108 results about "Model tuning" patented technology

The invention relates to the technical field of three-dimensional model adjustment, in particular to a three-dimensional model adjustment method and system and a medium. The method comprises the following steps: obtaining a multi-angle image of an original model, carrying out multi-view normalization on the multi-angle image, generating a normalized view image set, extracting feature points of the original model, carrying out parallax correction on the feature points, reconstructing a simulation three-dimensional model, collecting basic purpose data of the model, carrying out ideal demand mapping through the data, and carrying out ideal demand mapping. The method comprises the following steps: determining an ideal three-dimensional model structure, carrying out core region segmentation on a reconstruction model according to basic purpose data to obtain key region model slices, carrying out highlight region comparison with the ideal model structure, analyzing model differences, determining a structure adjustment amplitude interval according to a comparison result, and carrying out cyclic fine adjustment correction on the key region model slices to obtain a three-dimensional model. And the three-dimensional model is consistent with the ideal three-dimensional model in structure, so that the optimized three-dimensional model is generated. According to the invention, efficient and accurate three-dimensional model adjustment and optimization are realized.

The invention discloses an image classification and recognition system and method based on an AI training model, relates to the technical field of image classification and recognition, and solves the problem that an existing method lacks dynamic adaptability. Comprising a data monitoring module, a dynamic label correction module, a model distillation module, a model tuning module and a model version control module. The data monitoring module evaluates input data distribution offset in real time by adopting a maximum mean value difference and KL divergence algorithm, and triggers model updating; the dynamic label correction module realizes automatic label correction through clustering active learning and semantic segmentation; the model distillation module uses adaptive incremental learning and a knowledge distillation mechanism to reduce computing resource consumption; the model adjusting and optimizing module adopts an elastic weight integration method to maintain the stability of the model; performance fluctuation is prevented through the model version control module; according to the invention, the flexibility and response speed of electric power facility inspection are greatly improved, and the defect detection precision and the long-term adaptability of the model are remarkably improved.

The invention discloses an electric power knowledge question-answering system construction method and system combined with engineering ethical analysis, and the construction method comprises the steps: constructing all modules, and carrying out the fine adjustment of a large language model based on a P-Tuning method, so as to construct an electric power system question-answering knowledge base; the question understanding and classification step is responsible for receiving input questions of a user and performing semantic analysis, technical classification and ethical risk identification on the input questions; searching background knowledge; carrying out engineering ethical analysis; generating questions and answers; and a feedback and improvement step: entering a model tuning stage, and carrying out adaptive tuning on the model by adopting a reinforcement learning framework. According to the method, comprehensive engineering ethical analysis can be carried out on technical schemes and operation suggestions, finally, answers generated by the system are accurate and feasible in technology and have ethical compliance at the same time, it is ensured that a user can balance technical requirements and ethical responsibility in practical application, the intelligent level of an electric power knowledge question-answering system is remarkably improved, and the user experience is improved. And important support is provided for safe operation and sustainable development of the power industry.

The invention discloses an automatic model tuning system based on an AI training intelligent workbench, and relates to the technical field of artificial intelligence, and the system comprises the steps: setting a data collection instrument and an environment monitoring point, collecting model real-time data in a model training process, and carrying out the preprocessing; performing model performance analysis according to the model real-time data and the model original data to obtain final parameter configuration; and performing model training by utilizing the final parameter configuration, dividing the model training process into different stages, dynamically adjusting an intelligent control strategy based on model performance requirements, and performing optimization on the different stages of the model. According to the method, through multi-dimensional performance evaluation, parameter configuration is optimized, and the robustness and generalization ability of the model are improved. Through dynamic adjustment of an intelligent control strategy, fine management of a training process is realized, efficiency and resource utilization are improved, model convergence is accelerated, and finally an efficient, stable and high-adaptability model training effect is achieved.

The invention discloses an ice condition prediction method based on multi-feature fusion and physical constraint, and belongs to the technical field of hydrological forecasting. Firstly, various types of data of a target area are collected and preprocessed to serve as a data set, features of the various types of data are extracted, feature fusion is conducted on obtained image features, time sequence features and environment features through a multi-head attention mechanism, and a fusion feature vector is generated. Secondly, adopting a physical information neural network model, taking the fusion feature vector as input, taking ice thickness and ice stress as output layers, carrying out constraint by using a composite loss function, carrying out model optimization by using a verification set, carrying out processing through a full connection layer in the network, and carrying out end-to-end training and regularization of a prediction model; and finally, evaluating the final prediction model obtained by training through the test set, and verifying the effectiveness and generalization ability of the final prediction model. The method combines multi-source and multi-mode observation data, can effectively capture complex relations between ice surface changes and various factors, can improve prediction precision, stability and reliability, and can improve model training efficiency and generalization ability.

