102 results about "Pattern detection" patented technology

The invention relates to the technical field of data analysis, in particular to a hazardous chemical substance transportation risk prediction method and system based on the Internet of Things, and the method comprises the steps: obtaining hazardous chemical substance type information of each cargo unit in a cargo compartment of a transportation vehicle, recognizing a coupling risk pair, and extracting a taboo reaction type and a coupling triggering condition parameter set; configuring a gas cross detection mode according to the partition position information of the related goods by the coupling risk, and detecting the characteristic gas of the hazardous chemical substance in the adjacent area; dynamically adjusting a sampling frequency and an alarm threshold value of a related partition sensor in combination with a threshold value parameter; coupling risk precursor triggering is judged, dynamic characteristics of a taboo reaction corresponding to the coupling risk precursor triggering are extracted, a time window needed by the taboo reaction to evolve from the current state to the danger degree is estimated according to the relation between the temperature and the reaction rate, and the time window serves as a safety intervention time limit to be output; and when the safety intervention time limit is lower than a preset threshold value, retrieving a corresponding blocking measure according to the coupling risk type, and issuing a control instruction to execute a blocking operation.

The invention discloses an elevator steel belt damage detection and quantitative analysis system based on eddy current and magnetic flux leakage dual-mode detection, and aims to solve the problems of single-mode information loss and serious industrial field strong noise interference in the existing steel belt detection. The system synchronously integrates an eddy current sensor, a magnetic flux leakage sensor and an encoder through a multi-probe array adapter; and multi-dimensional damage physical information in the steel strip is obtained. An improved wavelet packet transform-empirical mode decomposition (WPT-EMD) collaborative noise reduction algorithm is adopted, and in combination with a sub-band energy entropy and a self-attention mechanism, non-stationary mechanical noise is effectively filtered out. A multi-rule feature extraction engine is used for extracting smooth residual errors, derivative mutation and other features, a support vector machine (DE-SVM) model introducing physical priori knowledge weights is constructed, and the small sample recognition problem is solved in combination with a virtual sample generation technology. The method can realize high-precision positioning and quantitative evaluation of steel strip damage under complex working conditions, and has the characteristics of strong anti-interference capability, high identification accuracy and good generalization performance.

A system, method, and device for predicting a risk of wildfire are provided. The system includes a satellite imaging device for collecting data covering a target area and a processing server including an indices module for processing the data to generate key vegetation indices, a map generation module configured to generate weather and static maps, an analysis module configured to analyze historical wildfire data to identify past fire locations in the target area, an integration module configured to integrate the received, generated, and analyzed data to obtain a comprehensive dataset for the target area, and a risk prediction module configured to analyze the comprehensive dataset using a risk prediction model trained to predict the risk of wildfire for the targeted area, the risk prediction model including a machine-learning-based pattern detection model for receiving the comprehensive dataset as an input and generate prediction data describing a predicted risk as an output.

The invention relates to a power transmission and transformation equipment fault detection method and equipment based on photoelectric sensing, and relates to the related technical field of power transmission and transformation equipment detection, and the method comprises the steps: carrying out the topological correlation analysis based on the structure distribution data and attribute characteristic data of a power transmission and transformation equipment set; key node identification is carried out on the power transmission and transformation equipment topology network, and photoelectric sensing modules are deployed in sequence; building multiple equipment fault mode detection channels, carrying out fault switching detection based on an MOSFET switching circuit, and collecting N equipment mode detection feature sets at the same time; and performing global fault analysis on the N equipment mode detection feature sets, and outputting a power transmission and transformation equipment fault detection result. The technical problem that in the prior art, fault detection means are isolated, the real-time performance is poor, coverage is not comprehensive, and consequently a fault source is difficult to locate accurately is solved, and the technical effects that intelligent fault diagnosis of power transmission and transformation equipment relevance is achieved, and the fault detection efficiency and the fault locating precision are improved are achieved.

The invention relates to a multi-source partial discharge fault spectrogram detection and separation method and device and a medium. The method comprises the steps that a multi-source partial discharge fault spectrogram is collected and preprocessed; inputting the preprocessed multi-source partial discharge fault spectrogram into a pre-trained detection separation model to obtain a separated partial discharge fault spectrogram; wherein the detection separation model is pre-trained based on a multi-source partial discharge fault spectrogram data set, the multi-source partial discharge fault spectrogram data set is subjected to sample expansion through a Wasserstein generative adversarial network with gradient penalty, and the detection separation model uses a VoVNet network as a backbone feature extraction network. By generating a multi-scale feature map corresponding to different sizes of fault features in multi-source partial discharge, the detection and separation model effectively detects different types and sizes of fault features in multi-source partial discharge, and a multi-source partial discharge fault spectrogram is separated. Compared with the prior art, the characterization and detection capability of the model on the partial discharge characteristics of the small target is remarkably enhanced.

