1927 results about "System stability" patented technology

The invention provides an optical storage charging and discharging station aggregation control and optimization method based on a virtual power plant, and aims to solve the problems of multi-target collaborative optimization, dynamic resource response and uncertainty robustness. By introducing a Markov decision process and an adaptive clustering algorithm, the system can dynamically aggregate photovoltaic, energy storage and charging pile resources according to equipment characteristics, and power dispatching is optimized. A multi-objective optimization model is adopted, economical, technical and environmental objectives are combined, a dynamic weight factor is introduced, and optimal scheduling is generated in combination with a fuzzy decision theory. And real-time compensation is carried out by adopting a rolling time domain control framework and deep reinforcement learning, so that the scheduling precision and the response speed are improved. The edge computing and cloud collaboration mechanism reduces the communication load through a lightweight federated learning model, and improves the scheduling response efficiency. According to the invention, the scheduling efficiency of the optical storage charging station can be obviously improved, the operation cost is reduced, the system stability is improved, and the system has good adaptability and expandability.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

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 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 provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

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 relates to the technical field of precise injection molding control, and discloses an injection molding process parameter self-adaptive compensation system which comprises a multi-source sensing module used for collecting mold cavity pressure, melt temperature and screw displacement information in the injection molding process in real time; the data fusion module is used for performing time sequence synchronization, space registration and noise suppression on the multi-source sensing data; the dynamic modeling module is used for constructing a dynamic model of an injection molding process state based on the fused process data and outputting a state estimation value; and the stability analysis module is used for carrying out system stability analysis based on the dynamic model and generating a control compensation strategy. Cooperative control of precise positioning and high-pressure response is realized through a composite driving framework and a dynamic disturbance compensation technology; an incremental learning algorithm and a multi-dimensional quality evaluation system are combined, a process parameter self-evolution mechanism is constructed, material characteristic drift and complex working condition disturbance are effectively dealt with, and closed-loop optimization from process control to quality feedback is formed.

The invention relates to the technical field of intelligent power grids, and provides an intelligent operation centralized control center intelligent scheduling interaction method and system, and the method comprises the steps: generating a heat distribution gradient through environment noise filtering, reflecting the temperature field change of equipment, obtaining a voltage amplitude envelope through fundamental wave and harmonic wave separation, and monitoring the power quality; a current fluctuation coefficient is extracted by combining segmented deviation analysis to evaluate a line load state, and frequency fluctuation parameters are captured by using time domain statistics to track system stability. By constructing a dynamic association map, heterogeneous data such as thermodynamics, electrical quantity, load characteristics and synchronous states are mapped to power grid topological nodes, and a cross-dimension association relationship is established to realize whole-network state coupling analysis. An overload risk, a voltage out-of-limit probability and a system instability threshold are quantified through a hierarchical evaluation mechanism, a multi-target optimization constraint condition is formed, and finally a scheduling strategy considering equipment safety, economy and power supply reliability is generated. When the power system operates, intelligent scheduling is carried out according to the operation state of the power grid.

The invention discloses a multi-target task and resource intelligent modeling method, particularly relates to the field of complex adversarial simulation, is used for solving the problems of dynamic constraint optimization and robustness improvement in multi-dimensional task planning, and aims at realizing multi-dimensional coupling of space-time resource parameters by constructing a three-dimensional hypergraph model, mining a parameter association rule by means of tensor decomposition, and realizing multi-dimensional optimization of the space-time resource parameters. Dynamic constraint quantization is supported, multi-dimensional index priority evaluation is fused in a constraint layered injection stage, hard constraints are recognized, a solution domain is compressed, a hybrid optimization strategy regulates and controls balance between global exploration and local optimization, and after annealing is simulated to jump out of a local extreme value, a multi-target particle swarm algorithm is used for screening a space-time resource equilibrium solution in a trimming solution domain. Digital twinborn verification promotes physical and virtual space interaction data closed loop, a parameter correlation degree matrix is corrected, scheme robustness is enhanced, efficient generation and adaptive optimization of a task planning scheme under complex constraints are realized, and system stability and multi-target cooperation capability under sudden disturbance are 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 discloses a control method and device for heat dissipation equipment, and relates to the technical field of computers, and the method comprises the steps: employing a load dynamic perception and prediction model cooperative control technology, building a dynamic correlation model of a load and heat dissipation, and achieving the mode upgrade of a heat dissipation system from lagging regulation to advanced response. A control instruction is generated by predicting a load change trend in real time, and a dual-mode protection mechanism is combined, so that energy efficiency loss caused by traditional temperature control delay is avoided, system stability and energy efficiency performance are optimized, an efficient and energy-saving active heat dissipation solution is provided for high-load equipment, and the service life of the system is prolonged. The problems of low control efficiency of the heat dissipation equipment and energy efficiency loss caused by delayed adjustment in related technologies are solved, and the technical effects of improving the control efficiency of the heat dissipation equipment and reducing energy consumption are achieved.