A combined hyperparameter and proxy model tuning method is described. The method involves multiple search iterations. In each search iteration, candidate hyperparameters are considered. An initial (‘seed’) hyperparameter is determined, and used to train one or more first proxy models on a target dataset. From the first proxy model(s), one or more first synthetic datasets are sampled. A first evaluation model is fitted to each first synthetic dataset, for each candidate hyperparameter, enabling each candidate hyperparameter to be scored. Based on the respective scores assigned to the candidate hyperparameters, a candidate hyperparameter is selected and used to train one or more second proxy models on the target dataset

The invention discloses a method for randomly predicting and assigning parameters of an excavated soil body of a beach-overtopping foundation pit. The method comprises the following steps of: collecting geological survey data and construction information of an excavated area of the beach-overtopping foundation pit, screening redundant parts of characteristic parameters, retaining variables which have influences on mechanical parameters of the soil body, and constructing a complete original database; collected data is divided into a training set and a verification set, the training set is used for preliminary training of the model, and the verification set is used for optimization and verification of the model. According to the method, the nonlinear relation of the complex variables is captured through the random forest model, dynamic monitoring and real-time optimization are combined, the soil parameter prediction precision is remarkably improved, and the construction uncertainty risk is reduced; the dynamic monitoring function quickly responds to soil body changes in construction, the prediction result is optimized, and the construction safety is improved; the random field assignment method effectively reflects the spatial variability of soil parameters, supports multi-scene risk assessment, and provides scientific basis and reliable guarantee for engineering design and construction.

The invention provides a method for training an end-to-end automatic driving strategy, and the method comprises the following steps: S1, data collection and preprocessing, S2, defining a loss function and an optimizer, and constructing a model, S3, data division, S4, model training, S5, evaluation on a verification set, S6, model adjustment and optimization, and S7, test set evaluation. S8, weight pruning and evaluation: calculating and evaluating the importance of each weight by adopting a gradient norm calculation mode, evaluating the model after weight pruning by using the test set in the step S7 according to an importance evaluation result, starting from a relatively small proportion, gradually increasing and removing unimportant weights, and if the performance of the model does not reach the standard at the moment, evaluating the model after weight pruning by using the test set in the step S7. According to the method, through weight pruning, the applicability of a model system is wider, and the training efficiency of the system is higher.

The invention discloses a livestock health state monitoring method, apparatus and device, and a storage medium. The livestock health state monitoring method comprises the steps of obtaining sensor monitoring data in a livestock factory; constructing a sample data set according to the sensor monitoring data; and performing model pre-training and model tuning training on a pre-constructed monitoring network model by adopting the sensor sample data until a preset model training ending condition is met, and taking the trained monitoring network model as a livestock health monitoring model. By means of the technical scheme, the model performance of the livestock health monitoring model applied to different livestock factory areas is improved, and meanwhile the training efficiency of model training is improved.

A combined hyperparameter and proxy model tuning method is described. The method involves multiple search iterations. In each search iteration, candidate hyperparameters are considered. An initial ('seed') hyperparameter is determined, and used to train one or more first proxy models on a target dataset. From the first proxy model(s), one or more first synthetic datasets are sampled. A first evaluation model is fitted to each first synthetic dataset, for each candidate hyperparameter, enabling each candidate hyperparameter to be scored. Based on the respective scores assigned to the candidate hyperparameters, a candidate hyperparameter is selected and used to train one or more second proxy models on the target dataset.

The invention discloses a tensor program generation method and system based on a large language model, and the method comprises the following steps: (1) generating a random ONNX computational graph through an AI compiler test tool nnsmith; (2) importing an ONNX calculation graph generated by nnsmith into a TVM, and converting the ONNX calculation graph into a RelayIR intermediate representation; (3) based on a model intermediate representation RelayIR in the TVM, adjusting and optimizing the model through an automatic adjusting and optimizing device Ansor in the TVM, generating an optimal tensor program, and outputting a JSON file measured during adjusting and optimizing; (4) continuously carrying out model tuning and tensor program measurement, generating a large number of measurement records during tuning, and forming a tensor program data set which comprises features of a tensor program and a corresponding optimization pass list; and (5) training a large language model according to the generated tensor program data set to realize understanding and generation of the tensor program. Through combination of an automatic tool and a large language model, efficient tensor program generation and optimization can be realized.

