118 results about "Symbolic regression" patented technology

The invention relates to the technical field of coast engineering, and discloses a multi-source data-based beach restoration decision-making method and system, and the method comprises the following steps: obtaining multi-source data of beach monitoring, carrying out the data fusion processing, and generating a feature tensor with a unified temporal-spatial resolution; performing symbol regression analysis based on the feature tensor, and automatically discovering a control equation of beach evolution; and inputting the evolution equation into a decision optimization framework to generate a beach restoration decision scheme. According to the method, through organic combination of multi-source data fusion, symbolic regression analysis and a decision optimization framework, accurate description of a beach evolution process and intelligent generation of a repair decision are realized; according to the scheme, information of various monitoring data can be fully utilized, a control equation of beach evolution and time-varying characteristics of the control equation can be automatically found, and a physical rule is directly converted into a specific engineering decision suggestion, so that prediction precision and decision efficiency of beach repair are improved.

The invention discloses a grid-connected photovoltaic power station reverse power protection monitoring method and system, and the method comprises the steps: collecting the time sequence data of each grid-connected node, and carrying out the normalization and time sequence alignment processing; inputting the standardized data into a TimesNet model, extracting time sequence features and predicting a reverse power risk; generating a mathematical expression through symbol regression in combination with historical abnormal data and model output; a protection strategy is generated according to the prediction result and the expression, and the linkage device executes and collects feedback; and the feedback data is used for updating the model and the expression, and a self-adaptive closed-loop control mechanism is constructed. According to the invention, by introducing the time sequence depth model and the symbol regression fusion method, accurate prediction and adaptive protection control of a reverse power event are realized.

The invention is suitable for the technical field of material mechanics and engineering, and provides a multi-axial fatigue life prediction method and device and computer equipment, and the method comprises the steps: obtaining original data from a multi-axial fatigue test database, and obtaining target features based on the original data, designing a plurality of initial multi-axial fatigue life prediction equations based on a semi-empirical multi-axial fatigue life prediction method, constructing a corresponding neural network architecture according to each initial multi-axial fatigue life prediction equation, and training the neural network architecture in combination with the target features and the physical constraint loss function to obtain a target neural network; and performing interpolation sampling on each network module of the target neural network to construct an enhanced data set, extracting an interpretable quantization equation of each network module through symbolic regression based on the enhanced data set, combining the interpretable quantization equations, performing generalization screening, and outputting a final multi-axial fatigue life prediction equation. And precision, interpretability and generalization are considered, and engineering application requirements are met.

The invention relates to a cross-system lattice constant modeling method, device and medium based on Pareto frontier optimization and symbolic regression collaboration, and the method comprises the steps: collecting theoretical calculation and experimental data of a perovskite and spinel cubic phase system, and constructing an initial data set containing geometric and electronic structure parameters; preprocessing the data; performing global search by adopting a Pareto frontier optimization algorithm to improve prediction precision and feature consistency to obtain an optimal feature subset; generating a lattice constant analytical model with physical significance through genetic programming by using a symbolic regression algorithm; the model is finely adjusted through experimental data, and a cross-system prediction general formula suitable for experimental conditions is obtained. Compared with the prior art, the method has the advantages that the global optimal feature subset is efficiently searched in the multi-material system, the explicit quantitative relation between the lattice constant and the key material attribute is established, and an efficient and reliable calculation means is provided for lattice constant prediction and mechanism analysis of the complex material system.

The invention discloses a concrete nonlinear creep explicit modeling method and system, and relates to the technical field of concrete structures and materials.The concrete nonlinear creep explicit modeling method comprises the steps that concrete creep test data under all conditions are collected, and a test data set is constructed; preprocessing the concrete creep test data of the test data set; a DBO algorithm is adopted to optimize hyper-parameters of the LGBM model, and the LGBM model is constructed and trained through the test data set and the optimal hyper-parameters; the input features of the trained LGBM model are screened through an SHAP additive interpretation method, and key feature parameters are obtained; and establishing a concrete nonlinear creep explicit calculation formula by using a PySR symbol regression tool and taking the key characteristic parameters as input and the creep strain as output. The concrete nonlinear creep explicit modeling method provided by the invention has both calculation precision and engineering practical value.

