398 results about "Active control" patented technology

The invention discloses a digital twinning-based major engineering construction safety active control method, system and equipment, and belongs to the technical field of construction active control. The problems of low safety risk identification efficiency and inaccurate risk control means in the major engineering construction process are solved, major engineering construction site environment and structural feature data are obtained through space-air-ground-water multi-way intelligent means and equipment, a digital twinborn model of a real-time mapping construction site is established, and the safety risk identification efficiency of the major engineering construction site is improved. Correcting the digital twinborn model by using real-time data; a digital twinborn model is used as a basic model of finite element analysis, mechanical properties of different construction stages are analyzed, dynamic early warning is performed on an analysis result in combination with real-time data, a safety risk derivation model is established to perform real-time analysis on key problems such as a risk point location, a risk influence range and risk derivation prediction in the digital twinborn model, and the safety risk derivation prediction result is obtained. Data support is provided for engineering safety active control, an active control strategy model is established, a control strategy is output to a servo control device according to risk information, strategy dynamic adjustment is performed according to a real-time effect, and high-precision cooperative control of major engineering safety monitoring is realized.

The invention discloses a heat dissipation control method, a heat dissipation control system, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a running state information sequence of a server in a preset time window, so as to sense a continuous power consumption and temperature change trend; according to the running state information sequence and preset architecture information, adopting a running mode recognition model to obtain a current running mode of the server; the adaptability and pertinence to different load scenes are improved; and according to the current operation mode, the operation state information sequence and the preset architecture information, obtaining thermal field evolution prediction information of the server by adopting a thermal field evolution prediction model, and according to the preset architecture information, the current operation state information and the thermal field evolution prediction information, obtaining a heat dissipation strategy, and outputting a heat dissipation instruction. The heat dissipation is converted from passive response to active control, the technical problem of poor timeliness of heat dissipation control is solved, and the effect of improving the timeliness and accuracy of heat dissipation control is achieved.

Systems, methods, apparatuses, and computer program products for enhancing dynamic CORESET adaptation with limited or no control channel overhead. One method may include receiving, by a UE, a plurality of PDCCH CORESET configurations associated with at least one SS; determining, by the UE, at least one of the PDCCH CORESET configurations is associated with a symbol or slot format from a corresponding set of symbols; and performing, by the UE, PDCCH monitoring on an active control resource set configuration associated with the set of determined slots or symbols.

The invention provides a vibration active control method based on a secondary channel online identification FxLMS algorithm, a vibration active control system comprises a sensor, a vibration signal conditioner, an actuator, a piezoelectric power amplifier, a real-time control simulation system and a computer, and a built SPMFxLMS algorithm physical model is embedded in a computer controller. The SPMFxLMS algorithm physical model comprises a vibration response error signal calculation module, a control channel adaptive filter, an identification channel adaptive filter and an unknown secondary channel model, and the unknown secondary channel model is constructed according to the identification channel adaptive filter and updated in real time. The algorithm physical model constructed by the invention adopts an improved power scheduling strategy and an identification adaptive filter to update and calculate the vibration response time domain data of the controlled object, so that the obtained control voltage signal is reliable, the residual vibration quantity caused by identification noise is avoided, the vibration suppression effect is excellent, and the method has excellent noise immunity and applicability.

The invention relates to the technical field of wing-mounted flight simulation, in particular to an immersive active control wing-mounted flight simulation system. The system comprises a physical simulation platform used for bearing an experiencer and providing immersive sensory feedback; the motion capture subsystem is used for collecting motion data of an experiencer in real time; the wing-mounted flight aerodynamic modeling subsystem is used for calculating aerodynamic characteristics in the wing-mounted flight process; the dynamic wind feeling simulation subsystem is used for dynamically adjusting the intensity and direction of a wind field and simulating wind pressure distribution in different flight states; the dynamic stress control subsystem is used for applying dynamic force feedback to the experiencer and simulating stress change in the flight process; the three-dimensional visual subsystem is used for simulating a visual picture in real time; and the sound subsystem is used for comprehensively simulating the environment sound effect in the flight process. According to the invention, the immersion and reality of wing-mounted flight simulation are greatly improved, and the safety and reliability are ensured.

