248 results about "Pattern analysis" patented technology

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention relates to the technical field of multi-agent cooperative control, and discloses a multi-agent autonomous decision-making method based on deep reinforcement learning. The method comprises the steps of synchronously detecting an initial collaborative state of a cluster, performing joint situation assessment, and judging a collaborative operation mode according to a quantitative situation. And analyzing the capability of each agent and the real-time task load, and constructing a distributed task knowledge graph. And utilizing the atlas to drive a deep reinforcement learning network, coupling computing resource allocation and a task path, and generating a preliminary behavior strategy of each agent. And performing cluster-level conflict detection and iterative negotiation adjustment, and finally issuing an executable action instruction sequence. According to the method, integrated optimization of resource allocation and action paths is realized, the situation understanding and negotiation mechanism is enhanced through the knowledge graph to guarantee the collaborative consistency, and the collaborative decision-making efficiency and task execution robustness of a multi-agent system in a dynamic environment are improved.

The invention discloses a wind power gear box online fault diagnosis method based on multi-source data fusion, and particularly relates to the field of mechanical fault detection, and the method comprises the steps: S1, collecting high-frequency dynamic, medium-frequency working condition and low-frequency thermal state data, and outputting a standardized data set through time alignment, quality verification and physical constraint verification; s2, performing multi-scale decomposition to retain a fault sensitive frequency band, inverting physical parameters such as gear contact stress and the like, constructing a physical cause and effect graph, and determining fault sensitive characteristics and threshold values; s3, constructing a multi-modal feature tensor, and obtaining a low-dimensional health representation vector through CP decomposition fusion, graph neural network reasoning and variational auto-encoder dimension reduction; s4, calculating a weight by using an entropy weight method, calculating a dynamic health degree in combination with a health benchmark, predicting a trend by using LSTM, and establishing a five-level health system; s5, judging a fault mode through double-layer identification, analyzing a root cause and formulating a hierarchical operation and maintenance suggestion; the method is based on multi-source fusion and data mechanism dual drive, and precise diagnosis and operation and maintenance guidance are achieved.

The invention relates to the field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle communication frequency band monitoring method and interference method.The method comprises the following steps that 1, background noise signals of a full frequency band are collected, Fourier transform is conducted on the collected background noise signals, and a signal frequency band with energy exceeding a preset threshold value is extracted; step 2, acquiring each signal frequency band with energy, and performing adaptive IMF component decomposition on each signal frequency band with energy; 3, screening the IMF components according to the impact energy of the IMF components, and performing signal reconstruction on the screened IMF components; and step 4, inputting the reconstructed signal into the neural network model for rule analysis, and generating a risk coefficient of existence of communication in a signal frequency band according to the rule analysis.

The invention relates to the technical field of network security, in particular to a security monitoring method and system for network traffic. The method comprises the following steps: capturing a network flow data flow in real time, and extracting a network entity and a direct communication relationship to construct a basic communication graph; identifying and quantifying a high-order interaction mode between network entities, and taking the high-order interaction mode as an implicit feature enhanced basic communication graph to generate an enhanced security graph; processing the enhanced security map by using a multi-scale time sequence diagram neural network, and capturing a short-term burst mode and a long-term evolution mode at the same time; a dynamic anomaly score is calculated based on the network entity historical behavior baseline and the current network situation, and a security alert is generated when an adaptive threshold is exceeded. The system correspondingly comprises a flow capture module, a feature extraction module, a high-order mode analysis module, a security map construction module, a multi-scale analysis module, a dynamic risk assessment module and an intelligent alarm module, and comprehensive and accurate network threat detection is realized.

