489 results about "System parameters" patented technology

The invention relates to the technical field of artificial intelligence application, and discloses an information technology auxiliary consultation system based on artificial intelligence. The system comprises a data acquisition module, a knowledge graph construction module, an intention analysis module, a decision engine module, a strategy optimization module and a feedback correction module. The data acquisition module acquires multi-dimensional data such as a semantic type, an intention label and a historical interaction record of a user consultation request in real time; the knowledge graph construction module dynamically generates a hierarchically associated domain knowledge graph according to the domain database; and the intention analysis module completes user intention classification and analysis through a multi-level attention mechanism. The decision engine module combines the analysis result and the knowledge graph to generate candidate strategies, and the strategy optimization module screens out target strategies meeting real-time response requirements through an adaptive weighting algorithm. The feedback correction module utilizes user interaction data to update system parameters, improves service precision, and is suitable for various information technology consultation scenes.

The invention relates to the technical field of industrial automatic monitoring, and provides a mining equipment remote monitoring method and system and a storage medium. The method comprises the following steps: dividing mining equipment into core equipment and general equipment based on equipment operation parameters; performing fault risk assessment on the core equipment by adopting a dynamic threshold value adaptively generated based on historical data, and performing deviation detection on the general equipment by adopting a fixed threshold value; fusing the fault risk assessment result of the core equipment and the deviation detection result of the general equipment to generate global maintenance decision information; optimizing a field maintenance path according to the geographic position and the emergency degree of the maintenance task; and continuously calibrating system parameters through a closed-loop feedback mechanism. According to the invention, through hierarchical monitoring and adaptive optimization, the problems of uneven distribution of monitoring resources, rigid threshold setting and isolated maintenance decision in the prior art are solved, and accurate configuration of mining equipment monitoring resources and remarkable improvement of fault early warning capability are realized.

The invention belongs to the technical field of analog simulation, and particularly relates to an intelligent simulation system with an autonomous decision-making capability, which comprises simulation management used for task scene initialization, system parameter configuration and simulation process control and operation. The display system is used for 2D / 3D visual display and playback of simulation results; the model implementation system is connected with the simulation management and display system and is used for implementing a target model and a flight mode model according to configuration parameters; the intelligent target generation system is used for automatically generating a maneuvering combination strategy, a task load control strategy and a radar control strategy according to the simulation situation information generated by the model implementation system, and feeding back the maneuvering combination strategy, the task load control strategy and the radar control strategy to the model implementation system to drive simulation deduction; wherein the system can execute a plurality of simulation modes including large sample space random task simulation, task simulation without autonomous capability level and task simulation with certain autonomous capability level according to simulation types set by the simulation management and display system.

The invention discloses a city management AI dispatch algorithm and system based on historical mining and responsibility matching, and the method comprises the steps: building and dynamically updating a city management element evolution graph through obtaining the multi-mode description information of a city management case and the real-time state data of disposal resources; calculating potential disposal effects of different candidate dispatching schemes by using a causal inference engine, and generating a comprehensive efficiency estimation vector; on the basis, a multi-target reinforcement learning strategy is adopted to generate an optimal dispatch instruction, and system parameters are continuously optimized through online element learning during execution; cooperative processing network analysis is activated for sudden complex events, and responsibility atlas reconstruction is triggered when the matching efficiency is low. According to the method, the accuracy and efficiency of case disposal are remarkably improved, disposal timeliness optimization, resource load balancing and improvement of the first solution rate are realized, and meanwhile, the adaptive capacity and continuous optimization efficiency of the system to complex scenes are enhanced.

The invention discloses a wind-solar-water-storage complementary system short-term risk scheduling method considering uncertainty, and the method comprises the steps: converging historical physical operation data and multi-subject behavior data, and constructing a training data set and a system parameter set; based on the training data set, constructing a combined robust radius and wind-solar combined scene containing behavior risk quantification; based on the system parameter set and the training data set, constructing a dynamic risk scheduling unit fusing multi-dimensional risks; solving a candidate short-term scheduling scheme by combining a joint robust radius and a dynamic risk scheduling unit and adopting a behavior risk-oriented Bayesian optimization method; and performing multi-subject consensus evaluation on the candidate short-term scheduling scheme, determining a final execution short-term scheduling scheme, and performing uplink execution. According to the method, the problem of separation of physical risks and behavior risks is solved, and the behavior acceptability of the scheme is improved.