The invention relates to the technical field of body intelligence, and discloses a body intelligence-oriented sensing active control method and system, and the method comprises the steps: obtaining multi-modal sensing data, and determining a feature extraction unit and a control center node based on a heterogeneous data fusion framework; selecting a target coding module according to the spatial-temporal correlation index of the adaptive coding module, and generating a feature fusion path; determining a signal synchronization moment by combining the dynamic response delay and the sub-layer coupling degree, and marking a real-time fusion topological graph; and detecting a signal phase conflict, and planning a multi-modal control instruction based on a conflict result. The technology also relates to perception mode priority grading, feature fusion path dynamic generation, spectrum interference detection, path re-planning and the like. Microsecond-level time-space stamp marking and modal feature decoupling are realized through a distributed heterogeneous sensor array. According to the invention, the multi-modal data fusion precision, the signal synchronization control and the multi-modal coordination decision ability are improved, and the environmental adaptability and robustness of the intelligent system are enhanced.

The invention discloses an intelligent network connection automobile active safety teaching control method based on digital twinning. The method comprises the steps that S1, multi-source sensor data are collected and preprocessed to generate a driving state data set; s2, inputting the state data into a digital twin model to drive a traffic scene and outputting a synchronous state; s3, based on Dueling-DDQN, executing strategy learning to generate an active safety control instruction; s4, the instruction response effect is simulated and verified in the virtual environment; s5, collecting driver operation behaviors, inputting the improved MHA-BiLSTM model to extract time sequence features, and outputting behavior features; and S6, comparing the driving behavior with a standard instruction item by item, calculating an operation deviation and a response difference, and generating a personalized active safety teaching task. According to the invention, efficient comparison and teaching feedback of the driving behavior and the active control strategy can be realized, and the intelligent level of driving training is improved.

The invention discloses a micro-mechanical gyroscope zero offset online temperature compensation method based on Q value active control, and belongs to the technical field of micro-mechanical gyroscope zero offset temperature compensation. According to the method, on the basis that asymmetry of quality factors of a driving mode and a detection mode is a main cause of an in-phase zero offset error, improvement of in-phase zero offset is achieved in a mode of controlling the quality factors of the modes through parametric excitation. According to the method, online identification of the intrinsic quality factor and the equivalent quality factor of the detection mode is realized by using a signal injection method, and online compensation of the equivalent quality factor is realized through parametric excitation. The method solves the problem of zero offset error drift caused by mismatching of quality factors of a driving mode and a detection mode in a temperature change environment, and a universal scheme is provided for the zero offset temperature stability of the high-performance micromechanical gyroscope.

The invention discloses an anti-settlement method and system for a conjoined building with asynchronous foundation construction. The method comprises the following steps: step 1, establishing a multi-stage coupling settlement prediction finite element model of the conjoined building; 2, a settlement active control system composed of a central control unit, a distributed sensor network and a servo hydraulic actuator array is arranged at the structure connection interface of the asynchronous construction areas of the connected building; 3, in the whole construction process, relative settlement data of all the areas are continuously monitored in real time; 4, comparing the real-time settlement data with the prediction model data, and calculating a deviation value; and 5, according to the deviation value, the needed active supporting force is calculated, the servo hydraulic actuator array is instructed to adjust the output force of the servo hydraulic actuator array in real time, and therefore differential settlement in the construction process is actively controlled within the preset minimum threshold value range. According to the invention, the internal stress of the structure is greatly reduced, and a long waiting period required by technologies such as post-cast strips is canceled.

The invention provides a rapid die change active regulation and control method and system applied to a paperboard processing line, and the method comprises the following steps: obtaining real-time order data and current production data of a production line, and configuring a die database based on the real-time order data; dynamically matching the real-time order data, the mold data and the current production data of the production line, and judging the mold changing priority of the real-time order data; constructing a balance model of the die change cost and the production efficiency, and obtaining an optimal production path based on the balance model; and simulating an optimal execution path action through a digital twin platform, predicting an interference risk and optimizing a production path to obtain a final production path. The method does not need to depend on a complex parameter identification process of a traditional rotating shaft physical model, six-degree-of-freedom error cooperative compensation can be achieved only through sensor data synchronization and a dynamic optimization algorithm, the method is suitable for computer rotating shafts of different loads and motion scenes, and the motion precision and reliability of equipment are remarkably improved.

