2184 results about "Safety control" patented technology

The invention discloses a driving vehicle road cloud collaborative safety management and control method and system based on trusted computing, and the method comprises the steps: generating an HMAC-SM3 data fingerprint through employing an SM3 Hash algorithm engine, and proposing an LSTM lightweight dynamic trust evaluation model based on a TinyML framework; through a step-by-step Hash verification and rollback mechanism, through Geohash coding and SM3 Hash joint calculation, based on an aggregation signature of a BLS12-381 curve and public key aggregation, the communication overhead during multi-RSU collaborative signature is reduced; distributed private key fragmentation decryption is realized by adding a homomorphic aggregation ciphertext; aggregating and delaying the compressed ciphertext; dynamically evaluating the credibility of the nodes by a Krum dynamic defense mechanism; the system comprises a vehicle side credible sensing layer, a roadside credible broadcast layer and a cloud credible decision-making layer. According to the invention, through layered credibility verification, dynamic trust chain extension and a data privacy protection mechanism, the security and credibility problems of multi-node cooperation in an automatic driving scene are solved.

The invention discloses a container wharf hazardous chemical transport vehicle intelligent tracking and positioning system comprising a data acquisition module used for acquiring vehicle positioning, driving behaviors and environment state data; the data fusion and time synchronization module is used for performing time alignment on the multi-source data; the behavior modeling and trajectory prediction module is used for extracting driving behavior characteristics and predicting a driving trajectory; the risk assessment and anomaly detection module is used for calculating an anomaly score and predicting a driving risk; the security strategy optimization module is used for constructing a zero-sum game security control model and optimizing speed limit, path and early warning strategies; the remote control and intelligent intervention module is used for executing remote speed regulation, emergency braking, path optimization and alarm pushing in the intelligent dispatching system; and the cloud monitoring and data analysis module is used for storing a track, an environment, a driving score and a safety strategy and predicting a long-term risk trend. According to the invention, based on intelligent game and multi-modal fusion, accurate tracking and safety management and control of the hazardous chemical substance transport vehicle are realized.

The invention relates to the technical field of vehicle lateral stability control, and discloses a multi-source sensing fusion vehicle lateral stability coordination control system which comprises a multi-source sensing module, a fusion decision module, a control execution module, a state evaluation module and a coordination optimization module. The multi-source sensing module is provided with multiple types of sensors, and vehicle transverse motion parameters, tire attachment parameters and the like are collected and preprocessed. The fusion decision module constructs a data fusion model and a control rule, and dynamically simulates control parameters; the control execution module transmits the rule to a steering mechanism, a braking mechanism and the like, adjusts the output power and returns feedback data; the state evaluation module performs data standardization processing, calculates a first evaluation index, compares the first evaluation index with a reference threshold value, and preliminarily evaluates the stability; and the coordination optimization module calculates a second evaluation index in combination with the road environment parameters when being unstable, and performs secondary evaluation and adjusts the control instruction. The system is suitable for the field of active safety control of various vehicles.

The invention relates to the field of cableway transportation safety control, in particular to a cableway lift carriage operation deflection attitude monitoring device and a control method, and the method comprises the steps: collecting the position, speed, attitude angle and local micro deflection disturbance information of a lift carriage in real time by constructing a multi-mode sensing network; the frequency of the sensor is dynamically adjusted by adopting an adaptive sampling mechanism, and space-time alignment and fusion of heterogeneous data are completed in combination with dynamic time warping and minimum mean square estimation; strengthening the key disturbance dimension based on a disturbance-driven attention mechanism, and predicting a future deflection angle sequence; a decoupling torque is generated through spatial deflection vector field modeling, regional coupling tensor analysis and a dynamic direction alignment matrix, and power consumption constraint and a dynamic environment parameter optimization control instruction are fused; and finally, driving the quadrilateral moment gyroscope to execute correction, and constructing a multi-dimensional evaluation index based on the attitude response residual error. According to the method, adaptive closed-loop adjustment of the control strategy is realized, and the robustness under wind speed disturbance and load variation is improved.

