923 results about "Analyser" patented technology

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.

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 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 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.

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 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.

A system and computer-implemented method facilitate expansion of knowledge. The system allows for characterization of natural language documents and of search queries to locate those documents. A natural language processing (NLP) analyzer finds subject-verb-object (SVO) triplets in received text and assigns initial hierarchical classifications to word components of the SVO triplets. An SVO analyzer generates variation hierarchical classifications by varying the initial hierarchical classifications assigned, selects at least one hierarchical classification from the initial hierarchical classifications and variation hierarchical classifications, and produces a token stream of tokens. The tokens represent respective hierarchical classifications of the at least one hierarchical classification selected. The token stream produced may represent a natural language (NL) document to be stored to facilitate matching the NL document to a subsequently independently specified query. Alternatively, the token stream produced may represent a query and the token stream is used for generating a response to the query.

A code analyzer implements machine learning to detect vulnerabilities in computer code. The code analyzer trains a machine learning model using training vectors that characterize vulnerable programming patterns. The code analyzer evaluates a topological representation of the computer code using the machine learning model to identify a potential vulnerability. The potential vulnerability corresponds to a portion of the computer code where an attack can be used to bypass a security procedure. The code analyzer tests the potential vulnerability by emulating a fault injection during execution of the portion of the computer code. Upon confirming that the potential vulnerability can be exploited via a fault injection, the code analyzer generates a training vector that characterizes a vulnerable programming pattern associated with the portion of the computer code. The training vector can be used to further train the machine learning model.

The invention discloses an ANTLR4-based multi-manufacturer network equipment configuration analysis method and system, and the method comprises the steps: carrying out the preprocessing of a configuration file; based on a machine learning algorithm of multi-dimensional feature analysis, automatic identification of configuration file manufacturer types is realized; based on the identification result of the manufacturer type, loading an ANTLR4 grammar parser corresponding to the manufacturer type; a lexical analyzer in ANTLR4 realizes lexical analysis on the configuration file through lexical rule definition, a Token classification system and an optimized analysis algorithm; a syntactic analyzer in ANTLR4 constructs an abstract syntax tree according to a lexical analysis result and a syntactic rule defined based on a context-independent grammar; traversing the abstract syntax tree, and extracting key configuration information; and converting the specific configuration format of each manufacturer into a unified standard model. According to the method and the system, network equipment configuration files of different manufacturers can be automatically identified and analyzed, and key configuration information is extracted and converted into a unified model.

An alternating pulsed lidar system comprising a laser beam generator, a receiver, and an analyzer. The laser beam generator is configured to sequentially emit laser beam pulses from ports for an aircraft into an atmosphere on an alternating basis between the ports. The receiver is configured to receive a backscatter light generated in response to sequentially emitting the laser beam pulses and generate backscatter data from the backscatter light. The analyzer is configured to determine a set of parameters for the aircraft using the backscatter data.

Systems and methods for interfacing with data profilers using a machine learning model. In some aspects, the system receives a data profiler configured to create and interface with a plurality of data profiles. The system trains a profile query model to generate responses to user queries relating to data profiles. The system receives a user query concerning data profiles associated with the data profiler. Using an interpretation model, the system pre-processes the user query and data profile attributes to determine an activation pattern for the profile query model. The system uses the profile query model to process the user query and the data profile attributes of the data profiler and generate a preliminary response. The system post-processes the preliminary response to generate a verified response by applying a corrective program. The system then transmits the verified response in a conversational program related to the user query.

The invention provides an early warning method based on microseismic energy accumulation tendency, and belongs to the field of seismic data processing. The method comprises the following steps: acquiring a historical micro-seismic log set of an earthquake monitoring sensor array in a coal mine area; performing staged processing in combination with a timestamp to obtain a micro-seismic energy characteristic sub-sequence set; executing a first-order difference operation, and constructing a difference energy characteristic subsequence set; performing trend correlation analysis by using a space-time guiding module to obtain a trend feature set; constructing an energy accumulation tendency analyzer based on the trend feature set; and obtaining a real-time micro-seismic log, carrying out abnormity identification by combining with an analyzer, and triggering an early warning instruction. The technical problem that in the prior art, the dynamic change trend of the micro-seismic energy cannot be accurately captured, so that the early warning accuracy of the energy accumulation is insufficient is solved, and the technical effect of improving the early warning accuracy and timeliness of the micro-seismic energy accumulation through differential energy feature analysis and space-time trend association is achieved.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

Conditions are identified in a telecommunications network based on data collected from the telecommunications network. The data comprises time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and raw error logs collected alongside the time series telemetry data for the telecommunications systems. Outputs from a time series insight generator and a sentiment analyzer are combined to generate an output report indicative of anomalous metrics in the telecommunications network.