Aspects of the present disclosure illustrate embodiments of systems and methods for deidentification of unstructured data using semi-structured elements. A system for deidentification of unstructured data using semi-structured elements includes at least a processor, and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the processor to implement method for deidentification of unstructured data using semi-structured elements. The method includes receiving a plurality of case data, inputting the plurality of case data into a pattern detection machine-learning model, receiving, from the pattern detection machine-learning model, a template structure corresponding to a set of case data from the plurality of case data, inputting the template structure and case data into a data deidentification module, and receiving a deidentified set of case data.

An apparatus for data processing and related non-transitory computer-readable medium are provided. In the method, the apparatus computes a burst threshold for a data stream. The burst threshold is associated with a throughput of the data stream. The apparatus further identifies a set of bursts in the data stream based on the burst threshold, and detect a pattern in the data stream based on the set of bursts. The apparatus further estimates at least one subsequent burst in the data stream based on the pattern and the burst threshold, and outputs an indication of the at least one subsequent burst in the data stream. The method enables a device to predict one or more future data bursts based on characteristics of existing data. The predicted data bursts allow the transmission resource and power to be adaptively arranged to significantly reduce power consumption and improve transmission efficiency.

Methods, systems, and devices supporting techniques for pre-fetching information using pattern detection are described. Some memory systems may support pre-fetching information, such as logical-to-physical (L2P) mapping tables, data, or both, if a sequential pattern of read commands is detected. In some examples, the memory system may store a list of logical addresses indicated by received read commands and may determine whether the list corresponds to a sequential pattern independent of intervening write-alike commands. The list may store previous logical addresses for read commands, allowing the memory system to determine whether subsequent read commands form a sequential pattern. Additionally or alternatively, the memory system may track a ratio of hibernate commands to other commands (e.g., sequential read commands) and may refrain from pre-fetching L2P mapping tables for a detected sequence if the tracked ratio satisfies (e.g., exceeds) a threshold ratio.

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 automated, analytics-driven invention that generates, validates, and orchestrates unified RBAC postures spanning cloud infrastructure, SaaS applications, and on-premise assets. The method ingests entitlement data, HRIS attributes, behavioral data, and security policy constraints to identify common access patterns, detect anomalies, and recommend consolidated roles that embody the principle of least privilege. A dual-perspective algorithm—bottom-up clustering of shared connections and top-down evaluation of organizational context—produces high-fidelity roles annotated with confidence scores. The system surfaces actionable insights such as over-entitlement, under-entitlement, segregation-of-duties conflicts, and duplicate connections, then prescribes remediation playbooks or just-in-time enforcement. By continuously comparing the current state to a dynamically computed ideal state, the invention guides enterprises toward an access model that minimizes risk, streamlines audits, and reduces administrative drag.

An attack detection device (120) includes an anomalous pattern detection unit (123) to detect whether an anomalous pattern is included in a time-series recognition score that is time-series data generated using a plurality of recognition scores that are calculated respectively using a plurality of pieces of image data captured of a range within an image-capture range at mutually different time points within an image-capture time range, and indicate results of detecting objects respectively in the plurality of pieces of image data. The anomalous pattern is a pattern that occurs when an adversarial sample patch attack has been conducted against at least one of the plurality of pieces of image data.

A system and method for learning a predictive function that can automatically learn different operating modes for a multi-modal system and predict the number of operating states for a multi-modal system and additionally the detailed structure for each state. Once learned, the predictive function (model) can be used to determine a mode of a new sample (an asset). Based on the determined components that maximize a log likelihood function, a mode of the new sample is detected into the model via dependency graphs. One aspect includes enforcing a lower bound for the number of sample points to form an operational mode for an asset. While a mode relates to sample points which maximizes like log-likelihood, an ability is provided to remove artifact modes due to noisy data by considering a sufficient sample data condition and maximizing log-likelihood. Domain knowledge can be incorporated into the model via dependency graphs.

The application discloses a Chinese continuous speech tone change automatic detection method and system, and belongs to the technical field of speech signal processing.The method comprises the following steps: a speech forced alignment step is used to acquire syllable time boundaries; a fundamental frequency transition trajectory extraction step is used to extract a fundamental frequency feature vector in an adjacent syllable connection area; a tone change rule matching step is used to acquire an expected mode from a knowledge base containing necessary and variable rules; a tone change mode detection step is used to calculate a matching score and determine whether the tone change is correct, missing or excessive; and a feedback generation step is used to generate a pitch curve labeling graph and rule explanation.The application realizes accurate tone change detection by focusing on the fundamental frequency transition features of the connection area, provides reasonable evaluation by using a hierarchical rule knowledge base, and helps learners to improve pronunciation by visual feedback.

