4039 results about "Data detection" patented technology

The invention discloses an energy efficiency data detection processing method and system for a data center, and the method comprises the steps: deploying a sensor and a distributed collection network, and achieving the collection of energy efficiency data through the calculation of an energy efficiency change rate and the adjustment of a self-adaptive sampling frequency; dynamic energy efficiency prediction is realized by adopting adaptive time window adjustment, short-term and long-term error feedback correction, energy efficiency trend modeling and multi-level prediction fusion; an energy efficiency perception load evaluation index is introduced to realize energy efficiency maximization; an intelligent group decision model is constructed, and global allocation of computing resources is realized through dynamic balance scheduling optimization, task allocation stability analysis and adaptive convergence adjustment; based on an energy efficiency autonomous learning framework, initial strategy training, reinforcement learning optimization scheduling strategy, adaptive tuning and dynamic adjustment are carried out by utilizing imitation learning, and adaptive optimization is realized. According to the invention, under the condition that the business of the data center changes rapidly, the allocation of the computing resources can be adjusted accurately, rapidly and dynamically, and the optimal energy efficiency is achieved.

The embodiment of the invention provides a distributed server cluster log processing method and device, and the method comprises the steps: constructing a distributed server cluster log collection network, and dynamically distributing collection tasks through a load balancing scheduling center. An incremental data acquisition channel is designed, efficient transmission is realized by using a websocket long connection pool, and the transmission efficiency is optimized in combination with real-time compression coding and a repeated data detection mechanism. An intelligent log analysis model is constructed, the intelligent log analysis model comprises an anomaly detection sub-model, a mode recognition sub-model and an event association analysis sub-model, anomaly score calculation, log classification labeling and multi-dimensional association analysis are achieved based on a deep learning method, and visual display and data export functions are provided. According to the method, the defects of the traditional technology in the aspects of distributed acquisition, data transmission, intelligent analysis and the like are effectively overcome, and the performance and practicability of a log processing system are remarkably improved.

An enhanced multi-compartment connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with a plurality of sensors. A first subset of the sensors is disposed relative to a first monitored receptacle compartment and a second subset of the sensors is disposed relative to a second monitored receptacle compartment. The plurality of sensors monitor for a change in state of the storage receptacle. The wireless accessory sensor node detects the storage receptacle's state change based upon sensor data and broadcasts a signal with a flag set indicating there is event information available for upload. The bridge node detects the flag set, retrieves the event information available for upload from the wireless accessory sensor node, and transmits the retrieved event information to a backend server to cause the backend server to initiate a dispatched logistics operation related to the storage receptacle.

The invention relates to the technical field of anomaly detection, in particular to a measurement abnormal data detection method and system based on a rule engine, and the method comprises the following steps: extracting a transmission path and a timestamp sequence based on a source point number, a transmission link number and a real-time state code of a measurement data packet, comparing a data flow direction path with a preset abnormal flow direction, and determining a data flow direction; and identifying an abnormal mode of which the risk path fitting degree exceeds a preset threshold value, and obtaining an abnormal flow direction feature set. According to the method, abnormal point locations in the data can be effectively identified and corrected by monitoring the change of the submission times and the activeness of the real-time measurement data of the power grid and the data synchronism, and the dynamic abnormal threshold setting and real-time data analysis further enhance the capability of capturing the abnormal trend, can adjust the processing strategy in time, and improve the accuracy of data processing. The method has the advantages that accumulation and propagation of error data are avoided, data redundancy and error repetition are effectively reduced due to generation of a data calibration set and application of data fingerprints, and data quality is guaranteed.

A system for dynamically scaling microservices in cloud environments, including: a predictive scaling module that uses machine learning algorithms to analyze historical and real-time workload data and predict resource requirements; an auto-scaling decision engine configured to determine and execute dynamic scaling actions based on predictions and current system performance; a cloud orchestration module that deploys and manages microservice instances in cloud environments to ensure optimal resource allocation; an intelligent load balancer that dynamically redistributes incoming requests across microservice instances to avoid bottlenecks and maintain service reliability; and a real-time monitoring module configured to continuously collect system performance data, detect workload fluctuations, and provide feedback for adaptive scaling.