A hypergraph workload manager in a server is configured for failure tolerant and explainable state machine driven hypergraph execution. The hypergraph executor comprises a query optimizer, a hypergraph enlister, a pipeline analyzer, and a state machine generator. The query optimizer translates a user query into a query operator graph. The hypergraph enlister enlists the query operator graph into a hypergraph containing a set of query operator graphs representative of already submitted user queries. The enlistment is configured to join query operator graphs where it makes sense to optimize query executions. Updates to the hypergraph based on the enlistment results in a set of disconnected graphs. The pipeline analyzer performs an analysis of all operators of all queries in the hypergraph to find an optimal sequencing of execution. The state machine generator is configured to generate a hierarchical state machine for all operators of a disconnected graph of the hypergraph.

In order to allow an analyzer to be calibrated or a sampling channel to be purged without removing a sampling probe from a sampling location, provided are a sampling channel through which an exhaust gas from an internal combustion engine flows, an analyzer that is connected to the sampling channel and analyzes the exhaust gas, a diluter provided upstream of the analyzer in the sampling channel, a recirculated channel that branches from a branch point set between the analyzer and the diluter in the sampling channel and joins the diluter, a pump that is provided in the recirculated channel and guides part of the exhaust gas from the branch point to the diluter, an additional channel that is connected to the recirculated channel or the sampling channel and through which a calibration gas or a purge gas flows, and an opening and closing mechanism provided in the additional channel.

The invention discloses an automobile industry code quality automatic compliance method based on a large language model. The method comprises the following steps: calling a static code analyzer to generate a violation report; the method comprises the following steps: intelligently classifying violation into Tier1, Tier2 and Tier3 based on a multi-feature analysis algorithm of a rule document; for Tier1 violation, performing three times of retry iteration repair by using a large language model, automatically detecting compilation errors and performing rollback; for Tier2 violation, multiple technical schemes are extracted by a large language model for a user to select; a multi-LLM antagonistic cooperation mechanism is adopted for Tier3 violation, automatic restoration of complex violation is achieved through a Generator generation restoration scheme, a Disscriminator review challenge scheme and antagonistic iteration of Arbiter arbitration conflicts, and an optimal scheme is selected through a weighted voting algorithm when conflicts occur. The method obviously reduces the manual intervention rate, improves the repairing quality, and is low in cost and high in universality.

The invention discloses an intelligent data value mining method and system, a storage medium and a computer program product, and relates to the technical field of data asset management.The method comprises the steps that an enterprise internal source data document is obtained, a pre-constructed localized deployment agent is adopted to analyze the enterprise internal source data document, and a key metadata set is extracted; calling a pre-trained dynamic consanguinity analyzer to perform consanguinity analysis on the key metadata set to obtain a data value evaluation matrix; loading a pre-trained asset prediction model to perform hierarchical prediction analysis on the data value evaluation matrix to obtain a table entry feasibility prediction result, the table entry feasibility prediction result including a table entry success rate and a recommended asset type; and generating an intelligent data value disclosure report based on the entry success rate and the recommended asset type. A localized deployment agent, a data asset prediction engine and a dynamic blood relationship analyzer are mutually combined, and the data asset value and compliance risk of an enterprise are efficiently and accurately evaluated.

