1508 results about "System stability" patented technology

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

The invention discloses a stability analysis method of a network tracking / constructing hybrid station based on an impedance model, and relates to a modeling and stability evaluation technology of a wind power plant grid-connected system. The method comprises the following steps: firstly, respectively establishing sequence impedance models of a following network type direct-driven fan and a construction network type direct-driven fan based on a harmonic linearization method, and verifying the accuracy of the established models in different frequency bands by combining an impedance sweep frequency method; secondly, on the basis of considering the influence of line impedance, deducing to form an impedance network structure of a hybrid wind power plant grid-connected system, and constructing a stability judgment condition suitable for system analysis; and finally, based on the established impedance stability criterion, identifying possible oscillation risks of the system in subsynchronous and supersynchronous frequency bands. The method is suitable for wind power system stability analysis in various hybrid access scenes, and provides theoretical basis and technical support for subsequent system parameter setting, control strategy optimization and oscillation suppression measures.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention relates to the technical field of computing power scheduling, provides a distributed computing power scheduling method and system, breaks through the limitation that a traditional computing power scheduling system depends on a single index or a static strategy by constructing an integrated architecture of data perception, load prediction and intelligent scheduling, and constructs a self-adaptive and closed-loop optimized intelligent scheduling system. The scheduling system obtains multi-source data in real time and fuses the multi-source data into a perception vector to accurately describe the running state of the system, accurate pre-judgment of load changes is achieved through hierarchical load prediction, and resources can be efficiently allocated in different scenes in combination with double scheduling paths and execution feedback optimization; according to the scheduling system, the resource utilization rate of the distributed system is effectively improved, the energy consumption cost is reduced, the task execution delay is reduced, the system stability and the fault-tolerant capability are enhanced, and an innovative computing power scheduling solution is provided for a large-scale distributed computing scene.

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention discloses a current transformer real-time state diagnosis system based on Internet of Things, which relates to the technical field of electrical equipment state diagnosis and comprises a reference construction module, a disturbance excitation module, a modeling identification module, a driving unwrapping module, a self-adaptive setting module and a closed-loop control module, in the operation process of a secondary winding loop of the current transformer, a nanosecond time synchronization reference is established, a phase reference baseline is locked, current vector trajectories are continuously collected based on the time synchronization reference and the phase reference baseline, and a discrete phase residual image is constructed. According to the method, through high-precision time synchronization, phase disturbance excitation, sparse modeling identification, driving unwrapping and self-adaptive setting control, hysteresis oscillation of the secondary circuit of the current transformer is accurately identified and suppressed, the fault diagnosis accuracy and the system stability are remarkably improved, and the limitation of a traditional method in the aspects of identification sensitivity and response capability is broken through.

The invention relates to the technical field of cooperative control, in particular to a multi-station wind and light cooperative energy storage optimization control system, which comprises a wind and light output change identification module, an energy storage window state screening module, a linkage section intersection calibration module, a multi-station energy storage priority allocation module and a cooperative regulation and control instruction arrangement module. According to the method, the wind power climbing extreme value and the photovoltaic descending section are extracted, the matching relation between the power generation complementary characteristic and the energy storage response time is established, the time sequence scheduling precision is enhanced, the SOC and the operation state are fused to perform multi-dimensional screening on the energy storage units, the non-adjustable resources are eliminated, the execution reliability is improved, and the output fluctuation and energy storage window intersection is constructed; standardized scheduling time slices are generated, unified positioning of tasks is achieved, energy storage resource grouping and conflict elimination are completed based on available duration, priority ranking is clear, scheduling coordination is guaranteed, redundant conflict items are removed, instruction stream integrity and control coverage are improved, and execution efficiency of multi-station cooperative scheduling and system stability are enhanced.

The invention relates to the technical field of artificial intelligence, and particularly provides a low-sample NL2SQL intelligent generation method and device, and the method comprises the following steps: S1, enabling a dynamic sample extension module to solve a training data sparse problem in a small sample scene through an intention clarification and data enhancement technology; s2, the clause dependency chain type generation framework converts natural language query into structured query language (SQL) statements with clear structures through semantic analysis, clause generation and dependency modeling; s3, the multi-agent cooperation platform performs iterative optimization through intention recognition, SQL generation and code execution; s4, enabling an automatic evaluation and iteration mechanism to pass a standardized test and continuous optimization; and S5, carrying out field adaptation and security enhancement. Compared with the prior art, the complex query processing capacity can be improved, and the stability and safety of the system are guaranteed through automatic evaluation and a safety mechanism.