The invention provides a metro driver abnormal behavior recognition method based on multi-modal data fusion. The method comprises the steps that metro driver abnormal behaviors are recognized based on multi-modal data fusion, track positioning and platform screen door sensor data are fused to detect the position of a train, and a station entering confirmation instruction is generated; according to the data of the seat pressure distribution sensor, driver off-seat inspection is judged; generating an outbound completion event in combination with a track circuit signal, an ATO instruction and the like; analyzing console touch pressure, voice instructions, hand actions and the like to verify operation compliance, and triggering an alarm when the operation is abnormal; aggregating results at edge computing nodes, encrypting and transmitting the aggregated results to a central scheduling system, dynamically mapping driver behaviors in a digital twin cockpit, and automatically triggering hierarchical alarms and associating data snapshots when the driver behaviors are abnormal; according to the invention, accurate recognition and real-time early warning of subway driver behaviors can be realized by means of multi-modal data fusion and intelligent analysis, and the subway operation safety and the emergency response efficiency are greatly improved.

An enhanced node-based connected logistics receptacle system includes a storage receptacle, a bridge node, and wireless accessory sensor node with at least one sensor monitoring for a change in state of the storage receptacle. The system's wireless accessory sensor node detects the storage receptacle's state change based upon sensor data, records timestamped information reflecting the detected change, and broadcasts a signal with a flag set indicating there is event information available for upload. The system's bridge node is operative to detect the flag set, retrieve the event information available for upload from the wireless accessory sensor node, and transmit a message related to the retrieved event information to a backend server to cause the backend server to initiate the dispatched logistics operation related to the storage receptacle.

The invention discloses a dynamic optimization updating method of a credible data intelligent detection system, which relates to the technical field of credible data detection, and comprises the following steps: deploying heterogeneous feature acquisition nodes, acquiring data association features, operating behavior fingerprints and setting a dynamic credible evaluation threshold value; constructing a gradient aggregation channel based on differential privacy encryption, performing security fusion, generating a global model optimization vector, and generating an adversarial sample set; carrying out weight distribution, configuring a dynamic update step length, and generating an update instruction set; and activating the adversarial sample generation network, generating an incremental update package, triggering asynchronous hot deployment of the edge computing nodes, reconstructing a model topological structure of the federated incremental learning framework, and generating an update log. The technical problems of poor adaptability and low real-time performance in the aspects of data credibility evaluation, privacy protection and security defense in the prior art are solved, and the technical effects of improving the data credibility, enhancing the privacy protection capability and improving the attack defense capability are achieved.

An enhanced connected logistics receptacle system that selectively retrieves and reports event information to a backend server to initiate a dispatched logistics operation related to a delivery item. The system includes a storage receptacle for receiving the delivery item, a bridge node mounted to the storage receptacle, and a wireless accessory sensor node coupled to a plurality of sensors. The wireless accessory sensor node is operative to detect a change in state of the storage receptacle based upon sensor data, record timestamped information reflecting the detected change in state, and broadcast an updated advertising signal having a data available flag set within the updated advertising signal. The bridge node processor is operative to detect the data available flag, selectively retrieve event information from the wireless accessory sensor node, and transmit the retrieved event information to the backend server to cause the backend server to initiate the dispatched logistics operation.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention relates to the technical field of building construction monitoring, in particular to a construction site real-time monitoring system and method based on an unmanned aerial vehicle technology. The method specifically comprises the steps that an unmanned aerial vehicle carries a high-definition camera, an infrared thermal imager and a laser radar and collects multi-modal data of a construction site in real time; a deep learning algorithm is adopted to carry out multi-modal semantic segmentation, RGB images, infrared thermal imaging and laser point cloud projection data are fused, YOLOv8 target detection and time sequence behavior modeling technologies are combined, and abnormal risks are dynamically identified; by constructing a context-aware comprehensive risk scoring model, the risk level of a construction scene is quantified, and a graded early warning mechanism is triggered; and meanwhile, based on a three-dimensional modeling technology, tank vertex cloud data is analyzed by utilizing an LIO-SLAM algorithm, and the inclination degree of the structure is detected and is linked with emergency response. According to the invention, through multi-sensor fusion, dynamic risk assessment and closed-loop control, intelligentization and precision of construction monitoring are realized.

The invention relates to the technical field of remote monitoring, in particular to a wireless vehicle state monitoring system which comprises a state change evaluation module, a data transmission adjustment module, a power anomaly detection module, a health state evaluation module and a risk early warning optimization module. According to the method, the vehicle operation parameters are obtained, the change amplitude and the rate of the parameters are analyzed in multiple time windows, the change amplitude and the rate are compared with the preset threshold values, the transmission frequency is adjusted according to the stability of the state, important data are updated in time, and the abnormality of the power system is detected by analyzing the data transmitted in real time. By matching specific data of engine torque and gear shifting modes, prediction of potential faults of the vehicle is more accurate, data of main components of the vehicle are integrated to be compared with health standards, accurate evaluation of the overall health state of the vehicle is achieved, risk early warning can be given out in advance, driving safety is optimized, and the driving safety is improved. And the service life and the operation efficiency of the vehicle are improved.