The invention discloses a self-adaptive optical system parameter adjusting method and device combining offline pre-training and online reinforcement learning, and belongs to the field of intelligent control combining self-adaptive optics and reinforcement learning. Multi-modal state modeling, off-line strategy pre-training and an online strategy enhancement mechanism guided by behavior advantages are introduced, and key control parameters in the AO system are dynamically optimized by using a reinforcement learning algorithm. According to the method, multi-modal heterogeneous data can be effectively integrated, the dynamic modeling and prediction capability of the system is improved, and stable, efficient and robust control strategy updating is realized.

The invention discloses a scanning control method and system for a magnetic resonance spectrometer, and the method comprises the following steps: an upper computer analyzes and loads sequence file parameters meeting the Pulseq standard, and integrates the sequence file parameters and pre-configured system parameters into a scanning parameter set; the upper computer generates an executable spectrometer hardware control instruction based on the scanning parameter set, and writes the spectrometer hardware control instruction into a register of a PCIE board card through a PCIE driving module; the PCIE board card generates a hardware control signal according to the content written in the register, controls the spectrometer hardware module to execute corresponding operation, and generates magnetic resonance original data; and the upper computer reads the magnetic resonance original data and reconstructs the magnetic resonance original data into magnetic resonance image data based on analysis of the magnetic resonance original data. According to the invention, the compatibility and flexibility of the system are significantly improved, different upper computers can communicate and cooperate with the spectrometer hardware module, sequence sharing and use among different devices are facilitated, and the integration and maintenance costs of the system are reduced.

The invention relates to the field of steel rolling control, in particular to a cold continuous rolling high-pressure large-flow hydraulic energy-saving control method and system.The method comprises the steps that rolling system parameters and real-time data are collected and preprocessed, and basic data used for pressure regulation and control are obtained; carrying out model coupling analysis on the steel coil process parameters to obtain adaptive rolling pressure data; non-linear fusion evaluation is carried out on acceleration and thickness disturbance, and a self-adaptive dynamic safety margin is obtained; performing real-time synthesis on the adaptive rolling pressure data and the adaptive dynamic safety allowance to obtain dynamic target pressure setting data; and closed-loop energy-saving control is performed based on the dynamic target pressure setting data, so that the energy-saving effect of the hydraulic system is improved.

The invention discloses a thermodynamic system parameter distribution method and device, and the method comprises the steps: S1, analyzing a block definition diagram and an internal block diagram from a SysML model to obtain a module structure description, a module value attribute, an interface specification of an equipment unit and a thermodynamic parameter description, and generating an analysis result, the analysis result comprises equipment unit port and connector information. And S2, based on the analysis result, mapping the equipment module to a simulation software element through name matching, and generating a steady-state thermal simulation model. And S3, associating the module value attributes with element attributes of the steady-state thermal simulation model through a parameter mapping mechanism, and generating attribute mapping data. And S4, generating a complementation parameter list according to the external design value and the heat balance demand. Therefore, the efficiency and accuracy of parameter allocation are improved, manual intervention is reduced, and it is ensured that the allocation result meets the requirements of system performance.

The invention provides an energy storage charging and discharging time sequence optimization scheduling method and system based on revenue maximization, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: obtaining price data and system parameters, recognizing a price local extreme point, constructing an envelope line, and stripping an oscillation component to obtain a price trend component; and establishing a corresponding relation between a charge state target value and a time period identifier, enabling the charge state track to converge to the target value step by step, and dynamically correcting the target value and adjusting a price peak value identification method according to real-time feedback data. According to the invention, price fluctuation can be effectively coped, the economic benefit of the energy storage system is improved, and self-adaptive optimization control of the charging and discharging process is realized.

The invention discloses an arch bridge tensioning method and system based on a physical constraint space-time diagram attention network, and the method comprises the steps: constructing a dynamic topological graph of an arch bridge cantilever construction structure, taking a structure initial state parameter, a control force system parameter and a real-time environment parameter as node input features, and taking a structure state response parameter as an output label, generating a training data set based on finite element model simulation; designing a deep learning network model combined with space diagram attention and time sequence causal convolution, and modeling a space-time dependency relationship; defining a mixed loss function of the data fidelity item and the physical residual loss item of the structural mechanical control equation; and training the model through a back propagation algorithm and deploying the model to a field decision control system. According to the method, through graph structure abstraction, spatio-temporal joint modeling and physical constraint embedding, multi-source monitoring data driven structure state online prediction and tension control strategy intelligent optimization are achieved.

