1421 results about "Analyser" patented technology

The invention provides a real-time deviation analysis method and system for grid-connected operation data of a new energy station, and relates to the technical field of data deviation analysis, and the method comprises the steps: collecting the real-time grid-connected operation data of the station, and carrying out the adaptive decomposition based on a data fluctuation trend, and obtaining a plurality of groups of original feature sequences; screening features through a recursive support degree model and information entropy, and generating a reconstructed feature matrix by using a manifold embedded network; the double-branch verification network analyzes the features from the time sequence and the frequency domain, and generates time sequence feature vectors and frequency domain feature vectors; the topological continuous coherence analyzer extracts the topological relation between the features; the dynamic coupler constructs a multi-dimensional feature space, a deviation source is tracked, analyzed and positioned through a feature propagation link, and a propagation path is established; constructing a fractional order differential operator based on a propagation path, depicting a long-range dependency relationship among parameters, generating a dependency intensity matrix, performing causality test, constructing a dynamic causality graph, quantifying contribution degrees of propagation nodes, analyzing a deviation evolution mechanism, and outputting a deviation analysis result.

The embodiment of the invention provides a cross-platform compatibility automatic testing method, which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, collecting chip platform information to construct a testing environment database, constructing a testing case feature model according to the instruction set features and the testing environment database, designing a test case generator based on the test case feature model, and generating a test task scheduling scheme based on the test execution framework; inputting the abnormal behavior characteristics and the performance baseline threshold value into a test result analyzer, constructing a scoring dimension containing functional integrity, a performance standard-reaching rate and a stability index, and mapping the abnormal behavior characteristics and the performance baseline threshold value to the scoring dimension to generate a test result scoring matrix, calculating a weighted average value of the test result scoring matrix and the optimized weight coefficient as a compatibility level, generating a test report based on the compatibility level, and executing regression verification; according to the invention, the comprehensiveness and accuracy of the test can be improved, and more efficient compatibility evaluation is realized.

The invention is an advanced deep learning system that combines a latent transformer core with a latent dynamics analyzer. This system processes input data into latent space vectors, which are then analyzed in parallel for both prediction and dynamic modeling. The latent transformer generates short-term predictions, while the latent dynamics analyzer derives equations of motion describing the underlying system dynamics. By integrating spectral analysis and change detection, the system can identify significant shifts in behavior, particularly useful for complex systems like financial markets. The invention enables more accurate predictions, interpretable insights, and early detection of regime changes. Its end-to-end training approach ensures all components work harmoniously, balancing predictive accuracy with physical plausibility and interpretability.

A computer system may have one or computers and one or more data storage devices storing instructions, which when executed by the one or more computers implements a characterization manager, comprising: a test data database storing a plurality of test data sets; the characterization manager configured for selecting one or more test data sets from the test data database and apply the selected test data sets to a Generative AI system to derive from the Generative AI system an output; an output analyzer for processing the output to generate characterization data describing one or more facets of the output.

A method for managing a framework includes: receiving, by a GUI, a query that is sent to a first module; analyzing, by the first module, the query to infer intention; making, by the first module, a determination that the intention is not searching for an object in a database; sending, by the first module, the query to a second module; transforming, by the second module, the query into a vector that is sent to a third module; performing, by the third module and using the vector, a search for a nearest image in the database; identifying, by the third module, a path associated with the nearest image, which is sent to an analyzer, in which the analyzer further sends the path to a fourth module; and fetching, by the fourth module and based on the path, an image from a store, which is sent to the GUI.

A method for managing an anomaly in a client includes: obtaining, by an analyzer, historical metadata (HM); obtaining, by the analyzer, an error description that is associated with the HM; analyzing, by the analyzer, the HM to generate a first data frame (DF); generating, by the analyzer, a second DF and a third DF based on the first DF, in which the second DF and the third DF are sent to an engine; generating, by the analyzer, a fourth DF based on the first DF and error description, in which the fourth DF is sent to the engine; tuning, by the engine, an anomaly detection model (ADM) to obtain a tuned ADM using: a first target parameter (TP) and the second DF; a second TP and the third DF; a third TP and the fourth DF; and initiating, by the engine, notification of an administrator about the tuned ADM.

