854 results about "Data selection" patented technology

The systems and methods disclosed herein generate responses using data retrieved by validating vector embeddings using hash signatures. An output generation request is received via a computing device and includes an input that includes a content set and a command set. The content set includes data chunks, which refers to text, audio, image, and / or video data. A subset of the data chunks that fail to associate with existing hash signatures are selected. Each selected data chunk is validated against predefined constraints that define the operative boundaries of a guideline set and subsequently assigned a unique hash signature to indicate a degree of satisfaction of the data chunk with the guidelines. Using an artificial intelligence (AI) model, a response to the output generation request is generated in accordance with the data chunks, where each data chunk is associated with a corresponding unique hash signature.

The invention discloses an automatic leveling method for intelligently controlling an underwater leveling machine. The method comprises the following steps: acquiring real-time attitude data and working surface flatness requirements of a plurality of execution mechanisms of the underwater leveling machine, and constructing state and operation action information characteristics; an execution mechanism is mapped into an agent with a body, information is shared through an underwater acoustic communication network, and each agent selects and executes a leveling action based on self and neighborhood interaction data; after execution, obtaining a reward value, storing the reward value in an experience pool, sampling and updating the state-action function, and performing iterative training to obtain a multi-body agent reinforcement learning optimization model; continuously monitoring the state of a mechanism when the model runs, dynamically updating the features of an affected area when a fault or hydrodynamic abnormality is identified, and recalculating a leveling strategy to obtain a dynamic re-planning scheme; the scheme is combined with model output to generate a real-time leveling instruction sequence, and the real-time leveling instruction sequence is decomposed and then issued to each execution mechanism, so that self-adaptive accurate leveling control of the underwater leveling machine is realized, and the working efficiency and reliability of a complex underwater environment are effectively improved.

A video editing system leverages a generative artificial intelligence (AI) model to identify songs to overlay on a video. The video editing system extracts a set of key frames from the video and prompts the generative AI model to generate a video narrative for the video. A video narrative is a text description of the plot, theme, feel, or other characteristics of the video. The video editing system uses the video narrative to prompt the generative AI model again to generate a set of descriptor tags for the video based on the video narrative. Descriptor tags are strings that represent themes, features, or characteristics of the song. The video editing system uses an audio tagging system to score a set of songs based on the set of descriptor tags and presents a selected subset of the set of songs based on the scores of the songs.

The invention relates to a monitoring method and system for the full life cycle of a transformer substation project, and the method comprises the following steps: deploying an unmanned plane automatic airport to collect environment data, and selecting a transformer substation project construction site according to the actual construction demands and the environment data; modeling the power generation and transformation project based on the monitoring data of the power generation and transformation project construction site, and constructing a hidden danger sample library; constructing an intelligent monitoring system which comprises a hidden danger recognition module, an expert rechecking module and a decision generation module; wherein the hidden danger identification module performs hidden danger identification by using an improved YOLOv8n network to obtain a hidden danger target; the improved YOLOv8n network is specifically characterized in that a convolution attention module and an adaptive feature fusion module are embedded in a backbone network; the hidden danger target passing through the expert rechecking module is input into a decision generation module, and a hidden danger decision is generated and issued to an unmanned aerial vehicle automatic airport; the unmanned aerial vehicle automatic airport receives and controls the unmanned aerial vehicle to execute the hidden danger decision.

The invention relates to the technical field of language model integration, in particular to a large language model integration method supporting semantic correction. The method comprises the following steps: S1, selecting several large language models, and sorting and preprocessing text data for training and testing; s2, training each source model and an optimization model by using an intelligent fusion language model technology; s3, fusing parameters of each source model by using an intelligent fusion language model technology, selecting model parameters for fusion according to the text data, and adjusting the fused model; s4, evaluating the semantic correction capability of the fused model, and adjusting a fusion strategy and regularization parameters according to an evaluation result; and S5, integrating the trained model into a target system. According to the large language model integration method supporting semantic correction, through an intelligent fusion language model technology, a context sensing and hybrid regularization technology is utilized to train and optimize a model and enhance adaptability, and a FuseLLM fusion technology is adopted to merge all source model parameters.