The invention discloses a child electric motorcycle intelligent safety control system based on the Internet of Things, and relates to the technical field of electric motorcycle safety control. The problem of insufficient driving risk perception and control response of children is solved. The method comprises the following steps: firstly, fusing obstacle trajectory prediction, a road adhesion coefficient and an environment contour, constructing a dynamic risk vector field, and generating a collision probability and out-of-control coefficient fused risk assessment matrix; then, the matching degree of handle operation and vehicle body response is calculated through cross attention, and a third-level behavior label is output; according to the behavior level and the risk direction, anti-sideslip control or progressive power limitation is executed, and intervention characteristics are recorded; and finally, constructing a virtual training task by using parent operation data, extracting a personalized control threshold, dynamically optimizing system parameters, and realizing a child driving behavior-oriented closed-loop safety control system.

The invention discloses a gate valve automatic pressure relief control system and a gate valve, and belongs to the technical field of valve safety control systems.The gate valve automatic pressure relief control system comprises a valve assembly which comprises a valve body and a valve element, the valve element is installed in the valve body, and a pressure relief hole is formed in the valve element; the pressure relief assembly comprises a sealing column, a pressure bearing piece and a driving piece, the sealing column is arranged in the valve element in a sliding mode, the driving piece and the pressure bearing piece are both installed on the valve element, one end of the driving piece is connected with the sealing column and applies acting force for blocking the pressure relief hole to the sealing column, and one end of the pressure bearing piece bears fluid pressure of the inflow end of the valve element; the pressure sensor is used for monitoring the fluid pressure of the inflow end of the gate valve, once the fluid pressure exceeds the set safety pressure, hydraulic oil in the hydraulic pipe is discharged through the hydraulic system, the pressure bearing piece drives the sealing column to slide under the action of the fluid pressure, the flow guide hole communicates with the pressure relief hole, and automatic pressure relief is achieved.

The invention discloses a UWB-based multifunctional integrated vehicle-mounted safety control method and system, and the method comprises the following steps: collecting the dynamic environment data around and in a vehicle in real time based on a plurality of sensors, carrying out the path adjustment and driving mode optimization through generating a multi-level three-dimensional environment model, and carrying out the control of the driving mode. The system performs risk assessment and dynamic adjustment by using external data such as obstacles, road conditions, weather and the like collected by a sensor and combining a behavior mode of a driver and a health state of a passenger, and in a collision prediction and protection mechanism, the system gradually activates a protection system by analyzing factors such as vehicle speed and acceleration, so that the safety is guaranteed, and the safety is ensured. The system optimizes the energy distribution of a key system through an energy scheduling model, improves the energy efficiency, and preferentially guarantees the operation of a safety system in an emergency. According to the invention, a self-adaptive learning algorithm and a multi-scene optimized driving assistance function are integrated, and self-adaptive adjustment and safety control of the vehicle in different driving environments are realized.

The invention discloses an intelligent automobile cooperative cruise safety control method under Dos attacks and physical faults, and relates to automobile intelligent safety and automatic driving. A hierarchical control framework is adopted and comprises an observer layer and a tracking layer. The method comprises the following steps: establishing a vehicle longitudinal dynamics model and a DoS attack model for DoS attack in a V2X communication network and the problems of sensor and actuator faults and parameter isomerism of a vehicle; a completely distributed self-adaptive preset time observer based on event triggering is designed for each following vehicle in the observer layer, and rapid and accurate estimation of the state of the pilot vehicle is achieved; the method comprises the following steps: constructing an augmentation system at a tracking layer, designing a distributed intermediate observer, carrying out online estimation and compensation on faults of a sensor and an actuator, designing a distributed active fault-tolerant controller based on a fault estimation value and a pilot vehicle state estimation value, and calculating a wheel driving torque to realize safe cruise control. Multiple threats of coexistence of network attacks and physical faults are effectively handled, and the stability and safety of the cooperative cruise system are ensured.