The invention discloses a wavy feathering method and system of a digital direct injection printer for flag cloth and electronic equipment, the method abandons a traditional gradual feathering mode, and adopts a wavy distorted feathering pattern, and the method comprises the following steps: firstly, analyzing the permeability characteristic of the cloth; according to the method, a two-way complementary template is finally generated by setting morphological parameters, creating an S-shaped fluctuation track, filling ink dots and leaving white space, so that adjacent PASS patterns can be occluded in an interlocking manner at a seam; and finally controlling the nozzle to spray ink according to the pattern. The system correspondingly comprises a pattern analysis module, a pattern generation module and a printing control module, through irregular ink dot distribution of wavy patterns, smooth gradual change is formed through natural permeation of ink, the contrast ratio of a feathering area and a non-feathering area is effectively reduced, accurate alignment of joints can be ensured through real-time error compensation, accordingly, PASS channels are eliminated, and the printing quality is improved. And the overall quality and the front and back color consistency of a flag cloth printing image are improved.

The invention relates to the technical field of image recognition, and discloses an artificial intelligence-based image recognition method and system, and the method comprises the following steps: constructing a multi-modal fusion network based on a multi-modal data set, carrying out the feature complementation between modals through employing a cross attention mechanism, and outputting the fused three-dimensional dynamic semantic features; performing cross-scene element training by using an MAML framework to generate a lightweight model with a rapid adaptation capability; based on a lightweight model, behavior pattern analysis is carried out on the three-dimensional dynamic semantic features through a hierarchical space-time Transform, and behavior semantic tags containing time sequence action units are extracted; and generating a trained image recognition model based on the behavior semantic tag, and outputting a recognition result based on the image recognition model. According to the invention, the technical bottlenecks that the dynamic scene performance is reduced and the cross-scene adaptation efficiency is low in the prior art are fundamentally solved.

The invention relates to a turntable data calibration method and device and computer equipment. The method comprises the following steps: acquiring target rotation plane data and initial rotation plane data corresponding to a tracking turntable; under the condition that the initial rotation plane data and the target rotation plane data are different, the calibration mode of the tracking rotary table is analyzed by using the initial rotation plane data, and calibration mode analysis data are obtained; performing operation control analysis on the tracking turntable according to the calibration mode analysis data to obtain turntable calibration control data; controlling the tracking turntable to move according to the turntable calibration control data to obtain turntable calibration data of the tracking turntable; the turntable calibration data is used for eliminating the difference between the target rotation plane data and the initial rotation plane data. By adopting the method, the tracking precision of the tracking turntable during the tracking task can be improved.

The invention relates to the technical field of image recognition, in particular to a personnel abnormal behavior supervision method and system for important places. The method comprises the following steps: acquiring a monitoring video frame image in real time, matching in combination with a preset abnormal foreign object image library, recognizing an abnormal behavior image, and confirming the identity of an abnormal person through face recognition to generate first alarm information; then, through analyzing the action sequence of the abnormal behaviors, identifying a second abnormal behavior image and a third abnormal behavior image, and calculating a situation response deviation degree and a behavior coherence imbalance index, generating second alarm information; finally, based on the two pieces of alarm information, the system performs alarm processing on the abnormal personnel, and abnormal behavior monitoring and timely response are effectively realized. According to the invention, through accurate behavior identification, intelligent mode analysis and time sequence association evaluation, an efficient and accurate abnormal behavior monitoring and alarm function is provided, and the place safety is improved.

A new computational approach may provide improved detection of disease conditions and comorbidities, such as PTSD, Parkinson's, Alzheimer's, depression, etc. For example, in an embodiment, a computer-implemented method for detecting a disease condition may comprise receiving a plurality of data streams, each data stream representing a measurement of a brain activity comprising physical and chemical phenomena and performing pattern analysis on the plurality of data streams to detect at least one fundamental code unit of a brain code corresponding to a disease condition based on a combination of the plurality of data streams.

The invention discloses a big data medical record linkage analysis method based on a knowledge graph. The method comprises the following steps that medical record data are collected and preprocessed; generating a medical entity set and a relation set by using an improved BioBERT model; constructing patient-level, disease-level and region-level medical record knowledge maps by taking the medical entity set as a node and the relationship set as an edge; carrying out representation learning by adopting an improved Grapher model on the basis of the medical record knowledge graph, and generating low-dimensional vector representation; a Dual-AMN method is adopted to map the low-dimensional vector representation, medical entity alignment is completed through structural attention and semantic attention mechanisms, and similar medical record recommendation, complication prediction and group pattern discovery results are generated; an analysis result is displayed through graph visualization and interactive query, and case tracking, similar case retrieval and disease evolution mode analysis services are generated. According to the invention, intelligent analysis and auxiliary decision-making driven by the medical record knowledge graph are realized.