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

The invention discloses an electronic component surface defect analysis system based on an attention mechanism, which comprises an image acquisition module, a data processing module, an attention mechanism module, a surface defect analysis module, a system maintenance and feedback updating module and a report generation and user interface module, the data processing module is used for preprocessing the collected image, the attention mechanism module is used for highlighting key area features in the image, the surface defect analysis module is used for detecting and analyzing device defects, and the system maintenance and feedback updating module is used for system maintenance and updating. And the report generation and user interface module is used for generating a detection report and providing an interactive interface. According to the electronic component surface defect analysis system based on the attention mechanism, a key defect concentration algorithm based on the attention mechanism is provided to extract key image defects; and providing a surface defect analysis algorithm based on Fourier transform to detect and analyze the surface defects of the device.

The invention relates to the technical field of civil engineering monitoring, and discloses a structure health monitoring system based on civil engineering. A high-density sensing network of the system collects strain field distribution, vibration spectrum and environmental corrosion parameters of a structure through a distributed multi-mode sensor array; performing clock drift compensation and space coordinate normalization on the asynchronous sampling data by a space-time alignment engine to generate an original feature tensor of a unified space-time reference; the semantic modeling unit is combined with a design drawing and a material parameter library, the original feature tensor is mapped to a component semantic space, and hierarchical structure features with topological marks are output; the adaptive fusion core executes dynamic weight distribution, eliminates sensor conflict data and generates an anti-interference fusion diagnosis index; the edge computing node operates a lightweight damage detection model according to the fusion diagnosis index, and outputs a local component health state level; and the cloud collaborative analyzer aggregates multiple edge node results and predicts the overall residual life of the structure in combination with historical degradation data.

The invention discloses a medical report generation method, a model training method, equipment and a medium, and the model training method comprises the steps: constructing a medical report generation model framework which comprises a global semantic collaborative multi-modal enhancement module, a visual encoder, a text encoder, a medical insight analyzer and an LLM decoder; wherein the global semantic collaborative multi-modal enhancement module respectively enhances a medical image and a medical report by utilizing a selected image enhancement strategy and a text enhancement strategy, and the medical insight analyzer comprises a fine-grained structure learning device and a global context guide learning device which are connected in sequence so as to enhance the cross-modal alignment capability; and performing intelligent collaborative optimization by taking a strategy set formed by an image enhancement strategy and a text enhancement strategy and architecture configuration parameters of the medical insight analyzer as optimization targets to obtain an optimal medical report generation model. The medical report generation performance can be effectively improved.

The invention discloses a legal instrument generation method and device, electronic equipment and a storage medium, and relates to the technical field of legal instrument generation or other related fields, and the method comprises the steps: receiving first language case information and a legal instrument generation demand; analyzing the first language case information through a semantic analyzer corresponding to the first language to obtain case factors of the target case, and translating the first language case information through a translator to obtain second language case information; querying a target legal knowledge base based on the case factors to obtain a query result; and determining a target document template based on the legal document generation demand, and generating a target legal document based on the target document template, the second language case information, the case factors and the query result. According to the method and the device, the technical problem that the generated legal document is inaccurate and even not compliant due to the fact that professional legal terms and contexts cannot be accurately understood and converted in the cross-language generation process of the legal document in related technologies is solved.

Disclosed in the present invention are a method and system for building a compilable customized module. The method comprises: customizing a module on the basis of a process, defining a modeling language syntax, and generating a modeling language file corresponding to the customized module; establishing a text matching rule and a grammar matching rule, using flex to generate a source program file of a lexical analyzer from the text matching rule, and using bison to generate a source program file of a syntax analyzer from the grammar matching rule; performing lexical analysis and generating an abstract syntax tree; traversing the syntax tree, generating a translation C++ file for a modeling language on the basis of a syntax-based translation configuration file, and compiling the translation C++ file; and importing the built customized module and an executable binary file into a simulation platform, and performing flow simulations for different scenarios including process simulation, optimization and parameter tuning. The present invention can realize the rapid commissioning of a process and can seamlessly switch a customized model to various application scenarios.