The invention discloses an artificial intelligence automobile authority safety control method and device, and relates to the technical field of automobile electronics. According to the technical scheme, physiological monitoring data, driving behavior data and environment sensing data in a current period are acquired; calculating a physiological anomaly index, an operation deviation degree and an environment danger coefficient of the current period; inputting the physiological anomaly index, the operation deviation degree and the environmental risk coefficient into a risk prediction model fused with an attention mechanism to obtain a dynamic risk value of a next period; correcting the dynamic risk value according to the risk aging decay factor, and matching a target risk level according to the corrected dynamic risk value; determining a dynamic permission adjustment strategy corresponding to the target risk level by using a risk-permission linkage rule; and performing real-time verification on the dynamic permission adjustment strategy based on the driving scene where the vehicle is located, generating a controllable function list, and executing permission authorization operation in the next period. The main purpose is to realize accurate permission adaptation of vehicle functions.

The invention relates to the technical field of vehicle drive-by-wire chassis, and provides a drive-by-wire chassis safety guarantee method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining various types of vehicle sensor data, carrying out the data fusion, and generating fusion data; based on the fused data, multi-modal anomaly detection is carried out, a fault detection result is generated, and the fault detection result comprises a fault type and a fault confidence coefficient; determining different levels of security control strategies according to the fault detection result; and controlling a redundant actuator system of the drive-by-wire chassis according to the safety control strategy. According to the technical scheme provided by one or more embodiments, the reliability and safety of the drive-by-wire chassis in a complex scene can be effectively improved.

The invention relates to the technical field of hazardous chemical substance production, and provides a hazardous chemical substance safety production management and control system, in the aspect of data acquisition, all-around acquisition of equipment operation, environment and personnel operation data is realized, a high-precision sensor ensures data accuracy and far exceeds traditional finite dimension and precision acquisition, and during data analysis and processing, edge calculation is combined with deep learning, so that the safety production management and control of hazardous chemical substances is realized. A risk assessment model is dynamically adjusted, the limitation of a traditional fixed model is broken through, production change and risk early warning can be adapted in real time, risk levels are dynamically adjusted, multi-element early warning and visual display are achieved, compared with single early warning and simple display, risk perception is more timely and accurate, accurate reasons are found through a unique accident analysis model, and the risk assessment efficiency is improved. According to the method, the production management and operation improvement can be promoted, each module of the system can be optimized, a closed-loop lifting mechanism is formed, the safety production management and control level of the hazardous chemical substances is comprehensively improved from data acquisition, analysis, early warning and optimization, the safety risk is greatly reduced, and the production safety and stability are guaranteed.

The invention relates to the technical field of safety control, in particular to an intelligent control method and system for disaster coping with a wind-resistant and fire-resistant rolling shutter door, and the method comprises the following steps: extracting and calculating a wind speed difference value and a temperature gradient value based on a wind speed sensor signal and a temperature sensor signal of the rolling shutter door, carrying out the attribution classification through combining the load pressure data at the two sides of a door curtain, and comparing and judging with the node state of the roller shutter door to obtain a node disaster response priority index. According to the method, the wind speed difference value and the temperature gradient are calculated by extracting wind speed and temperature signals, multi-node cooperation is ensured, transmission shaft operation parameters, infrared signals and the pressure state are analyzed, the operation state is mastered in real time, the power of the transmission shaft is dynamically adjusted to improve adaptability and stability, output of a driving motor and configuration of sealing parameters are optimized, and the sealing performance is improved. The roller shutter door can keep balance between operation and sealing performance under strong wind and high temperature, wind resistance and fire resistance are remarkably improved, disaster loss is reduced, and response efficiency and safety protection capacity are enhanced.