The invention relates to the technical field of cutter suction dredger steel pile trolley control, and discloses a cutter suction dredger steel pile trolley control method and system based on hydraulic excitation early warning, and the method comprises the steps: collecting the influence degree of multiple parameters on the system stability, calculating a pressure fluctuation factor and the like to form a state feature set; constructing a dynamic pressure balance model, inputting data such as a pressure time sequence queue and a sea condition level, and outputting a dynamic pressure adjustment value; constructing a cooperative control correction model, inputting steel pile attitude data and outputting a cooperative correction coefficient; and calculating a final control output quantity by combining the adjustment value and the correction coefficient, and generating a hydraulic excitation control instruction set. The system comprises a multi-source sensor module, a state feature calculation module, a dynamic pressure balancing module and the like, and multi-dimensional data fusion and intelligent control are achieved. The stability, the control precision and the environmental adaptability of the steel pile trolley system are improved, and the method is suitable for the control scene of the cutter suction dredger steel pile trolley.

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 an intelligent management system based on metal powder production, and relates to the technical field of industrial automation intelligent management. Sensor arrays are deployed in an ultrasonic atomizer and a vacuum drying furnace, the dust concentration, the vibration frequency, the pressure, the humidity, the temperature and the oxygen concentration are measured in real time, and the current stage is obtained through a production plan; dynamically and finely adjusting the weight matrix according to the sensor parameters corresponding to different stages, generating a weighted comprehensive index, calculating theoretical control parameters based on a physical model, correcting the theoretical control parameters through an LSTM neural network in combination with real-time environmental data, outputting an equipment control instruction, and extracting the sphericity and the moisture residue of the metal powder after the production of the metal powder is completed, so as to obtain the spherical degree of the metal powder. And the production quality is judged, and different responses are adopted according to the judgment result. The multi-parameter cooperative control precision is further improved, and the system stability is enhanced.

The invention relates to the technical field of intelligent edge computing, and discloses a heterogeneous data processing optimization system based on intelligent edge computing. Comprising a heterogeneous data acquisition module, an edge preprocessing module, an intelligent optimization scheduling module, a heterogeneous fusion analysis module and a self-adaptive feedback control module. According to the system, data streams of different formats and protocols can be collected from a plurality of heterogeneous data sources in real time, and multi-modal correlation analysis is carried out after preprocessing and intelligent scheduling. Meanwhile, the system performance is optimized through self-adaptive feedback control, and a resource efficiency evaluation module and a safety compliance verification module are further arranged. According to the method, the problems of delay, accuracy, resource allocation, safety compliance and the like in heterogeneous data processing are effectively solved, the data processing efficiency and the system stability are improved, and the method is suitable for heterogeneous data processing scenes in the fields of industry, traffic, medical treatment and the like.

The invention discloses a servo control method and system based on a PLC. The method comprises the steps of track data acquisition, interpolation instruction generation, control instruction output, state information acquisition, synchronous error evaluation, error compensation, track continuity control and the like. Wherein the PLC generates a multi-axis interpolation control instruction according to trajectory data issued by the upper computer, periodically issues the multi-axis interpolation control instruction to each servo driver through a high-speed timed task, collects feedback information in real time, and evaluates multi-axis synchronous deviation. On the basis, the PLC constructs an error vector and dynamically corrects interpolation parameters of the next period to realize synchronous control. Meanwhile, a buffer transition area is arranged at the track section switching position, acceleration and speed changes are adjusted, and it is guaranteed that the motion track is continuous and smooth. The system comprises a PLC, a trajectory data input module, an interpolation generation module, an error evaluation and compensation module and the like, has high real-time performance and high synchronization precision, and can improve motion coordination and system stability in complex trajectory execution.