The invention discloses an examination and research word efficient mnemonic system based on an AI agent, and belongs to the technical field of artificial intelligence and natural language processing, and the system comprises a multi-dimensional semantic knowledge graph construction module, a cognitive state self-adaptive evaluation module, a personalized memory strategy generation module and a closed-loop feedback optimization module. The multi-dimensional semantic knowledge graph construction module generates a vocabulary semantic association network based on root and affix structures, semantic similarity and context co-occurrence frequency; the cognitive state self-adaptive evaluation module determines cognitive forgetting probability distribution of the user in combination with the semantic difficulty reference value; the personalized memory strategy generation module generates a multi-dimensional mnemonic scheme through an AI agent and determines a personalized review interval; the closed-loop feedback optimization module collects learning effect data and generates optimization parameters to realize system parameter iteration, and the four modules form a deep coupling closed-loop collaborative architecture to realize mutual promotion and superposition synergy.

The invention is suitable for the technical field of automatic control, and provides an MPC heating furnace oxygen-enriched combustion control system parameter optimization method based on reinforcement learning, which comprises the following steps: step 1, production line data is acquired, and the acquisition of the production line data is mainly to acquire real data of an actual cement production control production line, including MV, OV and DV data; and step 2, obtaining a state space model, the obtaining of the state space model comprising establishing a model of converting a first-order transfer function into a state space, the MPC parameter adjustment is performed by using the prominent learning ability and adaptive ability of a reinforcement learning (RL) method, the dynamic adjustment according to the change of the system can be realized, and the control accuracy of the system is improved. And training the current oxygen-enriched combustion control algorithm according to the state information under the existing decision-making condition so as to output the expected furnace temperature, and evaluating the effectiveness of the control algorithm through the prepared evaluation standard.

The invention relates to the technical field of data processing, in particular to a reasoning acceleration method based on a prefix shared KV cache and request aggregation, which comprises the following steps: constructing and persisting a shared prefix KV cache capable of cross-request multiplexing; the method comprises the following steps: after receiving an inference request, normalizing candidate prefixes of the request, calculating the similarity between the candidate prefixes and representative prefixes in a shared prefix KV cache, and loading corresponding shared prefix KV data when judging that the candidate prefixes are hit; the hit requests are aggregated to a request bucket queue based on prefix similarity, a continuous batch is generated when a triggering condition is met, prefix KV data are shared by requests in the batch, and forward calculation is only executed on the difference token; a quantization, aging, recovery and rollback integrated strategy is adopted to manage and share a prefix KV cache; system parameters are dynamically adjusted by monitoring closed-loop periodic acquisition performance and quality indexes. According to the method, the technical problems of repeated prefix calculation, high TTFT and P95 delay, limited system throughput, uncontrollable quantization quality, rigid scheduling threshold and the like in the prior art can be solved.

The invention discloses a BO-GRU-KAN-MOGWO-based shield performance online multi-objective optimization method and system, and the method comprises the steps: selecting important muddy water large shield tunneling parameters and slurry system parameters as input characteristic variables, selecting key muddy water large shield construction performance indexes as output variables, and collecting data to construct an original sample set; bayesian optimization is adopted to optimize hyper-parameters of a GRU-KAN algorithm, a BO-GRU-KAN prediction model is constructed, the tunneling speed, the cutterhead abrasion, the overturning moment and the over-excavation flow are predicted, and four regression prediction functions are obtained; a SHapley addition interpretation method is introduced, and key influence parameters are determined; taking the four regression prediction functions as fitness functions, and constructing a multi-objective optimization objective function; carrying out global optimization by adopting an improved multi-target grey wolf optimization algorithm, and determining an optimal muddy water large shield construction parameter combination; and the tunnel construction requirements of high efficiency, low consumption, safety and accuracy under complex working conditions can be met.

