1501 results about "Model selection" patented technology

A variety of generative models are trained that are trained on a reference data set. The generative models are evaluated by candidate metrics to determine the relative rankings of the models as evaluated by the different candidate metrics. Rankings as generated by the models is compared with human evaluation of the generated results as simulated and the candidate metrics that most align with the human evaluation may then be used to automatically evaluate subsequent generative models. The candidate metrics may include various types of encoding models trained for non-generative purposes, such that the selected candidate metric may represent selecting an encoding model that performs well on the generative data.

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

The invention relates to an intelligent routing model selection method, device and equipment and a computer readable medium. The method comprises the steps of performing model classification based on a model parameter scale and a model deployment mode; creating an intelligent router, extracting multi-dimensional key features in the user task request and generating a multi-dimensional key feature vector; performing multi-dimensional resource pool evaluation on each large model through the intelligent router to obtain a first evaluation result, and constructing a multi-dimensional model capability vector; determining a model candidate set based on the multi-dimensional key feature vector and each multi-dimensional model capability vector; if a plurality of candidate large models exist, multi-dimensional element scoring is carried out on each candidate large model to obtain respective second evaluation results, and elements are respectively matched with element weights which are dynamically adjusted based on user requirements; and constructing a target function, and selecting a target large model for response based on the target function, the decision constraint condition and each second evaluation result. The problems of low model selection flexibility and low data security are solved.

Described herein are systems and methods for verifying the integrity of data, such as data used for training machine learning models. Some implementations are directed to verifying the provenance of datasets, the contents of datasets, or both. In some implementations, multiple filters are selected for verifying the contents of datasets. Filters can be selected based on rules, random selection, or using a machine learning model in some implementations. In some implementations, data cleaning is provided.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a machine learning task execution and feedback method, device, equipment and medium. And performing semantic reasoning by using a pre-training language model, generating a decision result in combination with an entity relationship associated with a task type and a data type in the knowledge graph, executing a corresponding machine learning task, converting a task execution process and an execution result into natural language feedback information, and outputting the natural language feedback information to a user side. According to the method, natural language input is structured into machine learning task description, large model semantic reasoning and knowledge graph knowledge association are combined, an executable decision result is generated, automatic closed loop from task recognition, model selection to result feedback is achieved, the user operation threshold is lowered, and the model development efficiency and the interaction intelligence level are improved.

The invention provides an electric power multi-modal data hierarchical routing feature processing and fusion method and system, and relates to the technical field of electric power multi-modal data process.The method comprises the steps that multi-modal data are obtained and preprocessed; performing feature extraction on the preprocessed multi-modal data; selecting the most suitable expert model through a hybrid expert model, and carrying out feature extraction on the preprocessed multi-modal data; performing feature alignment and fusion on features of different modes after feature extraction; aligning the features of each modal with the features of other modals through a cross-modal attention mechanism, and fusing the features of other modals during alignment to obtain middle-level features of the corresponding modals; and carrying out weighted fusion on the middle-level features of each mode to obtain a fused global feature. According to the invention, efficient processing and fusion of multi-modal data can be realized.

Methods and systems for source machine learning (ML) model selection for the transfer learning. A method may include receiving a source ML model request from a target domain, determining candidate source ML models, calculating a model quality score for each of the candidate source ML models, using the calculated model quality scores to select candidate source ML models, sending the selected candidate source ML models to the target domain, receiving fine-tuned ML model weights for fine-tuned ML models, and calculating a model quality score for each of the fine-tuned ML models. The method may include determining, for each of the fine-tuned ML models, a ranking and / or a deployment recommendation for the fine-tuned ML model based on the model quality score for the fine-tuned ML model and sending, for each of the fine-tuned ML models, the ranking and / or the deployment recommendation for the fine-tuned ML model to the target domain.