The invention provides a breathing mode analysis method and device based on a calm breathing pressure difference waveform and electronic equipment, and relates to the technical field of medical signal processing and artificial intelligence, and the method comprises the following steps: obtaining a calm breathing pressure difference original signal of a target user; preprocessing the calm breathing pressure difference original signal to obtain a target waveform sequence; performing feature extraction based on the target waveform sequence to obtain a plurality of dimension features; combining the at least two dimension features to obtain a multi-modal feature; and inputting the multi-modal features into a trained classification model, and outputting a breathing pattern classification result. According to the method, the whole analysis process is completely based on natural calm breathing, special cooperation or forced breathing actions of the user are not needed, the pain point that traditional lung function examination has a high requirement for the user cooperation degree is solved, and auxiliary decision making of chronic respiratory diseases is achieved.

The invention provides a soil ecological environment monitoring and management method based on big data, and relates to the technical field of soil ecological environment monitoring and management. According to the method, multi-source data including soil and pasture are collected and integrated to construct a central database through field stratified sampling, classified deployment of sensors and butt joint with a third-party mechanism; space-time pattern analysis, pollution source analysis and ecological risk evaluation are carried out based on the central database, and a pollution traceability report is generated; the method comprises the following steps: constructing a multi-dimensional risk evaluation system, dividing a region into four classes and implementing differentiation management, constructing a governance technology database, screening an optimal scheme based on multiple criteria, performing demonstration verification in a typical region, comparing monitoring data, judging a governance effect and generating a demonstration application summary report; the problems that the soil monitoring and treatment accuracy of the heavy metal mining area is insufficient and the scheme adaptability is poor are effectively solved, and the monitoring and management efficiency and scientificity are remarkably improved.

The application discloses an I / O mode analysis method and device based on elastic storage and a storage medium, and the method comprises the following steps: capturing kernel I / O mode in a storage backend; analyzing and abstracting the I / O mode to obtain an analysis result; and importing the captured I / O mode and the analysis result into a new storage scene for reconstruction and playback. By implementing the application, the I / O mode is intercepted from the bottom layer, and the problem that the existing tracking technology cannot detect non-file services is solved. Meanwhile, the analysis method proposes a complete systematic design of extraction-analysis-reinjection, combines the current popular pattern matching and big data analysis and prediction technology, and makes the traditional technology play a greater role in the emerging cloud computing scene. In addition, through the analysis method, the cloud computing front-end service mode can be better understood in the distributed storage scene, and the bottom layer can be more targetedly optimized, so that the end-to-end quality is truly improved.

The invention discloses a curved surface conformal phased array directional diagram analysis method, system and device and a storage medium, and belongs to the technical field of phased array antennas. The method comprises the following steps: performing spherical harmonic function expansion on electric field pattern data of an antenna unit in a local coordinate system to obtain an analytical expression of a unit pattern under the local coordinate system; the unit pattern factors of all the antenna units under the global coordinate system can be obtained by using the analytical expression, then the pattern contributions of all the antenna units are obtained, and the curved surface conformal phased array beam scanning pattern is synthesized after superposition. According to the method, the simulation quantity is extremely small, the problem that calculation is slow when the number of interpolation points is large due to an interpolation method is solved, the calculation efficiency and precision are greatly improved, and the method can be applied to analysis of any curved surface conformal phased array directional diagram, especially application scenes with the large number of antenna units.

