling the hardware root key to serve as a starting point of a whole trust chain; after the flight control system of the unmanned aerial vehicle is electrified, started and initialized, based on the hardware root key and the equipment unique identification code of the flight control system, deriving a system key by using a Hash algorithm; after the unmanned aerial vehicle takes off, deriving a task key by using a hash algorithm based on the system key and the identifier of the current flight stage; when the unmanned aerial vehicle needs to communicate with a ground station or other unmanned aerial vehicles in the current flight stage, based on the task key and the environmental risk level corresponding to the current flight stage, an interaction key is derived by using a Hash algorithm. According to the method, a data communication dynamic trust security protection mechanism of the flight control system of the unmanned aerial vehicle is realized, and malicious threats and viruses can be effectively defended.

The invention relates to a method for authentication, designed to allow access and may find application for the purpose of ensuring a higher level of security in electronic identification and / or communication and protection against unauthorized access, including visual, video or another surveillance of the process. The method is applicable and works successfully in various physical, virtual and operational environments, the protection being achieved by combining secret elements with decoys and implicit rules, that can be further changed by hidden instructions. The method significantly limits the possibilities of using standard methods for tracking user actions by recording key strokes and / or cursor movements (keyloggers), or taking screenshots (screen recorders), or surveillance, as visible actions do not reveal the secret combination. Also, the method does not require the use of additional security devices / keys of any kind.

The invention discloses a button switch adaptive optimization method and a storage medium. The method comprises the following steps: acquiring a tactile signal through a high-density piezoelectric sensor, and extracting pressure distribution, gradient change rate and energy concentration degree characteristics in combination with a space-time convolution kernel and a dynamic baseline calibration algorithm; integrating time-frequency domain analysis of the historical operation sequence and neural differential equation modeling, predicting an operation probability and marking an anomaly; and an anti-interference control instruction is generated by fusing environmental parameters, and multi-source data are dynamically weighted by adopting a gating attention mechanism. The system constructs a multi-stage reliability protection mechanism, and fault immunity is realized through feature drift detection, digital twin residual verification and hybrid control switching. And responding to a real-time demand, dynamically optimizing heterogeneous computing resource allocation, executing three-dimensional storage reconstruction and precision bit width switching, and forming a closed-loop cognitive evolution link.

The invention discloses a virtual power plant collaborative optimization scheduling method and system based on deep reinforcement learning, and the method comprises the steps: building a multi-time scale dynamic model covering second-level frequency response and hour-level economic scheduling through collecting the operation data of a virtual power plant, constructing a mixed sensing network, and taking the multi-time scale dynamic model as the input, extracting multi-scale time sequence features by combining a multi-head hierarchical attention encoder, obtaining equipment dynamic association features through a dynamic heterogeneous graph neural network, and generating comprehensive feature representation; constructing a hierarchical deep reinforcement learning control framework based on comprehensive feature representation, optimizing energy storage charging and discharging power, a load regulation instruction and a unit output strategy by adopting a deep reinforcement learning algorithm based on an experience playback mechanism, and constructing a multi-target reward function; and finally performing training-deployment process and simulation verification. According to the method, the response time of a protection mechanism can be shortened to be within 0.1 second under the working condition that the SOC is lower than 20%, the comprehensive operation cost of the system can be reduced under the normal working condition, and the signal-to-noise ratio of input signals of a control layer is remarkably increased.