The invention discloses a fusion graph recognition and symbolic expression-based resin-based composite material automatic native modeling and reverse design method and system, belongs to the field of composite material modeling and design, and particularly relates to a graph neural network and symbolic regression fusion-based composite material native modeling and structure reverse optimization method. The method comprises six steps of microcosmic image structure extraction, topological graph construction and graph embedding, constitutive relation symbol modeling, graph structure homogenization, performance-oriented reverse design and multi-modal performance prediction, and can realize an automatic process from microcosmic image to macroscopic performance prediction to structure optimization design. The problems that an existing method is low in efficiency, poor in interpretability and difficult in reverse design are solved, and the modeling efficiency and the design intelligence level of the composite material under multiple scales and multiple targets are improved.

The invention relates to the technical field of wind power plants, in particular to a wake flow evaluation method and device for a floating wind power plant, equipment and a storage medium. According to the method, high precision of numerical simulation and high efficiency of data driving are combined, namely, CFD is firstly adopted for simulation, and then a symbol regression model based on a particle swarm optimization algorithm is trained based on data obtained through simulation, so that a wake flow velocity loss expression is constructed, and the influence of wake flow interference existing in the floating wind power plant is simulated. And meanwhile, compared with the traditional method that fan integrated numerical calculation is carried out by using CFD, the calculation time consumption and the cost are very high, the method carries out multi-equation comparison through a particle swarm optimization algorithm, an optimal symbol regression equation is optimized, and rapid prediction of a key region of interest in engineering is realized. Besides, for the wake flow area, a smooth step function is adopted to replace an indicator function, so that the problem that the wake flow expansion rate contains jump discontinuity, and sharp change of the wake flow width can be caused can be avoided.

The invention relates to the technical field of machine learning, and discloses a concrete filled steel tube member lateral impact deflection prediction method based on machine learning, comprising: generating a lateral impact sample set; respectively training a support vector machine model, a random forest model and an extreme gradient lifting model based on the lateral impact sample set, and screening an optimal model; inputting the characteristic parameters of each lateral impact sample into the optimal model, and performing performance analysis on the optimal model by adopting an interpretable method to obtain optimal characteristic parameters; constructing a relational expression between the maximum deflection and the optimal characteristic parameters, selecting a basic quantity, and after converting the relational expression into a dimensionless relational expression based on a theorem, performing fitting by adopting a symbolic regression method of genetic coding to generate a fitted maximum deflection prediction formula of the component under lateral impact so as to obtain the optimal maximum deflection; according to the method, the maximum deflection prediction precision of the component is improved.

The invention discloses an energy storage system fault prediction and diagnosis method based on artificial intelligence. The method comprises the following steps: acquiring operation data of an energy storage system; preprocessing the operation data to generate a preprocessed multi-dimensional feature sequence; performing feature representation transformation on the multi-dimensional feature sequence, and extracting low-dimensional potential feature representation; constructing a symbol regression network; executing symbol expression search, applying dynamic weight penalty in the search process, and outputting a target expression; the future operation state of the energy storage system is predicted, and fault early warning information is generated; and analyzing the contribution degree of each variable and an operator in the target expression, determining a key parameter causing a fault, and judging a corresponding fault category. According to the method, the energy storage mechanism constraint operator and the residual error distribution driven game adaptive penalty mechanism are introduced, so that high-precision prediction and interpretable identification of the fault of the energy storage system are realized.

The invention discloses an unmanned heavy-load vehicle operation monitoring method and system based on big data, and the method comprises the following steps: building an interpretable operation behavior model through a symbolic regression algorithm, carrying out the modeling of the healthy operation state of an unmanned heavy-load vehicle under various working conditions, and extracting the function relation between key variables. According to the method, a first operation baseline is generated through initial stable data, a second operation baseline is constructed under a model output stable condition, a double-baseline reference system is formed, real-time operation data is compared with the baseline after model prediction, a residual trajectory is generated, multi-dimensional residual features are extracted, and the features are input into a CUSUM judgment module. And statistical magnitude updating and sensitivity parameter self-adjustment are executed, dynamic recognition and multi-level early warning output of the degradation trend are achieved, and the method is suitable for deployment of the vehicle-mounted edge equipment.

