943 results about "Model selection" patented technology

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 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 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.

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 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.

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 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 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.

The invention belongs to the technical field of automatic driving automobile testing, and particularly relates to an intelligent automobile key scene optimization generation method based on large model knowledge guidance. Comprising the following steps: S1, constructing a knowledge expression module, and designing a knowledge result, a large language model application mode and a dynamic updating process from scene theoretical knowledge and scene exploration knowledge; s2, constructing a parameter optimization module, designing a multi-stage optimization strategy including global exploration, local optimization and spatial pruning, and fusing the multi-stage optimization strategy with the knowledge model; s3, establishing a simulation test module, and based on a Carla simulation platform, establishing a scene establishment component, a to-be-tested system component, a simulation execution component and a data export component through LLM-Agent; s4, selecting a model and pre-training a large language model, designing a model selection index and selecting the model; and S5, constructing an evaluation and memory module, evaluating a test result, and storing all test and knowledge data. According to the method, the key scene of the intelligent automobile can be quickly generated through mutual combination of the knowledge model, the large language model and optimization search.

The invention relates to the technical field of building information models, in particular to a BIM (Building Information Modeling)-based intelligent model selection and real-time collaborative design method for supports and hangers. Comprising the following steps that S1, building, structure and electromechanical multi-professional BIM models and external data are integrated, and a foundation database of support and hanger model selection is constructed; and S2, based on a rule engine and a machine learning algorithm, automatically matching an optimal support hanger type and generating a parameterized model. Through multi-physics field coupling simulation, stress, displacement and other core indexes calculation, collision check and mechanical checking calculation are combined, layout and component specifications are dynamically adjusted, the reliability of the support hanger under the extreme working condition is ensured, the later reworking risk is reduced, and the anti-seismic indexes such as base shear force and interlayer displacement angle and the bearing indexes such as axial stress and bending stress are adopted, so that the reliability of the support hanger is improved. The performance of the support hanger is comprehensively evaluated, the failure problem caused by a single factor is avoided, and the safety and stability of engineering are greatly improved.

The invention relates to the technical field of rail transit detection, in particular to a machine vision detection method and system for railway sleeper supporting fastener missing, and the method comprises a data acquisition step, a detection model construction step, an environment judgment step, a model selection step and a defect detection step. And the optimal detection model is selected according to the environment type, so that the problem of long processing time of a single model in a complex environment in a traditional method is avoided, the detection efficiency is improved, potential safety hazards can be fed back in time, and the problems of long missing detection response time and low detection efficiency in the prior art are solved.

A method (1100) by a radio node (110, 210, 310) includes transmitting (1102), to another radio node (120, 220, 320), information indicating an activation or a deactivation of one or more AI and / or ML models at the radio node. For example, the radio node may include a UE and the other radio node may include a base station such that the base station is able to inform and / or suggest modifications in the node configurations to enhance communication performance and model selection at the UE.

The invention relates to a correlation analysis method and device for computing power resources of a data center and energy consumption of refrigeration equipment, and the method comprises the steps: obtaining related data of the computing power resources of the data center and the energy consumption of the refrigeration equipment, carrying out the correlation analysis of the preprocessed related data, and obtaining key parameters related to the computing power resources of the data center and the energy consumption of the refrigeration equipment; selecting a model according to the key parameters and the modeling target, training the selected model by using historical data to learn the correlation between the computing power resource and the energy consumption of the refrigeration equipment, and evaluating the performance index of the selected model; according to the modeling target and the constraint condition, an optimization algorithm is selected to carry out joint optimization logic of the data center computing power resources and the refrigeration equipment energy consumption, the optimization algorithm is operated, and distribution of the data center computing power resources and operation parameters of the refrigeration equipment are adjusted through iteration; and obtaining a performance index for evaluating the correlation between the computing power resource of the data center and the energy consumption of the refrigeration equipment. According to the method, the prediction accuracy, generalization ability and interpretability of the model are remarkably improved.

