951 results about "System stability" patented technology

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention belongs to the technical field of crossing of environmental engineering and ocean dynamics, and particularly relates to a tidal dynamics prediction method and control device for a seawater desulfurization system, and the method comprises the steps: constructing a time-space coupling prediction model fusing multi-source hydrological observation data, and introducing a nonlinear dynamic weight distribution mechanism; the influence of terrain constraint, wind stress disturbance and upstream runoff on tidal propagation is quantified in real time, and a rolling prediction sequence of tide level phase, flow velocity gradient and salinity disturbance in the next three hours is output; the prediction result drives the scheduling of a desulfurization pump set, the adjustment of spraying density and the matching of aeration intensity, and the model weight is corrected on line based on the actually measured feedback of desulfurization efficiency to form closed-loop control. By means of the technical scheme, accurate cooperation of the operation parameters of the desulfurization system and tidal dynamics is achieved, meaningless energy consumption is reduced while the desulfurization efficiency is guaranteed, and the utilization rate of the desulfurization agent and the stability of the system are improved.

The invention discloses a force control joint torque self-correction method based on digital twinborn mapping, which comprises the following steps of: acquiring and preprocessing multi-source operation data of a force control joint to generate a standardized input data set; establishing a three-layer digital twinning mapping model to realize physical, simulation and cognitive layer mapping; a bias load device is arranged, and angle, torque, current and temperature signals are collected; space-time tensor compensation is executed on the simulated twinborn layer, and a dynamic compensation value is generated; bootstrap consistency correction is executed on the cognitive twin layer, and comprehensive correction parameters are fused; and inputting comprehensive parameters to adjust a simulation layer structure, outputting a correction torque and performing closed-loop updating. By constructing the three-layer digital twinning mapping model and introducing the torque compensation algorithm, torque dynamic error identification, adaptive compensation and real-time self-correction of the force control joint under complex working conditions are realized, and the measurement precision and the system stability are improved.

The invention relates to the technical field of micro-grid control, and discloses a micro-grid adaptive switching control method and system based on multi-source information fusion, and the method comprises the steps: obtaining the internal operation parameters of a micro-grid, and the state parameters of an external large power grid connected with the micro-grid; the internal operation parameters and the external large power grid state parameters are fused, and a real-time comprehensive operation state model of the micro-grid is constructed; based on the real-time comprehensive operation state model, adaptively determining a target operation mode of the micro-grid; and according to the target operation mode, generating and executing a smooth switching control instruction of the micro-grid. According to the method, the multi-source operation information of the large power grid inside and outside the microgrid is fused, the comprehensive operation state model is constructed in real time, the target operation mode is adaptively selected, and switching control is smoothly executed, so that efficient and stable switching of the microgrid between multiple modes such as grid connection and island can be realized, and the power supply reliability, the system stability and the electric energy quality are improved.

The invention discloses a system and a method for analyzing stability and oscillation transmission of a network-forming converter system, and belongs to the technical field of virtual synchronous generator control, and the system comprises an active power control loop which takes an active power deviation value as input, and outputs a power angle of a VSG system through an active integration part and a feedback part; the reactive power control loop takes the reactive power deviation ratio as input, and outputs the voltage amplitude of the VSG system through an active integration part and a feedback part; according to the analysis method, whether each state variable is stable or not is judged firstly, and then the intensity of oscillation transmission between the state variables is judged. According to the method, the stability of the system can be evaluated more accurately, and the accuracy of system stability analysis under dynamic disturbance is ensured; an oscillation transfer effect function is introduced, and the mutual influence between different state variables is quantitatively analyzed; and the dynamic fluctuation range of each state variable in the system can be accurately evaluated.

