959 results about "Safety control" patented technology

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 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 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.

The invention discloses a starling group-based unmanned aerial vehicle cluster safety control and steering consensus method, which comprises the following steps of: firstly, constructing a three-layer leader-follower network simulating a pigeon group hierarchical structure, and establishing a dynamic physical neighbor topology in combination with a communication radius; an improved artificial potential field method is designed, a damage area, a rejection area, an alignment area and an attraction area are divided, a smooth concave-convex function is introduced to adjust a potential field, and cooperative force and obstacle rejection force for maintaining an expected distance are generated; an inertial spin mechanism is introduced, when a maneuvering intention is detected, expected spin and perceptual spin are calculated, a spin kinetic equation is utilized to update a state, and a transverse consensus coupling force for driving a cluster to rapidly and synchronously steer is generated. And finally, for different levels of unmanned aerial vehicles, differentially fusing the control force, and updating the cluster state through numerical integration. According to the method, the internal safety distance of cluster high-density flight is guaranteed, the overall steering speed and consistency of the cluster facing sudden obstacles are remarkably improved, and the robustness of the system in a dynamic environment is enhanced.

The invention belongs to the technical field of stage equipment boundary safety protection control, and particularly relates to a variable constraint control method for stage equipment based on risk perception and a dynamic safety domain. The characteristic that stage equipment is usually in a fixed application scene in the performance process is utilized, the inherent safety level and active protection capacity of a stage equipment system are greatly improved through tight combination of real-time collection and variable constraint, and therefore safer, more accurate and more smooth control is achieved. According to the method, a construction algorithm is embedded in software, a dynamic region division and segmentation threshold adjustment strategy is utilized, and a multi-stage braking redundancy cooperation mechanism is triggered, so that the anti-interference capability and fault tolerance performance of the control system are effectively enhanced, the implementation cost is low, a large amount of manpower and material debugging can be saved, and the system is suitable for large-scale popularization and application. And a high-precision and high-robustness safety control scheme is provided for large stage machinery such as a seat vehicle platform and a rotating stage.

The present invention relates to a safety steering device for manual intervention by a safety driver in an autonomous vehicle or a vehicle with a driver assistance system. The safety steering device comprises a control unit and a joystick operable by the safety driver, from which a steering intervention signal can be transmitted to the control unit, wherein the steering intervention signal overrides an autonomous steering signal. A safety control element is arranged on the joystick, which, when activated by the safety driver, generates a release signal to enable the transmission of the steering intervention signal to the control unit.

The invention discloses a combustor safety control system for a negative pressure furnace, relates to the technical field of combustion control of negative pressure furnaces, and aims to solve the technical problems that single closed-loop control response of an existing negative pressure furnace combustor is lagged, and the system stability is poor. According to the system, a double-closed-loop control framework of cooperative work of an inner-layer combustion stability closed loop and an outer-layer negative pressure closed loop is constructed, flame characteristic parameters and the gas / air proportion serve as core control objects of the inner-layer closed loop, the opening degree of a gas proportional valve and the rotating speed of a draught fan are adjusted in real time through a PID algorithm, and the combustion state is kept stable; the outer-layer closed loop dynamically adjusts the opening degree of the hearth baffle valve based on the inner-layer adjusting result so as to maintain the negative pressure within a preset range; when any closed loop is abnormal, the system automatically triggers a cross-closed-loop compensation mechanism, and the other closed loop optimizes self-adjusting parameters in real time to realize dynamic compensation. Through double-closed-loop cooperation and abnormal compensation design, the control response speed and operation safety of the negative pressure furnace combustor are greatly improved, the problem of response lag of single closed-loop control is effectively solved, and the method is suitable for safety control of various industrial negative pressure furnace combustion systems.

The invention provides a reinforcement learning steering control method and system based on a safety barrier, electronic equipment and a storage medium, and relates to the field of automatic driving safety control. The method comprises the following steps: sensing current states of each participating object and a vehicle in an environment; respectively performing state estimation and future moment position prediction on the participating objects and the vehicle by applying Kalman filtering to obtain relative position probability distribution of each participating object and the vehicle in a plurality of steps in the future; calculating the collision probability of the vehicle and each participating object, and determining the overall collision risk according to the probability; the total collision risk is compared with a preset collision safety barrier threshold value, when the total collision risk is larger than the collision safety barrier threshold value, a safety barrier is triggered, candidate wheel turning angles are projected into an action set meeting safety constraints based on minimum change projection, and the projected wheel turning angles are issued and executed; if not, triggering is not conducted, and a reinforcement learning decision maker is adopted to output candidate wheel rotation angles and issue for execution.

