50 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 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.

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 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.

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 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 discloses a quality authentication method combining model fine tuning and a knowledge graph, belongs to the technical field of natural language processing, and aims to solve the problem that an existing quality authentication method based on a pre-training language model is insufficient in model performance, the method comprises the following steps: firstly, constructing a GLM model, and performing parameter fine tuning on the GLM model by adopting a low-rank adaptation method; the computing resource demand in the model fine tuning process is reduced, and the model tuning efficiency is improved. Meanwhile, by adopting integration of the knowledge graph, on one hand, the understanding and generation ability of the model to long texts and sentence levels is enhanced, and on the other hand, knowledge in the professional field of quality authentication is effectively integrated into the learning process of the model, so that the model can learn general knowledge from large-scale general texts and can also learn general knowledge from the sentence level. And moreover, professional tasks can be better completed by utilizing the injected knowledge in the quality authentication field, and the application effect of the model in the quality authentication field is improved through optimization and implementation of the model.

To tune a machine-learning model for a deployment system, a machine-learning model training system generates multiple tuning steps for the machine-learning model and generates an accuracy loss sensitivity for each tuning step. Each of the tuning steps indicate a corresponding set of one or more parameters and hyperparameters that reduces the impact of the machine-learning model on the system performance of the deployment system. Based on the accuracy loss sensitivities, the machine-learning model training system selects a tuning step with the least impact on the accuracy of the machine-learning model and modifies the machine-learning model based on the selected tuning step. After also modifying the tuned machine-learning model based on a threshold accuracy, the machine-learning model training system provides the tuned machine-learning model to the deployment system.

A kind of H&E image ovarian cancer polyploid giant cell (PGCCs) detection method based on deep learning, including constructing specific data set, image annotation and data division, using OCDet model to carry out feature learning and optimization, and model training and evaluation.The method first establishes the H&E staining image data set containing PGCCs, then accurately labels the image and divides it into training, verification and test set.OCDet model takes CSPDarkNet as the core, combines ECA mechanism, focuses on the deep learning and re-encoding of pathological semantic features.Through the training data set, the model updates parameters through back propagation and gradient descent, optimizes to identify PGCCs features.The verification set is used for model tuning, and the test set is used for evaluating the performance of the model.The automatic detection technology of the application can help doctors improve the diagnosis efficiency and reduce the error, provide an important reference for clinical treatment and prognosis evaluation, and show significant clinical application value.

The invention discloses a mechanism data hybrid process system modeling method based on a genetic programming algorithm, and the method comprises the steps: carrying out the induction and construction of a normative mechanism symbol system from the perspective of energy transmission for the hybrid modeling demands of the complementation of the mechanism and data in the process industry, and carrying out the modeling of the mechanism data hybrid process system based on the genetic programming algorithm. Furthermore, through three steps of a model generation step based on genetic programming, a model setting step based on heuristic Powell and a model evaluation step based on graph complexity, a high-quality explicit model considering fitting accuracy and structure simplification is autonomously constructed, so that a good twinborn analysis basis is provided for a complex industrial system data monitoring scene in the process industry.

The embodiment of the invention relates to the technical field of artificial intelligence, and discloses an LLM dialogue system-based intention recognition model tuning method, device and equipment and a storage medium, and the method comprises the steps: obtaining first sample labeled data and sample unlabeled data input by a user in an LLM dialogue system, and obtaining an initial intention recognition model; the model is used for performing intention recognition on information input by the user to predict the real intention of the user; based on the initial intention recognition model, the sample unmarked data and the first sample marked data, second sample marked data and error sample data are obtained; generating third sample marked data based on the error sample data and the large language model; and performing optimization processing on the initial intention recognition model based on the second sample marked data and the third sample marked data to obtain a target intention recognition model. The large language model is utilized to expand the sample data, and a large amount of manual annotation of the sample data is not needed, so that the manual annotation cost is reduced, and the recognition accuracy is improved.

The invention discloses an intelligent processing method and system for multi-modal data of an oral broadcast video based on deep learning, and relates to the technical field of oral broadcast video data processing, and the method comprises the following steps: 1, collecting multi-modal oral broadcast data in real time, recognizing emotion types, and carrying out the data preprocessing; step 2, constructing an oral playing animation generation model, judging the synchronization degree of the mouth shape, the head action and the audio in the oral playing animation, and taking coordinated regulation measures; 3, judging the consistency of the mouth shape and the emotion intensity in the oral playing animation, and implementing an anomaly repair measure; 4, constructing a depth time sequence prediction model, evaluating the difference degree between the actual oral playing animation and the prediction result of the depth time sequence prediction model, and carrying out video optimization and model tuning; the problems that when mouth shape supplement and animation filling are carried out by AI, precise synchronization of mouth shapes, expressions, head actions and emotional fluctuations is difficult to achieve, so that visual abnormity is caused, and the reality sense and the professional degree of videos are affected are solved.