The invention relates to the technical field of industrial process automation control, and discloses an intelligent adjusting method for an industrial waste liquid low-temperature evaporation process, which comprises the following steps of: superposing pressure step test excitation to a vacuum adjusting valve, synchronously acquiring temperature response data flow, calculating absolute error integral and SIAE and reply time characteristics of a response curve, and calculating the temperature response data flow according to the absolute error integral and SIAE and reply time characteristics of the response curve; substituting into an inverse model to solve an equivalent process gain and a time constant of the controlled object; based on the internal model control principle, PID controller parameters are dynamically calculated and updated through the calculated gain and the time constant, a control law is reconstructed to generate an execution instruction, and the problem of control divergence of a non-linear time-varying object under model mismatch is solved through active energy feature identification and a pole assignment mechanism; decoupling control and self-adaptive adjustment of fluid viscosity sudden change and equipment scaling are achieved, and the stability and control precision of the system are improved.

The invention discloses a photovoltaic energy storage cooperative control method and system, and the method comprises the steps: obtaining the real-time state data and environment information of distributed photovoltaic and energy storage equipment, and constructing a system network topology; constructing a hierarchical prediction model including photovoltaic power generation prediction, load demand prediction and electricity price prediction based on the system state data set; constructing a multi-objective function containing an economic benefit objective and a system stability objective based on a prediction result, and performing objective decomposition through recursive iteration; according to a local optimization target, generating a photovoltaic power adjustment strategy and an energy storage charging and discharging strategy by reducing a distributed maximum matching gap and reducing an order to break a distributed maximum independent set obstacle algorithm; and based on the distributed control strategy set, the system operation state is monitored in real time, and control parameters are dynamically adjusted by adopting an event triggering mechanism. According to the method, the problems of calculation complexity and communication dependence of traditional centralized control in a large-scale photovoltaic energy storage system are solved.

The invention provides a distributed energy storage device group intelligent cooperative control optimization method and system, and relates to the technical field of group intelligence, and the method comprises the steps: obtaining source load fluctuation data, constructing a dynamic defense topological graph with energy storage devices as nodes and electrical coupling relations as edges, recognizing a disturbance propagation path and a node disturbance arrival time sequence through graph convolution operation, and obtaining a distributed energy storage device group intelligent cooperative control optimization model. Obtaining a node disturbance sensitivity quantized value; dividing the energy storage equipment group into a front defense domain and a back-up defense domain based on the quantized value and a preset layering threshold value; reversely deducing a power regulation sequence aiming at the front defense domain to generate an active defense instruction, and generating a following type scheduling instruction aiming at the back-up defense domain; issuing and executing the instruction, collecting topological response data, and jointly updating the edge weight and the propagation coefficient based on the deviation. According to the method, disturbance pre-compensation control is realized through dynamic topology modeling and a layered defense strategy, and the cooperative response efficiency of the energy storage equipment group and the system stability are improved.

The invention belongs to the technical field of artificial intelligence, and discloses an artificial intelligence model training resource adaptive distribution system. The method comprises the following steps: acquiring and calculating graph structure data and hardware resource state data in real time, and calculating a data reuse rate and generating a candidate operator fusion scheme by constructing an operator execution time sequence constraint matrix and identifying an operator cluster of data locality characteristics; a resource competition hotspot prediction mechanism is introduced, memory bandwidth occupation fluctuation characteristics are analyzed, a resource conflict probability is calculated for a fusion scheme, and a dynamic balance optimization model of fusion income and resource conflicts is constructed. An optimal operator fusion decision sequence and a resource allocation strategy are generated through iterative solution, and accurate dynamic adjustment of computing resources in the training process is achieved. The training efficiency and the resource utilization rate are improved, the energy consumption is reduced, and the system stability is enhanced.

The invention discloses an embedded motor torque ripple real-time compensation system based on deep learning, and relates to the technical field of motor control and artificial intelligence crossing, the system comprises a hardware data acquisition module, a signal feature extraction module, a deep learning compensation model module, an embedded real-time compensation module and a compensation effect verification module; a motor position and a current signal are acquired in real time through the hardware data acquisition module, dynamic analysis and multi-dimensional feature construction are performed on torque ripple related features by combining the signal feature extraction module, and the deep learning compensation model module learns a fluctuation rule in historical data by using a space-time sequence model to generate a high-precision compensation current. The model adapts to an embedded environment through lightweight design, real-time deployment and dynamic optimization of a compensation algorithm are achieved, compared with traditional PID control, the scheme can adapt to changes of motor operation conditions, the influence of torque fluctuation on system stability is reduced, and the smoothness and efficiency of motor operation are improved.