The invention discloses an electric power field operation guidance interaction system and method, and belongs to the technical field of electric power construction. The method comprises the steps that double-end communication connection is constructed, continuous supervision data and operation abnormal performance of target power equipment are acquired, a blank analysis space is constructed, and a suspended potential discharge abnormal signal is generated; according to the method, the core problems that a guidance scheme flows on the surface and interaction communication is low in efficiency and delayed are fundamentally solved, and sustainable and optimized intelligent supporting capacity of power field operation is formed through data driving and knowledge precipitation in the whole process; the early warning capability, the handling efficiency and the safety management and control level of potential faults of complex power equipment are remarkably improved, an innovative development direction of a power field operation guidance interaction technology is represented, and multi-source deep diagnosis, dynamic risk assessment and intelligent decision interaction are organically fused to achieve the purpose of improving the safety of the power field operation guidance interaction technology. And a closed-loop intelligent interaction system integrating monitoring, analysis, decision making and guidance is constructed.

The invention belongs to the technical field of industrial combustion safety control, and provides a double-channel industrial furnace gas combustion safety interlocking control system which comprises a gas / air flow ratio dynamic monitoring module, a flue CO concentration online detection module, a double-channel data conflict arbitration processor and a visual human-computer interface. Aiming at the prominent problems of incomplete combustion of coal gas, carbon deposition, detonation risk and the like which are easy to occur under the working condition of accelerated temperature rise, the system integrates visual double-insurance safety control of dynamic calculation of a flow ratio and real-time analysis of tail gas components, and all-directional safety protection in the combustion process is realized through a multi-parameter cooperative monitoring and intelligent interlocking mechanism; the detonation risk is obviously reduced; meanwhile, the air-fuel ratio fluctuation is accurately controlled within the range of + / -5%, and the carbon deposition phenomenon is effectively reduced; and remarkable economic benefits are created while reliable operation of equipment is guaranteed.

The invention provides a collision detection and safety control method for a mobile mechanical arm in a man-machine co-fusion environment. The collision detection and safety control method comprises the steps that multi-source sensor data of the mobile mechanical arm is collected in real time; inputting the preprocessed multi-source sensor data into a pre-trained collision detection neural network model to obtain a collision risk prediction value; based on the collision risk prediction value, a detection threshold value dynamically adjusted based on the historical fluctuation trend of sensor data is combined, and collision event judgment is carried out; when it is judged that the collision event occurs, compliant safety recovery control is executed, specifically, cached historical motion tracks within a preset duration before collision occur are called, and the historical motion tracks comprise a historical pose sequence of a mechanical arm joint and a historical path of a movable chassis; based on the historical movement track, a mechanical arm joint rollback track and a movable chassis rollback path are generated through a minimum Jerk track planning method; and the mechanical arm joint and the movable chassis are controlled to conduct collaborative rollback motion according to the rollback tracks and the rollback paths generated by the mechanical arm joint and the movable chassis respectively.

The invention discloses a pipeline gas safety valve control method and system, and relates to the technical field of gas safety control, and the method comprises the following steps: obtaining the operation data of a gas pipeline, constructing a gas pipeline operation state portrait, extracting evolution characteristics, and generating an operation state characteristic vector; according to the running state feature vector, grading the running state, dynamically correcting a division threshold value according to historical running data, and outputting a corresponding safety valve control decision instruction; the safety valve is driven to execute opening and closing actions according to the safety valve control decision instruction, the consistency between the actual action state of the valve and the control instruction is verified, and valve execution result data is generated; writing back and updating the operation state feature construction process according to the valve execution result data; through the running state portrait and closed-loop feedback control, the problems that in traditional pipeline gas safety valve control, the control decision adaptability is not high, and the execution result lacks feedback are solved.