The invention discloses a commercial customer service system based on large model emotion recognition labeling and correction, and belongs to the technical field of natural language processing and deep learning. In order to solve the problems that an existing emotion recognition system depends on large-scale manual labeling, label quality is unstable and small sample performance is poor, a multi-model collaborative labeling and iterative optimization mechanism is adopted, and fusion labels are generated through automatic basic model selection, small sample LoRA fine adjustment, double-model divergence detection and large model arbitration. And a refining training set is constructed for iterative fine tuning to form a closed-loop optimization system. The method can effectively reduce the labeling cost, improves the label consistency and the emotion recognition precision under complex semantics, and is suitable for business information, financial public opinions and intelligent customer service scenes.

Methods and systems are described herein for minimizing development time in artificial intelligence models by automating model selection based on dataset fittings of time-series data prior to hyperparameter optimization. For example, the system may apply a profiling model using a time-series embedding of the dataset combined with the aggregate statistical profile. In either case, the profiling model may be trained on the scoring policy and / or a time-series embedding of the dataset combined with the aggregate statistical profile to determine a likelihood of the effectiveness of a given model on the given dataset and / or likely hyperparameters for the given model.

A computing device may perform training of a set of machine learning models on a first data set associated with a first domain. In some examples, the training may include, for each machine learning model of the set of machine learning models, inputting, as values for a set of parameters of the respective sets of parameters and for an iteration of a set of iterations, a moving average of the set of parameters calculated over a threshold number of previous iterations. The computing device may select a set of model states that are generated during the training of the plurality of machine learning models based on a validation performance of the set of model states performed during the training. The computing device may then generate an ensembled machine learning model by aggregating the set of machine learning models corresponding to the set of selected model states.

The invention discloses a model selection method and device. The method comprises the steps that a to-be-recognized data set is received, feature extraction is conducted on the to-be-recognized data set, element features of the to-be-recognized data set are obtained, and the element features comprise at least one of the data scale, the feature type, target variable information and the data sparsity; determining a to-be-executed target task type according to the meta-features; selecting a plurality of candidate models from a preset model database according to the target task type; a target model is selected from the multiple candidate models according to a predetermined reward function, and the reward function is determined according to the performance index of the model and the training efficiency index of the model. The technical problem of low model selection efficiency in related technologies is solved.

The invention relates to a backlight module LED model selection method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a data configuration operation in a graphical interface, and obtaining a configured reference spectrum data source, a diaphragm penetration rate frequency spectrum and a liquid crystal panel penetration rate frequency spectrum; performing spectrum modulation simulation processing on the reference spectrum data source, the diaphragm penetration rate spectrum and the liquid crystal panel penetration rate spectrum according to an optical structure hierarchical relationship of a simulation backlight module to obtain colorimetric parameters of the simulation backlight module; and determining a backlight module LED model selection result according to the colorimetric parameters. In this way, the actual assembling and measuring process of the backlight module is converted into mathematical operation, the backlight scheme of the simulated backlight module can be evaluated under the condition that the backlight module is not actually assembled, then the optimal LED model selection result of the simulated backlight module is determined, the development efficiency of the newly-assembled screen backlight module is improved, and the development cost is reduced.

The present disclosure is related to machine learning model swap (MLMS) framework for that selects and interchanges machine learning (ML) models in an energy and communication efficient way while adapting the ML models to real time changes in system constraints. The MLMS framework includes an ML model search strategy that can flexibly adapt ML models for a wide variety of compute system and / or environmental changes. Energy and communication efficiency is achieved by using a similarity-based ML model selection process, which selects a replacement ML model that has the most overlap in pre-trained parameters from a currently deployed ML model to minimize memory write operation overhead. Other embodiments may be described and / or claimed.