The invention provides an intelligent fault diagnosis method and system for electromagnetic valve test data and a computer program product, and relates to the technical field of electromagnetic valve fault diagnos.The intelligent fault diagnosis method comprises the steps that firstly, multi-dimensional test data of an electromagnetic valve under multiple working condition combinations are collected, and multiple types of data including action response and the like are contained; a data behavior collaboration model is constructed based on the behavior collaboration relation between the data, and laws such as synchronous response are embodied; an abnormal collaboration chain deviating from a normal collaboration mode is mined from the model, and abnormal performance of each link is determined; reversely deducing a fault core cause through the abnormal collaborative chain, and determining information such as cause types; and finally, generating an executable instruction sequence based on the fault core inducement and the abnormal collaborative chain conduction characteristic, and controlling the maintenance system to execute fault handling operation. Therefore, faults can be comprehensively and accurately diagnosed, and the operation reliability and the system stability of the electromagnetic valve are improved.

The invention discloses a high-speed data processing and exchanging method and system based on Zynq UltraScale + MPSoCs, and belongs to the technical field of data processing.The method comprises the steps that nanosecond-level synchronous pulses are generated through a PL end of Zynq, initial multi-modal data are collected, the transmission rate and integrity are analyzed through a double-SERDES controller of the PL end, and a dynamic data processing matrix is constructed; pL and PS end data flow distribution is simulated and optimized through a preset multi-data granularity scale, and an exchange instruction set is generated; in combination with an on-chip bus topology, a first data exchange path is formed through DMA-OCM interaction and hardware accelerator adaptation, and a second data exchange path is obtained by adjusting sub-channel allocation and bus priority; and the operation state parameters of the second data exchange path are written back to the data frame state attribute bar corresponding to the dynamic data processing matrix in real time, so that efficient processing and dynamic optimization of the multi-modal data are realized, and the system stability and the transmission efficiency are improved.

The invention belongs to the technical field of crossing of artificial intelligence and computer system architecture, particularly relates to a computing power center equipment power supply intelligent system of a knowledge graph, and aims to solve the problem of low energy efficiency caused by extensive power supply management, unbalanced energy consumption and lagged load response. The system comprises a task analysis module, a dynamic load prediction module, a multi-source power supply scheduling module, an equipment-level power supply control module and a feedback optimization module, through load prediction and multi-source power supply cooperative scheduling driven by task semantics, voltage and current dynamic adjustment and sleep wake-up control are achieved, and a strategy is optimized online based on operation feedback. The scheme improves energy efficiency, equipment life and system stability.

The invention discloses a deviation correction method, system and equipment based on a tool bit, and relates to the technical field of tool bit correction.The method comprises the steps that after a tool bit replacement correction request sent by a user side is received, the system executes first-time destination cutting, generates a destination cutting groove virtual model, extracts cutting parameters and compares the cutting parameters with preset parameters, and destination cutting deviation data is obtained; the system compensates the tool bit track and then executes second-time return cutting, a return cutting groove virtual model is generated, cutting parameters are extracted, and return cutting deviation data are obtained; the residual deviation is compared with a preset residual deviation threshold value, and whether the tool bit is corrected or not is judged; and if the correction is not completed, calculating a correctable value, and judging whether the return cutting deviation data can be continuously used for subsequent compensation or not. According to the method, whether the tool bit deviation can be compensated or not can be dynamically judged according to actual conditions, system oscillation caused by blind correction is avoided, rapid detection, effective compensation and intelligent decision making of the tool bit deviation are achieved, and the cutting precision and the system stability are improved.