The invention discloses an intelligent task planning method and device based on environment perception, equipment and a medium, and the method comprises the steps: fusing abstract environment information into a large language model, and carrying out the fine adjustment of the large language model, so as to enhance the environment perception capability of the large language model; inputting a task target and an environment state table into the fine-tuned large language model, and generating a multi-step action sequence with the highest probability through a step-by-step beam search strategy; according to the generated action sequence, the environment data state table is updated by simulating interaction between the robot agent and other objects, and therefore interaction tracks corresponding to the steps are generated. According to the method, the model is adjusted into an environment perception large language model fine tuning structure, so that higher accuracy and practicability are brought to intelligent task planning; the large language model can explore a wider search space by utilizing a step-by-step beam search strategy, so that more comprehensive search is beneficial to generating a more reasonable and effective plan.

The invention relates to the technical field of welding detection, in particular to a welding seam strength detection method, which comprises the following steps of: based on welding process monitoring, monitoring welding seam temperature, welding speed and load pressure, recording and screening data, eliminating abnormal data points, generating a primary screening data set, applying an autoregressive moving average model to the primary screening data set, and adjusting model parameters, and generating a model tuning result. According to the method, the accuracy is improved by monitoring the welding process in real time, screening data, eliminating abnormal points, improving data quality and analysis precision, applying an autoregressive moving average model, improving prediction capacity, predicting weld temperature change and failure time points, comparing with historical data to verify the model and finely adjusting parameters; and a fault early warning mechanism and a threshold value are set to automatically trigger maintenance reminding, so that the maintenance efficiency is remarkably improved, the downtime is shortened, fault early warning logic is updated in real time, the response speed of a monitoring platform is increased, welding parameters are continuously optimized, and the welding seam strength and the service life prediction accuracy are improved.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide a contextual content generation system that trains and implements a unique machine learning architecture to generate context-specific digital content items based on a digital guideline document. In particular, the disclosed systems select a content generation method from among prompt engineering and / or updating one or more machine learning models to generate digital content. For example, the disclosed systems utilize machine learning models to extract key elements from a digital guideline document comprising context-specific guidelines for digital content. Further, the disclosed systems generate an augmented prompt comprising indications of key elements from the digital guideline document. In addition, the disclosed systems select a content generation method from among prompt engineering and / or updating machine learning models to generate the digital content item which incorporates digital content corresponding to the context-specific guidelines based on the augmented prompt.

The invention provides an adjusting and optimizing method of a positioning model, electronic equipment and a storage medium, and relates to the technical field of positioning. The method comprises the steps that in the process of obtaining a first positioning result through a first positioning model in a historical period, historical model data are extracted from the first positioning model according to a preset period and stored, and the historical model data comprise observed quantity data and state data corresponding to one or more extraction time points; the observed quantity data comprises observed quantity input into the first positioning model at the extraction time point, and the state data comprises state parameters of the first positioning model at the extraction time point; and according to the historical model data, the first positioning model is adjusted and optimized to obtain an optimized positioning model, so that the adjusting and optimizing efficiency is improved.

A method for model tuning and a system therefor are provided. The method according to some embodiments may include obtaining a deep learning model into which an adapter is inserted, wherein the adapter is a module inserted to adjust the output of the deep learning model using a plurality of weight matrices, and the plurality of weight matrices include a diagonal weight matrix, calculating loss by performing a specific task on the deep learning model and updating the plurality of weight matrices of the adapter based on the loss.

The invention provides a large model tuning method and system based on multi-modal information and AI, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting multi-modal parameter features and interaction features, and dynamically adjusting a contribution coefficient to achieve feature migration, and obtaining a cross-modal fusion vector; aligning the modal features by adopting an adversarial mechanism to obtain domain invariant features; constructing a prompt chain based on the modal confidence, optimizing the prompt chain by using AI reinforcement learning, and iterating to converge to obtain an optimization result; and finally updating the large model parameters. According to the method, effective fusion of multi-modal information is realized, and the tuning effect and generalization ability of the man-machine interaction large model are improved.