The invention discloses a multi-heat-source coupling heat pump control system for recycling waste heat of industrial circulating cooling water, which comprises the following steps: monitoring key parameters of various heat sources in real time through an environment sensing module, screening and preprocessing through a data processing unit, screening out a heat source set suitable for the current condition, and sending the heat source set to a control module; the recovery potential and the use efficiency of the heat source are evaluated by utilizing an adaptive learning model, the use priority and the distribution proportion of the heat source are intelligently determined in combination with real-time load change, and the working time and the switching time of the heat source are dynamically adjusted by adopting a heat source time window distribution mechanism. The operation parameters and the heat source coupling mode of the heat pump are optimized through the intelligent complementary adjusting module, the heat recovery path is dynamically adjusted through the autonomous temperature control unit, the scheduling strategy and the operation mode of the heat source are optimized through the self-adaptive feedback mechanism and the intelligent learning module, and the adaptability and the energy-saving effect of the system are improved. According to the system, efficient cooperative utilization of multiple heat sources is achieved, and the heat recovery efficiency and the system stability are improved.

The invention provides a virtual synchronous machine dynamic inertia control method and system based on an artificial intelligence technology, which are applied to voltage source inverter control, and mainly comprises the following steps: data acquisition and preprocessing are carried out, and dynamic parameters are output through a time sequence neural network and are respectively used for control links of a virtual synchronous machine; a convolutional neural network is adopted to calculate a phase compensation angle for phase correction of pulse width modulation, and then a deep reinforcement learning network is utilized to output duty ratio correction to adjust the pulse width modulation so as to cooperatively control the dynamic inertia of the virtual synchronous machine. According to the invention, dynamically adjusted parameters are obtained through data time sequence processing and neural network application, and adaptive adjustment of core parameters can be realized; the convolutional neural network participates in modulation signal calculation, hardware phase delay is effectively compensated, and the dynamic response precision is improved; and cooperative optimization of inertia control and dynamic response is realized by combining deep reinforcement learning, current overshoot is reduced, harmonic waves are suppressed, and the electric energy quality and the system stability are improved.

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 oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention relates to an intelligent power security situation assessment method based on a multi-source sensor network, and the method comprises the steps: obtaining multi-dimensional data from a power system through the multi-source sensor network, and constructing an original data set containing time sequence features; preprocessing and denoising the original data set, and extracting dynamic change characteristics of key parameters to obtain a cleaned parameter change sequence; based on the cleaned parameter change sequence, analyzing an interaction relationship among the key parameters, and obtaining spectrum feature data; judging whether a resonance risk exists in the system or not according to the frequency spectrum characteristic data in combination with a resonance phenomenon theoretical model, and generating a risk assessment report; for the risk assessment report, identifying unstable factors and extracting a high-risk parameter combination, and generating unstable factor distribution feature data; and according to the system stability evaluation index, judging whether the power grid operation state is close to an unstable boundary, and generating a risk early warning signal and a parameter adjustment suggestion.

The invention relates to the technical field of grinding machining, and discloses a blade grinding wheel abrasion loss online compensation method which comprises the steps that a dynamic hexagonal detection grid is constructed based on the rotating phase of a grinding wheel, non-repeated high-density sampling is achieved through a Fibonacci spiral expansion path, a detection area is dynamically adjusted to reduce the overlapping rate, and the blade grinding wheel abrasion loss is obtained. Micron-sized wear transition is accurately captured; dispersing the abrasion loss into minimum compensation units, constructing a dynamic state equation in combination with grinding force, dressing displacement and workpiece errors, introducing an attenuation coefficient related to the service life of a dressing wheel, and automatically correcting a model drift error; a grading triggering strategy of residual uncompensated quantity is adopted, dynamic decision making of a processing period is combined, and through integral multiple compensation and a margin temporary storage mechanism, it is ensured that long-term accumulative errors are restrained at the submicron level while overmodulation oscillation is avoided. Through the synergistic effect of space-time coupling detection, discrete-continuous double-domain modeling and intelligent compensation decision, the grinding wheel abrasion compensation precision and the system stability are remarkably improved.