The invention provides an efficient and accurate frequency modulation screen printing color management color matching auxiliary system which comprises a collector, the collector comprises an image collection module, the image collection module can capture a multi-channel filter array of a printing manuscript through a multispectral imaging device, and an analyzer comprises a spectral analysis module and a color matching prediction module. The spectral analysis module is in communication connection with the image acquisition module, after low-dimensional feature vectors are generated, candidate schemes are listed, and the color matching prediction module predicts an ink matching scheme based on the low-dimensional feature vectors and a deep convolutional neural network; relates to the technical field of frequency modulation screen printing toning. According to the method, the limitation of traditional single-factor color matching is broken through by introducing a multi-modal data fusion (spectrum + base material + ambient light) and Bayesian optimization feedback mechanism, the nonlinear mapping problem of high-dimensional spectral data is solved through cooperation of manifold learning dimension reduction and deep neural network prediction, and efficient and accurate frequency modulation network color matching adjustment is realized.

The invention discloses a new media content creation and distribution platform, and the platform comprises a user portrait construction module which is used for collecting user behavior data and generating a multi-dimensional interest label; the multi-modal content generation module supports automatic generation and editing of texts, images, audios and videos; an advanced algorithm is integrated through a multi-modal content generation module, intelligent collaborative production of texts, images, audios and videos is achieved, creation efficiency is improved, title variant generation and style imitation are supported, and the manual creative bottleneck is broken through; the intelligent distribution engine adopts reinforcement learning to dynamically optimize weight distribution and combines a platform feature analyzer and an A / B test unit to improve cross-platform distribution accuracy and traffic conversion rate; the block chain evidence storage and cross-platform crawler system constructs a full-link copyright protection network, so that content fingerprints can be quickly chained, infringement contents can be automatically released, and the right protection efficiency is improved; and the virtual digital human driving module and the multi-mode conversion unit extend the content adaptability, so that the immersion of the user is enhanced.

A system and method for automated qualitative analysis of computerized conversational agent (CCA) conversational data. In an embodiment, the system comprises a system configuration manager for establishing CCA characteristics and factors to be included in analyses, a data ingestion engine for ingesting conversations from a CCA, a conversation indicator extractor for identifying and classifying key aspects of conversations, a fallback analyzer for identifying the type and frequency of CCA failures, a topic modeling engine for identifying trends in conversations, and a CCA score generator for generating score assessments for qualitative aspects of the CCA's performance.

The invention discloses a data privacy protection method and system based on a zero trust principle, and particularly relates to the field of data privacy protection.The method comprises the steps that identity information and biological characteristics are provided when a user registers, and a security token is obtained; during login, the system verifies identity information and inquires role permission to ensure access compliance; a quantum encryption algorithm is adopted for data transmission, and the data security is ensured through quantum state selection, quantum channel transmission and error rate detection; the system continuously monitors user behaviors, equipment states and environment information, dynamically adjusts access authority, divides risk levels based on a risk assessment result and takes corresponding measures; sensitive data are processed through a desensitization technology and an anonymization algorithm, and privacy is protected; and the system collects operation logs and data access logs, analyzes and excavates the logs by using an audit analyzer, identifies abnormal behaviors and generates early warning information.

The invention discloses an efficient data compression and transmission method based on a computing power host management operation platform, and relates to a large-scale data concurrent management technology. Comprising the steps of 1, integrating a lightweight data feature analyzer at a Kafka producer end, extracting feature indexes of data streams in real time through the data feature analyzer, and generating compression strategy parameters according to the feature indexes, and 2, performing feature recognition on structured data and unstructured data, extracting data features, and generating compression strategy parameters according to the compression strategy parameters. And step 3, based on data characteristics, CPU load of producers / consumers and network bandwidth prediction, constructing a multi-objective optimization model, dynamically selecting a compression algorithm and a compression level, selecting compression strategy parameters according to the compression algorithm, introducing a compression income ratio index, calculating a ratio of compression rate increase to compression time consumption, and performing compression strategy optimization. And preferentially selecting a compression algorithm with a high gain ratio to perform data compression and transmission.

A method for managing an anomaly in a client includes: obtaining, by an analyzer, historical metadata (HM); obtaining, by the analyzer, an error description that is associated with the HM; analyzing, by the analyzer, the HM to generate a first data frame (DF); generating, by the analyzer, a second DF and a third DF based on the first DF, in which the second DF and the third DF are sent to an engine; generating, by the analyzer, a fourth DF based on the first DF and error description, in which the fourth DF is sent to the engine; tuning, by the engine, an anomaly detection model (ADM) to obtain a tuned ADM using: a first target parameter (TP) and the second DF; a second TP and the third DF; a third TP and the fourth DF; and initiating, by the engine, notification of an administrator about the tuned ADM.