The invention provides an intelligent teaching system based on an AI robot coach, and relates to the field of intelligent teaching. Comprising a hardware equipment module, a software platform module, a safety control module, a hierarchical intervention module, a physiological monitoring module and a cognitive load regulation module, the hardware equipment module collects vehicle state, environment and student physiological data in real time through a multi-mode sensor; the software platform module generates a teaching strategy based on a deep reinforcement learning model and analyzes a student behavior mode; the safety control module triggers an automatic braking mechanism through the obstacle distance and the vehicle speed; the grading intervention module is divided into three-level intervention strategies of an L1 unit, an L2 unit and an L3 unit according to the operation error type, and voice prompt and braking intervention are dynamically adjusted; the physiological monitoring module non-inductively evaluates a tension index through micro-expression, grip strength and voice data; the cognitive load adjusting module dynamically adjusts the teaching content difficulty and the information density in combination with the steering frequency, the accelerator and brake alternating times and the tension index.

The invention provides a charging pile dynamic power regulation and control system and method based on multi-parameter coupling. Belongs to the field of charging of charging piles, and comprises the steps that when the total power of a bus reaches a load reduction threshold value, a phase cooperative load reduction strategy is activated, a multi-stage response mechanism is configured through a safety control execution unit, peak clipping and valley filling control is implemented in the time periods t1-t2, and a total load curve is smoothed through time shifting power distribution; and when the power is close to the power limit, the power of the non-emergency charging pile is automatically reduced to a safety threshold, and the equipment utilization rate is intelligently improved in a load sufficient period. And the concurrent access number of the charging piles is maximized on the premise that the bearing limit of the phase lines is not exceeded. The user capacity under the rated load of the charging station is improved, the user charging cost in the time-of-use electricity price period is reduced, the utilization rate of electric power resources is improved, millisecond-level power adjustment response is achieved, the full-time-period compliance of dynamic load limitation on the power grid side is ensured, and the operation benefits, safety and reliability of charging infrastructures are remarkably improved.

The invention discloses an intelligent safety control method for an exhibition, and the method comprises the steps: multi-sensor data fusion, real-time modeling and prediction, intelligent obstacle avoidance and planning, emergency braking and safety protection, cooperative control and communication, risk assessment and early warning, learning and self-adaption, and monitoring and manual intervention. The motion states of actors and stage equipment can be monitored and predicted in real time, and collision accidents are effectively avoided. And meanwhile, through the arrangement of an emergency braking system and protective equipment, the safety in the exhibition process is further improved. Besides, the control system has learning ability and adaptive ability, can automatically adjust safety control parameters according to different performance contents and stage arrangements, and improves the flexibility and practicability of the system.

The utility model discloses an intellectualized safety control take-up straining device, including handle subassembly, base subassembly and long braid, handle subassembly includes handle and ratchet wheel, base subassembly includes base, handle and base are pivoted and rotatingly connected through take-up shaft, ratchet wheel is fixed on take-up shaft, and the winding drum is fixed between the ratchet wheel, and the long braid is fixed on the base. The ratchet wheel, the winding drum and the tape winding shaft rotate synchronously, the winding drum is sleeved outside the tape winding shaft and is coaxially arranged, an opening for the long braid to pass through is arranged on one side of the winding drum, one end of the long braid is sleeved on the tape winding shaft and is led out from one end of the base through the opening on the winding drum, one end of the tape winding shaft is connected with the coil spring assembly, and the coil spring assembly comprises a coil spring, a coil spring box and a coil spring box cover. The coil spring box is covered with the coil spring box, a brake assembly is further arranged in the coil spring box, and one end of the tape winding shaft can be provided with a C-shaped opening, an eccentric shaft or a concentric screw hole according to the structure of the brake assembly. According to the utility model, the long braid can be prevented from swinging to hurt an operator, and the personal safety of the operator is guaranteed.