The invention provides a Markov jump-based mechanical arm preset time trajectory tracking control method, which comprises the following steps of: calculating a trajectory tracking error by establishing a two-degree-of-freedom mechanical arm dynamic model with a Markov jump characteristic; a trajectory tracking error is subjected to nonlinear transformation, a nonsingular terminal sliding mode surface is constructed in combination with a preset convergence time upper limit function, and then a control torque is synthesized through an equivalent control item and a robust switching item containing a sliding mode surface sign function and a switching gain to drive a mechanical arm. And the trajectory tracking error is difficult to converge in the preset time.

The invention belongs to the technical field of converter stability analysis and control, and discloses a new energy converter low-pass control dominated voltage oscillation parameter feasible region construction method, system and equipment and a medium, so as to solve the problem that accurate description of a system stability boundary is difficult to realize in the prior art. The method comprises the following steps: establishing a converter grid-connected system model considering low-voltage ride-through control, and generating a data set comprising a multi-parameter combination and a corresponding repeated low-voltage ride-through state tag based on the model; taking the data set as training data, and training the repeated low-crossing risk discrimination model by adopting a fast kernel learning algorithm to obtain a trained model; and inputting converter grid-connected system parameters needing to be analyzed into the trained model for classification prediction, and drawing a parameter region without repeated low-voltage crossing based on a result to obtain a multi-parameter feasible region schematic diagram. According to the method, the technical blank of carrying out accurate quantification and visualization on the repeated low-penetration stable boundary in a complex parameter scene is filled.

The invention relates to the technical field of multi-objective optimization algorithm verification and intelligent decision making, in particular to an algorithm verification method and application system based on multi-objective optimization, and the method comprises the steps: obtaining data, and forming a training data set; performing joint representation learning and decoupling on a Pareto solution set based on the data set to obtain independent latent variables of three dimensions of business stability, performance expression and environmental sensitivity; constructing simulation environment associated environment sensitivity information and disturbance parameters, generating a training scene sequence to drive an intelligent body to explore, and outputting a robustness evaluation result; learning an implicit preference function in combination with an implicit interaction sequence of a decision maker, recommending a candidate solution set and adjusting network parameters according to feedback; and establishing a knowledge graph to store historical data, and performing joint optimization on system parameters by extracting experience. According to the method, interpretability representation of the Pareto solution set, robustness verification in a dynamic environment and adaptive learning of decision preference are realized, and algorithm verification efficiency and decision reliability are effectively improved.

The invention relates to the technical field of intelligent design of optical lenses, and discloses a multi-series superposed complex periodic structure arrangement method and system.The method comprises the steps of normalized coordinate transformation, multiple sine wave superposition, an iterative function system, a Julia set algorithm, an LSystem grammar system, parameter adjustment, moire fringe fusion and the like; and a complex periodic structure with high complexity, adaptability resistance and natural morphological characteristics is generated. Advanced technologies such as logarithm uniform distribution wave number generation, a chaos game algorithm, linear mapping complex number conversion, a turn graph interpreter, multi-scale entropy complexity quantification and an Otsu automatic threshold method are adopted, and accurate parameterization control and manufacturing adaptation are achieved. The system comprises a coordinate transformation module, a wave interference calculation module, a fractal generation module, a complex power system module, an LSystem processing module, a parameter control module and a contour surface extraction module. According to the invention, a systematic solution can be provided for complex structure design of optical devices such as myopia prevention and control lenses.

The application discloses a method for realizing a conservative charge-controlled memcapacitive hyperchaotic circuit with multiple coexisting attractors. First, a six-dimensional conservative memcapacitive circuit model is constructed by a charge-controlled memcapacitive device, and the memcapacitive characteristics are verified. Then, the balance point and dissipation are used to verify the conservativeness. By changing the system parameters and initial conditions, a larger Lyapunov exponent can be obtained, and it is found that the internal parameter e of the circuit can not only cause the circuit to exhibit complex transient chaotic phenomena, but also has large-scale range chaotic characteristics. The attractor basin, coexisting attractors and complexity reflect the extreme multistability and complex nonlinear dynamic behavior of the circuit. In addition, the initial offset boosting behavior is also found. The application combines the FPGA technology to design a digital conservative charge-controlled memcapacitive hyperchaotic circuit model, and the hardware simulation result is consistent with the numerical calculation result, thereby providing a new idea for the digital realization of a nonlinear storage system.