The application belongs to the technical field of financial transaction security, and particularly relates to a financial behavior mode analysis and prediction method based on time sequence chart representation learning, which comprises the following steps: obtaining transaction flow data; obtaining flow embedding vectors based on an event encoder and a time encoder; learning the flow embedding vectors based on an autoregressive model, and obtaining a representation matrix through self-supervised contrast learning training; constructing an interaction graph, modeling node interaction on graph structure features using a graph neural network to form a graph enhancement module, and enhancing the representation matrix; and constructing a machine learning classification model to realize financial behavior mode analysis and prediction. Compared with the prior art, the application can automatically capture more complex and abstract information from massive information, mine the time sequence, interaction and business correlation between account flow and labels, and realize rapid and wide application of the model in the absence of business expert experience analysis.

The invention discloses a query intention understanding and execution path optimization system and method based on a multi-modal deep learning model, and the method comprises the steps: a multi-modal intention analysis module is used for receiving unstructured query input from a user, and uniformly extracting the unstructured query input as a standardized SQL; the cross-model intention recognition module is used for receiving the standardized SQL output by the multi-mode intention analysis module, recognizing a data entity, an operation type and a data model to which the data entity and the operation type relate in a query intention, and outputting an entity structure table and a query plan tree; the access mode analysis module is used for using the query plan tree, identifying a connection relationship between node objects in the tree by contrasting with an entity structure table, performing cost estimation on access of each node in the tree, and constructing a logic plan tree; and the path optimization generation module is used for executing generation, evaluation and screening of candidate execution paths on the basis of the logic plan tree output by the analysis module, and finally determining an optimal execution plan. According to the method, the problems of semantic analysis, cross-model query intention recognition and execution path intelligent selection of complex inputs such as natural languages in a multi-mode database can be solved, and the query accuracy and the execution efficiency are improved.

This application discloses an AI-based intelligent diagnostic and prescription recommendation system based on famous prescriptions from renowned doctors throughout history. The system includes a human-computer interaction interface, a natural language processing module, a semantic analysis module, a speech recognition module, an image processing module, a medical terminology processing module, a feature extraction and pattern analysis module, and a prescription screening module. The speech recognition module converts captured speech signals into text information. The natural language processing and semantic analysis modules perform semantic understanding on the text information and extract structured TCM syndrome feature elements. The image processing module acquires and processes facial and tongue images of patients, recognizing visual features. The medical terminology processing module connects to a standard TCM terminology database, standardizing and mapping TCM syndrome feature elements and visual features. The feature extraction and pattern analysis module uses a deep learning model to perform diagnostic logic reasoning based on the standardized mapped features and outputs the diagnostic results.

The application discloses a reliability simulation method and system for a ship transmission shaft based on deep reinforcement learning, and belongs to the field of ship transmission shaft design. The application organically combines a deep reinforcement learning algorithm with fault features and reliability characteristic quantities, substitutes reliability characteristic quantities of transmission shaft parts into a fault tree for quantitative analysis, calculates state transition probabilities of each node, uses a Markov chain model to summarize state transition rules of each node fault mode to establish a system fault state transition probability formula; a fault mode analysis establishes a fault state space; a probability correlation degree between a bottom event and a top event of the fault tree qualitative analysis constitutes an action space; a reward function weight factor of a Markov decision model is generated through a mathematical relationship of reliability characteristic quantities of each level unit, a reliability simulation event table column is solved and generated, and transmission shaft reliability change conditions can be timely reflected, so that the reliability comprehensive prediction precision of the transmission shaft is greatly improved, and the reliability and availability of the transmission shaft during operation are ensured.

The invention relates to the field of building vertical traffic system optimization, in particular to an elevator layout scheduling collaborative optimization method based on SLP and simulation, and the method comprises the steps: firstly obtaining elevator operation data, determining the unique uplink dominant characteristics of a hospital through flow mode analysis, and modeling a passenger arrival mode through Weibull distribution; then, building an inter-floor relation strength matrix by using an SLP method, and carrying out floor layout optimization based on a total movement distance minimization objective function; an elevator system discrete event simulation model is further constructed, multiple partitioning strategies are designed and evaluated, and the effect of a requirement-based cross region partitioning scheme is optimal; finally, layout and scheduling collaborative optimization is achieved through an SLP-simulation two-way data exchange mechanism, implementation can be achieved without replacing equipment, and the method is suitable for elevator system optimization of various high-rise hospitals and other public buildings.