The present invention relates to a system for generating a clinical score based on symbolic regression. The system for generating a clinical score based on symbolic regression groups a plurality of responses into a plurality of response groups, assigns the same partial weight to responses included in the same response group, generates a plurality of partial weight groups, determines a final partial weight group by iteratively evaluating the partial weight groups, determining top-ranking partial weight groups, and generating new partial weight groups over a plurality of generations, and generates a score table on the basis of the final weight group.

The invention provides a thermoplastic laying bandwidth stabilization method based on nerve guidance symbol regression and a process three-parameter selector, and the method comprises the steps: synchronously collecting power, speed, pressure, strip temperature, effective bandwidth and material identification, and calculating a compaction condition; constructing a process influence item set by using input alignment, segmented gating and a symbolic regression structure, explicitly calculating an edge outflow risk value, and identifying safety adjustment intervals and maximum adjustable amplitudes of power, speed and pressure; constructing a single-parameter adjustment path in a process three-parameter selector, performing path scoring on the risk reduction amount, the compaction influence amount and the response time delay, and generating a first adjustment parameter instruction; and through single-step adjustment and adjustment response data acquisition, according to risk change, an effective bandwidth difference value, response time delay and a compaction condition, carrying out bandwidth restability judgment, and outputting a sequence control instruction. The edge outflow risk of the strip can be reduced, the compaction quality is guaranteed, and the stability and the forming quality in the thermoplastic laying process are improved.

The invention discloses a high-speed aerodynamic derivative analysis modeling method and system based on symbolic regression, and belongs to the technical field of aircraft aerodynamic modeling and artificial intelligence cross, and the method comprises the steps: obtaining an aerodynamic data set of a high-speed aircraft, and carrying out the preprocessing; defining grammar for generating candidate symbol expressions and constructing an expression tree; modeling based on the training set by adopting a layered symbol regression framework to obtain an analytical model; the prediction precision of the analytical model is verified on the test set, and physical consistency analysis is carried out; carrying out uncertainty quantification on the analytical model to obtain predicted uncertainty estimation of the analytical model; and finally, outputting an analytic model in a mathematical expression form for describing the relationship between the aerodynamic derivative and the flight state variable and uncertainty estimation of the analytic model. The method overcomes the defects of a traditional method in precision, generalization and interpretability, realizes full-automatic efficient modeling from data to the analytic model, and improves the modeling efficiency. The method is suitable for control design, stability analysis and real-time simulation of the high-speed aircraft.

The invention discloses a turbulence model construction method for aerodynamic simulation of a hybrid three-compartment vehicle, and relates to the field of model simulation, and the method comprises the steps: S1, employing a flow field inversion method, and obtaining the dense space distribution of a turbulence model correction term; s2, screening local flow field characteristics related to the correction distribution characteristics; s3, training by adopting a symbol regression method to obtain an analytical expression of a turbulence model correction term; s4, through mathematical derivation, an analytical expression of a correction term is migrated into the standard model, so that the correction term is added in front of a damage term of a transport equation of the dissipation rate of the standard model; s5, selecting an initial value of a to-be-calibrated coefficient in the correction item according to the smoothness degree of the chassis of the hybrid three-compartment vehicle; and S6, calibrating the to-be-calibrated coefficient in the expression in the step S4 on the basis of real vehicle experimental data of the hybrid three-compartment vehicle to obtain the turbulence model for aerodynamic simulation of the hybrid three-compartment vehicle.

The invention relates to the technical field of hazardous chemical substance safety management, and discloses a hazardous chemical substance physicochemical property management system and method.The hazardous chemical substance physicochemical property management method comprises the steps that hazardous chemical substance physicochemical property data are preprocessed, and a standardized data set is obtained; carrying out feature extraction and transformation; pre-learning a feature extraction and transformation result by using a deep neural network containing physical consistency constraint, and extracting a complex mode in the data; self-adaptive symbol regression is carried out, wherein expression search space is defined, nerve-guided search space pruning is carried out, and an improved genetic programming algorithm is applied; carrying out optimization and verification, and selecting an optimal mathematical expression as a prediction model of the physicochemical properties of the hazardous chemical substances; according to the method, through a mathematical expression generation technology based on symbol regression, high precision and interpretability of the prediction model are realized, and a user can intuitively understand a calculation process and physical significance of a prediction result.

