1794 results about "Sensitivity analysis" patented technology

The invention relates to the field of digital twinborn modeling, and discloses a digital twinborn visual modeling method and system based on a neural network, and the method comprises the steps: collecting the multi-dimensional perception data of a target physical system, carrying out the format unification and normalization processing of the original data of a sensor through a heterogeneous data fusion module, and obtaining a data fusion module; constructing a high-dimensional input feature set for neural network modeling by combining a structured embedding algorithm; carrying out stability pre-evaluation on the constructed input feature set, and screening core features by adopting a disturbance sensitivity analysis mechanism; a dynamic residual feedback mechanism is introduced to carry out enhanced training on the preliminary twin model, and modeling is carried out on the space-time dependency relationship of different components through a graph neural network; tracking a stability index of visual representation in real time in a model training process; and according to a multi-dimensional visualization result output by the final twin model, performing interpretation in combination with an industrial scene semantic rule base. The method has the advantage of improving the practicability of the twin model in the industrial scene.

The invention relates to the technical field of smart power grids, and discloses a smart power grid power dispatching optimization method, which comprises the following steps of: firstly, acquiring power grid operation data, and processing data missing and noise problems by utilizing federal learning; and constructing a load prediction model through a dynamic time warping algorithm and a specific network. A multi-energy coupling scheduling model and a demand response game model are constructed, and a multi-time scale rolling optimization framework is established. And carrying out sensitivity analysis on scheduling parameters, designing a hierarchical collaborative optimization mechanism, and constructing a robust optimization model to cope with the power flow uncertainty. And integrating a scheduling instruction verification module, and deploying an online incremental learning mechanism. The method can effectively process data, accurately predict load, optimize multi-energy scheduling, guide demand response, deal with uncertainty, verify scheduling instructions and update the model in real time, improves the safety, reliability and economy of smart grid power scheduling, and realizes optimal configuration of power resources.

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

The invention relates to the technical field of carbon emission management, in particular to an expressway construction project carbon emission accounting evaluation method and system, and the method comprises the steps: dividing a system boundary into stages of an expressway construction full life cycle; carbon emission sources are divided into direct emission and indirect emission, and a source list of carbon emission in each stage and corresponding activity data are obtained; selecting carbon emission factors of various emission sources, constructing a carbon emission accounting model, and calculating the carbon emission of each stage; performing uncertainty analysis and sensitivity analysis on various emission source carbon emission factors and activity data, and optimizing a carbon emission accounting model; and performing comprehensive evaluation on each stage of highway construction to obtain an evaluation result of each stage. According to the invention, the problems of lack of full life cycle coverage and insufficient accounting precision of the existing highway construction carbon emission accounting method are effectively solved, the data accuracy and the model reliability are improved, and the carbon emission management and low-carbon construction targets are comprehensively supported.

The invention provides a 3D model multi-scale lightweight method and system based on topological optimization, and relates to the technical field of model lightweight, and the method comprises the steps: carrying out the mesh generation of a target 3D model, and generating a finite element model; a multi-scale topological optimization model is established, density distribution is adjusted through a density filtering function, and a macroscopic lightweight structure is obtained in combination with minimum size constraint and gray scale gradient projection processing; based on a macroscopic lightweight structure, a skeleton structure is obtained, based on an adaptive polygon segmentation rule, a porous filling unit is constructed, a multi-parameterized model is fused, a parameterized representation method of three variables of density, size and shape is established, and an intermediate scale structure is obtained through a multi-target genetic algorithm in combination with a sensitivity analysis technology; and embedding the intermediate-scale structure into the macroscopic lightweight structure, generating a multi-scale structure in combination with cascade optimization, calculating equivalent material attributes by adopting a homogenization method, iteratively adjusting model parameters, and finally obtaining a multi-scale lightweight result.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The present invention relates to the technical field of heat sink structure design, and in particular to a micro-channel heat sink structure design method based on a multi-objective function. The method comprises the following specific steps: S1, constructing a general mathematical description of a topology optimization problem; S2, acquiring input conditions for a specific design scenario, and dividing a topology optimization design domain; S3, establishing a governing equation for a topology optimization model; S4, on the basis of an adjoint method, performing sensitivity analysis of a multi-objective function with respect to design variables; S5, solving the topology optimization model by means of a method of moving asymptotes (MMA); S6, obtaining a two-dimensional flow channel configuration of a heat sink on the basis of contour reconstruction, carrying out finite element analysis of a two-dimensional flow channel structure, and narrowing down the selection range of constraint conditions; and S7, on the basis of a two-dimensional finite element analysis result, constructing a corresponding pseudo-three-dimensional model, carrying out finite element analysis, and determining optimal constraint conditions to obtain a final topology-optimized heat sink configuration. Compared with traditional methods, the present invention can more efficiently solve the problem in respect of heat sink structure optimization under different scenarios.