The invention discloses an anomaly detection model selection method and system based on index portraits, and relates to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: collecting historical data of a target monitoring index, extracting multi-dimensional features to construct an index portrait, and classifying the index portrait; screening candidate anomaly detection models from the matching rule base, performing adaptation degree scoring in combination with a model compatibility evaluation mechanism, determining an optimal anomaly detection model to perform anomaly detection, and outputting an anomaly judgment result; when a plurality of models exist, generating a final abnormal result through a confidence-driven arbitration mechanism; for multi-index abnormity, causal reasoning is carried out in combination with an electric power knowledge graph, main alarm indexes are determined, and secondary indexes are processed according to a delay strategy; meanwhile, incremental updating of index portrait features, adaptive adjustment of model parameters and dynamic optimization of matching rules are supported, and a whole-process closed-loop mechanism covering'portrait construction-model matching-result fusion-alarm decision-feedback updating 'is constructed.

A system and method for optimizing automated textual prompts in artificial intelligence (AI) conversational systems is disclosed. The system comprises a network interface, processors, and memory-storing instructions for performing operations to optimize prompts. These operations include receiving and preprocessing input data, tokenizing the data, verifying data authenticity, performing temporal analysis, calculating prompt complexity scores, and selectively expanding or refining prompts based on complexity thresholds. The system further incorporates context-aware optimization, multi-faceted prompt refinement, variation generation, and evaluation using machine learning models. Additional features include a technological hub with advanced processing capabilities, sensor-augmented input apparatus, device-specific prompt optimization, AI model selection, multimodal context integration, and an AI-driven creativity booster. The system provides interactive prompt visualization, certification, and uniqueness verification modules. This comprehensive approach ensures the generation of optimized, contextually relevant, and creative prompts for various AI applications while maintaining data integrity and user engagement.

The invention relates to the technical field of natural language processing, and discloses a power generation industry data intelligent treatment method, device and equipment based on a large model, and the method comprises the steps: constructing a multi-source heterogeneous task data set covering structured and unstructured information, completing the fine tuning training of a plurality of industry sub-fields based on a language model, forming a large language model set with specific scene adaptability; constructing a multi-view semantic representation structure for actual input data, integrating modeling task intention, application scene and model adaptability, predicting an optimal target model and a Top-K candidate model, and generating a unified semantic embedding vector; and realizing accurate matching of the structured knowledge fragments through the graph neural network. The problems that an existing model is insufficient in semantic understanding, inflexible in model selection and inaccurate in knowledge calling in the data management process are solved, the requirements of diversified tasks for accuracy and specialty are met, and then management of data assets is facilitated.

Methods and apparatus for dynamically selecting an action to be taken in an environment. A method comprises receiving, at a ML agent hosting the plurality of ML models, information about a state of the environment, wherein the information about the state of the environment comprises safety information, and analysing the safety information to determine a risk value for the state of the environment. The method further comprises selecting one of the plurality of ML models, wherein the selection is based on the determined risk value, and processing the information about the state of the environment using the selected ML model to generate a state prediction. The method also comprises generating one or more suggested actions to be performed on the environment using the state prediction.

The systems and methods disclosed herein enable dynamic selection of a routing model for generation of an output in response to a provided input (e.g., a prompt for a large-language model). Based on the selected routing model, the data generation platform can evaluate the input and / or other suitable system parameters (e.g., system resource usage) to determine a suitable model for processing the provided input. For example, the routing model can determine a technical application associated with the input and dynamically determine to modify the input prior to generation of the output based on system resource measurement values and / or other suitable information, thereby conferring efficiency, security, and accuracy benefits while preserving system resilience.

The present invention relates to the technical field of industrial boilers, and provides a prediction method based on adaptive boiler thermal energy. It includes data collection and preprocessing, model selection and training, adaptive adjustment, prediction and evaluation stages. Data collection covers multiple types of parameters, environmental data and historical data, and corrects abnormal values; model selection and training adopts a multi-model fusion strategy to build a hybrid model; adaptive adjustment uses a variety of algorithms to dynamically adjust model parameters and structures through real-time data monitoring and working condition identification; the prediction and evaluation stage combines the working condition optimization model fusion prediction, calculates the comprehensive performance evaluation index, and visualizes and analyzes the results. The present invention improves the prediction accuracy and stability, helps to optimize energy management, reduce costs, and ensure production safety.

