36 results about "Automated learning" patented technology

The invention relates to a distributed early warning log-based automatic learning and fault prediction method and system. The method comprises the following steps: analyzing historical log traffic characteristics, generating a log rate prediction value, dynamically adjusting a sampling frequency according to the rate prediction value, and outputting a lightweight feature vector with a timestamp; generating a real-time contribution weight of each node through a dynamic weight calculation function to obtain a region-level model parameter; inputting the regional-level model parameters into a feature distiller to generate representation vectors, and performing network superposition on the representation vectors to obtain a network topological relation matrix; tensor splicing is carried out on the feature vectors and a real-time network topology relation matrix, a graph neural network model is input, and a fault probability prediction value is generated in combination with a historical feature sequence; and when the real-time contribution weight of the node continuously decreases for N times and exceeds a preset threshold value, triggering an anomaly detection instruction, injecting a feature compensation vector into the FPGA preprocessing unit, and synchronizing the model updating frequency of the feature distiller.

This invention relates to an automated learning system and a computer implemented method employing programs, such as, artificial intelligence, large language models, multilingual speech recognition, speech-to-text, text-to-speech, speech-to-speech and speech synthesis integrated with telephony systems, to facilitate interactive learning. The system enables a user to engage in real-time voice interactions with an emulated human instructor's voice for learning a target subject of study in a conversational setting. The system operates via telephonic voice calls over analog or digital phone lines, voice over internet protocol lines and web real-time communication systems.

A method and a system for improving neural network training in object processing. The method comprises: receiving, from an object classification process using scene images of objects in an object processing facility, a set of segmented and classified objects captured during a pre-determined period; grouping the object images of the segmented and classified objects by a grouping routine based on their visual likeness to generate grouped object images, the grouping routine comprising at least one neural network; evaluating the objects of the object images based on comparison scores by a comparison routine, and generating a plurality of object sequences; and executing, by an automated learning routine, unsupervised and semi-supervised learning tasks by using the plurality of object sequences.

The invention provides a bolt looseness detection method and system for a railway vehicle, and the method comprises the steps: obtaining an image containing a bolt and an anti-looseness line, constructing a target detection network which introduces programmable gradient information, a general high-efficiency layer aggregation network and a Ghost module, constructing a bolt looseness network employing a reversible cylindrical network, a channel attention mechanism and the like, and carrying out the detection of the bolt looseness. A corresponding model is obtained through data set training, then the to-be-analyzed image is detected, the feature extraction capacity and the detection speed precision can be improved, key features are focused, interference is suppressed, and the low-resolution detection problem is solved; template comparison is not needed, feature automatic learning is achieved, and robustness and universality are high; automatic detection can be achieved, efficiency is improved, cost is reduced, missing detection is avoided, and rail traffic safety is guaranteed.

A system and method for performing automated learning of an Internet-of-Things (IoT) application are disclosed. The automated learning is based on generation of application-agnostic events, allowing the automated learning to be performed without prior knowledge of the IoT application.

The invention provides a logging analysis model construction method and system based on multilevel regional feature association, and belongs to the technical field of oil-gas exploration artificial intelligence. In order to solve the problems of high cost, poor generalization and the like of a conventional method for performing data correction by using a standard well, the method comprises the following steps of: firstly, constructing multi-level geographic region embedding according to well location hierarchical codes, and capturing regional geologic features; then, constructing a regional relation graph based on well location space coordinates; and finally, aggregating features of adjacent regions through a graph neural network, and fusing multi-level region features and logging data through gating weight. According to the method, automatic learning of regional features is realized through a technology of performing gating weight fusion on the logging data by using the multistage regional nesting embedding module, the graph neural network spatial modeling module and the logging data, dependence and cost on standard well correction are removed, and cross-regional prediction precision is remarkably improved by establishing the regional features.

Systems, methods, and computer program products of intelligent image analysis using object detection models to identify objects and locate and detect features in an image are disclosed. The systems, methods, and computer program products include automated learning to identify the location of an object to enable continuous identification and location of an object in an image during periods when the object may be difficult to recognize or during low visibility conditions.

The invention relates to the technical field of credit scoring, in particular to a credit scoring system for quantitative model service capability based on artificial intelligence, which aims at the pain points of current credit data output and traditional technical architecture, constructs a unified data table, breaks the independence of data distribution and storage, and improves the credit scoring efficiency. Real-time integration and linkage of multi-source heterogeneous data are achieved, characterization, tagging and business based on data are provided, original information and fields scattered in different data sources are collected in a unified mode, indexes are processed and integrated in a unified mode, a unified tag library with enterprises as the granularity is formed, and support is provided for decision-making type model development and business decision making. And in the aspect of the model, a machine learning algorithm is introduced, and the automatic learning capability and the dynamic adjustment characteristic of the machine learning algorithm are utilized, so that rapid iteration and accurate prediction of the risk control model are realized, the high requirements of banks on real-time data processing and dynamic risk strategies are met, and credit investigation companies are comprehensively assisted to promote general financial services.