The invention discloses a method, system and medium for a voltage clamping unit in a chip circuit, and belongs to the technical field of chip circuit protection, and the method comprises the steps: obtaining a maximum allowable voltage deviation window and a maximum allowable voltage rise rate of a target input / output pin of a chip, and constructing a target clamping characteristic parameter set; selecting a clamp sub-model based on the parameter set; adjusting capacitance matching unit parameters according to the equivalent input capacitance of the target pin by combining a support vector regression algorithm with a genetic algorithm; connecting a clamping model and injecting a test voltage waveform, and extracting clamping response time and clamping voltage to construct feedback characteristics; a mathematical model between the clamping response time and the delay gate series is generated through a symbol regression algorithm, and a control delay unit structure is corrected; according to the method, closed-loop adaptive adjustment of clamping parameters is realized, the transient interference suppression capability and clamping precision of the chip under high-frequency and low-voltage conditions are improved, and the method has good reliability and expandability.

The invention discloses a multifunctional catalyst performance prediction method based on high-throughput calculation and machine learning, and relates to the technical field of catalytic material prediction. The method comprises the following steps: acquiring basic structure information of a target catalyst, correspondingly calculating catalytic performance data and related characteristic data of the target catalyst, and pairing the basic structure information and the related characteristic data to establish a data set; importing the data set into a plurality of machine learning models for training, and performing hyper-parameter tuning by using multi-target Bayesian optimization; based on the screened optimal model, sorting and discriminating key features influencing the performance of the catalyst by using an SHAP value, and importing the key features into an interpretable machine learning model SISSO for multi-task training; based on an interpretable machine learning model SISSO and through a symbol regression method, obtaining an explicit mathematical relationship between the feature combination and the catalytic performance as a descriptor formula for application; the multi-aspect catalytic performance of the catalyst can be rapidly predicted, and the research and development efficiency of the catalyst is greatly improved.

A fatigue life prediction method of a turbine blade based on logic constraint-enhanced symbolic regression, includes: constructing a symbol library based on a turbine blade fatigue test dataset; performing dimensionless preprocessing on input variables in the library; constructing a logic constraint-enhanced symbolic regression model with a reinforcement learning module with an RNN as a carrier and a logic constraint rule module, selecting a node from the library to construct expressions, and selecting an expression with best fitting effect as a prediction formula by using a real fatigue test benchmark; guiding the node selection and optimization of the constructed expression structure, and applying a logic constraint rule in the selected node; and obtaining basic mechanical property parameters of a dangerous part under different working conditions, which are used as an input of the fatigue life prediction formula, and outputting a fatigue life cycle number to predict turbine blade fatigue life.

An embodiment for generating impact functions for geospatial climate hazards based on user interactions. The embodiment may receive input data associated with a target geospatial climate hazard and a corresponding asset, the input data including one or more of a first dataset corresponding to a predetermined set of prompts, and a second dataset corresponding to a natural language exchange. The embodiment may generate, based on the first dataset an entity knowledge graph including a series of candidate variables. The embodiment may generate, based on the second dataset, a universal knowledge graph including a series of candidate function formulas. The embodiment may generate, using a graph neural network, embeddings corresponding to the entity knowledge graph and the universal knowledge graph respectively. The embodiment may perform symbolic regression, using the embeddings, to generate one or more impact functions for the target geospatial climate hazard and the corresponding asset.

The invention belongs to the technical field of artificial intelligence, discloses a rusted RC beam shear strength prediction method and system fused with domain knowledge, and solves the problems of low accuracy, insufficient transparency and poor interpretability in rusted RC beam shear strength prediction in the prior art. According to the specific scheme, the rusted RC beam shear strength prediction method fusing domain knowledge comprises the steps that input parameters related to the rusted RC beam shear strength are determined, and the input parameters serve as input to generate a rusted RC beam shear strength explicit calculation formula through symbolic regression analysis on the basis of a gene expression programming algorithm; taking an existing empirical model as domain knowledge, fusing the existing empirical model with an explicit calculation formula based on symbolic regression through a decision tree algorithm, and constructing a rusted RC beam shear strength prediction model based on physical information machine learning; and carrying out visual analysis on the decision-making process of the rusted RC beam shear strength prediction model based on physical information machine learning.