The invention relates to a vehicle safety control method, a vehicle and electronic equipment, and belongs to the technical field of vehicle safety, and the method comprises the steps: obtaining front multi-source sensing information, and judging whether a target pedestrian has a road crossing intention through a multi-mode intention prediction model based on the front multi-source sensing information; when it is determined that the target pedestrian has the intention of crossing the road, performing lateral risk identification based on the acquired lateral target motion data and pedestrian motion data to obtain a lateral risk level; performing rear risk identification based on the obtained rear target motion data to obtain a rear risk level; determining a comprehensive risk level based on a preset mapping relation according to the lateral risk level and the rear risk level; and according to the comprehensive risk level, performing control configuration on at least one of the vehicle exterior voice module, the vehicle exterior light module and the vehicle exterior interaction screen, and executing a corresponding vehicle exterior interaction control strategy.

The invention discloses a multi-dimensional seat safety monitoring control system and method.The system comprises a multi-sensor module, a seat safety controller and an alarm module which are sequentially arranged, and the multi-sensor module is used for collecting seat safety multi-dimensional data of all seats in a vehicle through multiple sensors; the seat safety controller is used for carrying out data fusion on the seat safety multi-dimensional data to obtain a seat state result, sending an early warning instruction to the alarm module according to the seat state result, and adjusting an airbag detonation parameter according to the seat state result, and the early warning instruction is a multi-level early warning instruction matched with the seat state result; the alarm module is used for giving an alarm in response to the early warning instruction. According to the multi-dimensional seat safety monitoring control system and method, the monitoring accuracy is improved, accurate identification of passenger identities, safety belt compliance tying and sitting posture states is achieved through multi-sensor fusion, misjudgment is avoided, the monitoring accuracy is improved, and the risk of false triggering of safety air bags is reduced.

The invention discloses a method and a system for measuring an electrode constant of an oil conductivity valuing device, relates to the technical field of measurement, and breaks through the limitation of roughly taking an average value of an area of a polar plate traditionally by constructing an actual conductance cell model and defining parameter information, accurately deduces a conductance cell constant, and reversely calculates an effective relative area to determine the electrode constant. According to the method, the coaxial annular structure characteristics are attached, the influence of the edge effect and electric field line diffusion is avoided, and electrode constant calculation is more scientific and rigorous. Therefore, the accuracy of the constant value of the oil conductivity standard substance is guaranteed, a reliable reference is provided for verification and calibration of an oil conductivity meter, an effective oil conductivity measurement result is ensured, the electrostatic accident risk is reduced from the source, and the oil fuel quality and safety management and control are enhanced. The problems that an existing coaxial annular cylinder type electrode conductance cell is not strict in constant calculation and low in standard value credibility are effectively solved, and the electrode constant measurement accuracy of the oil conductivity valuing device is remarkably improved.

The invention discloses an unmanned aerial vehicle wall-attached disturbance suppression method based on strategy optimization, and aims to solve the problems that in the wall-attached flight or operation process of an unmanned aerial vehicle, strategy optimization needs to be updated online, strong constraints such as the minimum safety distance, the attitude angle, the speed and the acceleration are easily violated, airborne computing power is limited, and safety and real-time performance are difficult to consider at the same time. According to the invention, reachability analysis is carried out based on a dynamical model and wall geometry to train a feasible region approximate network so as to construct a safety supervisor, an initial control instruction is generated by adopting a hierarchical strategy, and optimization and correction are carried out under the constraint of a feasible control region to obtain a safety control instruction. Performing event-triggered online updating based on the intervention amount of the safety supervisor, introducing supervision constraint learning which takes a safety control instruction as a supervision target, and replacing a baseline strategy when a candidate strategy meets a switching condition; the technical effects of improving the adherence disturbance suppression capability and reducing the on-line calculation burden on the premise of meeting the safety constraint are achieved.

The invention relates to the technical field of railway traffic safety control, and discloses a crossing centralized control method and system based on multi-source sensing, and the method comprises the steps: collecting multi-source sensing data, and carrying out the time synchronization of the multi-source sensing data; carrying out preprocessing and feature extraction on the time-synchronized multi-modal sensing data stream; carrying out multi-source sensing fusion by adopting an environment self-adaptive dynamic weight distribution mechanism; obtaining sensing results of adjacent crossings, performing time-space associated cooperative sensing enhancement, and outputting train arrival time prediction and confidence; performing end-to-end deep learning model decision making; executing graded early warning and fence gate control, and outputting a crossing protection execution state and an execution process log; carrying out model training and optimization; according to the invention, all-weather reliable perception, intelligent decision and cooperative control are realized, and the safety and passing efficiency of the crossing are improved.