The invention aims to provide an abnormal access detection method and system based on data analysis, and relates to the technical field of network data communication, user behavior classification is carried out based on a support vector machine, only normal behavior data is needed to establish a detection model, abnormal data is not needed, the model training complexity is highly simplified, and the user experience is improved. Compared with a traditional method, the OCSVM has distinct advantages in the aspects of single-class data processing and anomaly detection; according to the method, log data are analyzed by using an LEX lexical analyzer and a YACC syntactic analyzer, high-precision description of user behaviors is realized, feature selection is performed on original logs, sensitive information such as user names, operation time and client IPs is extracted, data redundancy is reduced, and high efficiency of model training and recognition is ensured; according to the anomaly detection method provided by the invention, the user behavior anomaly information in the database can be efficiently analyzed, and an alarm can be given in time and necessary countermeasures can be taken, so that potential safety risks or data leakage can be prevented.

A computer hardware system includes a static analyzer, a load estimator, and a hardware processor configured to perform the following executable operations. Using the static analyzer and for each egress API call site respectively associated with an ingress API handler of a microservice, a weight is generated and is included within a static analysis of the microservice also generated by the static analyzer. Using the load estimator and for each of the egress API call sites, a load is determined based upon the weight for the egress API call site, a number of times, over a predetermined period of time, a particular ingress API handler associated with the egress API call site is called, and a cost of a call to the egress API call site. Based upon the load, the microservice is modified.

The invention discloses a lightweight disease and insect pest early warning and capturing system, and the system comprises an execution unit which comprises an intelligent insect sticking plate, a trapping device, early warning equipment, and a controller; the acquisition unit comprises a multi-sensor array and an image acquisition device; the calculation unit comprises an edge processor, a data analyzer, an early warning calculation module, a configuration module and a decision module; the edge processor is configured with an image preprocessing method and a lightweight target detection and recognition algorithm; the early warning calculation module is configured with an insect pest counting algorithm, a density heat map generation method and a prediction model; the configuration module is used for configuring a threshold dynamic adjustment mechanism, an early warning grade division mechanism, an early warning trigger condition and a board change reminding mechanism; and the decision module is connected with the expert system. The technical problems of low manual inspection efficiency and limited coverage range are solved, the monitoring efficiency, the early warning precision and the prevention and control effect are improved, and the cost and risk of agricultural producers are reduced.

The invention discloses a power distribution cabinet intelligent monitoring method and system based on deep learning, and relates to the technical field of data processing, the system comprises an acquisition module, a detection module and a decision module, the acquisition module is used for acquiring operation parameters and environment parameters of a power distribution cabinet and transmitting the parameters to the detection module; performing multi-level anomaly identification on the operation parameters and the environment parameters through a multi-level detection framework, a threshold detection engine, a change rate analyzer and an association detector by adopting a cascade detection framework, acquiring an anomaly detection result, transmitting the anomaly detection result to a decision-making module, and performing decision-making on the basis of the anomaly detection result. A grading early warning mechanism and a disposal strategy library are set according to correlation analysis of operation parameters and environment parameters, a disposal plan is executed in a matched mode, accurate monitoring response is achieved by constructing the grading early warning and strategy library of the power distribution cabinet, robustness is enhanced through multi-abnormity coordination, data reliability is guaranteed through distributed sensors, and the strategy is optimized through feedback. Environmental parameter monitoring linkage is enhanced, safety and efficiency are improved, and the false alarm rate is reduced.

Examples described herein provide a computer-implemented method that includes, in response to receiving a request against the workload in an environment comprising predetermined cloud-based containers, searching predetermined container runtime interface metadata across a plurality of compute nodes in the environment to locate runtime processes. The method further includes selecting, for each runtime process located, a respective applicable profiler from a set of predetermined profilers sharing a transactional database. The method further includes injecting, for each runtime process located, predetermined code libraries for each respective applicable profiler. The method further includes re-linking the predetermined code libraries for each respective applicable profiler. The method further includes executing, for each runtime process located, each respective applicable profiler to produce a set of results.

The invention relates to the technical field of ion characterization and preparation, in particular to a high-throughput preparation mass spectrum device and method giving consideration to high-resolution characterization. The device comprises an ion source, an ion transmission cavity, a quadrupole mass screening cavity, an ion dissociation cavity, an ion deflection cavity, an ion deceleration and deposition cavity and a high-resolution mass spectrum characterization cavity which are sequentially arranged, wherein the high-resolution mass spectrum characterization cavity is formed in one side of the ion deflection cavity in the vertical direction; soft ionization is combined with a quadrupole analyzer to screen ions, the ions are dissociated through a dissociation cavity, an ion deflection cavity is used for selecting a high-resolution characterization or high-throughput preparation function, a high-resolution mass spectrum characterization cavity is used for achieving high-resolution characterization of screened and fragmented ions, and an ion deceleration and deposition cavity is used for high-throughput preparation of the ions. According to the method, perfect compatibility of high-throughput preparation and high-resolution mass spectrum characterization is realized through ion deflection design, and the method has a wide application prospect in the fields of efficient preparation of biological macromolecules, synthetic macromolecules and the like and the like.