According to the collision detection method for the high-reduction-ratio and large-load mechanical arm, the problems of sensitivity and reliability of collision detection of the high-reduction-ratio mechanical arm are solved by combining a two-stage detection mechanism with tail end vibration characteristics and joint torque errors; according to the first-stage detection, tail end vibration signals are captured in real time through a three-axis acceleration sensor, and rapid recognition of slight collision is achieved; in the second-stage detection, joint torque errors are calculated based on current feedback, the nonlinear effect of the speed reducer is compensated in combination with a kinetic model, and the accuracy of violent collision detection is improved. High-precision collision identification and multi-level safety control of the mechanical arm and an external object under the heavy load working condition are achieved, and meanwhile the anti-interference capacity and reliability of a detection system under the complex working condition are improved.

The invention provides an unmanned system security monitoring method based on multi-modal perception and autonomous decision discrimination, belongs to the technical field of artificial intelligence and industrial automation crossing, and aims to solve the problem that a machine learning decision security monitoring system of an existing unmanned system is difficult to quickly perceive environment change and make accurate response. The method solves the problems that in a high-dynamic scene, recognition of sudden risks is lagged, decision adjustment is slow, and the safety and stability of task execution are affected in the prior art, and comprises the steps that S1, key parameters of an unmanned autonomous system are screened; s2, modeling a parameter coupling relation; s3, time sequence driven key parameter extreme value boundary modeling and statistical analysis are carried out; s4, discovering danger cognition; s5, building a real-time prediction framework; s6, constructing a risk assessment model; and S7, determining an optimal safety switching mechanism. The method is suitable for real-time safety control of unmanned equipment such as an unmanned aerial vehicle, an automatic driving vehicle and an industrial robot.

The invention discloses a capacity adaptive coordination control system and method for distributed energy storage charging equipment, and relates to the technical field of charging safety control. The method is used for solving the problems of capacity sudden drop, vehicle charging delay and power grid interference caused by lithium precipitation. The method comprises the following steps: firstly, applying micro-current excitation in a charging pulse turn-off period by using a lithium precipitation characteristic monitoring module, extracting a phase deviation angle in a voltage relaxation curve, judging a lithium precipitation risk and generating a risk tag; then, the risk decision-making module calculates a charge migration proportionality coefficient and a floating coefficient in combination with the battery type and infrared thermal imaging information; then, the demand coupling module fuses the departure moment, the electric quantity gap and the power grid frequency fluctuation to generate a urgency degree weight and a pulse compensation parameter; and finally, the power reconstruction module completes charge migration and pulse sequence reconstruction according to the weight and the compensation parameter, triggers relaxation retest, and constructs a whole-flow dynamic coordination mechanism of the energy storage system.

The invention discloses an automatic driving safety operation system integrating environmental perception and decision reasoning. The system generates and dynamically updates a security risk map covering an operation area by fusing real-time environment perception, historical operation data and traffic management information. On the basis, the collaborative safety decision-making module further carries out behavior modeling and intention prediction on other traffic participants, and in combination with map risks and traffic instructions, a driving strategy is actively adjusted under a dynamic game framework. According to the invention, the safety control is improved from passive response to an active mode of behavior pre-judgment and game dominance, and the safety and reliability of the operating vehicle in a complex environment and the cooperative capability of the operating vehicle and traffic management are obviously enhanced.

The invention discloses a thermal management control method for a power battery. The method comprises the following steps: establishing a basic model for calculating heating and cooling of the battery; basic control logic is set, and the basic model is substituted into different temperatures to select different control modes; data management and analysis including real-time state monitoring, historical data calling, cloud platform data calling and big data comparison rule searching; a model learning algorithm is introduced, historical working conditions, environmental parameters and user behavior data are fused, the system temperature trend is predicted, and temperature management is started in advance; a temperature control model is dynamically optimized through big data; the optimal energy consumption of the control temperature is used as a target function; and 5) fault early warning: obtaining characteristic laws of temperature, voltage, time and the like during a thermal event and each fault event, and realizing early warning when the vehicle has similar characteristics. According to the invention, temperature uniformity control in the whole life cycle of the power battery can be realized, the service life of the battery is prolonged, and timely early warning can be carried out on thermal safety control.