The invention discloses a personalized acupuncture treatment scheme recommendation method and system based on data analysis, and relates to the technical field of acupuncture. The method comprises the following steps: constructing a feature vector matrix according to multi-dimensional feature data of a target patient; taking the matrix as a constraint, retrieving similar historical cases in a pre-constructed acupuncture case database, and obtaining a sample case set and a similarity set; configuring a data selection strategy based on the similarity set, constructing a sample training set, and training to generate an acupuncture parameter evaluation plug-in; and defining an acupuncture parameter optimization space based on the sample case set, performing iterative optimization in the space by using the evaluation plug-in, and finally outputting an acupuncture treatment parameter scheme adaptive to the target patient. According to the method, the whole-process personalized recommendation of the acupuncture treatment scheme from case screening, model evaluation to parameter optimization is realized, and the accuracy, objectivity and clinical operability of treatment are improved.

The embodiment of the invention provides a snow depth inversion method and system based on a random forest. The snow depth detection method is applied to the technical field of snow depth detection, and comprises the following steps: selecting an acquisition area, acquiring snow depth data in a predetermined acquisition area, and preprocessing to obtain processed data; selecting a prediction variable and a target variable, training the random forest model to obtain a trained random forest model, and performing precision verification; and inputting snow depth data acquired in real time into the trained random forest model, performing snow depth prediction, and outputting to obtain a prediction result. In this way, active microwave high-resolution snow depth data and passive microwave inversion of the mountain snow depth are combined, and Sentinel-1 inversion snow depth data are taken as a training target, so that the inversion precision of the mountain snow depth is improved.

The invention discloses a server cluster load balancing cooperative processing system and method and electronic equipment, and relates to the technical field of servers, and the method comprises the steps: receiving node operation data sent by each edge node in a region; carrying out load prediction in the region according to the node operation data and a pre-trained neural network model to obtain a prediction result; determining a dynamic scheduling weight; solving calculation is carried out according to the multiple pre-stored node mapping schemes, preset solving parameters and a pre-established target function, and a multi-target Pareto solution set is obtained; performing preferential processing according to the Pareto solution set and the dynamic scheduling weight to obtain a target node mapping scheme; and according to the target node mapping scheme, controlling each edge node in the region to execute load balancing cooperative operation. The problem that the load balancing performance of the server cluster is poor is solved, and the server load balancing performance of the server cluster is improved by selecting a proper target node mapping scheme based on the node operation data collected by each edge node of the edge layer.

The invention relates to the technical field of data backup, and discloses an iOS device data backup and recovery method and system, and the method comprises the steps: carrying out the SSL certificate verification and EscrowBag key authentication of an iOS device, and obtaining a data path list; according to the data path list, parallel scanning of APFS file system B-Tree traversal, NAND flash memory page reading and SQLite WAL log analysis is carried out on the iOS device storage partition, and an original data set is obtained; performing integrity evaluation based on the original data set to obtain an effective data set and constructing a virtual file index; the data selection list of the user is confirmed based on the virtual file index, safety recovery is executed, and a data recovery result is returned. The SQLite page Cell reconstruction and fragment file signature matching technology is combined, intelligent repair and quality prediction can be conducted on damaged data, the user experience and operation efficiency are improved, and the user experience is improved. And the security and the reliability in the data recovery process are ensured.

A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

The invention discloses a self-adaptive time sequence control circuit and method applied to an SRAM (Static Random Access Memory), relates to the technical field of integrated circuits, and aims to solve the problem that the traditional SRAM time sequence control circuit easily causes unstable performance of the SRAM under different temperature conditions. The adaptive time sequence control circuit comprises a temperature compensation code generation circuit and a time sequence compensation circuit. The time sequence compensation circuit at least comprises a delay chain circuit, a first AND gate and a second AND gate; the output end of the first AND gate is connected with the data input end of the delay chain circuit and the first input end of the second AND gate, the data selection end of the delay chain circuit is connected with the output end of the temperature compensation code generation circuit, the output end of the delay chain circuit is connected with the second input end of the second AND gate, and the output end of the second AND gate is connected with the SRAM. The self-adaptive time sequence control circuit applied to the SRAM is used for improving the performance stability of the SRAM under different working temperature conditions.