The invention relates to an agricultural machine operation area optimization calculation method for efficiently and intelligently identifying and eliminating Beidou satellite positioning driving track drift points, and aims to solve the problems of operation track distortion and virtual height of area caused by positioning drift point interference generated in a parking state of an agricultural machine. According to the method, a set of multi-stage intelligent trajectory analysis and cleaning algorithm is constructed, drifting data is preliminarily eliminated by using static point clustering and speed anomaly detection means, and adaptive judgment and elimination of direction anomaly line segments are realized through an intelligent recognition mechanism combining direction clustering and a shallow neural network. And finally, an Alpha Shapes space geometry algorithm is adopted to accurately extract the boundary of the operation area, and area calculation is completed. The whole process does not need additional sensor input, has the advantages of high automation, high learning ability, high adaptability and the like, remarkably improves the reliability of the operation track of the agricultural machine and the calculation precision of the operation area, and is particularly suitable for agricultural operation monitoring and management under complex terrains and variable working conditions.

The invention provides a vehicle tire pressure monitoring learning method and device, a storage medium and equipment, and the method comprises the steps: obtaining a frame number of a vehicle and tire pressure sensor identification information corresponding to four tires when the vehicle flows to a tire pressure station, carrying out the verification and sequence adjustment of the tire pressure sensor identification information, and carrying out the verification and sequence adjustment of the tire pressure sensor identification information; and establishing an association relationship between the tire pressure sensor identification information which passes the verification and completes the sequence adjustment and the vehicle frame number, storing the association relationship to an electric detection server, and when the vehicle flows to an electric detection station, querying the corresponding tire pressure sensor identification information in the electric detection server through the vehicle frame number and writing the tire pressure sensor identification information into a vehicle ECU (Electronic Control Unit). Therefore, automatic learning of vehicle tire pressure monitoring is realized, the labor cost is effectively reduced, and the processing efficiency is improved.

We provide the system. [Solution] A means of collecting surrounding information using various detection devices, An automated learning model means for analyzing the aforementioned information and detecting anomalies, A means for performing an evaluation based on anomaly detection and generating an alarm, A means of controlling mobile devices to patrol the site, A means of evaluating and optimizing the operation record of the entire system, A means to enable real-time confirmation of the aforementioned anomaly from a mobile device, A system that includes this.

The invention relates to an inspection control method and system for multiple inspection robots of a nuclear power station and computer equipment. The method comprises the following steps that an inspection point list is determined, and the inspection point list comprises a plurality of inspection points; performing the following steps on each inspection point: acquiring inspection point information of the current inspection point and historical inspection data of each inspection robot for the current inspection point; according to the inspection point information and the historical inspection data, the inspection matching value of each inspection robot for the current inspection point is calculated; and determining an inspection robot of the current inspection point according to all the inspection matching values so as to inspect the current inspection point. The robot for inspecting the inspection point is selected through automatic learning according to the historical inspection data, high adaptability and flexibility are achieved, and manual intervention and labor cost input can be reduced.

The present disclosure provides a system for automated learning for a digital worker comprising one or more processors and a non-transitory machine-readable medium storing instructions. The instructions cause the processors to receive a video recording captured by a user demonstrating steps for executing a specific task, generate a raw instructions file comprising recorded steps and user input events, and process the video recording and raw instructions file using a machine learning module to generate a processed instructions file adapted to the task context. Processing comprises analyzing video frames to detect user interface elements and correlating detected elements with user input events. The operations further comprise validating the processed instructions file, assigning the specific task to a digital worker configured to execute automated tasks according to the processed instructions file, and executing the specific task by the digital worker.

This application provides an enterprise-level financial big data model platform, relating to the fields of artificial intelligence and fintech. The platform includes: a model layer for collaboratively integrating the capabilities of models with different modalities and parameter quantities, providing model capabilities to the component layer and application scenario layer; a component layer for encapsulating model capabilities into standardized components; and an application scenario layer for calling standardized components corresponding to task requirements based on financial business scenarios, combined with multimodal data, to meet the task requirements of the financial business scenarios. The method of this application improves the development efficiency of financial applications, reduces development costs, and shortens the development cycle by integrating the interfaces of different models through the componentization of big and small model capabilities. Through an automated learning mechanism, it continuously optimizes model capabilities based on actual business feedback, achieving continuous model optimization, enhancing the intelligence of financial applications, and reducing the labor and maintenance costs of commercial banks.