The invention discloses a wireless communication control method and system based on a LoRa module, and relates to the technical field of wireless communication, and the method comprises the steps: configuring LoRa module communication parameters; rSSI and SNR monitoring is carried out on a currently used channel, and an optimal spreading factor parameter is evaluated and recommended; multi-channel optimization selection is carried out based on a spreading factor evaluation result, transmission capability evaluation is carried out on a selected channel, and an optimal main channel and an optimal standby channel are determined; analyzing and formulating a hierarchical resource scheduling strategy and a power consumption scheduling strategy based on the communication behavior data of the terminal node, and generating an active control instruction set; executing hierarchical access control and dynamic migration scheduling based on the active control instruction set, and executing a data transmission task; and counting performance indexes of each channel, and adjusting related control parameters to form adaptive optimization closed-loop control. According to the method, the channel adaptability is improved through real-time signal monitoring and spreading factor dynamic optimization, accurate resource configuration is realized based on a hierarchical scheduling strategy of behavior prediction, and the energy efficiency and robustness of the system are remarkably optimized.

The present invention discloses an active control wind tunnel for separately simulating mean wind and pulsating wind, belonging to the technical field of wind tunnel experimental research. This wind tunnel is a single test section direct current unsteady wind tunnel. A power section A is arranged at the front section of the wind tunnel. The power section A includes an array of fans for generating pulsating wind located at the very front end of the wind tunnel and a fan A for generating mean wind located in the flow direction of the pulsating wind air flow. The air flows generated by the array of fans and the fan A pass through the test section and are finally discharged from the air outlet at the end of the wind tunnel. A rainfall device is arranged in the test section. The wind tunnel is controlled by two independent active control modules, namely the fan A control system and the array of fans control system. The array of fans and the fan A can be used separately or in combination to achieve the simulation of complex wind field changes. A steady wind tunnel can also be arranged under the unsteady wind tunnel. The two layers of wind tunnels can be used separately or in combination to achieve specific functions such as simulating natural phenomena.

The invention provides a virtual synchronous generator control method based on adaptive RBF-LADRC, and the method comprises the steps: building a mathematical model of a virtual synchronous generator, introducing LADRC into a virtual speed regulator of the virtual synchronous generator as an outer loop frequency controller of an active control loop, and adjusting the active output of the virtual synchronous generator; adaptively adjusting a virtual coefficient in the mathematical model by using an RBF neural network; a voltage outer loop in voltage and current double-closed-loop control is designed based on linear active disturbance rejection control, a PI controller is replaced with an LED RC controller, a voltage and current double-closed-loop control system is obtained, and after reference voltage obtained through coupling of a virtual exciter and a virtual regulator is input into the voltage outer loop, double-closed-loop parameter adjustment control is carried out. Compared with traditional control, the method has the advantages that the system overshoot can be remarkably reduced, the active power tracking speed is increased, the power grid frequency stability is enhanced, and good anti-disturbance capacity is shown under the wind power plant short-circuit fault and low-frequency disturbance conditions.

The invention relates to the technical field of underground engineering construction monitoring and control, and discloses a multi-source monitoring data-based settlement risk early warning system for shield wall grinding undercrossing of a station, and the system comprises a data collection module which obtains shield and structure response data; the preprocessing module executes space-time correction to generate a synchronous data stream; the spectrum energy analysis module solves a mechanical-structural impedance coupling index; the rigidity inversion module uses the index to correct the specific energy and invert the residual thickness of the wall body; the active control module updates the kinetic model based on the thickness to calculate a frequency detuning factor; the decision module constructs a multi-dimensional feature vector, calculates a dynamic time warping distance, generates a control instruction according to a detuning factor, and achieves the active closed-loop control of the wall grinding risk. According to the method, a spectrum energy coupling analysis and dynamic stiffness inversion mechanism is introduced, and the apparent mechanical specific energy is corrected by using the mechanical-structural impedance coupling index, so that the accurate identification of the real physical state of the cutting medium and the real-time inversion of the wall residual thickness are realized.

The invention relates to the technical field of vibration active control and artificial intelligence, and discloses an aero-engine fan rotor keyless phase virtual dynamic balance method and system based on a finite element method, and the system comprises a modeling and simulation module which is configured to obtain the first-order critical speed and vibration mode of an initial rotor dynamics simulation model; the model calibration module is configured to calibrate the initial rotor dynamics simulation model according to the first actual amplitude to obtain a calibrated rotor dynamics simulation model; the model updating module is configured to update the calibration rotor dynamics simulation model to obtain an updated rotor dynamics simulation model; and the trial weight and counterweight module is configured to substitute the second actual amplitude into particle swarm optimization, calculate the actual total unbalance amount of the rotor system and the trial weight angle difference, and finally add a counterweight and select a direction until the vibration is reduced to realize dynamic balance. According to the invention, the position of the unbalance can be accurately determined only through amplitude analysis.