The invention relates to a turbine blade cooling optimization method and device based on non-Newtonian fluid. Comprising the following steps: step 1, carrying out grid division and simulation processing on a three-dimensional geometric model of the micro-channel of the turbine blade containing a shear groove to obtain a simulation grid model; 2, on the basis of the simulation grid model, determining a non-Newtonian fluid constitutive model suitable for the simulation grid model, and associating the non-Newtonian fluid constitutive model to the simulation grid model to obtain a fluid characteristic grid model; step 3, calculating index data based on the fluid characteristic grid model and the target equation; 4, adjusting the structure parameters of the shearing grooves in the simulation grid model, repeatedly executing the step 1 to the step 3 according to the adjusted structure parameters of the shearing grooves, and recording index data corresponding to the structure parameters of each group of shearing grooves to obtain a candidate data set; and 5, selecting the structure parameter of the shear groove with the comprehensive optimal resistance performance and heat transfer performance from the candidate data set according to the index data, and taking the structure parameter as an optimized target shear groove parameter.

The invention discloses a multi-modal document automatic proofreading method and system based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: receiving an original document, and converting the original document into structured data composed of a plurality of data of different formats and types; according to the data of each format type, selecting a corresponding check model to check the structured data to obtain a difference item corresponding to the data of each format type; determining a risk level corresponding to each difference item through a preset quality grader; and correcting each difference item according to each risk level. According to the method and the device, the corresponding verification models are selected to verify the structured data through the data of different formats and types in the structured data, the risk level of the difference item obtained through verification is determined through the preset quality classifier, and the difference item is corrected based on the risk level, so that automatic correction of the structured data is realized; and compared with a manual proofreading mode, the proofreading efficiency is effectively improved.

The invention discloses a federal data selection method and device based on a proxy verification data set, and the method comprises the steps: a central server selects participated clients according to the selection probability of the clients, and transmits a prediction model and meta-model parameters to the clients; the client calculates a sample and a client weight by using the meta-model, selects a training sample to update a prediction model according to the sample weight, and uploads the client weight and the updated prediction model to the central server; the client with the highest weight updates the meta-model by using the proxy verification data set, and uploads the updated meta-model to the central server; the client updates the proxy verification data set by using a meta-marginal function; and after receiving all the prediction model parameters, the central server carries out parameter aggregation to obtain an updated prediction model, and updates the client selection probability to carry out the next round of model training. According to the method, the dependence on an extra unbiased verification set is eliminated, and the calculation efficiency is high.

The invention relates to power load prediction, in particular to a dynamic hierarchical granularity model power load prediction method considering time sequence factors. According to the method, the fitting capability of periodic features, trend changes and sudden influences in load fluctuation is remarkably enhanced, and the problem of feature loss caused by neglect of a time sequence dynamic weight of an existing model can be solved. Comprising the following steps: S1, performing time sequence characteristic analysis on historical power load data, and converting an original non-stationary time sequence into a stationary time sequence through difference and logarithm transformation; s2, obtaining a cleaned power load data set; s3, based on the data cleaned in S2, selecting a Gaussian radial basis function (RBF) as a kernel function, and analyzing temperature, holiday identification and linear change trend key influence factors at the same time; s4, performing dynamic multi-level granulation on the data set, and dynamically adjusting the particle level according to the data mixing degree and the particle density; and S5, fusing the time sequence kernel function and the influence factor through a decision function, and predicting the future power load.

Systems, methods, and computer-readable storage media for measuring spinal canal volume in vertebrates, and more specifically to using Artificial Intelligence (AI) to predict how surgical options will affect spinal canal volume. A system configured as disclosed herein can receive two or more pre-operation medical images capturing at least one functional spinal unit, then calculate an initial spinal canal regional volume using the pre-operation medical images for at least a portion of the at least one functional spinal unit. The system can then calculate, using a neural network, a predicted spinal canal regional volume for at least a portion of the at least one functional spinal unit undergoing various spine surgery options separately, resulting in predicted spinal canal regional volumes corresponding to the plurality of spine surgery options. The system or a surgeon can then select, using that data, one or more of the spine surgery options.