Auto-tuning covariances associated with a set of noise models for a variety of sensor modalities and / or perception components such that the covariances are leveled respective to one another may include whitening the covariances and / or error models and determining scalars to apply to the covariances. Determining these scalars may comprise using the residuals that result from generating the set of noise model (e.g., such as may be determined as part of least squares estimation) along with the hat matrix of the process model to determine the scalars. The covariances may iteratively be updated until the scalar adjustments converge or until another end condition is met.

The invention relates to the technical field of circuit automatic generation, in particular to a circuit automatic generation method and system based on an FPGA, and the method comprises the steps: inputting a circuit design requirement into a module generation model, obtaining an initial circuit function module set, carrying out the normalization processing of an initial module characteristic parameter set, obtaining a standard module characteristic parameter set, and carrying out the calculation of the standard module characteristic parameter set; inputting the standard module characteristic parameter set into the module splicing model to obtain a module splicing circuit, performing a simulation test according to the HDL file to obtain a simulation test result, judging whether the simulation test result conforms to a simulation test standard or not, if not, performing model tuning, and if yes, performing a performance test according to the HDL file to obtain a performance test result. And judging whether a performance test result meets a performance test standard, if not, performing model optimization on the module splicing model, and if yes, constructing an FPGA configuration file according to the HDL file. The problems of long circuit design period and low efficiency can be solved.

The invention provides an integrated learning-based bird buzzing intelligent recognition method and system, and solves the problem that the recognition precision is insufficient when an existing single deep learning model processes high-noise and low-signal-to-noise-ratio audio data. In order to solve the problems that bird species with limited long-tail data distribution or limited data volume are particularly unsatisfactory, the method mainly comprises the following steps: S1, collecting and preprocessing bird buzzing data to construct a standardized bird buzzing database; s2, using a Bootstrap sampling scheme to perform random sampling with playback on audio samples in the standardized twitter database, and generating a plurality of different independent sub-data sets; s3, respectively constructing and training an independent deep learning model for each sub-data set, including model configuration, data preparation, model training and model tuning; and S4, based on a Bagging ensemble learning strategy, fusing output results of the plurality of models to generate a final prediction result.

Embodiments of the present disclosure provide techniques for adaptive tuning of motion stabilization models using artificial intelligence. A motion stabilization model for use with a device may be identified. Context data associated with a device may be identified. Metadata comprising one or more model parameters for the motion stabilization model may be identified. Model adjustment data may be generated based on the context data and the metadata by applying the context data and the metadata to a machine learning tuning model. The model adjustment data may be applied to the motion stabilization model to tune the motion stabilization model. The motion stabilization model may be configured to facilitate motion stabilization to account for screen motion of the display of the device and eye motion of a user relative to each other in a vehicle.

A training data store may contain training data associated with monitoring node values during normal operation of an industrial asset and simulated abnormal data. An offline model tuning platform accesses the training data from normal operation of the industrial asset and the simulated abnormal data in the training data store. Based on the training data from normal operation of the industrial asset, the simulated abnormal data, an abnormal operating condition, and a constrained optimization solution, controller tuning parameters are created for at least one tuned data-driven adaptive controller such that an operating condition of the industrial asset will move from the abnormal operating condition to a normal operation condition through a stable trajectory. An online monitoring platform receives a stream of current monitoring node values and, when the abnormal operating condition is detected, utilizes the controller tuning parameters to implement the at least one tuned data-driven adaptive controller.

Systems and methods for model tuning and inference can include leveraging quality signals for determining resources to be more closely utilized during training and inference. The systems and methods can include obtaining a training dataset that includes a plurality of content items associated with a plurality of resources. The systems and methods can determine a plurality of quality scores for the plurality of resources. The systems and methods may then tune the parameters of the generative model based on the plurality of content items and the plurality of quality scores. The plurality of quality scores may be leveraged to train a reward model to be utilized for model-generated output selection.

The invention relates to the technical field of power prediction of a power system, and discloses a sample adaptive iterative training method and system for existing model tuning, and the method comprises the steps: obtaining the net load and solar irradiance data of a power distribution network; determining a payload-irradiance response intensity indicator based on the response slope within the local window; determining reference values of a truncation mode and a response mode according to statistical distribution of the indexes, and calculating a non-truncation probability of the sample in a normal power generation state; calculating the marginal response intensity of the prediction model to the solar irradiance; constructing a critical factor for capturing an inverter action boundary and a sample training weight by using a non-truncation probability; constructing a photovoltaic bistable constraint item, and constraining marginal response intensity to approach corresponding modal reference values in different states; and finally, performing parameter updating on the model based on the weighted prediction error and the bistable constraint term. According to the method, the problem of model tuning failure caused by equipment control truncation is effectively solved, and the prediction performance of the model under different operation conditions is ensured.