The invention relates to an intelligent power transformation micro-station equipment energy consumption prediction system and method based on a symbol regression algorithm, and the method comprises the steps: selecting daily average temperature, daily average humidity, sunshine duration t and traffic flow VF as four input features which affect the daily total energy consumption of an intelligent power transformation micro-station; three modules of a symbol regression algorithm based on adaptive iteration and control variable genetic programming: an adaptive iteration module, a control variable module and a genetic programming algorithm module are used for training a standard genetic programming algorithm; and inputting the sorted historical energy consumption data set into an intelligent power transformation micro-station equipment energy consumption prediction model based on a symbol regression algorithm to obtain a total energy consumption prediction value of the intelligent power transformation micro-station on the day. The method can effectively mine the potential relation between the characteristics in the data set, obtains the displayed prediction equation, and accurately predicts the energy consumption of the intelligent power transformation micro-station equipment. The search space of the algorithm can be reduced, the training time of the prediction model is shortened, and the model energy consumption prediction precision is improved.

The invention provides a drilling-expanding mixed machining material removal power prediction method based on SR-TELM, relates to the technical field of numerical control machine tool power prediction, and deeply excavates a physical mechanism between machining parameters and material removal power in drilling-expanding mixed machining material removal through a symbolic regression algorithm. By embedding a mechanism model into hidden layer neurons of the TELM, the advantage of analyzing a physical mechanism by a symbolic regression algorithm and the strong nonlinear approximation capability of the TELM are organically fused, the power prediction precision is effectively improved under a limited data condition, the physical interpretability of the model is ensured, and the power prediction efficiency is improved. And a new solution is provided for machine tool drilling-expanding mixed machining material removal power modeling.

This invention relates to the field of semiconductor manufacturing process monitoring technology, and discloses an online pH monitoring system for polishing slurry based on ultrasonic stirring. The system includes: an ultrasonic stirring module for uniformly stirring the polishing slurry and acquiring acoustic signals, extracting acoustic feature vectors through Fourier transform and wavelet analysis; a multi-sensor data acquisition module for acquiring pH, temperature, turbidity, and redox potential data; an anomaly detection module for constructing an LSTMAutoencoder model, calculating reconstruction errors, and triggering early warnings; a causal analysis module for constructing a causal network of pH changes; a rule generation module for extracting mathematical relationships using symbolic regression and converting them into predictive rules; and an intervention control module for calculating the effectiveness of intervention measures, generating and executing optimal control operations. Through structural causal models and counterfactual reasoning, this invention can accurately identify key factors leading to pH anomalies and their contribution, shortening troubleshooting time.

The invention relates to the technical field of fault diagnosis, and discloses a continuous casting bonding breakout forecasting method based on symbol regression. The method comprises the steps that state data in a target time period in the steel smelting continuous casting process are collected and preprocessed; identifying an abnormal thermocouple temperature region based on the temperature data, extracting abnormal thermocouple temperature features, and constructing an input feature set in combination with process parameters; constructing a forecasting model, performing training based on the input feature set, inputting state data collected in real time into the trained forecasting model, predicting and generating a real-time risk value, and performing contribution degree analysis on input variables in the input feature set to obtain corresponding contribution weights; and adjusting parameters of the forecasting model based on the contribution weight, retraining the forecasting model, outputting an optimized real-time risk value, setting a preset risk threshold value, and triggering breakout early warning in combination with the preset risk threshold value. According to the method, the accuracy and the real-time performance of steel breakout forecasting are improved.

The invention discloses a distributed joint debugging simulation platform construction method based on symbol regression. The method comprises the following steps: 1) collecting multi-source original data including sensors, experimental data, historical data and model simulation data; 2) carrying out de-noising processing on the collected original data; 3) constructing a symbol regression model based on the denoised data; 4) on the basis of obtaining the optimal analytic formula result of the symbol regression model, constructing a sequence simulation driving module based on the basic element preset library and the engineering element database, so as to perform simulation evaluation and optimization; according to the distributed joint debugging simulation platform based on symbolic regression, an interpretable analysis formula is generated through data-driven symbolic regression, and the model has the advantages of interpretability, high efficiency and adaptability.