The invention provides an intelligent control method for heat treatment furnace temperature, and relates to the technical field of metallurgical heat treatment control. The method comprises the following steps: establishing a blank temperature state space prediction model based on adaptive parameter estimation for predicting the time sequence change of the blank temperature; an optimization objective function is constructed, and an optimal furnace temperature setting sequence is calculated in a prediction time domain; establishing a system parameter adaptive adjustment mechanism, and updating system parameters in real time by adopting a recursive least square method; and valve control is executed based on self-adaptive PID control, and a gain compensation strategy is introduced to deal with equipment aging. According to the method, the furnace temperature can be accurately controlled under different working conditions and different equipment aging degrees without depending on the experience of operators, and the product quality stability is effectively improved.

The invention relates to the technical field of power systems and energy management, in particular to a source network load storage collaborative interaction optimization system for high-proportion new energy. Comprising a multi-source information sensing data acquisition module used for monitoring various objects and an external environment in real time; the data modeling prediction analysis module is used for constructing a system parameter model and carrying out prediction analysis; the source network load storage collaborative optimization decision-making module is used for realizing multi-time-scale and multi-main-body optimization scheduling; according to the invention, the multi-source information sensing data acquisition module, the data modeling prediction analysis module, the source network load storage collaborative optimization decision module, the execution control instruction issuing module and the like are organically combined, so that the multi-source information sensing data acquisition module, the data modeling prediction analysis module, the source network load storage collaborative optimization decision module and the execution control instruction issuing module are integrated; omnibearing perception and prediction of new energy output, load demand, energy storage operation and power grid state are realized.

The invention discloses an AI field control intelligent review management method and system based on a live broadcast room, and belongs to the technical field of live broadcast room intelligent management. Collecting interactive data such as bullet screens, comments, gift reward and attention behaviors in a cross-platform manner through a process automation component, writing the interactive data into a cache layer and a database in a layered manner, and generating a traceable collection log; performing semantic recognition, sentiment analysis and violation detection on the interactive data by utilizing a pre-trained language model, and classifying the interactive data into positive feedback, question consultation and violation content; generating a standardized lethal reply for the positive feedback, calling a large model for question consultation to generate a personalized reply in combination with a knowledge base, and performing risk control filtering before reply pushing; based on priority strategy differentiation response interaction, automatic protection is triggered for illegal content, and field control and review links are interlocked and coordinated; feedback data and historical data are collected, and system parameters are iteratively optimized through reinforcement learning. According to the method, automatic acquisition, intelligent analysis, precise review and cooperative field control of interaction in the live broadcast room are realized, the interaction processing efficiency and the content compliance are greatly improved, continuous iterative optimization can be realized, and the multi-platform and multi-scene live broadcast operation requirements are effectively met.

The invention discloses a file system event processing method and device based on an intelligent filtering rule, and the method comprises the steps: obtaining a file system original event of a monitoring directory, carrying out the cross-platform standardization processing, and extracting event feature vectors of four dimensions: time, a file, a process and a path; the method comprises the following steps: constructing a rule base containing software operation modes such as an editor, a compiler and a version control tool, performing mode matching on an event sequence, calculating confidence, determining a filtering strategy type based on a matching result, executing corresponding processing, and dynamically optimizing system parameters through statistical analysis. The technical problems that an existing file monitoring system is serious in event redundancy and frequent in false triggering are solved, effective file operation and temporary intermediate operation can be intelligently distinguished, invalid synchronous triggering is greatly reduced, the accuracy is improved to 95% or above from 60-70% of a traditional scheme, and invalid event processing is reduced by 90% or above.

An integrated method for supporting complex system design and optimization selects first graphical-textual model frameworks in a problem domain; implements functional requirement verification in a validation domain and guides a physical architecture model; selects second graphical-textual model frameworks in a solution domain to establish a physical architecture model; generates an integrated simulation model oriented towards functional and non-functional requirements in the validation domain; selects third graphical-textual model frameworks in an optimization domain to establish a multidisciplinary optimization model, and establishes binding with preset subsystem parameters in the physical architecture model of the solution domain and output values of variables related to an objective function; automatically feeds an optimal optimization scheme obtained through iterative solving based on intelligent optimization algorithms to a system design model for automatic verification. The integrated method breaks down barriers between models of various stages, improves the development efficiency, and avoids the difficulty of seamless integration.