The application discloses a magnetic tape storage adaptive interleaving method and system based on dynamic error mode detection, relates to the technical field of magnetic tape interleaving, and comprises the following steps: continuously collecting error information in a magnetic tape reading and writing process and forming a structured report; judging the received structured report according to a preset rule, dynamically calculating new interleaving parameters, and outputting a decision package containing a new interleaving depth factor and a new available track list; checking whether the parameters in the received decision package are within a preset range, and if yes, sending an instruction to a magnetic tape drive through a bottom driver interface, setting new interleaving configuration for the magnetic tape, and obtaining a success status code returned by the magnetic tape drive; and the interleaving method significantly improves the reliability and error correction efficiency of the magnetic tape storage system in a complex error environment, reduces redundancy, and improves the effective utilization rate of storage space.

The invention relates to the technical field of computers, and particularly provides an intelligent compensation method and device for a distributed system interface, and the method comprises the following steps: S1, a business party carries out the persistent storage according to an interface configuration code after compensating a call interface configuration page, configuring call parameters and receiving the configuration parameters; s2, the service system initiates a request through the proxy interface, transmits an interface configuration code value, obtains interface configuration parameters according to a code after receiving a call request of the service system, and then calls a configured interface URL (Uniform Resource Locator); s3, receiving a result returned by the calling interface, and entering fault mode detection and compensation based on the returned result; s4, based on the fault modes classified in the step S3, corresponding compensation strategies are executed for different modes; and S5, if the compensation strategy is executed completely and the compensation calling still fails, carrying out manual intervention. Compared with the prior art, the system compatibility can be adaptively improved, the misjudgment rate is reduced through accurate fault recognition, and resource waste is reduced through a differentiation strategy.

The application provides a station mark detection method and related equipment. First, the local part of the station mark is detected, and a candidate combination frame is determined according to a suspected frame pair in the result that meets a screening condition. The suspected frame pair includes a station mark pattern detection frame and a station mark character detection frame. The candidate combination frame is a circumscribed rectangle of the station mark pattern detection frame and the station mark character detection frame. The serialized features of the candidate combination frame are extracted and classified to determine whether the candidate combination frame is a station mark combination. Finally, the sub-frame and the parent frame in the station mark are output. The station mark pattern detection frame and the station mark character detection frame are the sub-frames, and the candidate combination frame is the parent frame. By using the topological relationship (correlation relationship) between the local structure of the station mark, the detection of the local part (sub-frame) and the whole (parent frame) of the station mark can be realized end to end, the situation of missing detection of the local part of the station mark is reduced, and the efficiency and accuracy of station mark detection and identification are improved.

The invention discloses a graph detection method and system, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining a to-be-detected layout which comprises a to-be-detected graph; acquiring a simulation exposure pattern corresponding to the pattern to be detected; obtaining the area ratio of the simulation exposure pattern to the corresponding pattern to be detected, and taking the area ratio as a first area ratio; whether the first area ratio is larger than a first threshold value or not is judged, if yes, it is judged that the to-be-detected graph corresponding to the first area ratio meets the requirement, and if not, it is judged that the to-be-detected graph corresponding to the first area ratio does not meet the requirement. According to the embodiment of the invention, whether the to-be-detected graph meets the preset requirement or not is judged through the area ratio, the actual difference condition between the simulation exposure graph and the to-be-detected graph can be well reflected, and correspondingly, the difference degree between the simulation exposure graph and the to-be-detected graph can be more accurately reflected, so that the accuracy of a detection result is improved, and the detection efficiency is improved. The pattern detection method is correspondingly improved, and the performance of the semiconductor structure is improved.

The invention provides a medical data credible circulation method and system based on multi-level authority dynamic management, and the method comprises the steps: constructing a multi-mode matching index, and carrying out the feature extraction and vectorization of medical text data, structured data and image data; establishing a three-layer index architecture and an intelligent routing layer; constructing a user-permission-data relation graph by applying a graph algorithm, evaluating a permission use mode in real time, and detecting an abnormal behavior; the abnormal judgment threshold is dynamically adjusted in combination with situation factors such as medical emergency degree, abnormal behaviors are subjected to grading processing, and evidence obtaining information is automatically collected. The invention further comprises a data classification method based on security classification and timeliness and a fine-grained permission control mechanism using the minimum permission principle, and dynamic permission granting and recycling are supported. By balancing security and availability, secure sharing and effective utilization of medical data are realized, and diversified data access requirements are met.