The invention discloses a PA6 accelerated aging performance prediction method and system fusing symbol regression and machine learning, and the method comprises the steps: S1, collecting aging data of PA6 in different environments, and carrying out the preprocessing of the aging data, and obtaining the preprocessing data; s2, extracting feature data of the preprocessed data, and inputting the feature data into symbol regression to generate derivative features; s3, based on a machine learning architecture, inputting the derivative features into a CatBoost algorithm for training, and constructing an aging performance prediction model; and S4, outputting aging data of PA6 collected in real time into the aging performance prediction model for prediction, and obtaining an accelerated aging performance prediction result.

The invention belongs to the technical field of alloy fatigue life prediction, and discloses an alloy fatigue life prediction method fused with an information mining technology, which comprises the following steps: introducing key physical quantities into a feature set by combining a fatigue theory; mining inherent information in the data by adopting a symbolic regression algorithm driven by genetic programming, and adding the inherent information into a feature set as derivative features so as to construct a fatigue life prediction model of the alloy; a prediction model without derivative features is constructed for performance comparison, so that the improvement effect of fusion information mining technology aided modeling on prediction precision is evaluated; derivative feature expansion is applied to fatigue life prediction of a multi-component alloy system, and the universality of mined information is verified. According to the method, the information mining technology is adopted, hidden information in the data is extracted to assist modeling, and compared with a traditional machine learning method, more universal and high-precision fatigue life prediction is achieved, so that more reliable prediction support is provided for risk assessment of structural components.

The invention discloses a flow field prediction method based on a multi-agent symbolic regression algorithm, and the method comprises the steps: obtaining known flow field data, and setting a preset number of agents; inputting the known flow field data into the intelligent agent to obtain a preorder traversal sequence of the symbol expression tree; obtaining a partial differential equation based on the preorder traversal sequence; updating the parameters of the intelligent agent based on a reward function and a risk seeking strategy gradient method until the calculation error meets the requirement when the partial differential equation is full, and obtaining an updated intelligent agent; acquiring to-be-measured flow field data and inputting the to-be-measured flow field data into the updating agent to obtain an optimal partial differential equation as a constraint term; constructing a loss function based on the constraint term and training the physical information neural network to obtain a trained physical information neural network; and inputting to-be-measured flow field data into the trained physical information neural network to obtain a flow field prediction result. And efficient and accurate prediction of any flow field data is realized.

The invention provides an engine system assembly fault diagnosis method based on genetic programming. The engine system assembly fault diagnosis method comprises the steps that 1, automobile engine assembly parameters and vibration data are collected; 2, data enhancement based on a neighborhood oversampling regression technology; 3, carrying out assembly parameter-vibration performance symbol regression modeling based on genetic programming; and step 4, system assembly fault diagnosis and key process parameter analysis are carried out. According to the method, a genetic programming symbol regression method is adopted, an explicit tree structure representing internal correlation between the assembly parameters and the vibration performance is automatically evolved, high-precision and highly interpretable vibration state classification is achieved, and fault diagnosis can be effectively supported. Through deep analysis of the optimal expression tree, key process parameters having significant influence on vibration performance are accurately identified, and a nonlinear coupling mechanism and a sensitive direction of the key process parameters are disclosed, so that a reliable theoretical basis and engineering guidance are provided for accurate optimization of an engine assembly process and effective traceability of vibration faults.

The invention discloses an artificial intelligence chip-oriented model performance automatic optimization method, and relates to the field of model automatic optimization, and the method comprises the steps: obtaining the hardware constraint of a target chip, carrying out the spatial sampling of a dynamic input dimension, mining training data containing hardware boundary features, and carrying out the reference symbol regression through genetic programming, thereby obtaining a target chip model; and thus, a reference parameter mapping model with generalization ability is constructed. Then, hardware nonlinear residual errors are extracted and characterized, and a regression decision tree is used for fitting specific performance fluctuation of the hardware; and finally, injecting the hybrid strategy model into a dynamic compiler, so that the executable file can adaptively generate optimal parameters according to the input dimension during operation. In this way, the unified acceleration strategy abstraction layer is combined, automatic combination verification of quantification and compilation optimization is achieved, and it is ensured that the model is always in the optimal performance interval in the complex heterogeneous hardware environment.