The invention discloses a hydroelectric generating set power active control method, system and device based on frequency prediction and a medium, and belongs to the technical field of power control, and the method comprises the steps: collecting basic operation data of a hydroelectric generating set adjusting system in real time, and carrying out the preprocessing of the basic operation data; time sequence characteristics of the power grid frequency data are extracted, and a neural network is adopted to generate a power grid frequency prediction sequence in a future time window; the power grid frequency prediction sequence is converted into a dynamic power feed-forward compensation amount, feed-forward compensation amount optimization calculation is carried out, the feed-forward compensation amount is superposed to the output end of a feedback controller, and a composite power instruction input value hydroelectric generating set is formed; and updating a neural network input sequence in each control period by adopting a rolling time domain prediction strategy, dynamically correcting a future prediction result, and realizing controlled quantity updating based on actual frequency deviation. According to the method, the compensation weight is dynamically updated by using the online adaptive algorithm, the prediction error and the system performance target are considered, and the compensation amount distribution is optimized.

The invention relates to the technical field of forklift safety analysis, in particular to a forklift safety analysis system fusing multi-modal data, which comprises a multi-modal data acquisition module, an environment-level safety analysis module, a vehicle-level safety analysis module, a behavior-level safety analysis module, a dynamic decision engine, a safety intervention module and a model optimization module. By collecting multi-modal data, constructing a dynamic environment risk model, a forklift comprehensive performance model and a sub-scene safety evaluation model and fusing multi-dimensional analysis results to generate a multi-level instruction, early warning and active control are achieved, and robustness is improved through self-optimization. The problems that a traditional technology depends on single data, a fixed threshold value fails, and closed-loop intervention is lacked are solved, and the forklift operation safety and efficiency can be comprehensively improved.

The invention discloses an anchoring system prestress active regulation and control method based on surrounding rock deformation monitoring, and relates to the technical field of geotechnical engineering supporting, and the method comprises the following steps: arranging various types of sensors in a surrounding rock supporting area, and collecting transverse displacement, vertical displacement, stress change and environmental parameters of surrounding rock in real time; and the data is transmitted to the central processing unit through the wireless data transmission module. By monitoring surrounding rock deformation in real time, the response speed of an anchoring system is increased, and the long-term stability of an engineering structure is ensured; the prestress is intelligently regulated and controlled, fuzzy control and a self-adaptive PID algorithm are combined, dynamic adjustment is achieved, human intervention is reduced, and the supporting efficiency is improved; the maintenance cost is reduced, the service life of an anchoring system is prolonged, manual inspection and material waste are reduced, and economic benefits are improved.

The invention discloses a network construction type energy storage transient stability optimization control method and system considering reactive power degradation, and belongs to the field of power system transient stability control. According to the method, a network construction type energy storage grid-connected system model is constructed, a reactive power degradation effect dynamically triggered by a reactive power control loop is analyzed, and an active power loop optimization control equation is established according to the reactive power degradation effect. On the basis, a self-adaptive torque adjusting module is embedded in an active control module, and when it is monitored that the internal voltage of the inverter is reduced to 50% of a rated value, the reference torque is automatically and dynamically corrected so as to improve the power angle transient stability margin of the system. Simulation results show that the method can effectively restrain the instability of the network construction type energy storage power angle, obviously enhance the transient stability of the system, and guarantee the safe and reliable operation of the power grid.

The invention relates to a self-adaptive drilling method and system based on hole wall stability and deep learning, and belongs to the technical field of coal mine underground drilling. Aiming at the technical problems that the drilling efficiency is low and the construction safety risk is high due to the fact that an existing drilling control method is difficult to effectively predict and avoid hole wall instability, the method adopts the core technical scheme that a drilling parameter sequence is collected in real time through an orifice sensor; calculating a hole wall instability risk probability in real time by using a pre-trained lightweight hole wall instability prediction network; further, the scheme evaluation network predicts future multi-target performance based on the current state and the candidate control set; and finally, constructing a scoring function by taking the hole wall stability as a primary constraint, preferentially generating a control instruction and issuing the control instruction to a drilling machine for execution. According to the method, intelligent prediction and active control over the stability of the hole wall are achieved, the safety and reliability of drilling construction are remarkably improved, meanwhile, the drilling efficiency and the hole forming quality are both considered, the system calculation load is low, and on-site deployment and application are easy.