The invention discloses an electric vehicle charging station intelligent recommendation method and system based on electric energy quality space-time optimization. The recommendation method comprises the steps that power grid operation data, charging station and electric vehicle states, road network operation data, traffic state data and personalized data of all transformer areas are collected in real time; extracting electric energy quality state parameters of each transformer area, and respectively calculating real-time electric energy quality factors; inputting the real-time electric energy quality factor into the electric energy quality prediction model to obtain an electric energy quality factor prediction value of each transformer area; searching a local candidate sub-graph according to the current position and the driving path of the user, and adjusting the edge weight according to the predicted value of the power quality factor in the searching process; and constructing a fusion graph structure, and selecting an optimal charging station as a recommendation result according to the electric energy quality factor prediction value, the traffic cost and the user preference data. The method can improve the power quality, gives consideration to the real-time traffic state, reduces the passing cost of a user, and improves the user experience.

The invention discloses a switch service quality optimization method and device based on intelligent prediction and adaptive scheduling, equipment and a storage medium, and relates to the technical field of network communication, and the method comprises the steps: selecting a multi-dimensional feature model based on real-time network flow data; obtaining a flow demand prediction result based on the multi-dimensional feature model; adjusting a service priority based on the traffic demand prediction result and a real-time network load; and setting a differential service code point value based on the adjusted service priority, and realizing the differential service of the network flow. According to the invention, modeling is carried out on the traffic through multiple dimensions, deep analysis and prediction of traffic behaviors are realized by using the model, the priority and resource allocation are intelligently adjusted according to the real-time change of the network load and the traffic prediction result, and a finer and more intelligent traffic scheduling decision is provided.

The invention discloses a PID parameter optimization system based on offline system identification, and the system comprises a data selection module which is used for screening modeling data in a historical database; the data preprocessing module is used for performing real data complementation and data filtering on the screened modeling data; the model system identification module is used for establishing a controlled object model by using the preprocessed data; and the PID parameter optimization module is used for performing off-line optimization of PID parameters on the controlled object model. The defects in the prior art can be overcome, any on-line closed-loop test is not needed, and the optimal PID parameter is output at a time.

The invention provides a standardized instant response system and method for a resource scheduling high-concurrency scene. The system comprises an associated service verification module which is used for monitoring the change condition of service data in real time, selecting a verification chain of the highest level corresponding to the service data to execute verification for the changed service data, determining the state information of a scheduling voucher associated with the verification chain according to the verification result, and sending the state information to the scheduling voucher; the state information of the scheduling voucher is provided for the scheduling verification and response module; and the scheduling verification and response module is used for returning standardized response data to the user side according to the state information of the target scheduling voucher provided by the associated service verification module in advance after the target scheduling voucher in the resource scheduling request is obtained through analysis. Through the technical scheme provided by the invention, the associated service verification time consumption can be greatly reduced, the resource scheduling full-chain duration is reduced, and the resource scheduling efficiency is greatly improved.

The invention discloses a question and answer intention recognition and classification method for an infant care robot. A question rewriting node, an intention recognition node and a branch node are included. The input data of the question rewriting node are questions input by a user, the training data of the question rewriting node are phrases for mothers, infants and child rearing scenes, the model parameters of the question rewriting node are obtained through adjustment based on the training data, and the question rewriting node processes the input data according to the model parameters of the question rewriting node and outputs the data; the intention recognition node obtains data output by the problem rewriting node and recognizes a user intention and an expected result; the branch nodes provide a plurality of large language models in mother and child breeding scenes, and the branch nodes select the corresponding large language models to execute data processing according to output data of the intention recognition nodes. Original user input is clarified through standardization and rewriting, then the accuracy and response efficiency of subsequent intention recognition nodes are remarkably improved, and finally the overall user experience is improved.

Methods, systems, and apparatus for training a smaller machine learning model through contrastive learning. The method includes obtaining data specifying a larger machine learning model, wherein the larger machine learning model has been trained through contrastive learning; obtaining a training dataset comprising a plurality of training examples; and training the smaller machine learning model on the training dataset, the training comprising, at each of a plurality of training iterations: generating a batch for the training iteration that comprises a subset of the plurality of training examples, the generating comprising selecting the subset of training examples according to performing an active data selection procedure based on respective contrastive losses of the larger machine learning model on one or more candidate batches that each include a respective subset of training examples from the training dataset; and training the smaller machine learning model on a contrastive loss function using the batch.