An on-board system and method for leveraging multi-modal means of detecting runway incursions to increase aircraft safety, and to provide a necessary enabler for both reduced crew operations and autonomous flights in the future. The system and method detect incursions using visual perception for all types of vehicles and objects that may incur, and an ADS-B for self-reporting vehicles. The system and method fuse prior known information (such as ownship pose and map data) with detections from sensors and other sources. The system actively tracks detections and computes the current and predicted future states to decide if there is an on-going incursion on the runway. The system reports the runway state to the pilots and / or autonomous receivers.

The system according to this embodiment aims to detect early signs of dementia and report them quickly to medical professionals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a pattern detection unit, and a reporting unit. The collection unit collects the subject's health status or conversation patterns and medical information. The analysis unit analyzes the data collected by the collection unit. The pattern detection unit captures characteristic patterns or changes of dementia based on the analysis results obtained by the analysis unit. The reporting unit reports to a medical professional based on the patterns or changes captured by the pattern detection unit.

The invention relates to the technical field of banknote plate making automatic detection, and discloses a banknote gravure plate pattern detection method and system, and the method comprises the steps: placing a to-be-detected gravure plate in a three-dimensional detection device, and judging whether a detection template is matched with the pattern morphology of the to-be-detected gravure plate; if the pattern morphology of the detection template is matched with that of the gravure plate to be detected, deviation of the gravure plate to be detected is automatically corrected; selecting a detection position on the single-open template image of the gravure plate to be detected, determining a detection task, copying the detection task to the same pattern position of the whole plate, and starting the detection task; the method comprises the following steps: automatically planning a detection path based on a detection point, carrying out automatic detection according to the detection path, and carrying out automatic focusing detection on a gravure plate pattern to be detected by utilizing a multi-focal plane superposition technology to obtain a detection result. Measurement errors caused by deviation of the gravure plate are avoided, automatic detection replaces manual point-by-point detection, and the overall detection efficiency is improved.

Disclosed herein a method for agile simulation of a stochastic computing image processing where the input operands are processed with the aid of a correlation-controlled contingency table (CT) construct without using actual stochastic bit-streams. The disclosed method utilizes contingency tables to perform (i) template matching, (ii) image compositing, and (iii) pattern detection. Results show that the proposed approach achieves similar computation accuracy to the traditional stochastic computing simulation while performing runtime- and memory-efficient computations.

The invention relates to the technical field of data processing, in particular to a bank outlet consumable abnormal use root cause diagnosis method and system and a computer readable storage medium. The method comprises the following steps: acquiring data by automatically capturing data of an acquisition system and manually inputting the data; processing the data in parallel based on four agents of service coupling degree analysis, time sequence mode detection, peer efficiency benchmarking and inventory strategy compliance verification, and respectively generating a service interpretation contribution rate, an abnormal mode label, an efficiency level and an inventory compliance label; performing standardized mapping and causal probabilistic reasoning on the structured evidence output by the intelligent agent through a Bayesian network, calculating the posterior probability of each root cause hypothesis, and screening competitive hypotheses; and generating a structured diagnosis report containing the most probable root cause, the competitive hypothesis, the key evidence chain and the action guidance according to the posterior probability. According to the method, multi-dimensional root cause diagnosis of abnormal use of the bank outlet consumables can be realized, and the management precision and interpretability are improved.

The invention provides a Bluetooth signal mode detection method and device and a storage medium, and belongs to the technical field of Bluetooth communication. The detection method comprises the following steps: acquiring a data packet of a to-be-detected Bluetooth signal; determining a signal bandwidth of the to-be-detected Bluetooth signal according to the data packet, and determining mode information of the to-be-detected Bluetooth signal according to a first bandwidth when detecting that the signal bandwidth is the first bandwidth; when it is detected that the signal bandwidth is the second bandwidth, decoding the data packet to obtain a decoded signal, and performing cyclic redundancy check on the decoded signal; when it is detected that the cyclic redundancy check is not passed, performing lead code detection on the decoded signal to obtain mode information of the to-be-detected Bluetooth signal; the first bandwidth is higher than the second bandwidth; when it is detected that the cyclic redundancy check passes, performing modulation detection on the decoded signal to obtain mode information of the to-be-detected Bluetooth signal; according to the detection method, the calculation complexity can be reduced while the detection precision of the Bluetooth signal mode is ensured.