The invention discloses a model tuning method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining an initial data set, an initial question and answer pair comprising an initial question and a corresponding initial answer; in order to extract data related to the initial questions, first data corresponding to all the initial questions are retrieved from a preset database, then a target question-answer pair is determined by adopting a target model according to the first data and the initial question-answer pair, and the target question-answer pair is used as a target question-answer pair. The target question is a corresponding initial question which is not answered by the target model. In order to improve the performance of model tuning, the target data set is generated according to the target question and answer pair and the initial data set, then the target data set is used for tuning the target model, and the effective data in the target data set for tuning the target model is improved, so that the tuning effect of the target model is improved.

The invention provides a model updating method, device and system, in the model updating method, computer equipment displays a first processing result obtained by processing system data of a network system according to a model, and a user can perform knowledge data input operation after seeing the first processing result. The computer device obtains target knowledge data obtained after format conversion is conducted on the knowledge data, then the computer device responds to the model updating instruction, a model is updated through the target knowledge data, an updated model is obtained, and finally system data of the network system is processed according to the updated model to obtain a second processing result. And displaying the second processing result. The user can directly participate in the model adjusting and optimizing process, and the model of the user can be updated in real time to meet changing business requirements and personalized expectation of the user.

The invention provides a computing power asynchronous parallel fine-tuning large model method and device based on an intelligent computing center, and the method comprises the steps: obtaining a to-be-processed task, and determining a training set, a test set, a base large model and an evaluation index; determining a hyper-parameter space; based on the hyper-parameter space, the training set and the computing power resource of the intelligent computing center, performing parallel fine tuning on the base large model; based on the test set, the evaluation index and the computing power resource of the intelligent computing center, performing parallel evaluation on each fine-tuned large model to obtain an evaluation result corresponding to each fine-tuned large model; and determining an optimal evaluation result, determining the fine-tuned large model corresponding to the optimal evaluation result as a target large model, and recording a hyper-parameter group corresponding to the target large model. Through powerful computing power resources of the intelligent computing center and in combination with an asynchronous parallel processing mode, large model fine tuning and evaluation tasks based on multiple hyper-parameter combinations can be processed at the same time, the model tuning time is remarkably shortened, and the model tuning efficiency is improved.

The present application discloses a method, system, and computer system for building a model associated with a dataset. The method includes receiving a data set, the dataset comprising a plurality of keys and a plurality of key-value relationships, determining a plurality of models to build based at least in part on the dataset, wherein determining the plurality of models to build comprises using the dataset format information to identify the plurality of models, building the plurality of models, and optimizing at least one of the plurality of models.

The invention relates to the field of dialogue systems, and discloses an active dialogue model fine tuning method based on knowledge editing, a terminal and a medium. According to the method, an active dialogue data set and a universal pre-training data set in a specific field are collected and preprocessed, and an active dialogue task in the data set is divided into three sub-tasks of fuzzy demand prediction, active questioning and user demand overview; performing instruction fine tuning and secondary pre-training on the pre-training language model added with the parameter efficient module by using the active dialogue data set and the pre-training data set; specific capability is extracted and enhanced on the parameter efficient module, so that model parameters after adjustment of the pre-training language model are obtained; and based on the adjusted model parameters, triggering an active questioning ability through context learning, and realizing dynamic mining of user intentions. According to the method, the active dialogue model is fine-tuned, so that the potential intention of the user is mined through active questioning of the model, the understanding of the user on the performability of the task is improved, and subsequent tasks are guided to proceed.

Disclosed in the present application are a data processing method and related apparatus, and a device and a storage medium. The method comprises: sending to a server K images which are captured in the current site environment, and the server acquiring K first prediction results by means of an image recognition model; constructing a fine-tuning training set according to the K images and the K first prediction results; by means of a model to be trained, acquiring a second prediction result of each image in the fine-tuning training set; according to the second prediction result of each image and a first prediction result of the image in the fine-tuning training set, updating model parameters of the model to be trained, so as to obtain a local recognition model and model tuning parameters; and if a model fine-tuning condition is met, sending the model tuning parameters to the server, and the server updating model parameters of the image recognition model according to a set of model tuning parameters. Thus, the present application enables an image recognition model to be applicable to various site environments, improves the precision of model recognition, and also saves on processing resources of a server and improves the efficiency of model learning.