The invention provides a multi-mode electronic fence forbidden zone intrusion identification method and system based on AI. The method comprises the following steps: firstly, acquiring synchronous multi-modal data acquired by a heterogeneous sensor network; secondly, according to the real-time production activity state, the type attribute and the real-time vehicle scheduling information, a differential perception focusing strategy is generated; performing weighted focusing fusion on the synchronous multi-modal data according to a differential sensing focusing strategy to generate dynamic intrusion sensing features; and then performing pattern analysis through an artificial intelligence model based on the dynamic intrusion sensing features, and finally identifying an intrusion behavior which accords with a preset factory safety regulation definition. According to the technical scheme provided by the invention, the identification precision of real threats in a complex industrial environment and the overall adaptability of the system are improved, and high-precision and self-adaptive intelligent identification of forbidden zone intrusion behaviors in the complex industrial environment is also realized.

The invention relates to the technical field of financial services, and discloses a customer behavior pattern analysis method, system and device based on homomorphic encryption, and the technical scheme comprises the following steps: collecting behavior data of a target customer; encrypting the behavior data through a homomorphic encryption algorithm, and sending a private key to an application end; storing the encrypted behavior data in a security database; in response to the analysis call instruction, sending the encrypted behavior data from the secure database to an analysis server through a secure transmission channel; after receiving the encrypted behavior data, the analysis server performs data aggregation and behavior pattern analysis on the encrypted behavior data in an encrypted state to obtain an encrypted analysis result; the encrypted analysis result is sent to the application end and decrypted by the application end through the private key, the analysis result of the customer behavior is obtained, the privacy protection advantage is achieved, the safety performance is enhanced, and the expandability is improved.

This invention relates to the field of automatic control and discloses a method, device, computer equipment, and storage medium for preventing pinching on intelligent doors. The method includes: detecting the target anti-pinch mode currently triggered by the intelligent door, wherein the target anti-pinch mode includes either an independent anti-pinch mode or a cooperative anti-pinch mode. The independent anti-pinch mode is used to characterize protection performed independently by an electronic anti-pinch system, and the cooperative anti-pinch mode is used to characterize protection performed jointly by an electronic anti-pinch system and a physical anti-pinch system; analyzing the probability value of the risk of the intelligent door being pinched by at least one sensed target within a preset range based on the target anti-pinch mode to obtain a risk assessment result; and controlling the intelligent door to execute the protective action corresponding to the target anti-pinch mode based on the risk assessment result. This invention solves the problems of incomplete protection, conflicting responses, and inability to achieve seamless passage in existing intelligent door anti-pinch technologies.

This system collects large amounts of business data, preprocesses it, and then performs feature extraction to analyze employees' work styles and know-how. [Solution] A system comprising: data collection means for collecting a large amount of business data; data preprocessing means for preprocessing the business data; feature extraction means for extracting features from the preprocessed data; pattern analysis means for analyzing business patterns based on the features; model training means for training an AI model based on the analysis results; robot generation means for generating a virtual copy robot using the AI ​​model; accuracy evaluation means for evaluating the accuracy of the answers generated by the copy robot; result presentation means for presenting the evaluation results; and data deficiency identification means for indicating missing data based on the presentation results.

A data structure and associated methods for analysis of a continuous 1,197,000-letter unvocalized Hebrew string referred to as the Word of God (WOG). The data structure contains only the twenty-two classical Hebrew letters and their five final forms, with no spacing, punctuation, vowelization, or editorial symbols. Intrinsic placement of the final letters enables deterministic segmentation of the string into 305,490 lexical units and 23,206 verses without external conventions. Fixed letter-number assignments provide a numeric architecture for evaluating substrings, detecting alterations, identifying encoded mathematical correspondences, and performing pattern analysis. The system preserves full semantic range by supporting multiple morphologically valid interpretations of unvocalized Hebrew strings. Methods for segmentation, numeric evaluation, reconstruction, integrity verification, semantic analysis, and mathematical pattern detection are provided thereby providing a reproducible foundation for computational and linguistic research.