The embodiment of the invention discloses an unmanned aerial vehicle soft fence control method, and the method comprises the steps: obtaining personnel state information, generating environment risk distribution information based on the personnel state information, and carrying out the time alignment processing of the environment risk distribution information, so as to obtain the risk distribution information corresponding to the current moment of an unmanned aerial vehicle; acquiring a current flight state of the unmanned aerial vehicle, predicting a motion state of the unmanned aerial vehicle in a future period of time, and determining a risk assessment result according to the motion state and the risk distribution information; performing constraint processing on the operator control input according to the risk assessment result to generate a safety control instruction; inputting the safety control instruction into the flight controller to execute flight control, and triggering a safety protection strategy when the risk degree meets a preset condition; the unmanned aerial vehicle can be effectively prevented from entering a crowded or high-risk area, and the flight safety and controllability of the unmanned aerial vehicle in a complex personnel environment are improved.

The invention relates to the technical field of industrial safety control, in particular to a dual-system redundancy safety circuit and a self-diagnosis method, the dual-system redundancy safety circuit is composed of a first safety system, a second safety system, a test load and a switching unit, and the first safety system and the second safety system realize information interaction through an optical coupling isolation communication interface. According to the self-diagnosis method, an encrypted diagnosis trigger pulse sequence is generated through the second safety system, a test contactor of the first safety system is driven to be connected to a test load, and a main safety loop generates simulated real safety action current; the second safety system performs high-speed sampling, filtering, correlation analysis and response delay extraction on the current waveform to obtain a diagnosis result of the contact performance and the load capacity; and when an abnormity is detected, the second safety system controls the switching unit to execute safety switching through the solid-state switching output point, so that control right taking-over is realized, and the reliability and the online diagnosis capability of the safety loop are improved.

The invention is suitable for the technical field of engineering machinery safety control, and provides a forklift anti-rollover self-adaptive balance control system and method, and the system comprises a sensor group, a controller and an execution mechanism group. The sensor group detects vehicle body postures, cargo weight and road conditions; the controller automatically identifies working conditions such as flat ground, ramp and high-position stacking according to the data and calculates a safety coefficient; and the executing mechanism group comprises a vehicle body leveling mechanism capable of adjusting left and right suspensions and a door frame posture adjusting mechanism capable of actively adjusting postures. The controller can command the two mechanisms to work in different cooperation modes according to different recognition results, for example, the two mechanisms mainly adjust a vehicle body on a ramp, stabilize the vehicle body and goods when the goods are stacked at high positions, and preferentially stabilize the goods and then switch the vehicle under the most dangerous composite working condition. According to the method, passive alarm is changed into active cooperative adjustment, and the working efficiency is effectively guaranteed while the operation safety of the forklift under the complex working condition is improved.

The invention relates to the technical field of high-speed train pneumatic safety performance optimization, in particular to a high-speed train crosswind pneumatic safety control blowing device which comprises a blowing assembly and an adjusting assembly. The air blowing assembly comprises a plurality of air blowing pipes, an air pump and an air conveying pipe, the air blowing pipes are arranged on the tops of the two sides of the train in a sectional mode or close to the top of the train, and a plurality of air blowing holes are formed in the sides, away from the train, of the air blowing pipes at equal intervals in the axial direction. The air pump is installed on the train and connected with the air blowing pipe through the air conveying pipe. The adjusting assembly comprises a motor, a wind speed and direction sensor and a controller. The motor is in transmission connection with the air blowing pipe and used for adjusting the angle of the air blowing hole. The wind speed and direction sensor is installed on the roof of the train and used for monitoring the wind speed outside the train in real time. The controller is arranged in a safety guarantee system of the train, and the controller is connected with the motor, the wind speed and direction sensor and an inertial measurement unit of the train.