The invention relates to an athletic ability analysis method for rehabilitation data enhancement processing, which relates to the field of data processing, and comprises the following steps: performing primary retrieval by taking a multi-modal rehabilitation data sequence as a reference to obtain a primary similar user set; determining exercise demand information in combination with a life scene, and screening the primary similar user set to obtain a secondary similar user set; obtaining an exercise ability analysis report set based on secondary similar user set analysis, and training to obtain an exercise ability analyzer; and performing analysis according to the multi-modal rehabilitation data sequence by using a motion ability analyzer, and outputting a target motion ability analysis report. Through the method, the problems that a traditional analysis method cannot comprehensively reflect the real exercise ability of a patient, and analysis accuracy and practicability are insufficient can be solved; athletic ability analysis is carried out by utilizing artificial intelligence and big data technologies and combining life scenes and exercise demands of patients, so that scientificity, accuracy and practicability of exercise ability analysis of the patients can be remarkably improved.

The invention discloses an optical glass production and processing monitoring system based on the Internet of Things technology, which relates to the technical field of optical glass production monitoring and comprises an Internet of Things data collector component, a data processing analyzer component, an intelligent monitoring and early warning component, a user interaction control screen component and an authority manager component. Key parameter data in the production and processing process are collected in real time through the Internet of Things data collector component, real-time processing and analysis are carried out through a big data processing engine and a machine intelligent learning algorithm library, a prediction algorithm is constructed in combination with historical data to predict the production trend, the system can predict the future production trend, and the production efficiency is improved. And scanning and one-by-one calculation are carried out for each time period in the production cycle, so that the predicted production capacity or production trend of each time point in the whole production cycle is obtained, the production efficiency and the quality control are improved, and intelligent management and optimization of the production process are realized through adaptive learning and fault prediction functions.

In some examples, an analyzer manager configured to select one of a program code analyzer, a static data analyzer, and an unused memory location analyzer for malware detection within memory of a system. The program code analyzer can be executed to evaluate instruction data for executing a computer program at a first set of memory locations within the memory for malware in response to being selected by the analyzer manager. The static data analyzer can be executed to evaluate static data for use by the computer program at a second set of memory locations within the memory for the malware in response to being selected by the analyzer manager. The unused memory location analyzer can be executed to evaluate null data indicative of unused memory locations at a third set of memory locations within the memory for the malware in response to being selected by the analyzer manager.

The invention relates to the technical field of agricultural intelligent equipment, in particular to a field sowing grain distance detection system and method based on computer vision, and the method comprises the steps that an image collection module collects 4KRGB and near-infrared data of a sowing area; the image processing module is used for processing and correcting the acquired image in real time; the grain distance feature deep learning analyzer strengthens seed feature extraction through an attention mechanism based on an improved YOLOv5 algorithm, accurately distinguishes weeds from seeds, and calculates a center distance deviation value of adjacent seeds based on a preset target grain distance; the positioning fusion module fuses Beidou positioning data and a visual odometer track, generates seed longitude and latitude and burial depth coordinates by using a high-precision farmland surface elevation model, and maps the seed longitude and latitude and burial depth coordinates to a farmland electronic map; and the dynamic regulation and control execution mechanism adjusts the rotating speed of the seeder or the seeding gap according to the grain distance deviation value and a priority strategy, and dynamically corrects the grain distance. Therefore, the problems of high detection error rate, single detection algorithm, poor data relevance and the like in the prior art are solved.