The invention discloses an integrated hyperspectral water quality analysis method and belongs to the field of hyperspectral water quality analysis. The method comprises the following steps: firstly, acquiring early-stage water quality data and water quality images, and preprocessing the data; secondly, three dimension reduction methods are adopted to carry out dimension reduction processing, and fusion dimension reduction is carried out through parameter tradeoff selection; thirdly, performing hyperspectral water quality inversion model training and testing on the dimension-reduced spectral data by adopting a machine learning algorithm; and finally, selecting and optimizing a hyperspectral water quality inversion model. According to the method, an innovative fusion strategy is adopted in the aspect of data dimension reduction processing, a better data dimension reduction effect can be achieved, noise and redundant information are effectively removed, and a more accurate and reliable data basis is provided; in the aspect of model training and testing, multiple machine learning algorithms are used to train and test the dimension-reduced spectral data, and the most suitable method is selected according to various indexes to train and predict the model, so that the advantages of different algorithms can be fully utilized, and the accuracy and reliability of water quality analysis are improved.

A computer implemented method for the detection of anomalies comprises: selecting an imaging dataset of a wafer and a hyperparameter value defining a machine learning model for anomaly detection; training and evaluating the machine learning model by computing an objective function value; and selecting one of the trained machine learning models and applying it to detect anomalies. A computer implemented method for the detection of anomalies in an imaging dataset of a wafer comprises: providing samples of a distribution of anomaly detection image values for each defect class; calibrating the anomaly detection image by training a machine learning model for anomaly localization; and applying a threshold to the calibrated anomaly detection image to detect anomalies.

The invention discloses a transformer current rising test data analysis method based on deep learning, and relates to the technical field of power transformation equipment, and the method comprises the following steps: S1, data collection and preprocessing; s2, performing feature engineering; s3, dividing a data set; s4, model selection and architecture design; s5, model training; s6, performing model verification and hyper-parameter tuning; s7, testing and evaluating the model; s8, performing anomaly detection and adaptive learning; s9, result interpretation and model interpretability; and S10, continuously monitoring and updating the model. According to the transformer current rising test data analysis method based on deep learning, based on deep learning, in combination with technologies of feature engineering, model selection and architecture design, anomaly detection and adaptive learning and the like, the accuracy and robustness of a transformer current rising test are improved through comprehensive data analysis; the generalization ability of the model for unseen data can be improved, and the model can adapt to environmental changes in real time.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention provides an illegal capital collection suspicious subject identification method and system based on reinforcement learning. Comprising an information sorting module, a data analysis module, a data feature extraction module, a data feature processing module, a model construction module, a model selection module, a model training module, an algorithm selection module, an algorithm optimization module, a risk identification module and an early warning module. According to the illegal capital collection suspicious subject identification method based on reinforcement learning, a reinforcement learning agent is trained by using key fund features and data, so that the illegal capital collection suspicious subject can be accurately identified, the accuracy and efficiency of risk identification are improved, implicit information in fund data is fully mined, and the risk identification efficiency is improved. And abnormal modes and risk points which are difficult to detect by a traditional rule model and a supervised learning method are identified.

Provided is a failure prediction device and a failure prediction method for predicting failures in various types of industrial equipment with different operating characteristics or environments using multiple deep learning models selectively, which includes a sensor unit collecting operational data detecting an operating state of the industrial equipment; a data prediction unit provided with multiple deep learning-based prediction models and predicting the operational data of the industrial equipment during a second period after a first period using the operational data collected by the sensor unit; a prediction model selection unit comparing the operational data collected by the sensor unit during the second period with the operational data predicted by the multiple prediction models and selecting any one of the multiple prediction models; and a failure prediction unit predicting the occurrence time of failure of the industrial equipment using the operational data predicted by the selected operational data prediction model.