The invention provides a safety analysis method and system for an automatic driving Transform model, a product and a vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: systematically recognizing the specific safety risk of the Transform model in the whole life cycle of an automatic driving system, and constructing a structured safety demonstration model and verifiable safety demands on the basis; further embedding the model into a vehicle dynamic control link, and positioning an unsafe control action caused by abnormal output of the model by using a system theoretical analysis method, so as to design and deploy a safety relief mechanism adaptive to a Transform architecture in a targeted manner; meanwhile, a multi-level verification system covering the internal mechanism of the model and the vehicle behavior is established, an empirical evidence supporting safety demonstration is generated, and continuous performance monitoring and closed-loop optimization are implemented in the operation stage, so that the long-term safety, reliability and compliance of the automatic driving system in a complex open environment are effectively guaranteed.

The invention discloses a learning and model hybrid driven autonomous vehicle safety control method and system, which are suitable for expressway convergence control in a hybrid traffic environment. According to the method, multi-agent reinforcement learning and a safety potential field theory are combined to construct a hybrid control framework; collaborative decision and adaptive control of ramp and main line automatic driving vehicles are realized through a multi-agent near-end strategy optimization algorithm; meanwhile, a novel relatively safe potential field energy index is introduced, the interactive collision risk between the vehicles is quantified in real time, and a reward function is designed based on the index to guide an intelligent agent to learn safer behaviors; in addition, a dynamic safety intervention mechanism is further arranged, when risk assessment exceeds a preset threshold value, protective action coverage is automatically triggered, and risk avoidance is implemented. According to the method, the collision rate is remarkably reduced, meanwhile, high passing efficiency and reward revenue are kept, and the safety, coordination and deployability of the automatic driving vehicle are effectively improved.

The invention relates to the technical field of unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle adaptive obstacle avoidance control method and system based on environment perception, and the method comprises the steps: obtaining environment perception data and flight state data; constructing a slender obstacle parameter model; constructing an equivalent dangerous volume field and a repulsive potential energy field; generating a controllable risk potential field; generating an obstacle avoidance control quantity and a reference trajectory; performing online self-tuning on potential field parameters; and generating a final flight control instruction. Compared with the prior art, the technical problem that slender obstacles cannot be effectively converted into safety control distances capable of participating in flight control calculation especially under the environmental conditions that a large number of slender obstacles such as electric wires, branches and vines exist in power grid polling, forest low-altitude flight and the like is solved. According to the method, the slender linear obstacle is mapped into the equivalent dangerous volume field, and the continuous guided repulsive potential energy field is constructed to participate in the control quantity solution, so that the flight safety and stability of the unmanned aerial vehicle are improved.

The invention provides an intelligent driving safety control method based on multi-source data fusion, and the method comprises the steps: collecting multi-source data such as high-precision map data, vehicle-mounted sensor data and Internet of Vehicles data, carrying out the fusion analysis of the multi-source data to generate fusion environment information, calculating a dynamic risk level based on the information, and carrying out the calculation of the dynamic risk level. And executing a grading response control strategy according to the risk grade. Through multi-source data fusion, the sensing limitation of a single sensor under severe weather or complex road conditions can be overcome, and the accuracy and robustness of environment sensing are improved; the dynamic risk level calculation integrates the lane occupancy rate, visibility and other multi-dimensional factors, and realizes the accurate assessment of real-time risks; the grading response control strategy is gradually upgraded from early warning to active intervention according to the risk level, the problem of response lag or overreaction of a traditional scheme is avoided, and therefore the driving safety and the system adaptability are remarkably improved. The method is suitable for intelligent driving vehicles with different configurations, and the accident risk can be effectively reduced.

Provided in the present invention are a virtual-robot and physical-robot collaborative safety control method and apparatus based on mixed reality and a digital twin, which method and apparatus specifically relate to the technical field of smart manufacturing and assembly involving virtual-robot and physical-robot collaboration. In the solution, the method comprises: acquiring first posture information of a physical robot, and acquiring second posture information of a virtual robot; on the basis of the first posture information and the second posture information, performing pose registration on the physical robot and the virtual robot, so as to obtain a registration result; on the basis of the registration result and a preset virtual-robot and physical-robot workspace in combination with deep reinforcement learning, performing path planning to determine a virtual-robot and physical-robot collaborative motion path; and on the basis of the virtual-robot and physical-robot collaborative motion path, executing a virtual-robot and physical-robot collaborative safety control action. The solution uses a mixed-reality device to extract virtual-robot and physical-robot posture information, implements virtual-robot and physical-robot pose registration by means of a virtual-real space mapping method, and designs a bidirectional virtual-robot and physical-robot collaborative safety interaction policy based on deep reinforcement learning, thereby effectively improving the safety and effectiveness of a virtual-robot and physical-robot interaction operation.