Tuning result records indicating tuned schedules for machine learning tuning tasks are stored at a data store accessed from a tuning service. A given schedule indicates at least an order in which sub-operations of a tuning task are to be executed. In response to determining that the data store does not include a result record whose tuning task meets a similarity criterion to a tuning task determined from a tuning request, performance predictions for a set of candidate schedules are obtained using a performance prediction model, without running code which executes the candidate schedules. In a response to the tuning request, executable code corresponding to a preferred schedule identified from the candidate schedules using the performance predictions is included.

The invention discloses a self-supervised Chinese character generation method introducing dynamic convolution to enhance font style extraction, which is suitable for the fields of computer vision, font design and calligraphy education digitization, and comprises the following steps: constructing a dynamic convolution enhanced self-supervised network model which introduces a dynamic convolution kernel into the font style extraction field, and the convolution kernel parameters are dynamically adjusted to adapt to the edge and structure characteristics of the Chinese characters of different styles, so that the accurate capture of the style information is realized. According to the method, font style information extraction is enhanced through dynamic convolution, and the problems that in a traditional font generation method, a fixed convolution kernel is depended, the kernel scale and weight need to be manually adjusted, and the fixed kernel and a style extraction target are obviously separated, so that the model adjusting and optimizing process is tedious, and the cross-style generalization ability is poor are solved; therefore, the feature extraction adaptability and accuracy of different styles of Chinese characters are remarkably improved.

A method of tuning a machine learning model associated with a lithography patterning process is disclosed. More particularly, a method of using a reinforcement learning tuning engine to accurately adjust a deep learning-trained lithography model and enable global view of a training dataset is disclosed. In the reinforcement learning process, the reward can be defined as a function of global metrics derived from model predictions. Given the reward, the accuracy agent can determine actions corresponding to delta variables of the model.

The invention provides a model adjustment method and device, and relates to the technical field of communication, and the method comprises the steps: adjusting a first model parameter according to the environment information of a wireless channel, and obtaining a second model parameter, the first model parameter being a part of the model parameter of a first target model; according to the second model parameter, updating information is sent, and the updating information is used for indicating updating of the first target model. According to the scheme provided by the invention, the adaptation of the model can be realized without adjusting all model parameters, and the performance of processing the communication service by the model is improved.

The invention discloses a federated learning-based model training method, device and equipment, which are applied to a main computing node, and comprise the following steps: determining a plurality of first target hidden layers based on an initial first large language model obtained by training, and obtaining initial first sub-parameters corresponding to the first target hidden layers; receiving an initial second sub-parameter which is sent by each slave computing node and corresponds to each second target hidden layer in the initial second large language model; performing parameter aggregation at least based on each initial first sub-parameter and each initial second sub-parameter to obtain an initial aggregation parameter; and sending the initial aggregation parameter to each slave computing node to enable each slave computing node to carry out next round of model tuning based on the initial aggregation parameter, receiving a current second sub-parameter sent by each slave computing node, stopping model tuning until a predetermined tuning condition is met, taking the current aggregation parameter as a target aggregation parameter, and sending the target aggregation parameter to each slave computing node. And obtaining the target large language model. According to the method and the device, the final tuning result can be more accurate.

The present application discloses a model tuning method, apparatus, device and storage medium, which relates to the field of data processing technology, including adjusting the current node to a training state and obtaining target data in response to meeting a preset model condition. The target data includes a plurality of sample data, the sample data is question-and-answer data, and the question-and-answer data is at least one of question-and-answer text data, question-and-answer image data or question-and-answer video data. In order to improve the tuning effect of the model, a target tuning algorithm is determined by the target data, and the target model is tuned using the determined target tuning algorithm according to the target data, thereby realizing automated model tuning, enhancing the effect of model training tuning, and thereby improving the efficiency of model tuning.

The application provides a large model optimization method and system based on multi-modal information and AI, relates to the technical field of artificial intelligence, and comprises the following steps: by extracting multi-modal parameter features and interaction features, dynamically adjusting a contribution coefficient to realize feature migration and obtain a cross-modal fusion vector; adopting an antagonistic mechanism to align the modal features to obtain domain invariant features; constructing a prompt chain based on modal confidence, and optimizing the prompt chain by using AI reinforcement learning, and iterating to convergence to obtain an optimization result; and finally updating the large model parameters. The application realizes effective fusion of multi-modal information, and improves the optimization effect and generalization ability of the human-computer interaction large model.

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.