The invention relates to the technical field of power converters, in particular to a power converter control method and device based on physical information machine learning and a storage medium. Comprising the following steps: setting up a control system; collecting, storing and preprocessing data; building and evaluating a model; model deployment; applying the model; and selecting a prediction model of the operation mode of the power converter. According to the design, physical constraint information is embedded into a loss function, a machine learning algorithm is used for mining the relation between the duty ratio, the frequency and the number of hopping pulses of a control signal and input voltage, input current, output voltage, output current and temperature, and a machine learning control model is deployed on an FPGA. The duty ratio, the frequency and the pulse hopping number of the control signal are predicted and controlled in real time; accurate modeling is performed through the nonlinear capability of a machine learning model, so that a large amount of cost can be saved, and the accuracy of the model is improved.

The invention discloses an automatic data analysis system and method based on artificial intelligence, and relates to the technical field of data management, and the method comprises the following steps: S1, data collection and preparation; s2, data annotation and classification; s3, feature selection and extraction; s4, data standardization and normalization are carried out; s5, model selection; s6, training and optimizing the model; s7, performing model evaluation; s8, model interpretation and visualization are carried out; s9, generating an automatic report; and the system is constructed based on the steps. According to the automatic data analysis system and method based on artificial intelligence, by setting a classification and labeling model, a training optimization mechanism and a dynamic model evaluation method, the efficiency and precision of automatic data analysis are remarkably improved, and the quality of data preprocessing is improved; meanwhile, through self-adaptive hyper-parameter adjustment and optimization, the efficiency of the training process and the model performance are optimized; the dynamic evaluation mechanism ensures that the model is more accurate in performance under different data characteristics.

The invention discloses a multi-model service construction and intelligent scheduling method based on a large model. The multi-model service construction and intelligent scheduling method based on the large model comprises the following steps: S1, constructing a multi-model integration platform; s2, registering and evaluating the model; s3, the semantic vector of the user service request is disassembled and understood; s4, model selection and priority screening; s5, sorting service response priorities and intelligently assigning resources; s6, optimizing and feeding back a feedback result; s7, performing hit labeling and result training; according to the multi-model service construction and intelligent scheduling method based on the large model, an artificial intelligence integration platform is constructed, multiple artificial intelligence models including the large model are subjected to unified services and interfaces, and intelligent understanding of user service requests, intelligent model selection, intelligent service response scheduling and feedback result optimization are achieved; and therefore, the multi-scene artificial intelligence technology application requirements of the user can be met.

The invention discloses a named entity recognition method in combination with a large model and dynamic examples. The method comprises the following steps: S1, constructing a dynamic example library; s2, constructing a basic cue word: defining a business entity annotation background, a rule and an output specification, and constructing a structured cue word in an XML (Extensible Markup Language) style; s3, recalling a dynamic sample; s4, training corpus construction, namely entity recognition corpus construction and recognition reasoning corpus construction; s5, selecting a large model; s6, identifying the large model, integrating the instance d, recalling the sample set R and the basic cue word, inputting the integrated instance d, recalling the sample set R and the basic cue word into the trained model, and outputting a final entity identification result; and S7, API service packaging: recalling the dynamic sample, and packaging the prompt word and the selected large model into AP I service for convenient calling. According to the method, the problem of bad case identification can be rapidly repaired, powerful support is provided for rapid landing of new field and new demand expansion, and the manpower and time cost of data annotation is effectively reduced.

The invention discloses an information transmission attack event intelligent prediction model training optimization method, which comprises the steps of a data preparation stage, a feature engineering stage, a model selection and construction stage, a model training stage and a model evaluation and optimization stage, and is characterized in that data are collected from multiple channels, noise is cleaned and removed, missing values and abnormal values are processed, and accurate labeling is carried out; then key features are extracted, redundant irrelevant features are removed through selection, standardized transformation is carried out, a proper model is selected based on data features, an integrated learning combination model is adopted, then a training set, a verification set and a test set are divided, a proper optimizer is selected, parameters are adjusted to be over, training is carried out through the training set, and over-fitting is prevented through the verification set; and finally, using index evaluation of accuracy and recall rate, and adjusting hyper-parameters and continuous optimization of the model structure through cross validation. According to the method, data collection, data imbalance processing and data noise interference processing are adopted, data diversity is improved, and interference of data noise is reduced.