The present invention provides a semi-active tuned mass damper based on intelligent materials, comprising: a passive control part, an intelligent control part, a first-stage vibration reduction part, a second-stage vibration reduction part and a health monitoring part, wherein the intelligent control part uniformly controls the first-stage vibration reduction part, the second-stage vibration reduction part and the health monitoring part; the tuned mass damper of the present invention can achieve two-way vibration control of a building structure, real-time monitor the vibration responses of the tuned mass damper and the main structure, and start the semi-active control of the intelligent materials after the computer control system collects and analyzes data.

The invention belongs to the technical field of vibration control, and provides a robust sliding mode control method and system for milling chatter of a frame beam type aviation thin-walled workpiece, and the technical scheme is as follows: determining cutter chatter active control application conditions; modeling is conducted on all corner milling technological processes through kinematics and dynamics, the corner milling process chip thicknesses in all the technological processes are established respectively, and the instantaneous milling force of a cutter is calculated in combination with the chip thicknesses; the flexibility of the cutter is considered, and a milling system flutter motion equation is established; based on a flutter motion equation of the milling system, considering cutting-in working condition change, unmodeled dynamics and external interference existing in the system, and designing a robust sliding mode controller of the system; the electromagnetic force in the milling system is calculated based on the designed electromagnetic force actuator control scheme, the control current of the electromagnetic actuator is calculated according to the input of the robust sliding mode controller of the system and the electromagnetic force, and non-contact control over the rotary cutter handle is achieved. The problem that milling chatter active control is poor in robustness and reliability is solved.

The invention relates to the technical field of automobile safety control, and provides an intelligent network connection automobile safety control system and method based on multi-source sensing fusion, and the system comprises a multi-source sensing module, a data processing module, and a cooperative control module. The multi-source sensing module comprises a sensor group and a data fusion unit, the sensor group senses information data and transmits the information data to the data fusion unit, and the data fusion unit performs fusion learning on the information data; the data processing module comprises a dynamic risk assessment module, an edge computing node and a cloud server, the dynamic risk assessment module analyzes and predicts data fused by the data fusion unit, the edge computing node is responsible for processing real-time data, and the cloud server performs global traffic flow optimization and historical accident data analysis; the cooperative control module comprises an active control layer and a passive control layer; the active control layer receives data processed by the edge computing node and performs active control; and the passive control layer receives data processed by the edge computing node and performs passive control.

The invention discloses an anti-overturning active control method and device for a single-column pier bridge and a medium. The method comprises the steps that a thundersight displacement monitoring system and a hydraulic driving system are deployed; leighting multi-mode fusion data are obtained, and the transverse overturning angle of a beam body is calculated; constructing a digital twinborn body for predicting the overturning state of the bridge structure, and predicting the overturning states of the bridge at multiple moments in the future; and constructing an intelligent agent, and controlling the hydraulic driving system to execute an anti-overturning action based on the predicted bridge overturning state. The device and the medium are both used for implementing the method. The beneficial effects of the invention are that the structure displacement monitoring method based on Reighting heterogeneous fusion is provided for a traditional mode of a single structure displacement detection means, interference factor identification and weighted fusion in different environments are realized, and the reliability of monitored structure displacement data is ensured.

The invention discloses a reinforcing system and method for structural damage of an arch bridge, and belongs to the technical field of bridge engineering. A prestressed carbon fiber grid reinforcing layer (material reinforcement), SMA anchorage device intelligent regulation and control (parameter self-adaption) and magneto-rheological damper active control (dynamic response optimization) realize deep coupling through a distributed sensing network and edge calculation to form system-level collaboration of strength improvement, rigidity adjustment and dynamic performance optimization; the subsystems are driven to work cooperatively through sensing data, and a new'prevention-reinforcement-regulation 'integrated bridge maintenance mode is constructed.