The invention relates to a collaborative optimization method and device for multi-source adaptive networking and load distribution, belongs to the technical field of power grids, and solves the problems of low system stability and low energy utilization efficiency in an existing mobile emergency microgrid. Comprising the following steps: constructing a hierarchical collaborative architecture which takes a multi-source network inverter as a bottom layer, model prediction control as a middle layer and multi-agent reinforcement learning as an upper layer; the upper layer receives the global state of the system and outputs an action reference instruction sequence to the middle layer through multiple agents; the middle layer optimizes and corrects the action reference instruction sequence of the upper layer by constructing a target function and a constraint condition based on the prediction model to obtain a control reference value to the bottom layer; and the bottom layer takes the control reference value as a reference target of cooperative control of the multi-source networking type inverter, executes stable control and self-adaptive networking of voltage and frequency, updates the global state of the system and feeds back to the upper layer and the middle layer to form cooperative optimization. And the system stability and the energy utilization efficiency are improved.

The invention relates to the technical field of generator set intelligent control, and discloses a generator set intelligent control method and system, and the method comprises the steps: obtaining the sensor data and vibration signals of a generator set; obtaining a corrected frequency characteristic value according to sensor data screening, denoising and frequency characteristic correction; generating and iteratively updating a control decision instruction sequence according to the real-time change of the vibration signal and the corrected frequency characteristic value to obtain a system stability index; and combining system stability index feedback adjustment to obtain a control decision reliability index. The method can solve the problem of noise interference under a dynamic working condition, realizes fault early warning, parameter optimization and system stability, and improves energy efficiency and operation reliability.

The invention discloses a permanent magnet synchronous motor PI double-loop control method based on deep reinforcement learning. The method comprises the following steps: establishing a permanent magnet synchronous motor double-loop coupling mathematical model; a traditional FOC composite controller is constructed; designing a state feedback and reward mechanism of the double-ring TD3; constructing a speed loop and current loop TD3 + PI composite controller; and carrying out system stability analysis. According to the method, a system is divided into an inner ring and an outer ring based on hybrid cooperative control fusing deep reinforcement learning and classical control, the problem that robustness is insufficient under system disturbance through PI control and the problem that tracking precision is reduced due to load sudden change and noise interference in PMSM operation are combined, nonlinear disturbance is dynamically compensated through the strategy learning ability of a TD3 algorithm, and the tracking precision is improved. Meanwhile, soft update is introduced to improve convergence stability, and a compound control strategy based on double-loop TD3 + PI is provided. According to the invention, complex non-linear processing is simplified, and the stability of the system and the accuracy of rotating speed tracking are improved at the same time.

The invention relates to the technical field of computer storage, and particularly provides a large model training storage resource dynamic allocation method, device and system, and the method comprises the steps: collecting a workload index and a storage system state index of a large model training task; inputting the real-time monitoring data into a convolutional neural network (CNN) model, and outputting a feature identifier of a current training stage; inputting the real-time data and the stage identifier into a long short-term memory network LSTM model, and predicting future bandwidth, IOPS and storage space requirements; and generating a resource allocation scheme through multi-objective optimization according to a prediction result in combination with a system state, and executing load allocation, data layering and bandwidth reservation operations. According to the method, training stage perception and resource demand prediction are realized through cooperation of the CNN and the LSTM, and dynamic allocation and advanced scheduling of storage resources are realized, so that the resource utilization rate, the training efficiency and the system stability are improved.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention discloses a vibration event early warning diffusion method based on edge calculation, and relates to the technical field of computer application, and the method comprises the following steps: constructing a topological phase coordinate under a unified time baseline, playing back a historical track of early warning diffusion based on the topological phase coordinate, recognizing a continuous path segment with a closed trend in the track, and carrying out the early warning diffusion. Outputting a loop suspicious region; a fingerprint code of early warning information is constructed on the basis of a loop suspicious area, a three-dimensional feature set containing time sequence features, path sequence features and phase sequence features is extracted, and a corresponding loop suspicion degree spectrum is generated. According to the method, through space-time modeling, path fingerprint construction, causal discrimination and duplicate removal mechanisms, accurate identification and repeated suppression of a vibration early warning information diffusion path are realized, dynamic threshold regulation and reverse intervention are combined, loop closed-loop treatment and topology self-healing are realized, and early warning accuracy, timeliness and system stability are remarkably improved.