A computer implemented system and process to determine a model for a domain includes identifying a schema that defines a possible causal element of a particular type of behavior. One or more concepts are determined, as well as one or more sub-concepts for each concept, where each concept and sub-concept are associated with a logical relationship. Multiple models are determined from the one or more concepts and the one or more sub-concepts. The multiple models may be calibrated using representative data collected from a real-world source. An optimal model is determined amongst a plurality of calibrated models.

The present invention provides a method and system for detecting loose bolts on rail vehicles, comprising acquiring an image containing bolts and anti-loosening wires, constructing a target detection network that introduces programmable gradient information, a universal high-efficiency layer aggregation network, and a Ghost module, constructing a bolt loosening network that uses a reversible columnar network, a channel attention mechanism, etc., training a corresponding model using a data set, and then performing image detection on the image to be analyzed. The method can improve feature extraction capabilities and detection speed and accuracy, focus on key features, suppress interference, and solve low-resolution detection problems; no template comparison is required, and feature automatic learning is achieved, with strong robustness and versatility; automated detection can be performed, improving efficiency, reducing costs, avoiding missed detections, and ensuring rail transit safety.

The present invention belongs to the field of data analysis technology, and particularly relates to a method for predicting crowd flow big data for 5G needs, comprising dividing a to-be-predicted area into a plurality of small areas, regarding each small area as a node, obtaining node features and edge weight features between nodes; converting node features and edge weight features into vector representations, and inputting the vector representations together with node and edge position codes as input data into a graph neural network; constructing graph data through node and edge weight relationships, iteratively updating each node in the constructed graph data using a graph neural network, and obtaining a first feature vector of each node; inputting the first feature vector of each node into a graph attention network, and obtaining a second feature vector of each node; dividing data at a certain moment in the to-be-predicted area into three layers of data, namely, real-time data, periodic data, and abnormal data, processing the current layer of data through an enhanced memory adjustment network model, splicing the current layer of data with the next layer of data as the input of the next layer of enhanced memory adjustment network model, and outputting a third feature vector from the last layer of enhanced memory adjustment network model; splicing the second feature vector and the third feature vector together and inputting them into a fully connected layer, and obtaining a flow prediction for the current area; the present invention effectively improves the accuracy and efficiency of crowd flow prediction by comprehensively utilizing automated learning and advanced network models, and is particularly suitable for the big data environment of the 5G era.

A method for determining by prediction, using automated learning of the machine learning type, time ranges during which a radio interface of a wireless access point of a communication network can be switched off or put on standby, for energy-saving purposes.

A method for optimizing an automated process to select and grip an object by a robot in an arrangement that includes a plurality of robots with regard to a specifiable optimization criterion, wherein the objects to be potentially gripped irregularly occur with respect to their spatial position and a time of their arrival, where detection of objects to be potentially gripped by robots is performed, detection of a priority characteristic as well as an assignment to one of the robots for the objects to be potentially gripped via an automated learning algorithm, taking the optimization criterion into account, and where selection and gripping depending on the assignment and the priority characteristic is implemented.

The invention relates to the field of machine vision, in particular to an on-line cosmetic contact lens printing ink dot detection method and system based on meta learning, and aims to realize rapid detection and quality judgment of ink dots of contact lenses with different patterns by adopting a Few-ShotCounting model in combination with a meta learning mechanism and through on-line training of a small number of template images. According to the system, through area division, ink dot counting and rule judgment, ink dots in an optical area and standard-exceeding ink dots on the edges of a printing area and the optical area are accurately recognized. The invention relates to a machine vision detection technology and a signal processing technology, and aims to further utilize a contact lens image collected by a high-resolution camera, automatically learn and identify the image, and judge whether a corresponding product is normal or not, so as to achieve the purposes of reducing labor cost, saving time and improving the production yield.

Conversational systems are intelligent machines that can understand language and conversing with a customer in writing or verbally. Embodiments herein provide a method for generating a universal conversational system using an ensemble of chatbots and a universal conversational system that adopts wisdom of crowd manifesting as an ensemble of chatbots. The ensemble of chatbots takes responses from NER and rule based conversational models. The knowledge based conversation models where complex queries that require question and answer, and the ensemble of generative knowledge chatbots are relying on a pre-trained models. The pre-trained models are complemented by domain specific training to answer queries that fall outside rule-based chatbot or knowledge graph-based conversation bot capability. The universal conversational system capable of building online virtuous automated learning loop where the models learn from each other and also from human response as wisdom of crowd.

