20 results about "Automated learning" patented technology

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.

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 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.

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.

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.

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 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.

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.

A subject knowledge multi-hop question and answer method based on structured planning and reflection reasoning comprises the steps that firstly, for input subject questions, a structured planning module is adopted to conduct decomposition and subtask modeling on complex questions, and a multi-stage reasoning path conforming to a subject knowledge system is generated; secondly, dynamically retrieving related knowledge contents from teaching materials and subject knowledge bases, and constructing a structural chemistry knowledge context expressed in a triple form in combination with an information extraction technology driven by a thinking chain; and finally, introducing an inverse reasoning module, and performing consistency analysis and self-adaptive correction on an intermediate reasoning result, thereby realizing accurate subject knowledge questions and answers. According to the method, controllability of the reasoning process in subject knowledge question answering is achieved through structured planning, correctness and robustness of a reasoning chain are improved through reflection reasoning, the understanding depth of textbook knowledge and question answering performance are improved in combination with structured knowledge contexts, and the method is suitable for scenes such as intelligent teaching tutoring and automatic learning evaluation.

The present disclosure relates to the technical field of intelligent model, and particularly relates to a model construction method and device, equipment and a storage medium. The method comprises: acquiring spatio-temporal data, and acquiring task parameters required for model construction; based on the task parameters, performing standardization processing on the spatio-temporal data to obtain spatio-temporal graph data expressed in a standardized form; based on the spatio-temporal graph data and the task parameters, constructing a search space corresponding to a target spatio-temporal prediction model; based on the search space and the spatio-temporal graph data, performing network architecture search and network parameter optimization corresponding to the target spatio-temporal prediction model to obtain the target spatio-temporal prediction model. The present disclosure is used to solve the defect that the spatio-temporal data cannot be directly applied to the automatic learning in the spatio-temporal graph modeling task in the prior art, and realizes the process of automatically constructing the target spatio-temporal prediction model based on the spatio-temporal data.

The embodiment of the invention provides a counterfeit application model construction method, a detection analysis method, a medium and equipment. The counterfeit application detection method aims at solving the problem that an existing detection technology cannot comprehensively and efficiently recognize multi-dimensional counterfeit applications, accurate recognition of the counterfeit applications can be achieved by combining the feature extraction technology of multiple data sources such as images and codes, and meanwhile the detection efficiency is guaranteed. The method is especially suitable for real-time detection of counterfeit applications in an application store, effectively improves the detection precision of the counterfeit applications, achieves continuous optimization through automatic learning, and guarantees that the efficient detection capability is kept in the face of continuously changing counterfeit technologies.

Processes can be employed to select cartographic reference system (CRS) recommendations from a CRS model where the CRS recommendations are matched to received seismic data. A learning mode can be used to build the CRS model where seismic data is matched to CRS. The learning mode can be automated using natural language processing system to parse the meta data for the seismic data, such as the name, area, or code, or label. The CRS model can be updated using an output from a user system, such as when a user manually matches a CRS to seismic data. The matched seismic data to CRS can be used as input to a user system or a computing system, such as a borehole operation system.