The invention discloses an amphibious robot energy efficiency optimization control method for amphibious cross-domain operation, and belongs to the technical field of robot control, and the method comprises the steps: obtaining multi-modal sensor data of an amphibious robot, carrying out the feature extraction of the multi-modal sensor data, and generating a coupling feature vector; and the stability constraint trained neural network generates a dynamic value function, predicts the time-varying constraint in a future time domain through a system evolution model based on the dynamic value function and the coupling feature vector, and generates a correction value to adjust the structure parameter of the dynamic value function. Performing real-time nonlinear optimization on the corrected dynamic value function, and solving an optimal control instruction; collecting actual response data of the system, and updating a model used for feature extraction and prediction on line by using the actual response data of the system; on the premise that the system stability is guaranteed, the environmental adaptability and the overall energy efficiency of the robot in the water and land cross-domain process are remarkably improved.

The invention belongs to the technical field of large model fine tuning, and particularly discloses a large model fine tuning parameter version chain management method and system, a terminal and a medium. Comprising the following steps: in the fine tuning process of a large model, performing difference calculation on model parameters after two adjacent times of fine tuning to obtain corresponding parameter increments; packaging the parameter increment, the time information, the model identifier and the fine tuning metadata into log entries; establishing a time index based on the time information of the log entries, establishing a model index based on the model identifier, and associating the log entries according to a time sequence to form a parameter version chain; when a model parameter recovery request is received, retrieving a log interval before a target time point according to the time index, loading and accumulating each parameter increment, reconstructing a model parameter state corresponding to the target time point, generating a parameter snapshot in a memory, and caching the parameter snapshot; according to the method, multi-version management and quick rollback can be realized under the condition that a full-quantity model is not stored, the storage occupation is reduced, and the parameter recovery efficiency and the system stability are improved.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention relates to the technical field of industrial digital twinning control, and discloses an optical fiber connector production line monitoring system based on digital twinning, and the system comprises a curing unit which generates a digital accompanying bill representing the hardness trend of a product based on thermal energy integration; the grinding unit reconstructs a targeted pressure and duration process formula according to the bill before the product arrives; according to the system and the method, cross-process feed-forward control logic is constructed, mandatory constraint is carried out on machining parameters by utilizing thermal historical data, error transmission caused by dispersion of a previous process is eliminated, and the system stability is improved.

The invention relates to an alternating-current trunk line or an alternating-current power distribution network, in particular to a method for improving the stability of a wind power system through combined control of network construction type energy storage and a nine-switch UPQC. The method comprises the following steps: firstly, proposing an active regulation and inertial support control method based on a virtual synchronous machine mechanism for network construction type energy storage; and secondly, by constructing a coordination control framework, dynamic response complementation of the energy storage system and the UPQC under disturbance is realized, so that the overall frequency stability and voltage supporting capability of the system are improved. And finally, building a system model in Matlab / Simulink, setting three typical working conditions of wind power fluctuation, frequency disturbance and serious voltage drop, and carrying out scheme validity verification. Simulation results show that the method can effectively stabilize the wind power output fluctuation, enhance the frequency and voltage support capability, and significantly improve the stability of a wind power system.

The invention relates to the technical field of motion control, in particular to a motion trail real-time optimization analysis method and system based on multi-modal perception, and the method comprises the following steps: a multi-sensor array obtains position and speed visual features, carries out the coordinate transformation, extracts a direction amplitude, and builds a multi-modal perception data set; the method comprises the following steps: inputting Kalman filtering prediction trajectory comparison deviation to generate a trajectory deviation vector field, calculating a deviation gradient, screening an over-threshold point marking time sequence, generating a dynamic weight correction parameter through weighted least square distribution normalization, and outputting a real-time optimization trajectory scheme through weighted correction fusion coordinates. The position information and the velocity vector of a moving target are obtained in real time by fusing multi-modal sensing data, a state estimation model is combined to predict a trajectory, a trajectory deviation vector field is generated, and a deviation point is subjected to weighted correction, so that the trajectory optimization precision is effectively improved, the weight is dynamically allocated to optimize the priority, and the trajectory optimization precision and the system stability are improved. And the self-adaption and robustness in a complex environment are enhanced.