The invention relates to the technical field of silicon carbide, in particular to a silicon carbide epitaxial process formula generation method, device and equipment and a storage medium. Acquiring historical experimental process data; solving the historical experimental process data and a preset target process index according to a preset data selection condition and the second component correlation coefficient to obtain a group of candidate feasible solutions; screening the group of candidate feasible solutions according to a preset least square regression algorithm and a preset expected dependent variable to obtain process parameters; generating a silicon carbide epitaxy process formula and an evaluation report according to the process parameters; according to the scheme, the blindness of experience trial and error is avoided by positioning key regulation and control factors of the silicon carbide epitaxy process; a group of candidate feasible solutions are obtained through a least square regression algorithm, and a process formula is finally generated, so that an interpretable reproduction scheme is provided for process research and development, the period is shortened, and the cost is reduced.

This disclosure describes methods for adaptive machine learning in distributed edge computing. An edge node collects local data, selects a suitable large language model (LLM) or small learning model (SLM), trains it, and shares updates with a federated server or peer nodes. Another method matches AI functions with appropriate models, uses datasets with confidence values, and applies a Kalman Filter to assign weights and update covariance matrix confidence. In collaborative training, edge nodes store trained models with per-layer covariance values, transmit them to a control node, and update models based on aggregated inputs. Blockchain may be used for secure model storage and distribution, with smart contracts managing access and updates. These approaches support efficient, privacy-preserving learning by adapting models using statistical confidence and decentralized coordination.

This disclosure provides methods, devices, and systems for planning and performing medical procedures. The present implementations more specifically relate to analyzing objects in 3D images. In some aspects, a segmentation system may receive image data representing a 3D image of an anatomy, select a seed location for a target in the 3D image, and infer a segmentation mask associated with the seed location from at least a portion of the received image data based on a neural network model trained to segment a class of objects associated with the target. The system further extracts a polygon mesh from the segmentation mask to produce a 3D model of the target. The system can determine a spatial relationship between an instrument and the target based on a position of the 3D model relative to the 3D image. The system can also estimate a geometry of the target based on the 3D model.

The invention discloses a robot data analysis decision method and system based on a neural network, and relates to the technical field of robot production, and the method comprises the steps: obtaining the state data of a local robot; acquiring state data of other local robots, and selecting the robot with the highest comprehensive score as an auxiliary robot; collecting local data and auxiliary data; performing modal noise reduction, feature extraction and fusion on the local data and the auxiliary data by using the neural network preprocessing layer to obtain an environment representation vector; determining a bottom risk by using a Bayesian network; analyzing a risk evolution trend by using a knowledge graph; and inputting the underlying risk and the risk evolution trend into the reinforcement learning model, and outputting an optimal decision. According to the method, the deficiency of single-robot sensing is made up through multi-robot cooperative data acquisition, noise reduction and deep feature extraction of interfered data are realized by means of neural network preprocessing, and the environment sensing accuracy, risk assessment precision and decision reliability of the robot in the interference environment are improved.

The invention discloses a cognitive function assessment data acquisition method and device, relates to the field of medical health, and is used for improving the convenience, efficiency and accuracy of assessment. Aiming at ADHD children, concentrated attention data, continuous attention data, selective attention data, alternate attention data, dispersive attention data, work memory ability data, suppression control ability data and cognitive flexibility ability data are respectively acquired by developing multiple rounds of interactive tasks. According to the invention, the portability of evaluation facilities is improved, and the accuracy and efficiency of data acquisition and evaluation are improved.

Embodiments select features for performance prediction. In one embodiment, a method comprises: receiving a request to select features to predict a performance issue of an application, the request indicating a set of key performance indicators (KPIs) for the application and data of performance metrics; selecting a first set of features, a feature being selected to the first set of features based on correlation between the feature and the set of KPIs; selecting a second set of features from the first set of features to predict the performance issue of the application, a feature being selected to the second set of features based on a causal relationship between the feature and the set of KPIs; and causing prediction of the performance issue of the application based on the second set of features and corresponding time lags between the second set of features and the set of KPIs.

The invention discloses an enterprise supervision method based on a cloud management platform and related equipment, and relates to the field of computers. The method comprises the steps that when an employee user logs in a cloud computer account, a work task plan of current login is acquired from the employee user, and the work task plan comprises at least one task type; based on the work task plan, evaluating cloud computer resource configuration data required by the employee user for logging in this time; and selecting a matched core time mode based on the cloud computer resource configuration data, wherein different core time modes have different rates. The problem that a charging mode of a cloud computer is generally based on calculation configuration and use time and is huge in expenditure for enterprises with large demands can be solved.