The invention discloses a gravity center self-adjustment control system and method based on an electric balance car, and relates to the technical field of electric balance car control, and the gravity center self-adjustment control system comprises a multi-source sensing module, a control decision module, a multi-mode execution module and a safety control module. According to the invention, attitude, pressure, motion and environment data are fused in real time through the multi-source sensing module, three types of instability risks of sudden lateral offset, continuous slope offset and load distribution abnormity are accurately identified, and the control decision module intelligently starts differential compensation, wheel track adjustment or counterweight adjustment according to risk types. The multi-mode executing mechanism can cope with composite unbalance in a cooperative mode, the safety control module adopts a dynamic threshold value to adjust intervention sensitivity, multiple guarantees are formed in combination with a fault diagnosis and speed reduction parking mechanism, and finally the control burden of a user is relieved while the trafficability of complex road conditions is improved.

The invention relates to the technical field of safety control, and particularly discloses a safety control method, device and system for a combine harvester, and the method comprises the steps: judging whether the combine harvester meets a starting condition or not based on the position of a handle of the combine harvester, and the rotating speed value, the battery voltage value and the excitation voltage value of an engine of the combine harvester; and after the combine harvester meets the starting condition, if a target control signal of a main clutch of the combine harvester is received, and the combine harvester meets a preset condition corresponding to the target control signal, the main clutch is controlled. The method can effectively solve the potential safety hazard caused by the fact that the vehicle starting condition is judged only by depending on the position of a handle in the prior art, makes up for the defect that multi-dimensional verification is not carried out in combination with the engine state, and can prevent illegal vehicle starting caused by sensor faults or instantaneous misoperation. And meanwhile, the problem that wrong actions are executed when a combination signal is triggered by mistake before the combine harvester is started is avoided, and the operation safety and the equipment reliability of the combine harvester are improved.

The invention relates to a safety air bag ignition control method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps that the running state of the automobile safety air bag control system is configured through the safety control state machine; under the condition that the running state is the normal state, the ignition function of the automobile safety air bag control system is configured to be in a forbidden state through a safety control state machine; in response to the fact that the automobile safety air bag control system is powered on for the first time and / or a watchdog error is detected, the running state is switched to a watchdog error protection state from a normal state through a safety control state machine; and under the condition that the operation result of the sensor data processing unit exceeds a preset threshold value, the operation state is switched from the normal state to the safety air bag ignition enabling state through the safety control state machine, and the ignition function is switched to the starting state. By adopting the method, the low-delay and high-precision response requirements of the chip on the ignition control instruction can be met.

The invention discloses automatic capping and screw automatic packaging equipment with torque control, and belongs to the technical field of mechanical automatic assembly. Through combined application of an infinite-angle rotating joint at the tail end of the mechanical arm and a fixed-torque screw, automatic screw tightening operation with 3% torque control precision is achieved, and the problem that a common torque control electric screw tool with a small torque range in the market is poor in torque control precision is solved; in addition, axial pretightening force and tightening torque borne by the screw in the tightening process are detected through a six-dimensional force sensor connected to the screw mounting mechanical arm, and the safety control measure that the axial pretightening force and the tightening torque of the screw do not exceed safety threshold values in the whole operation process is achieved. The defect that automatic screw tightening equipment or tools on the market usually only monitor the tightening torque but neglect the influence of the axial pre-tightening force is overcome.

The invention is suitable for the technical field of automatic driving, and provides an automatic driving vehicle blind area track prediction and planning method based on Transform, and the method comprises the steps: firstly constructing a key entrance point, calculating a shielding region, and then generating and maintaining a belief state of a ghost target in combination with a risk score; then, real and ghost targets are coded into Token in a unified mode, the Token is input into Transform for interactive modeling, and multi-modal track distribution is output; and finally, a variable anisotropic repulsive force potential field is constructed based on the predicted risk, and a safety control instruction is obtained through optimization solution and hard safety filtering. According to the method, continuous probabilistic estimation and interactive reasoning of the potential risk of the blind area are realized, a flexible defensive track can be generated according to the risk confidence and the dynamic state of the vehicle, and the traffic efficiency is guaranteed while the safety is improved.