The invention relates to a federated learning system based on multi-key homomorphic encryption and adaptive differential privacy, and belongs to the technical field of privacy computing. According to the system, on the premise that no trusted third party exists, a multi-client collaborative key generation and threshold decryption mechanism is achieved, it is ensured that model parameters are always in an encrypted state in the aggregation process, and leakage of a single node is prevented. By introducing a parameter sensitivity analysis and selective encryption strategy, the system only encrypts high-risk parameters, and the encryption burden is effectively reduced. Meanwhile, in combination with an adaptive privacy budget allocation mechanism, the system dynamically adjusts noise intensity according to a model training state, and model performance and convergence speed are maintained while privacy protection capability is improved. According to the method, high robustness and collusion resistance are realized, stable operation under the condition that part of clients are offline is supported, and the method is suitable for application scenes such as medical treatment and finance with high data sensitivity and strict performance requirements.

The present invention relates to the technical field of power system dispatching automation. Disclosed are a rapid identification and response method and system for a weak point-associated power flow based on a sensitivity factor. The method comprises: acquiring parameter data, and establishing a transfer factor matrix; assessing renewable energy integration of a distribution network and assessing a power flow over-limit line; and dispatching flexible loads in the distribution network on the basis of a sensitivity factor, and carrying out safety verification on a dispatching result. According to the rapid identification and response method for a weak point-associated power flow based on a sensitivity factor provided by the present invention, a weak point in new energy integration of the distribution network is identified, to obtain a blocking line that hinders new energy integration, providing a foundation for subsequent precise dispatching control for flexible loads. Sensitivity analysis is carried out on a weak point-associated power flow and flexible load user nodes on the basis of the sensitivity factor, and the line over-limit condition is eliminated by dispatching flexible loads of corresponding nodes, so that the power grid safety is ensured and new energy integration is implemented.

The invention provides a multi-parameter fusion drilling tool state intelligent diagnosis method, device and equipment, and the method comprises the steps: determining a stable working period through obtaining basic operation parameters of a drilling tool, and applying specific frequency excitation vibration to the drilling tool in the stable period to form an active propagation wave; a multi-point monitoring technology is adopted to obtain a response vibration signal and extract a time sequence, and a time sequence offset is obtained through differential processing; identifying a signal propagation delay section based on the time sequence offset, extracting actual propagation time, comparing the actual propagation time with standard propagation time to generate a time delay abnormal value, and determining an internal state change position; a high-damage section is determined by combining energy dissipation analysis, and fault positioning information is generated through spatial superposition; performing frequency sensitivity analysis by utilizing the damage characteristic parameters, capturing a resonance response peak value through frequency sweep excitation, and forming a secondary diagnosis result in combination with the wear severity; and finally, determining a fault development rate, generating a partition maintenance instruction, and completing intelligent diagnosis of the drilling tool state.

The invention relates to the technical field of coupling simulation, in particular to a river and underground water coupling simulation parameter generation method and system. The method comprises the following steps: collecting remote sensing and ground sensing monitoring data, and generating a multi-source space-time initial data set through time mark calibration, space resampling and signal-to-noise ratio weighted fusion; extracting features between the water level of the river and the water level of the underground water based on the data set, constructing a river-underground water space-time topological structure and identifying an interaction mode, and forming a space-time coupling feature map through significance screening and periodic enhancement; performing parameter inversion by adopting a hydrodynamic equation, and generating a physical inversion parameter set in combination with sensitivity analysis and local optimization; a parallelization hydrological simulation framework is constructed, error-driven optimization is executed, and dynamic optimization parameters are obtained; river and underground water exchange simulation is executed, and finally, coupling simulation parameter set updating is achieved through sliding window deviation evaluation and incremental parameter correction. Therefore, the precision and operability of a coupling simulation result are improved.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention relates to the technical field of layered surrounding rock inversion analysis, and discloses a layered surrounding rock three-dimensional crustal stress field inversion intelligent analysis system and method, and the system comprises a dimensional geological value module, an intelligent inversion analysis module and an inversion result verification module. The method comprises the steps of collecting geological data in target layered surrounding rock; carrying out normalization processing on the geological data, and carrying out sensitivity analysis; constructing a three-dimensional geologic model, and performing numerical simulation; a crustal stress field inversion intelligent analysis model is generated based on the three-dimensional geologic model; training the crustal stress field inversion intelligent analysis model by using the training sample; inputting the real-time geological data into the crustal stress field inversion intelligent analysis model to obtain a crustal stress inversion analysis result; carrying out visual output on the ground stress analysis result; and adjusting the crustal stress field inversion intelligent analysis model according to the actually measured data. Scientific ground stress field analysis basis can be provided for complex projects such as tunnel engineering, underground engineering and hydropower stations.