Methods, apparatus, systems and articles of manufacture are disclosed to determine audio quality. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal. Example apparatus disclosed herein also include an EQ analyzer to access a plurality of equalization settings determined by the neural network based on the query; and compare the equalization settings to an equalization threshold to determine if the audio signal is to be removed from subsequent processing.

Embodiments facilitate software analysis by machine learning (ML) models, through extensible software analysis architecture (ESAA) or software analysis work allocation (SAWA). Pluggable ESAA ML modules include a vetted prompt which is actionable for software analysis, with a vetting certification. Some ML modules contain computational cost information such as a token count or model round trip time. Tools are tailored to ML analyzers to control background execution, availability offerings, and results displays. SAWA determines how well a software analyzer meets a prompt's software analysis requirements, and an ML planning model generates an analysis plan that balances software analysis workloads among ML analyzers and non-ML analyzers. ML analyzers are favored for summarization, task decomposition, task scheduling, and source code change review, while non-ML analyzers are otherwise favored. Non-ML analyzers gather control flow, data flow, internal structure, and similar context which is then supplied to an ML analyzer.

A system includes a hardware processor and a memory storing a cross-language voice similarity analyzer (analyzer). The hardware processor executes the analyzer to generate an embedding vector representation of an audio sample of a human voice in a feature space including existing embedding vectors corresponding respectively to different reference voices, decompose the embedding vector representation to identify a linear or non-linear combination of vocal component vectors corresponding to the human voice, each vocal component vector representing a respective predetermined voice characteristic descriptor, increase the dimensionality of the linear or non-linear combination of the vocal component vectors to match the dimensionality of the embedding vector representation to provide a reconstructed embedding vector representation of the human voice, and identify, by comparing the reconstructed embedding vector representation with one or more of the existing embedding vectors, one of the reference voices as a match for the audio sample of the human voice.

The invention discloses an intelligent analysis system for the running state of a 3C vehicle-mounted contact network, and belongs to the technical field of crossing of intelligent operation and maintenance of rail transit and industrial artificial intelligence. And the system associates the multi-source heterogeneous observation data to a specific equipment unit through an equipment centralized data binding module. And the multi-modal feature extraction and state management unit processes the data and maintains a multi-dimensional state vector of the equipment by using the Kalman filtering updating unit. And the physical-data hybrid decision maker fuses the data driving rule and the simplified physical model to output a diagnosis result. The topology analyzer performs global verification based on a mechanical transfer rule. The system further comprises a long-term health state prediction and feedback module, early failure risks are predicted through a hidden Markov model, Kalman filtering process noise is dynamically fed back and adjusted, and cooperation of long-term prediction and short-term estimation is achieved. According to the method, the technical problems of multi-source data splitting, lack of physical basis in diagnosis and incapability of predictive maintenance are solved.

The invention provides a virtual equipment and variable data processing method for power monitoring, which realizes intelligent definition, calculation, alarm and visual data binding technology of virtual equipment and variables in a power monitoring scene by integrating MQTT, a memory database, a time sequence database and a relational database and combining a regular expression analyzer collaborative analysis mechanism. The system supports various data such as current real-time values, equipment attributes, accumulated value differences and historical data to form an expression, the real-time data is stored in a Redis database and the historical data is stored in a TD engine database by adopting an atomic storage strategy taking station + deviceid + measure as key values, and efficient query and atomic storage combination based on the Redis and the TD engine are realized through a unified API interface, so that the data display speed is greatly increased, and the data display efficiency is improved. The innovatively designed zero-code binding mechanism is based on a front-end data driving framework, and automatic binding of real-time acquisition variables, virtual variables and power monitoring display components can be completed without additionally developing an API (Application Program Interface).