The invention relates to the technical field of power generation, power transformation or power distribution, and discloses a control method for preventing overload of an energy storage system transformer, which comprises the following steps: monitoring the three-phase total power and the power of each phase of a power grid side ammeter in real time, and calculating the three-phase unbalance degree based on the three-phase total power when the three-phase unbalance phenomenon occurs; when the three-phase unbalance degree is greater than a preset unbalance degree threshold value, dynamically adjusting the charging power of the energy storage converter according to a starting threshold value, a charging limiting threshold value and a charging forbidding threshold value by adopting a multi-threshold value dynamic adjustment mechanism; the phase with the maximum deviation between the power of each phase and the average power of the three phases is calculated as an overload phase, load reduction is performed on the overload phase based on a load increasing and decreasing strategy, load increasing is performed on a non-overload phase, and the adjusted three-phase power is obtained; a power distribution instruction is calculated in real time in combination with a dynamic response mechanism, and the three-phase unbalance degree does not exceed a safety threshold value in cooperation with total power control. The problems of easy overload, three-phase imbalance and slow response of a transformer in the prior art are solved, and the purposes of accurate load control, high system stability and high response speed are achieved.

The invention discloses a digital calibration system and method for dynamic response parameters of a surge protection circuit, relates to the technical field of power electronic protection and test calibration, and is used for solving the problem that an existing surge protection circuit calibration method cannot reflect device degradation and dynamic response mismatch. Surge event voltage and current waveforms are collected through a synchronous trigger mechanism, non-stationary signal features are extracted through combined time-frequency analysis, and a voltage-current phase trajectory diagram is constructed to generate a response feature matrix; performing phase-space reconstruction on the matrix, calculating Kolmogorov entropy quantification system stability, and establishing a material degradation transfer function in combination with an energy absorption sequence to generate a grain boundary degradation index; key parameters are screened through L1 regularization regression, and a quantitative relation between a grain boundary degradation index and a protection threshold value is established to realize adaptive threshold value calculation; and executing strategy switching from calibration to predictive maintenance according to the degradation trend and threshold fluctuation change, and outputting a maintenance signal to complete dynamic accurate calibration and reliable operation of the surge protection circuit.

The invention discloses a stability analysis method for a hybrid grid-connected system of a grid-following converter, and the method comprises the steps: constructing precise impedance models of a grid-following type (GFL) converter and a grid-constructing type (GFM) converter respectively based on a phase-locked loop (PLL) mechanism and a virtual synchronous generator (VSG) control strategy, and the details are shown in an abstract figure 1; deducing the total equivalent impedance of the system at a point of common coupling (PCC) according to a circuit equivalence principle; performing stability analysis and sensitivity evaluation on different converter combination proportions, topological structures and key control parameters of the hybrid grid-connected system based on an impedance ratio criterion; and verifying the accuracy of the analysis result through frequency domain simulation. The method can be widely applied to a new energy power system, solves the problem of system stability evaluation and optimization under the high-proportion renewable energy access background, and has the characteristics of high adaptability, accurate model and comprehensive analysis.

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 relates to the field of energy management, and discloses a distributed energy intelligent configuration method and device based on Internet of Things big data, and the method comprises the steps: collecting the real-time energy flow and connection state of each node, integrating the real-time energy flow and connection state into initial node embedding, constructing a graph model, calculating the mutual influence weight of the nodes, and adjusting the interaction frequency; based on the mutual influence weight, the interaction frequency and the graph model, performing feature extraction on each node state, generating a node state feature vector, identifying a potential fault starting point exceeding an abnormal fluctuation threshold, predicting a propagation trend, and generating a risk path and a priority; extracting a hub based on a risk path and a priority, estimating an influence range and simulating diffusion, calculating system stability, if the system stability is lower than a threshold value, generating a resource optimization scheme, fusing position marks to generate an adjustment verification vector, updating an abnormal fluctuation threshold value, and reevaluating a potential fault starting point to obtain an adjusted propagation path; according to the invention, accuracy and robustness of dynamic energy management can be realized.