The invention relates to the technical field of territorial space planning, in particular to a construction land scale prediction method based on territorial space planning. The method comprises the following steps: acquiring historical construction land data, regional economic index data and control line space data of different regions; performing potential development turning point identification according to historical construction land data and regional economic index data, and performing stage trend analysis to obtain dynamic stage trend data; multi-scene prediction is carried out according to the dynamic stage trend data, so that multi-scene prediction data are obtained, and multiple scenes comprise a reference scene, a transformation scene and an innovation scene; and constructing an industry-land demand sensitivity analysis model according to the regional economic index data and the historical construction land data. According to the method, through development turning point identification and stage trend analysis, the dynamic characteristics of construction land change can be better grasped.

The invention relates to the technical field of hydrogen production, in particular to a bipolar plate runner structure-mass transfer efficiency parameter simulation optimization method. Comprising the steps that a flow channel structure is designed, specifically, a bipolar plate flow channel is constructed through a fractal tree-shaped network topological structure, the flow channel is composed of multiple stages of branch channels, the section size of each stage of branch channel is decreased progressively according to a self-similarity proportion, and a three-dimensional flow channel network with fractal dimensions is formed; multi-physics field coupling modeling: establishing a multi-physics field coupling model including fluid flow, electrochemical reaction and heat and mass transfer based on computational fluid mechanics and a finite element method, wherein the model considers a turbulence effect in a flow channel, interface impedance of a porous medium diffusion layer and reaction kinetic parameters at the same time; parameter sensitivity analysis: screening key parameters influencing mass transfer efficiency through orthogonal test design, including flow channel fractal dimension, branch angle, porosity and surface wettability parameters, and constructing a parameter response curved surface; the flow channel structure of the bipolar plate can be accurately optimized, and the mass transfer efficiency is effectively improved.

The invention provides a compliance test data generation method and device fusing differential privacy and a readable storage medium. The compliance test data generation device comprises a data acquisition module, a data sensitivity analysis module, a differential privacy parameter adjustment module, a data generation module and a test data verification module. The data acquisition module acquires and unifies a data format from multi-source heterogeneous data; the data sensitivity analysis module divides data into high, medium and low sensitivity levels by adopting a K-means clustering algorithm and integrating data types, sources and purpose quantitative weights; a differential privacy parameter adjustment module dynamically adjusts a privacy budget epsilon and a noise scale parameter lambda based on the sensitivity level and the test purpose; the data generation module selects a Laplace or exponential mechanism to generate test data according to the data distribution characteristics; and the verification module feeds back optimization parameters through statistical test. According to the method, the problem of test data deviation caused by insufficient privacy protection and static differential privacy parameters of a traditional anonymization method is solved.

The invention relates to the field of intelligent traffic safety, in particular to a hazardous chemical substance transportation path dynamic risk prevention and control system and method based on space-time fusion, and provides a method for calculating a multi-dimensional risk index matrix through a risk index quantification module and combining a space-time environment sensitivity prediction matrix generated by an environment sensitivity analysis module. According to a risk spreading probability cloud picture of the risk spreading prediction module, accurate evaluation of the dynamic risk of the road section is realized; the system establishes a collaborative risk factor assessment model, adjusts a safety threshold according to historical accident data, and generates a space-time road network risk scoring matrix; the risk prevention and control decision module determines an early warning level, selects prevention and control measures, optimizes a transportation path, formulates an emergency response plan for a high-risk road section, and outputs an intelligent decision instruction set; according to the system, comprehensive quantification of hazardous chemical substance characteristics is realized, and the comprehensiveness of risk assessment and the intelligence of prevention and control decision making are improved.