Artificial intelligence-based system (20) for analyzing business data, including: - a file reading module (Figure 1) configured to receive and process a variety of business files in various different formats; - a language conversion module (Figure 2) configured to analyze the contents of the files and translate them into a language that is simple and easy to understand; - a data comparison module (Figure 3) configured to compare the converted data with data from other business units; - a decision engine (Figure 4) configured to receive information from the language conversion and data comparison modules and to provide suggestions and strategies based on the results of the analyses performed; - a machine learning unit (Figure 6) configured to perform automated learning based on new data and file formats while continuously improving its performance and ability to adapt to different business requirements.

1. The name of this design product: three-axis mechanical module. 2. Purpose of this design product: PLC automation learning and practical equipment for motion control. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention relates to a method for sorting articles in a plurality of sorting devices, and a computer readable storage medium storing instructions for implementing the method, in which the method comprises the following steps: sorting (S1) a stream of articles (2) at a first sorting device (1) into at least one sorted portion (3) and optionally at least one unsorted portion (4); capturing (S2) a set of partial images; marking (S3) the partial image with a partial label; at least one classifier configured to correlate the item with the portion is constructed (S4), the at least one classifier being a machine learning model. Further, at the second sorting device (10), capturing (S5) an image of the items in the item stream (12); sorting of the articles in the stream of articles (12) into at least one sorted portion (13) and optionally at least one unsorted portion (14) is controlled (S6) using the images captured at the second sorting device (10) and at least one classifier.

The invention discloses a construction method and system for a regional industry map, and relates to the technical field of regional industry planning and management, and the method comprises the following steps: collecting regional industry related data, carrying out the cleaning, duplicate removal and standardization processing of the collected data, and carrying out the encryption storage of the standardized data; extracting a law and an influence mechanism of industrial space distribution as a basic principle of industrial map construction; the method comprises the following steps: automatically learning a spatial rule and a dependency relationship of industrial distribution by using big data analysis and spatial analysis technologies, fusing a complex network analysis technology, modeling a relationship between industries, and constructing an industrial network model through parameter adjustment and algorithm correction; the industrial network model is converted into a visual industrial map through a visualization technology, and the spatial layout and relation of the regional industry are displayed. According to the method, accurate drawing and visual presentation of the regional industrial map are realized, and scientific support is provided for regional industrial planning and policy making.

The invention relates to an artificial intelligence-based data analysis system for translating and comparing file content. The system comprises: - a reading module configured to receive and process files in various formats; - a language conversion module configured to analyze and translate file content into simple and understandable language; - a module for comparing the converted data with that of other business entities; - a decision engine configured to receive information from the language conversion and comparison modules and to provide suggestions and strategies based on the results of the analyses performed; - a learning unit configured to undertake automated learning based on new data and new file formats, while continuously improving its performance and its ability to adapt to different business needs.Figure for the abridged version: 1.

A system and method for performing automated learning of an Internet-of-Things (IoT) application are disclosed. The automated learning is based on generation of application-agnostic events, allowing the automated learning to be performed without prior knowledge of the IoT application.

The present invention discloses an automatic abstract generation method based on pointer key information. First, sentence vectors of multi-dimensional semantic features of an article are obtained through a pre-trained model. Secondly, pointer is used to select keyword semantic feature word vectors. Finally, an encoder is used to generate an abstract. In this method, a model framework is built using a neural network, and automated learning and training are used, eliminating excessive manual intervention. The training process adopts two methods of prediction and verification, and then the model calculates the loss and automatically improves the accuracy of the model. The method of the present invention has strong applicability and will have great applications in aspects such as news title generation and copywriting generation.

A computer implemented system and process to determine a model for a domain includes identifying a schema that defines a possible causal element of a particular type of behavior. One or more concepts are determined, as well as one or more sub-concepts for each concept, where each concept and sub-concept are associated with a logical relationship. Multiple models are determined from the one or more concepts and the one or more sub-concepts. The multiple models may be calibrated using representative data collected from a real-world source. An optimal model is determined amongst a plurality of calibrated models.

The invention provides a research and judgment strategy optimization method and device based on a large model, equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of large language models, intelligent agent technologies and network security. According to the specific implementation scheme, unsupervised clustering is carried out on difference samples of safety alarm research and judgment to form at least one sample cluster containing similar features; when any sample cluster meets a preset triggering condition, calling a large language model to carry out attribution analysis on the sample clusters so as to infer potential reasons causing research and judgment differences; based on the potential reasons, generating a structured new research and judgment strategy; and optimizing the strategy library based on the new research and judgment strategy. According to the scheme disclosed by the invention, self-reflection is carried out on the misinformation of clustering through the large model, the optimization strategy is automatically generated, and an intelligent and automatic learning closed loop of the research and judgment process is constructed, so that the research and judgment accuracy is continuously improved, and the operation cost of manual analysis and strategy iteration is remarkably reduced.