A deep reinforcement learning active control parameter adjustment method considering random time delay comprises the steps of determining a controlled structure model and an actuator installation position, and establishing a structure vibration control kinetic equation; determining a preliminary controller; random time delay is added in the actuator to simulate a real working environment, and a two-way communication environment is established; responding to the set feedback function by using the structure collected by the sensor; determining a deep reinforcement learning algorithm, a neural network framework and training parameters, and establishing an interactive interface; training a deep reinforcement learning agent in the interactive interface, and feeding back a function value through accumulated reward expectation of a deep reinforcement learning algorithm to carry out strategy updating iteration so as to obtain a mature agent; a mature intelligent agent is used as a parameter regulator in a preliminary controller, parameter values are regulated in real time, and vibration control of the structure under random time lag is achieved. According to the method, the robustness of the controller in a complex environment is ensured, and the method belongs to the field of structural vibration control.

The invention discloses a squeeze film damper active control method based on RBF neural network PID optimization. The method comprises the following steps: acquiring state parameters of a rotor system through a data acquisition module; inputting the state parameters into an RBF neural network, and performing online optimization on PID control parameters by using the RBF neural network; based on the optimized PID parameters, a piezoelectric ceramic actuator is driven through a piezoelectric ceramic controller, the damping force of the extrusion oil film damper is adjusted, and active control over vibration of the rotor system is achieved. According to the method, RBF neural network nonlinear approximation and PID control stability are fused, the hysteresis and locking problems of traditional passive control are solved, active suppression of rotor system vibration is achieved, and the control precision and reliability are greatly improved.

The invention relates to an exoskeleton safety redundancy strategy and switching method based on anomaly detection, and the method comprises the steps: collecting a tension state between the active control intention of a user and the response execution of an exoskeleton system, defining as a potential anomaly when continuous asynchronous tension exists between the expected control direction of the user and the inertial behavior of the system; a redundancy strategy triggering precondition is adopted; after abnormal triggering, the system outputs a control suggestion, and whether the control right should be taken over by a redundancy strategy or not is judged by combining user reverse force application and posture deviation trend behavior performance, and master control continues to be maintained or a forced freezing state is entered; the intervention level of a redundancy control strategy is dynamically adjusted, and continuous control evolution from low intrusion to high takeover is achieved; if it is detected that an irreconcilable confrontation trend exists between continuous reverse intention input of the user and control output of the system in the control evolution process, and intervention ratio unbalance of the system is continuously expanded, the system enters a soft freezing or conservative attitude mode; and in the freezing state, judging whether the man-machine trust relationship is reconstructed or not.

The invention discloses a multi-axis linkage machining precision optimization control method adopting real-time detection driving, and relates to the technical field of machining intelligent control. The method comprises the following steps: establishing a multi-source real-time detection system comprising a laser displacement sensor, a force sensor and an industrial camera, and collecting multi-source data; performing time-space synchronization calibration on multi-source data, and improving a fusion vector of a feature by borrowing an attention mechanism convolutional neural network; constructing a machining precision dynamic compensation model by combining the kinematics forward and inverse solution sub-model and the tool thermal deformation compensation sub-model, and iteratively optimizing parameters by adopting an improved particle swarm optimization algorithm; the compensation amount is converted into a numerical control instruction through a data conversion module; and after compensation, data feedback updates the feature vector, and compensation parameters are dynamically adjusted until machining is finished. According to the invention, an active control system of multi-source detection, intelligent analysis, dynamic compensation and dynamic feedback is constructed, precise control of the whole processing flow is realized, and a general solution is provided for precision upgrading in the field of high-end equipment manufacturing.

The invention discloses a fault diagnosis and self-healing control method, system, equipment and medium for a flexible interconnection device of a power distribution network, and belongs to the technical field of fault diagnosis and control of the power distribution network, and the method comprises the steps: building a system state vector, collecting operation parameters of the power distribution network, and carrying out the weighted fusion; performing quality inspection on the original data based on the acquired operation parameters, and dynamically adjusting model parameters through digital twin synchronization and model parameter updating; deep learning is carried out to carry out multi-level fault feature extraction, and multi-scale feature fusion is carried out; a fault mode identification module is started, and fault probability is calculated for fault classification; evaluating the severity of the fault according to a fault diagnosis result, selecting an optimal control strategy, and carrying out self-healing control; and fault early warning is started to estimate a future fault occurrence probability, and early warning information of different levels is issued to perform preventive maintenance. According to the invention, full-process automation and intelligentization from data perception and intelligent diagnosis to active control and prospective maintenance are realized.