An object of a system according to an embodiment is to analyze a viewing history and evaluation data of a user and recommend optimal content.SOLUTION: A system includes a viewing history collection unit, a viewing pattern analysis unit, and a content recommendation unit. The viewing history collection unit collects a viewing history and evaluation data of a user. The viewing pattern analysis unit analyzes the data collected by the viewing history collection unit, and specifies the user's preference. The content recommendation unit recommends an optimum content based on the preference specified by the viewing pattern analysis unit.SELECTED DRAWING: Figure 1

The invention discloses a sales training examination management method and system based on multiple scenes, and the method comprises the steps: determining a plurality of insurance sales scenes, and each scene corresponds to a specific sales strategy, a customer type and a business demand; according to the method, the question type weight is adjusted according to the scene complexity, the high-complexity scene focuses on strategy verification, and the low-complexity scene strengthens case analysis, so that the evaluation rule and the scene demand are accurately matched; biological feature recognition and behavior pattern analysis are integrated, abnormal operation is detected in real time, scene switching is triggered, and it is ensured that the evaluation process is traceable and verifiable; performing Z-score standardization comparison on score distribution and a threshold value, combining variance analysis to position a significant short plate dimension, generating a three-dimensional visual map containing scene performance data, and visually presenting capability short plate distribution; based on the capability atlas, the priority short board is identified, accurate training is realized, and the training resource utilization rate is improved.

The invention discloses a method for constructing a geological disaster meteorological risk early warning model based on rainfall pattern analysis. The method comprises the following steps: collecting basic geographic data and basic geological data of a research area; obtaining susceptibility grades of all slope units in the research area and obtaining effective landslide sample data induced by rainfall in the research area; establishing a rainfall pattern recognition module, and matching different types of rainfall threshold models for different rainfall patterns; and coupling the slope unit-based landslide susceptible partition and the rainfall threshold through the meteorological risk early warning criterion matrix so as to determine the geological disaster meteorological risk early warning of each slope unit and output the early warning result of the research area. The precision degree of the geological disaster meteorological risk early warning model can be improved, and the method has remarkable benefits in the field of geological disaster prevention and reduction.

The application belongs to the technical field of big data processing, and discloses an intelligent automobile accessory warehouse management system and method based on the Internet of Things; the method comprises the following steps: collecting automobile accessory attribute data, marking the accessories and cleaning the data in sequence, and constructing an accessory basic database; collecting original sensing data and associating the accessory basic database, obtaining a multi-source storage data set, performing accessory partitioning, and generating optimal warehouse layout information; obtaining real-time position information of the accessories, combining the optimal warehouse layout information, and outputting an efficient work optimization path; collecting accessory warehouse entry and exit records in real time, performing state prediction, and outputting referenceable inventory information; performing quality detection on the accessory basic database based on the referenceable inventory information, and outputting accessory evaluation data; collecting behavior data of workers and performing behavior pattern analysis, and generating behavior evaluation data; constructing a warehouse strategy, and sending the warehouse strategy to a preset automobile accessory management system; and high-efficiency and high-precision warehouse management is realized.

The invention discloses a method and a system for judging an end point of a mutton unfreezing state based on machine vision, particularly relates to the technical field of image recognition, and is used for solving the problems of image feature distortion and poor judgment accuracy caused by optical interference caused by a meat surface ice-water mixed phase state in an unfreezing process in an existing method. An unfreezing process image is obtained and converted into a color space containing a brightness component, a specular reflection area and a non-specular reflection area are segmented based on the brightness component, and the specular reflection area image is converted into a topological network diagram to calculate connectivity evolution parameters. And performing texture direction field analysis on the non-specular reflection region to calculate texture direction consistency recovery degree features, further performing collaborative mode analysis on a time sequence of the two types of features to obtain a feature vector, and finally inputting the feature vector into a pre-trained classification model to judge an unfreezing stage. And judging that an unfreezing end point is reached when the stage accords with a preset end point.