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 computing platform management system based on artificial intelligence, particularly relates to the technical field of distributed storage resource dynamic allocation, and is used for solving the problem of resource allocation oscillation caused by hardware performance difference and dynamic attenuation in the prior art. Acquiring the hardware performance, the load state and the data path of the storage node through a real-time acquisition module; the entropy judgment module calculates a path topology entropy and triggers a reconstruction instruction; the resonance analysis module analyzes the group association strength of hardware performance degradation and marks a resonance node group; the weight generation module fuses the parameters to generate a dynamic allocation weight; the error compensation module optimizes a weight coefficient in combination with historical error data; the migration execution module executes migration according to the weight and limits the load of the resonance node; through cooperative regulation and control of topological entropy monitoring and hardware resonance analysis, local oscillation diffusion is inhibited, unnecessary migration operation is reduced, system stability is improved, and the service life of hardware is prolonged.

The invention discloses a water pump station operation adjusting device and method, and relates to the technical field of pump station operation adjusting, a first prediction result is output through operation data of a water pump station, and whether the first prediction result needs to be corrected or not is judged based on environment characteristics, equipment state data and water quality data to obtain inlet and outlet valve adjusting parameters; constructing a water pump station digital model, receiving a simulation regulation feedback result, and optimizing inlet and outlet valve regulation parameters by utilizing the simulation regulation feedback result; an inlet and outlet valve adjusting stability threshold value is obtained, relation feedback verification is conducted through the inlet and outlet valve adjusting stability threshold value, and whether a protection mechanism is triggered or not is judged through the simulation adjusting feedback result and the relation feedback verification result; and when the protection mechanism is triggered, the dynamic current stabilizer is started, and the current stabilization parameters of the dynamic current stabilizer are adjusted. Intelligent accurate control, active protection and energy efficiency optimization are realized, the system stability is improved, and the operation and maintenance cost is reduced.

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 discloses an intelligent operation and maintenance distributed cloud native application computing method, and relates to the technical field of cloud computing, and the method comprises the steps: collecting heterogeneous data of an application layer, a middleware layer, a container layer and a service governance layer, constructing a dynamic multilayer dependency graph, and explicitly modeling a cross-layer dependency relationship and implicit dependency caused by resource competition; based on the spatio-temporal features of the spatio-temporal diagram neural network learning map, predicting a cascade fault risk and outputting a high-risk sub-graph; candidate operation and maintenance operations are generated through reinforcement learning, indirect negative influences of the operations on non-target components are evaluated in combination with a causal inference model, and risk filtering operations are verified through a multi-dimensional safety threshold value; and after the safety operation is executed, continuously optimizing the model and the atlas through a feedback closed loop. According to the method, deep insight, accurate risk prediction and self-adaptive decision making of side effect minimization can be carried out on a complex dependency relationship, and the system stability and the operation and maintenance efficiency are remarkably improved.

The invention discloses a power grid real-time data reasoning analysis system and method based on Rete algorithm optimization, and belongs to the technical field of smart power grids. In order to solve the problems of low rule matching efficiency and insufficient event triggering and operation optimization capability, a data acquisition module efficiently accesses a multi-source heterogeneous data stream, a streaming data processing module realizes high-throughput real-time data processing based on DAG task fragmentation and parallel computing, a rule engine module optimizes a dynamic rule network through a Rete algorithm, and a data processing module realizes real-time data processing based on DAG task fragmentation and parallel computing. The reasoning analysis module is combined with rule matching and event triggering, power grid faults are rapidly positioned, operation state evaluation and dynamic optimization suggestions are provided, and the graphical rule construction module reduces rule design complexity and enhances rule adaptability through visual rule editing and verification tools; the optimization monitoring module guarantees the system stability and the resource utilization efficiency through real-time performance monitoring, load prediction and automatic tuning.

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.