The invention relates to the technical field of multivariable analysis, and discloses a method and a device for improving the strength of flow-state solidified soil based on multivariable analysis, and the method comprises the following steps: collecting a material proportion parameter matrix, a stirring parameter matrix and an environment monitoring data matrix in flow-state solidified soil equipment, and executing feature extraction and feature integration; obtaining a target feature vector; performing intensity prediction of the time sequence on the target feature vector to obtain an intensity prediction value of the flow-state solidified soil; sensitivity analysis is carried out based on the intensity predicted value, and an intensity improvement potential index, a parameter adjustment stability index and an energy consumption influence index are obtained; according to the strength improvement potential index, the parameter adjustment stability index and the energy consumption influence index, adaptive parameter optimization is carried out through discount inverse reinforcement learning, and a target control parameter set is obtained. And the strength and the quality uniformity of the flow-state solidified soil are effectively improved while the operation stability is guaranteed.

The invention belongs to the technical field of industrial waste heat recovery, and discloses a flue gas waste heat recovery system optimization design method considering external parameter changes. The method comprises the steps of system monitoring data acquisition and feature extraction, thermodynamic modeling and parameter sensitivity analysis, external environment parameter and system performance coupling mechanism modeling, equipment failure prediction and reliability constraint determination, multi-objective optimization and self-adaptive control strategy generation and the like. Optimized operation of the flue gas waste heat recovery system under the condition of dynamic change of external environment parameters is achieved. According to the method, a dynamic influence matrix of external environment parameters and system performance is initiatively established, the reliability of the system is evaluated in combination with a stress-life analysis technology, efficiency and cost targets are balanced through a multi-target optimization algorithm, and a self-adaptive control strategy library for different working conditions is generated. The adaptability and stability of the system under complex working conditions are improved, the service life of equipment is prolonged, and the operation cost is reduced.

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

An optimization method based on measurement data of an ultrasonic gas meter, includes: if a relative strength index obtained by analysis does not meet expectation, calculating the degree of influence of a measurement condition on the relative strength index and determining whether to compensate the measurement data of the gas meter according to magnitude of the degree of influence; constructing an initial compensation model for the measurement data of the gas meter; performing sensitivity analysis on the optimized compensation model after multi-variable optimization; completing the correction of display data of the gas meter by a compensation factor combined with real-time temperature and pressure data and obtaining fluid features in a pipeline; matching the gas meter with the corresponding optimization plan from the pre-constructed gas meter optimization knowledge graph based on the correspondence between the fluid features and an optimization plan; and executing the optimization plan to optimize the gas meter.

The invention relates to the field of coating material production, and discloses a coating formula calculation method and system based on self-learning feedback parameter correction, and the method comprises the steps: obtaining raw material basic parameters, target performance requirements and process feedback data of historical production batches, combining a material screening mechanism and an environment working condition correction strategy, and calculating a coating formula; constructing a coating formula initial input matrix; performing multi-dimensional variable normalization processing on the initial input matrix of the coating formula, introducing a feature sensitivity analysis model, and extracting a key variable group influencing the coating performance in the feature sensitivity analysis model; based on the performance response relation mapping model, analyzing error distribution between model prediction output and actual detection data by using a prediction deviation recognition mechanism, and extracting learning error features; a self-learning feedback updating mechanism is introduced, and dynamic weight optimization is conducted on the parameter correction factor set; and performing sample test and performance verification on the corrected candidate formula set. The method has the advantage that the coating formula precision is improved.