Described herein are systems and methods for verifying the integrity of data, such as data used for training machine learning models. Some implementations are directed to verifying the provenance of datasets, the contents of datasets, or both. In some implementations, multiple filters are selected for verifying the contents of datasets. Filters can be selected based on rules, random selection, or using a machine learning model in some implementations. In some implementations, data cleaning is provided.

The invention discloses a modal language model image editing technology fusing non-perpetual culture elements. The modal language model image editing technology comprises the steps that a model is selected and trained, an LLaMA model is selected as a basis, LoRA is introduced for adaptive fine adjustment, and in this way, the model is subjected to adaptive adjustment under the condition that original parameter freezing is kept; according to the method, the understanding and reasoning capabilities in instruction editing are enhanced by combining MLLM (such as LLaVA), the MLLM can perform collaborative learning across text and image modalities and extract deep semantic information, so that the model can not only process basic instructions, but also understand complex non-perpetual culture elements, and the method has great significance in improving the understanding of the model on the non-perpetual culture elements. An enhanced bidirectional interaction mechanism is designed, and the mechanism realizes deep interaction between image and text features through a cross attention mechanism, so that the image features can be used as query and key value pairs to perform bidirectional communication with the text features, and the expression of a model in a complex non-perpetual scene is improved.

The invention discloses a visual detection optimization method and system based on data set classification. The method comprises the steps of introducing a scene classification label and carrying out standardization processing on scene features in a data set labeling and preprocessing stage, and carrying out scene classification multi-task learning in a model selection and training stage, so that a model dynamically adjusts feature learning in different scenes, and the multi-scene adaptive capacity is improved. And in the model evaluation and verification stage, scene-based hierarchical evaluation is introduced and corresponding optimization is carried out. In the model deployment and reasoning stage, a dynamic scene is introduced to adapt to reasoning, and the reasoning path of the model is dynamically adjusted to adapt to the characteristics of the scene, so that the reasoning accuracy is improved. In the later optimization, scene feature clustering and transfer learning are used, and the multi-scene performance of the model is optimized. According to the method, under the condition that the model is not jointly trained through multiple data sets, the multi-scene adaptive capacity of the visual detection model is improved through data set classification.

The invention discloses a mining mechanical equipment type selection management control platform based on a network, and relates to the technical field of mining mechanical equipment, and the control platform comprises a data collection module, a data processing and analysis module, and a type selection recommendation module. Multi-source heterogeneous data from a mining area geological exploration department, an equipment sensor, a satellite remote sensing image and a meteorological station are ingeniously fused, and the data are effectively integrated together through normalization processing and weight distribution, so that a comprehensive and accurate information basis is provided for subsequent model selection recommendation; the data processing and analysis module further performs cleaning, classification, coding and feature extraction on the data, a quantitative relation model between working condition factors and equipment performance is established, and the intelligent data analysis and processing capability enables the platform to perform quantitative analysis according to actual working condition requirements of a mining area. And equipment models and parameters with high adaptability and lowest full life cycle cost are accurately recommended.

The invention discloses a resume analysis method and system based on multiple large language models, and belongs to the technical field of large language models.The resume analysis method and system based on the multiple large language models.The resume analysis method and system based on the multiple large language models comprise the following specific steps that firstly, model parallel analysis is conducted, simultaneously inputting the data into at least two heterogeneous large language models through an application program interface; and each large language model independently performs information extraction and analysis according to the model structure and the training data of the large language model, and outputs a structured data result containing a plurality of preset fields. Through the multi-model parallel analysis and conflict re-judgment mechanism, the misjudgment risk of a single model is effectively reduced, the robustness of the whole system is improved, the resume analysis accuracy is remarkably improved, the model pool is automatically optimized and updated through the dynamic scoring mechanism, and the problem that the model is difficult to select and update is solved.