A system (100) for dynamic data pipeline configuration based on real-time enterprise integration context, comprising: (a) a context monitoring and event detection module configured to detect changes in enterprise systems in real time, including changes in data sources, schemas, system availability and regulatory conditions; b) an integration context analyzer configured to map and analyze the enterprise integration landscape, including data dependencies, formats, API endpoints, and metadata relationships; c) a pipeline orchestration and configuration engine configured to dynamically create, update, and deploy data pipelines in response to contextual triggers using the outputs of the integration context analyzer; (d) a rule-based decision and policy management module configured to apply user-defined and system-generated rules for data transformation, routing logic, security enforcement, and compliance controls during pipeline configuration; e) an AI and ML-driven optimization module configured to analyze historical data flow performance, predict bottlenecks, recommend schema transformations, and optimize pipeline efficiency based on learned patterns; f) a real-time data flow and transformation engine configured to perform adaptive ETL operations, handle schema evolution, support conditional transformations, and manage error recovery in live data streams; g) and an audit, logging and compliance module configured to maintain detailed records of pipeline actions, configuration changes and integration results for regulatory compliance, troubleshooting and traceability.

To provide favorable systems and method for correction of positionally dependent electromagnetic radiation detected from objects within a suitable fluid column.SOLUTION: A discrimination system forms a fluid column and interrogates objects within the fluid column with an excitation source. A fluid column forming a structure creates a fluid column containing objects at differing positions within the fluid column, and an excitation source generates excitation electromagnetic radiation directed toward objects in the fluid column at a measurement region. An optical arrangement collects output electromagnetic radiation emanating from the excited objects disposed within the fluid column and directs the output electromagnetic radiation to a detector. An analyzer reduces the positional dependency of the detected intensity by normalizing a value based on the position of each object.SELECTED DRAWING: Figure 1

The invention relates to the technical field of angle-resolved photoelectron spectroscopy experimental instruments, and discloses an angle-resolved multi-electron coincidence measurement system and method based on an event-type delay line detector, and the system comprises an angle-resolved energy analyzer; a detection surface of the event type delay line detector is divided into at least two independent areas, and the event type delay line detector is used for receiving photoelectrons in parallel and independently outputting event signals; the external trigger and synchronization module is used for generating a unified trigger period to establish a global time reference; the time digital reading module is used for receiving the event signal and generating a timestamp; and the data acquisition and processing module is used for carrying out association judgment and selection based on the timestamps in the same triggering period. The dead time is reduced through multi-region parallel reading, unified time base calibration is achieved through time base line calibration and delay compensation, multi-electron coincidence measurement can be achieved under the condition that the angular resolution capability is kept, and the method is suitable for pulse or continuous light sources.

The invention discloses a meteorological water conservancy disaster intelligent grading early warning linkage call routing method and a meteorological water conservancy disaster intelligent grading early warning linkage call routing system. A meteorological water conservancy two-factor early warning matrix is constructed, a cross-department data fusion channel is established, an early warning coupling model is developed, and multi-dimensional early warning fusion is achieved. And formulating a water conservancy special response strategy library, wherein the water conservancy special response strategy library comprises river water level grading routing and mountain torrent risk area customization strategies. And developing an intelligent water conservancy enhancement module, such as a flood control project topology analysis engine and a water and rain work condition semantic analyzer. The method is characterized in that a three-element risk rating model is constructed by an initiative dynamic coupling algorithm, and a water conservancy emergency response topology network based on double dimensions and a water conservancy project BIM data and meteorological early warning space superposition analysis module are adopted. The accuracy and comprehensiveness of disaster early warning can be improved, the timeliness and effectiveness of emergency response are ensured, and the intelligent level and disaster response capacity of the system are improved.

An analyzer module forms a hypothesis on what are a possible set of cyber threats that could include the identified abnormal behavior and / or suspicious activity with AI models trained with machine learning on possible cyber threats. The Analyzer analyzes a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and / or suspicious activity data with the AI models. A formatting and ranking module outputs supported possible cyber threat hypotheses into a formalized report that is presented in 1) printable report, 2) presented digitally on a user interface, or 3) both.

The invention provides a liver blood flow and anesthesia depth combined monitoring system in a liver operation. The liver blood flow and anesthesia depth combined monitoring system comprises seven modules including a multi-source data collector, a signal preprocessor, a physiological coupling model calculator, an operation recognizer, a brain-liver information flow analyzer, a risk prediction early warning device and a clinical decision assist device. According to the system, liver blood flow parameters and anesthesia depth parameters are collected and analyzed in real time, a dynamic coupling relation model between the liver blood flow parameters and the anesthesia depth parameters is established, and in combination with automatic recognition of surgical operation and quantitative analysis of brain-liver information flow, early prediction and early warning of surgical risks are achieved, and individualized clinical decision support is provided.