The application relates to the technical field of data analysis, and discloses a data analysis method based on model tuning, which comprises the following steps: when a model tuning control instruction is received, selecting a plurality of groups of hyperparameter combinations from hyperparameter combinations corresponding to preset type data according to a mapping relationship between data types and hyperparameter combinations; determining a target hyperparameter combination from the plurality of groups of hyperparameter combinations based on a first algorithm; constructing an initial data analysis model based on the target hyperparameter combination; performing first model variable equivalent simplification on the initial data analysis model based on a second algorithm to generate an intermediate data analysis model; performing second model variable equivalent simplification on the intermediate data analysis model based on a third algorithm to generate a target data analysis model; and calling the target data analysis model to perform data analysis on preset type data to obtain a data analysis result. The application can effectively improve the running performance of a data analysis model and the accuracy of data analysis.

This invention discloses a method for debugging a lubricating oil system based on digital twins, comprising the following steps: 1) Constructing a simulation model of the lubricating oil system and equipment by defining a lubricating oil system equipment model library; 2) The online model receives real-time operating data of the physical lubricating oil system collected by sensors, transmits the online measured data to the simulation model to drive simulation calculation, and transmits the calculation results to the demonstration model for simulation animation display; 3) Based on the lubricating oil user flow requirements under different pre-given operating conditions, and based on virtual-real fusion simulation, the action parameters of each actuator of the lubricating oil system are calculated through offline model tuning and transmitted to the control system as virtual debugging signals; 4) The physical lubricating oil system receives the control signals from the control system to adjust and control the operation of the physical lubricating oil system. This invention, by constructing a high-precision lubricating oil system model and using digital twin-based virtual debugging technology for lubricating oil systems, can be used for efficient debugging, accurate analysis, and virtual verification of physical systems.

The invention relates to the technical field of process engine control, and discloses a training planning process control method and system based on an Activity process engine. The method comprises the following steps: extracting an instance state snapshot from a process engine, analyzing to construct a time trajectory map of process evolution, marking a key path and a branch point, and generating a process evolution mode template library; and the current evolution stage is accurately determined by matching the real-time process with the template. And then dynamically loading a corresponding control strategy, analyzing the rule to generate a node-level operation instruction, and converting the node-level operation instruction into a model adjustment command which can be identified by an engine. Synchronously monitoring the synchronization state of the resource consumption and the flow track, and automatically calculating a resource quota adjustment scheme when deviation occurs. And finally, cooperatively scheduling a model adjustment command and a resource scheme, forming a control instruction set, and distributing the control instruction set to an engine and a resource manager to realize efficient and adaptive evolution of a flow according to an expected path. According to the invention, the intelligent level of process management and the resource utilization efficiency are improved.

The invention relates to a model self-adaptive optimization method and device, electronic equipment and a readable medium, and the method comprises the steps: obtaining a prediction model corresponding to target equipment, and enabling the prediction model to be obtained through training according to the historical fault data of the target equipment; in the process of performing fault prediction on the target equipment by using the prediction model, monitoring a prediction error of the prediction model and an operation condition of the target equipment; judging whether to trigger a model adjustment mechanism according to the prediction error and the operation condition; under the condition that a model adjustment mechanism is triggered, a corresponding target adjustment strategy is selected according to the size of the prediction error and the change of the operation condition, and the target adjustment strategy is a parameter adjustment strategy or a structure adjustment strategy; and adjusting the prediction model according to the target adjustment strategy to complete adaptive optimization of the prediction model. It is ensured that the model can continuously adapt to equipment state changes, and accurate prediction of equipment faults is achieved.

In a described embodiment, a system for predictive modeling is provided. The system includes a data input module configured to acquire raw data and a data pre-processing module 5 configured to process the raw data to generate sample data. The system further includes a feature engineering module configured to generate derived features using one or more specified criterion and a model generation and optimization module configured to generate and refine a predictive model using a plurality of machine learning techniques. A model evaluation module is configured to assess model performance using predefined metrics. A 10 model tuning system is configured to iteratively adjust model parameters of the predictive model based on performance feedback and a model deployment module is configured to implement the predictive model in a production environment.

The present application discloses a model tuning method, apparatus, device and storage medium, which relates to the field of data processing technology, including obtaining an initial data set, wherein the initial question and answer pair includes an initial question and a corresponding initial answer. In order to extract data related to the initial question, first data corresponding to each initial question is retrieved from a preset database, where the first data is data related to the initial question, and then a target model is used to determine a target question and answer pair based on the first data and the initial question and answer pair. The target question is the corresponding initial question that the target model lacks an answer to. In order to improve the performance of model tuning, a target data set is generated based on the target question and answer pair and the initial data set, and then the target data set is used to tune the target model. Since the effective data in the target data set used to tune the target model is increased, the tuning effect of the target model is improved.

A device and method with model determination are provided. The method includes identifying a pre-trained first model and at least one second model tuned based on the first model, for each of layers included in the first model, identifying a first weight value of the first model and at least one second weight value of the at least one second model, for each of the layers, determining a third weight value based on a location derived through a linear interpolation using a first location corresponding to the first weight value and at least one second location corresponding to the at least one second weight value that are in a weight value space, and determining a target model based on the first model including the third weight value.