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 discloses a stability evaluation and optimization method for a multi-infeed series-parallel new energy station based on equivalent external characteristics of a network construction type converter, and belongs to the technical field of electric power automation. Comprising the following steps: constructing a new energy multi-infeed hybrid system stability analysis closed-loop dynamic model, and simplifying a network construction type admittance transfer function; based on a stability analysis closed-loop dynamic model of the new energy multi-infeed series-parallel system, simplifying the new energy multi-infeed series-parallel system into an equivalent single-infeed system, and evaluating the stability of the equivalent single-infeed system through a generalized operation short-circuit ratio and a critical generalized operation short-circuit ratio; analyzing and quantifying the influence of key parameters of the network construction type equipment on the stability of the system through a parameter sensitivity analysis method; a node participation factor is used as an evaluation index, and control parameters are optimized based on a stability optimization method of key parameters of network construction type equipment of system weak nodes. The method provided by the invention has good effectiveness and reliability, and can realize gradual improvement and refined regulation and control of the system stability.

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 relates to the field of fault diagnosis, and discloses a drying machine fault diagnosis method based on deep reinforcement learning, which is used for improving the accuracy and control efficiency of drying machine fault diagnosis. A physical dynamic model fusing thermodynamics and mechanical dynamics is constructed, real-time quantitative evaluation is performed on system stability in combination with a Lyapunov function, and a fault propagation path is visually displayed by using a directed topology network. Firstly, dryer multi-source parameters are obtained to establish a dynamic model; positioning a fault source component and calculating the network influence degree of the fault source component; and a self-adaptive control instruction and a topology suppression instruction are generated, control parameters are dynamically adjusted, fault propagation is suppressed, the control instruction is converted into an actuator driving signal, and accurate control over the drying machine is completed. Through deep fusion of a physical model and data driving, the fault diagnosis accuracy and the control efficiency are remarkably improved, the troubleshooting time is effectively shortened, the production safety is guaranteed, and the method is suitable for intelligent operation of the drying machine under the complex working condition.

The invention relates to the technical field of ship fuel supply system control, and discloses a double-lug C-type LNG fuel cabin and methanol fuel self-adaptive switching control system which comprises a fuel state sensing module, a switching logic decision module, an execution mechanism control module, a safety redundancy guarantee module and a state feedback recording module. The fuel state sensing module is used for collecting and normalizing fuel parameters and mechanism operation state data; the switching logic decision module constructs a model to simulate switching dynamic characteristics and mechanism response characteristics, and performs parameter adaptation; the execution mechanism control module constructs an algorithm to calculate a switching pressure threshold value, a flow matching value and a valve group action time sequence; the safety redundancy guarantee module evaluates the switching safety; and the state feedback recording module performs secondary evaluation in combination with the environment working condition when the risk exists. According to the system, self-adaptive switching of LNG and methanol fuel is achieved, and switching accuracy, system stability and safety are improved.

The invention provides a mechanical arm predefined time trajectory tracking control method and system based on time delay estimation, and relates to the technical field of multi-degree-of-freedom mechanical arm trajectory tracking control. Constructing a predefined time error conversion function to define a system error and conversion error system, designing a barrier function and a feedback control rate based on the conversion error system, and designing a Lyapunov function to prove the stability of the system so as to track an expected trajectory; according to the method, time delay information is deeply fused, dependence on a complex model approximate structure is not needed, stable convergence of the mechanical arm system state within predefined time can be achieved when a mathematical model is not accurate, the system control performance and robustness are effectively improved, and the requirement of modern industry for mechanical arm high-performance control is met.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably 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 an intelligent fault diagnosis and safety protection method for a combine harvester, and the method comprises the steps: carrying out the quantitative analysis of the influence degree of a fault on the system safety and operation continuity through a risk assessment model, calculating a fault danger grade score through combining historical fault data and equipment aging state information, and carrying out the calculation of the fault danger grade score. A five-level danger classification result from slight danger to serious danger is obtained; acquiring system response data after isolation control is executed, monitoring running state changes and load redistribution conditions of residual normal parts, adjusting control parameters through a real-time feedback mechanism, and judging the effectiveness of isolation measures and the maintenance degree of system stability; a closed-loop feedback mechanism is adopted to continuously monitor the fault isolation effect and the system operation performance, key data in the processing process are collected to update fault diagnosis model parameters, the risk level evaluation accuracy and the protection range division rationality are optimized, and a continuously improved intelligent fault diagnosis and safety protection system is obtained.