An apparatus comprising an interface and a processor. The interface may be configured to receive sensor data from a plurality of sensors. The processor may be configured to detect an intruder in response to an analysis of the sensor data, activate an AI model in response to detecting the intruder and generate a countermeasure signal in response to a countermeasure selection by the AI model. The AI model may be configured to analyze the sensor data, compare the sensor data of the intruder to a database of pests to perform a classification of the intruder, determine the countermeasure selection in response to the classification of the intruder as a selected pest, monitor the sensor data for an outcome of the countermeasure selection, and generate a text description of the classification of the selected pest, the countermeasure selection and the countermeasure outcome.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

The invention provides a self-adaptive perception event element extraction method, which comprises the following steps of: firstly, acquiring data from open source resources of multiple fields, cleaning and labeling to construct a high-quality multi-field event element extraction data set, and then extracting event elements from the multi-field event element extraction data set by utilizing the constructed multi-field event element extraction data set. According to the method, two different types of event element extraction models are trained and finely adjusted, the two different types of models are a traditional deep learning model and a large model, then the length, syntactic structure, dependency relationship and vocabulary richness characteristics of a sentence are analyzed, complexity evaluation indexes are constructed, and the complexity of the sentence is evaluated. The method comprises the steps of automatically determining the complexity level of each sentence on the basis of the indexes, classifying the sentences, finally selecting a self-adaptive model according to the sentence complexity evaluation result, and finally integrating the result and outputting the result. By means of the scheme, self-adaptive extraction model selection is achieved, and the accuracy and efficiency of event element extraction are improved.

The invention relates to the technical field of research and development management, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an anomaly analysis method, device, equipment and medium based on a variation test. Generating a test case through the semantic perception model; selecting a variation strategy by using a state-driven model according to the service state, and generating a variation test case based on the access control strategy; executing the variation test case in a distributed environment, and collecting system resource index data and request execution monitoring data; and inputting the collected data into the abnormal prediction model, and outputting an abnormal behavior prediction result. Through combination of semantic perception generation, state-driven selection and anomaly prediction, coverage of a test case on protocol dependence and a service state is improved, and the accuracy of anomaly detection of the object storage system is remarkably improved.

The invention belongs to the technical field of sound source processing, and provides a sound source positioning and detecting method and device. The method comprises the following steps: acquiring delay estimation of microphones I and II and delay estimation of microphones I and III based on a three-path linear uniform microphone array; based on the delay estimation sum, respectively carrying out positioning calculation under near-field and far-field conditions; according to the sound source distance under the near-field condition, comparing the sound source distance with a distance judgment threshold value, and determining a far-field / near-field output sound source position; and carrying out feature extraction on the signals of any microphone array, and carrying out event classification based on a pre-trained convolutional neural network. According to the method, far / near field model selection is carried out according to the distance judgment threshold, large deviation generated by a single model in a critical region is avoided, continuous and stable positioning from short distance to long distance is achieved, time classification can be achieved while position calculation is carried out, and integrated output is achieved.

The invention discloses a target positioning method and system based on machine vision, and relates to the technical field of machine vision and target positioning, and the method comprises the steps: carrying out the model selection of machine vision key hardware, and collecting image information; carrying out image preprocessing, and automatically detecting key points and feature points of the image by using an SIFT algorithm; establishing a coordinate system to generate an internal reference matrix, establishing a camera coordinate system to generate an external reference matrix, and performing coordinate transformation by combining the internal reference matrix and the external reference matrix; through a camera calibration method, target positioning calibration is carried out. The SIFT algorithm adopted by the method has very strong robustness for scale change, rotation, illumination change and a certain degree of view angle change of the image, and is suitable for a complex environment in an industrial scene. In the calibration process, a chessboard calibration plate and a least square method are utilized to improve the calculation precision of internal and external parameters, the calibration error is effectively reduced, and the positioning precision is improved.

The disclosed data generation platform enables generation of an output in response to an output generation request based on tuning a routing model that enables model selection in a dynamic, system-sensitive manner. For example, the disclosed data generation platform receives an output generation request for a user device and generates a risk indicator associated with the output generation request. The platform can determine a current system state and generate a set of performance indicators and associated weighting values based on the risk indicator and the system state. The data generation platform can select a first routing model based on the weighting values. The data generation platform can provide the output generation request to the first routing model to generate an indication of a model with which to generate a model output responsive to the input. The data generation platform can enable access to the generated model output.