The invention provides a dynamic grouping method and device for a heterogeneous direct current delivery system, and relates to the technical field of dynamic analysis and control of a power system, and the method comprises the steps: deducing a linear time-varying variation equation which is satisfied by the trajectory sensitivity of a system state trajectory to a system parameter vector based on a parameterized differential-algebraic equation model; calculating the track sensitivity of each device to a system parameter vector, projecting the track sensitivity to a common connection point corresponding to each device, and constructing a unified sensitivity feature vector of each device; calculating the dynamic similarity between the devices based on the unified sensitivity feature vector, and constructing a mixed similarity matrix in combination with the electrical distance similarity between the devices; and feature decomposition and feature vector clustering are carried out based on the mixed similarity matrix, and dynamic clustering of heterogeneous equipment is realized. According to the dynamic grouping method provided by the invention, unified grouping of heterogeneous equipment such as a synchronous machine and a converter can be realized in a heterogeneous power system containing high-proportion new energy and direct-current power transmission.

The invention discloses a short-circuit parameter early warning method, system and device based on big data analysis and a medium, and belongs to the technical field of power short-circuit early warning, and the method comprises the steps: collecting multi-source data, carrying out the data preprocessing, extracting features through a depth auto-encoder, and evaluating the importance of the features; constructing and training an early warning model through joint optimization of multiple targets, and dynamically optimizing model parameters; and monitoring system parameters in real time to perform error evaluation, and performing risk evaluation and performance evaluation according to the early warning indexes. The invention provides the short-circuit parameter early-warning method which is higher in precision, earlier in timeliness, stronger in reliability and better in adaptability and has self-optimization capability, so that the safety early-warning level and the intelligent operation and maintenance capability of a power system are greatly improved, large-scale power failure accidents caused by short-circuit faults are effectively prevented, and the safety of the power system is improved. And the method has great economic and social benefits.

The application relates to a design method of a mass-spring damper vibration reduction structure, which can realize accurate and efficient control of specific multi-order and high-order modal responses of the structure, selects relative deformation of a mass-spring system installation position as a main degree of freedom, targets the shape of a structure deformation as a vibration mode of the structure, decouples a multi-degree-of-freedom original structure into a plurality of main degree-of-freedom systems of additional masses, calculates mass-spring system parameters required by a control target mode through an analytical expression, and determines an arrangement position. Compared with the prior art, the application can accurately reduce the response of a target mode without changing the period and vibration mode of other modes, and has the advantages of high precision, low calculation amount and easy realization.

This invention discloses a method for the collaborative optimization of turbine governor parameters and hydraulic system parameters. The method involves synchronously collecting multi-source operating data from a hydropower station using a sensor network to construct a hydraulic-governing coupled state-space model. Based on the UKF unscented Kalman filter algorithm, the method identifies the water flow inertial time constant, water hammer wave velocity, and turbine integrated transfer coefficient online, and evaluates the reliability of the identification results using a parameter identification confidence index. In a joint high-dimensional space of the control parameter domain and the hydraulic parameter domain, the method uses regulation quality, hydraulic safety, and grid stability as three objective optimization functions to solve for the Pareto front candidate set, and selects robust parameter schemes using a robust stability margin index. A three-level differentiated push strategy is implemented based on the parameter change amplitude, and real-time monitoring and an automatic anomaly backoff mechanism are implemented during the gradual push process. This invention achieves full-cycle collaborative optimization and safe tuning of hydraulic system parameters and governor control parameters.

The invention discloses a cement material ratio intelligent analysis method and system which can be executed through an intelligent chip, belongs to the technical field of computers, and realizes real-time quantification and improvement of cement production multi-source heterogeneous data quality by introducing dynamic quality risk assessment and collaborative enhancement processing. And a prediction modeling strategy is dynamically adjusted based on the risk level, and multi-objective optimization is carried out by fully considering prediction uncertainty, so that a recommendation matching scheme with higher robustness is generated. Besides, system parameters are cooperatively adjusted through a closed-loop feedback mechanism, continuous self-adaption and optimization of the whole analysis chain are ensured, and the reliability and stability of the cement material ratio under complex working conditions are effectively improved.