The invention relates to the technical field of synchronous reluctance motor structure optimization, in particular to a permanent magnet auxiliary synchronous reluctance motor structure parameter multi-objective optimization method. The method comprises the steps that motor structure optimization parameters are selected, and the change range of the parameters is determined; carrying out comprehensive sensitivity analysis according to the selected optimization target; performing hierarchical processing on the optimization variables according to the comprehensive sensitivity index, dividing the optimization variables into a strong sensitive layer and a weak sensitive layer, and taking parameters in the strong sensitive layer as to-be-optimized parameters of the next algorithm; comparing the prediction precision of the back propagation neural network, the radial basis, the extreme learning machine, the support vector machine and the kernel extreme learning machine, and selecting an agent model with an optimal prediction effect; combining the established high-precision agent model with a fast non-dominated sorting genetic algorithm, and searching an optimal combination of to-be-optimized parameters; and finally, carrying out single parameterization scanning determination on parameters in the weak sensitive layer by utilizing finite element simulation so as to obtain optimal structural parameters of the permanent magnet auxiliary synchronous reluctance motor.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention discloses a soil erosion combined forecasting method based on Bayesian weight. The invention relates to the technical field of soil erosion forecasting, and the method comprises the steps: building a watershed multi-source heterogeneous database, and building a soil erosion joint simulation frame based on a MUSLE model, a WEPP model, a LISEM model, an EUROSEM model and a SIMWE model; respectively carrying out parameter sensitivity analysis on each soil erosion model by adopting an FAST algorithm; respectively carrying out parameter calibration and verification on each soil erosion model by adopting a PSO (Particle Swarm Optimization) algorithm; carrying out Bayesian sampling on each soil erosion model by adopting a DREAM algorithm, and carrying out parameter posterior distribution estimation; and based on a Bayesian sampling result, evaluating the uncertainty of the soil erosion model, calculating Bayesian weights of different soil erosion models by adopting a BMA algorithm, and carrying out soil erosion combined forecasting.

The invention relates to the technical field of artificial intelligence, and discloses an agent rapid construction method and system based on enhanced retrieval, and the method comprises the steps: constructing a multi-dimensional task analysis system, retrieval task characteristics are automatically represented through characteristics such as data distribution, query complexity and semantic requirements; executing a progressive architecture evolution algorithm, and automatically searching an optimal architecture meeting multi-dimensional balance of retrieval precision, calculation complexity and memory occupation based on the task characteristic vector; constructing a hierarchical knowledge distillation transfer chain, and realizing efficient knowledge migration from a large model to a small model through feature matching and attention guidance; implementing mixing precision quantification and structured pruning on different hierarchies based on sensitivity analysis, and adapting to a target deployment environment; according to the method, the model volume is reduced, the reasoning speed is increased, the energy consumption is reduced, and the enhanced retrieval agent can efficiently operate on resource-constrained equipment.

The invention relates to the technical field of optimization control, in particular to an operation parameter optimization control method and system for an air condenser, and the method comprises the steps: firstly obtaining environment data, processing the environment data to obtain a self-adaptive environment monitoring value, and constructing a non-uniform frequency conversion strategy according to the self-adaptive environment monitoring value to control the optimization frequency of the operation parameters of the air condenser; then obtaining structural features and operation features of the air condenser, obtaining a first to-be-optimized parameter vector according to a principal component analysis method, obtaining the first to-be-optimized parameter vector, and carrying out sensitivity analysis to obtain a second to-be-optimized parameter vector; and then the optimization parameter prediction model predicts a parameter value vector of the second to-be-optimized parameter vector, the distributed parameter optimization control model sets a local agent of the to-be-optimized parameter, the parameter value vector of the second to-be-optimized parameter vector is acquired, and parameter optimization control is performed through a reinforcement learning algorithm. According to the method, through optimal control over the operation parameters, the parameter optimization speed and stability are improved, and the energy consumption of the air condenser is reduced.

The invention provides a power distribution network multi-time scale voltage regulation and control method adapting to topological changes, and aims to solve many problems of traditional power distribution network voltage regulation and control. According to the method, firstly, key parameters of the power distribution network are monitored in real time, topological changes are recognized through a clustering analysis algorithm, and a node-branch model based on the graph theory is dynamically updated; secondly, designing long-time and short-time scale strategies, adjusting equipment based on load prediction and a power generation plan for a long time, and rapidly adjusting load and distributed power supply changes for a short time; and then sensitivity analysis is carried out, and a regulation and control strategy is optimized according to the influence weight of the equipment on the voltage. A distributed cooperative control framework is constructed, and a regional controller coordinates a photovoltaic inverter and an energy storage device according to a selected protocol and mechanism. Finally, deviation is calculated through real-time data collection, and a control strategy is adjusted in a self-adaptive mode. The method effectively deals with topological changes, integrates the advantages of multiple strategies, improves the voltage regulation and control precision, enhances the power supply reliability of the power distribution network and the distributed energy consumption capability, and guarantees the stable operation of the power grid.