The invention belongs to the field of intelligent robots, particularly relates to an intelligent wheelchair following system based on single target tracking, and aims to solve the problem of poor effect of effectively avoiding obstacles in a complex scene and continuously following a single target under the condition of multi-target interference. The system comprises a sensor module used for restoring scene image data; the man-machine interaction module is used for acquiring a to-be-tracked target; the single-target tracking module is used for generating a moving strategy of the intelligent wheelchair; the intelligent decision-making module is used for generating an optimized path and dynamically adjusting the moving direction and speed of the wheelchair; the driving control module is used for realizing smooth steering and emergency braking of the intelligent wheelchair; and the communication module is used for realizing communication among the modules. According to the invention, three-dimensional positioning precision, target identification accuracy and tracking stability are improved, safety control and man-machine interaction are optimized, the method is especially suitable for people having difficulty in moving, and life self-care and social participation are improved.

The invention discloses a catastrophe rescue robot digital twin control method and system, and belongs to the technical field of robot intelligent control. Comprising a virtual-real mapping module which processes the body state of a robot and catastrophe scene point cloud data in real time, and constructs and synchronously updates a digital twinborn body containing a terrain elevation map at a cloud end; the hierarchical decision-making module is used for making a decision in the digital twinborn body by utilizing a hierarchical reinforcement learning model, and outputting a wheel-leg movement mode and an excavation arm cooperation instruction by a high-level strategy network according to the terrain complexity; and the safety deduction module introduces a safety reinforcement learning mechanism, deduces and verifies the generated action instruction in the virtual environment, and restrains the dangerous action by controlling a barrier function. According to the method, the decision risk of the heavy robot in the unstructured catastrophic terrain can be reduced, the stability of motion control is improved, and a high-safety control strategy is provided for various complex obstacle crossing actions based on simulation prediction.

The invention discloses a remote monitoring and routing inspection side station system for a wind generating set, and relates to the technical field of wind power generation monitoring system equipment. The remote monitoring and routing inspection side station system for the wind generating set comprises a data acquisition unit, a state analysis unit, a health analysis unit, a fatigue analysis unit and a processing unit, analyzes the running state of the wind generating set step by step, and realizes abnormal recognition, health assessment, fatigue judgment and power-off control; the multi-layer nested operation evaluation system with the power response index, the vibration temperature response index, the fusion health state index and the gearbox composite fatigue index as the core is constructed, so that the problems that in a traditional system, the state judgment granularity is coarse, the reaction mechanism is single, and fault logic is not closed are effectively solved, fault recognition is more sensitive, and the system reliability is improved. The response is more hierarchical and controllable, and the intelligent diagnosis capability and the safety control level of the remote monitoring system of the wind turbine generator are remarkably improved.

The invention discloses a double-seat cooperative safety control method and system for engineering machinery, relates to the technical field of engineering machinery control, and provides the following scheme that the method comprises the steps that a first operation instruction stream of a main driving seat and a second operation instruction stream of a co-driving seat are collected in real time, and a working condition parameter set of the engineering machinery is synchronously obtained; the operation instruction stream comprises all degree-of-freedom motion parameters of the engineering machinery, inputting the first operation instruction stream into a pre-constructed long-short-term memory model, outputting a prediction operation instruction stream in a future time window, and based on a difference vector of the first operation instruction stream and a second operation instruction stream on each degree-of-freedom motion parameter. According to the method, through the index curve fitting model and Simpson integral calculation, the defect that the co-pilot intervention weight is difficult to quantify is overcome, the dynamic intervention weight based on the physical equidistant interval absolute integral value is realized, and the accuracy of the control signal of the engineering machinery is improved.