The invention provides a vertical shaft type water inlet and outlet gate system of a pumped storage power station, and belongs to the technical field of water conservancy safety control. Comprising a cylindrical gate, a closed shaft, a trash rack section, a diffusion section, a straight pipe section, a bent pipe section and a tunnel section which are sequentially arranged on the cylindrical gate vertical shaft type water inlet and outlet in the power generation water flow direction. One end of the trash rack section is a horn mouth section, the trash rack section is provided with a cover plate, a diversion pier and a trash rack, and the lower portion of the cover plate of the trash rack section is connected with the diversion pier; digging a hole in the center of the cover plate; the trash rack is provided with an orifice serving as a water inlet / outlet orifice; a diversion tunnel is connected behind the bent pipe section; the gate is a cylindrical gate and arranged above the water inlet and outlet, the gate is vertically communicated, the bottom of the gate is fixed to the inner surface of the horn mouth section concrete, the top of the gate is fixed to a cover plate of the trash rack section, and the periphery of the gate is fixedly connected with a closed shaft through a diversion pier wall. The construction difficulty can be reduced, and the maintenance convenience degree can be improved.

The invention relates to the technical field of motor protection, and discloses a high-efficiency motor operation safety control method, which comprises the following steps: S1, based on an embedded sensor, adopting a multi-source data fusion algorithm, and through Kalman filtering and normalization processing, collecting and preprocessing operation data of a motor in real time, generating preprocessed multi-dimensional sensor data, and sending the preprocessed multi-dimensional sensor data to a server; and S2, on the basis of the preprocessed multi-dimensional sensor data, a physical model driving method is adopted, and a digital twin model of a motor is updated through a real-time simulation engine, so that a current motor state is mapped, and high-fidelity digital twin state mapping is generated. By collecting multi-source sensor data in real time and applying Kalman filtering and normalization processing, the data quality and consistency are remarkably improved, and a reliable foundation is laid for subsequent analysis; high-fidelity digital twinning state mapping is constructed based on physical model driving and a real-time simulation engine, dynamic and accurate representation of multiple physical field states in the motor is realized, and the state sensing depth is enhanced.

The invention provides a seat safety vibration control system and method based on health state dynamic reasoning. The method comprises the steps that physiological signals of a user are collected; analyzing according to the collected physiological signals to obtain a user health state grading result and a corresponding seat vibration and environment control execution scheme; according to the seat vibration and environment control execution scheme, the angles and the heights of the seats in all directions are adjusted, and opening and closing and the amplitude of vibrators of all subareas on all the seats are independently controlled. The intelligent health cabin system breaks through the limitation of single function guidance of traditional seat vibration, constructs an intelligent health cabin system from health monitoring to safety control to continuous optimization, has safety, accuracy, compliance and user experience, can be widely adapted to users in different health states and pathological types, and meets the industry development requirements of health cabins.

The invention discloses an electric tricycle controller intelligent parameter adjusting system and method based on Bluetooth, and the system is characterized in that the system comprises a mobile terminal which employs a smart phone to install an intelligent parameter adjusting APP, and comprises a parameter input and interaction module, a controller compatibility pre-detection module, a Bluetooth communication module A, and a data processing module; the vehicle-mounted controller serves as a core execution unit and comprises a hardware information storage and analysis module, an intelligent parameter adjustment module, a Bluetooth communication module B and a multi-dimensional safety control module, the communication stability is improved in a breakthrough mode, the communication error rate is reduced to 0.1% or below from 5%-8% in the prior art through the BLE 5.1 frequency hopping technology and XOR dual verification, the communication distance is increased to 15 meters, and the communication efficiency is improved. And no attenuation is caused when penetrating through a metal vehicle body.