The application relates to an artificial intelligence-based child growth and development evaluation method, device, equipment and medium. The method comprises the following steps: acquiring standardized growth and development data of a child and performing asynchronous time sequence alignment processing to generate a time sequence alignment characteristic vector sequence; inputting the time sequence alignment characteristic vector sequence into a cross-scale attention fusion model to perform feature fusion and generate a comprehensive feature representation; combining a pre-constructed hierarchical time sequence mode detection network to calculate a comprehensive anomaly index of the child; constructing an event-driven dynamic state space model based on the comprehensive anomaly index and generating a personalized growth corridor; comparing real-time monitoring data and a personalized growth corridor boundary, and generating a personalized growth and development evaluation report based on a comparison result. The method can effectively solve the technical defects of poor fusion effect of high-frequency data and low-frequency data in a traditional method, difficulty in capturing early weak development abnormalities and lack of personalized evaluation standards.

A system for detecting and mitigating application security threats comprises a processor associated with a server. The processor executes a static security model to analyze a group of development code sets for an application to detect threat objects. The processor determines that a number of threat objects in each development code set is lower than a threshold. The processor further executes a run-time security model to analyze the combined code sets to determine a set of threat object measurements of application product releases for the application. The processor generates a set of threat change ratios per application based on a first threat object measurement and the threat object measurements after the first threat object measurement. The processor determines threat change patterns of the threat change ratios associated with the application product releases for the application. The processor determines whether to trigger a development security model for the application.

The invention discloses an oil temperature abnormal mode detection method and system based on concept drift, a terminal and a medium, and belongs to the field of AMT gearbox oil temperature detection. The method comprises the following steps: acquiring and standardizing gearbox state parameters; dimensionality reduction is carried out through principal component analysis, oil temperature operation modes are divided based on principal components, and a mode label sequence is generated; directly taking the sequence as a mode transfer sequence S; counting transfer times among all pattern pairs in the sequence S, and constructing a pattern transfer time matrix P; a dynamic abnormality determination threshold [gamma] is set on the basis of the matrix P, and pattern transitions in which the number of transitions is less than or equal to [gamma] are determined as anomalies. According to the method, by discretizing the operation mode and modeling the transfer rule of the operation mode, concept drift in system operation can be self-adapted, timely and accurate recognition of abnormal mode transfer is achieved, and the robustness of oil temperature anomaly detection and the early warning reliability are effectively improved.

Software attacks directed to compromising an insecurely-stored credential on a device may be detected and mitigated by detection of each of an initial stage and a subsequent stage through identifiers within an activity log of the device. Detection of an initial identifier indicating a possible attempt to locate an insecurely-stored credential indicates an occurrence of the initial stage. Detection, within a search window commencing upon detection of the initial stage, of a subsequent identifier indicating a possible attempt to determine context for the insecurely-stored credential indicates an occurrence of the subsequent stage. Responsive to detection of the initial stage and the subsequent stage, a response target associated with the detected identifiers is identified, and a security response associated with the response target is performed to thereby mitigate exploitation of the insecurely-stored credential.

The invention relates to a method for detecting a fixed pattern in a dynamic image and a system for executing fixed pattern detection. The invention discloses a method and system for detecting fixed patterns in a dynamic image, and the method comprises the steps: firstly obtaining a continuous dynamic image, and then obtaining a frame image with multi-level resolution from the dynamic image, so as to detect a plurality of fixed patterns in the frame image with the multi-level resolution according to the features of a front frame image and a rear frame image; after a first fixed pattern with a higher resolution is converted into a fixed pattern with a lower resolution, a second fixed pattern is merged with the same lower resolution, and a merged fixed pattern is obtained. And then the combined fixed pattern under the lower resolution is subjected to erosion treatment and then converted into a pattern with the higher resolution, boundary detection is carried out, the first fixed pattern with the higher resolution can be subjected to erosion treatment according to a boundary detection result, and a clear fixed pattern is obtained.

A system, method, and device for predicting a risk of wildfire are provided. The system includes a satellite imaging device for collecting data covering a target area and a processing server including an indices module for processing the data to generate key vegetation indices, a map generation module configured to generate weather and static maps, an analysis module configured to analyze historical wildfire data to identify past fire locations in the target area, an integration module configured to integrate the received, generated, and analyzed data to obtain a comprehensive dataset for the target area, and a risk prediction module configured to analyze the comprehensive dataset using a risk prediction model trained to predict the risk of wildfire for the targeted area, the risk prediction model including a machine-learning-based pattern detection model for receiving the comprehensive dataset as an input and generate prediction data describing a predicted risk as an output.