The invention discloses a rural power distribution network-oriented network tracking / constructing type source storage stability evaluation and planning method, and relates to the technical field of rural power distribution networks, and the method comprises the steps: constructing an inverter control model, and building a quantitative relation between an inverter capacity ratio and a system short-circuit ratio; according to the control model, an inverter hybrid system is established, and stability weak nodes are identified through characteristic value analysis of a system state matrix; and on the basis of the quantitative relationship and the stability weak nodes, calculating the energy storage capacity of the network to be constructed, and performing iterative configuration according to a preset priority until the stability requirement of the system is met. According to the method, accurate identification of the stability weak nodes and accurate configuration of the constructed energy storage are combined, accurate processing of the risk of the stability weak nodes of the rural power distribution network is realized, the disturbance resistance and the new energy consumption level of the rural power distribution network are improved, and stable, safe and efficient operation of the rural power distribution network is realized.

The invention relates to the field of constructional engineering, and discloses a dynamic risk prediction system, which generates space-time alignment input through multi-source data fusion, adopts tensor field modeling to embed contract constraint to construct a risk dynamic model, and solves and outputs a continuous risk field through a partial differential equation; a propagation path is analyzed in combination with asymmetric causal analysis, model parameters are adjusted in real time through a dynamic optimization algorithm, and closed-loop optimization of a risk field is achieved; and finally, through four-dimensional thermodynamic diagram interaction early warning and resource intelligent scheduling, a whole-process closed-loop system of data modeling-causal analysis-dynamic optimization-visual management and control is formed. According to the method, dynamic optimization of risk field parameters is realized through adjoint equation back propagation, and the modeling precision of a complex scene is improved; a four-dimensional space-time thermodynamic diagram rendering technology is innovated to solve the problem of fragmentation of multi-modal information expression, and risk disposal response is accelerated; key task resource supply is guaranteed by combining video memory preemption and containerization scheduling strategies, and the system stability bottleneck in a high-load scene is overcome.

The invention discloses a landslide displacement double-layer fusion prediction method and model, and the method comprises the steps: carrying out the decomposition of an original displacement time sequence through employing an ICEEMDAN algorithm, and obtaining a plurality of IMF components; performing feature engineering on each IMF component, representing a displacement trend by adopting a trend slope and a window mean value, representing mutation early warning by adopting kurtosis and a frequency spectrum entropy, representing a period rule by adopting a main frequency and a zero-crossing rate, representing system stability by adopting a sample entropy and a standard deviation, and constructing a three-dimensional feature space fusing a time domain and a frequency domain; data standardization is carried out on the extracted features, the interference effect of dimensions on the model is eliminated, and it is ensured that all feature dimensions are within a unified calculation scale range; a CNN-BiLSTM model is constructed for each IMF component; a CPO algorithm is used to optimize the CNN-BiLSTM model; according to the method, the data acquisition difficulty during model training and use can be reduced, and the usability of the model in actual deployment is enhanced; and the prediction precision and the accuracy of landslide displacement prediction are improved.

The invention relates to the technical field of industrial robots and intelligent control, and discloses an explosion-proof robot MOS tube optimization driving control method and system based on an intelligent algorithm. The method comprises the following steps: acquiring multi-point temperature monitoring data of a power device to construct a virtual thermal gradient model; according to the virtual thermal gradient model, operation boundary parameter optimization calculation is carried out, and a dynamic safety threshold value is determined; acquiring real-time output state data of the driving system and generating a preliminary steady-state control strategy by combining the real-time output state data with the dynamic safety threshold; monitoring load efficiency feedback data, and triggering multi-target optimization of driving parameters by taking the dynamic safety threshold as a boundary constraint when an efficiency decline feature is identified, so as to obtain a to-be-verified driving parameter set; performing online prediction verification on the to-be-verified driving parameter set to generate a verification result identifier; and according to the verification result identifier, performing configuration updating or configuration keeping processing on a control register of the driving system during operation. The method effectively improves the system stability.