The invention provides a network security multi-mode intelligent detection system and method, relates to the technical field of network security management, and is applied to a power distribution system which comprises a communication device and a power distribution device. The network security multi-modal intelligent detection system comprises a multi-modal data acquisition module used for acquiring network flow data, equipment characteristic data and behavior data of a power distribution system; the detection engine module is used for analyzing and detecting the network flow data, the equipment characteristic data and the behavior data to obtain a detection result; and the decision and response module is used for performing risk assessment according to the detection result based on a preset risk assessment model to obtain a risk grading result and a response strategy. According to the invention, comprehensive monitoring and deep analysis of the network security condition of the power distribution system are realized. By integrating multi-modal data, the limitation of a traditional single data detection mode is effectively overcome, and the accuracy and timeliness of threat detection are improved.

The invention relates to the field of data detection, in particular to a network traffic anomaly detection method based on aggregated mimicry distillation. According to the method, protocol level analysis is carried out on traffic through a multi-branch feature extraction network, and a unified representation vector is generated through a cross-layer fusion mechanism; calculating a first abnormal score based on a protocol perception weighted confrontation soft contrast mechanism; a heterogeneous teacher model is constructed and dynamically weighted and aggregated, and the student model generates a second abnormal score through knowledge distillation learning; forming a heterogeneous redundant detection pool by the student model, the teacher model and the rule detector, dynamically selecting the detector and applying adaptive disturbance to obtain a third abnormal score; and dynamically fusing the three types of abnormal scores to output a detection result. According to the method, the problems of protocol semantic segmentation, weak boundary sample discrimination, knowledge migration simplification and defense path predictability are effectively solved, and the detection accuracy, robustness and dynamic defense capability are remarkably improved.

The embodiment of the invention provides a detection device, electronic equipment and a control method thereof, relates to the technical field of data detection, and aims to solve the problem that equipment with a health sign detection function is large in size. The detection device comprises a first circuit board, a light-emitting device, a photoelectric converter, a metal cover plate and a light-transmitting structure. The photoelectric converter and the light-emitting device are arranged on the same side of the first circuit board and are electrically connected with the first circuit board. The metal cover plate covers the first circuit board and is electrically connected with the first circuit board. The metal cover plate is provided with a first light through hole for exposing the light-emitting device and a second light through hole for exposing the photoelectric converter. The light-transmitting structure is filled in the first light-transmitting hole, the second light-transmitting hole and the accommodating cavity, wraps the light-emitting device and the photoelectric converter, and is connected with the light-emitting device, the photoelectric converter, the first circuit board and the metal cover plate to form an integrated structural member, so that the size of the detection device is reduced.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention discloses a data intelligent association analysis and application method based on a knowledge graph, and the method comprises the steps: S1, collecting and preprocessing multi-source heterogeneous data, and obtaining the preprocessed text data; s2, extracting entities and relationships from the preprocessed text data, and constructing a preliminary knowledge graph; s3, storing the knowledge graph by adopting a graph database; s4, expanding and supplementing the knowledge graph through an external data source; s5, performing data association analysis based on the knowledge graph, and identifying potential modes and association relationships in the data; s6, abnormal data in the knowledge graph are detected, and potential abnormal modes are found and marked; and S7, applying results of data association analysis and abnormal data detection to an actual scene. According to the method, an efficient and scientific optimization scheme can be provided in data intelligent correlation analysis and application design, and remarkable technical values and economic benefits are brought to practical application.

A control system for controlling a second unmanned aerial vehicle that flies while holding a first cable connected to a first unmanned aerial vehicle that performs work and a second cable extending from a cable reeling machine, includes a sensor to sense a surrounding environment and output sensor data, and a controller configured or programmed to control operation of the unmanned aerial vehicle and, during flight of the second unmanned aerial vehicle, detect the first cable and the second cable based on the sensor data, and upon predicting that at least one of the first cable and the second cable will contact the ground or an obstacle on the ground, change a trajectory of the second unmanned aerial vehicle to avoid the contact.

The invention relates to the technical field of mechanical equipment state monitoring, in particular to a method and sensor for detecting temperature and vibration data of dynamic equipment, and the method comprises the steps: collecting a temperature signal and a vibration signal of the dynamic equipment, and carrying out the fusion processing, thereby obtaining a fusion feature set; establishing a feature distribution baseline based on a Gaussian mixture model, calculating a relative entropy of the fusion feature set and the feature distribution baseline, and generating a dynamic threshold sequence; constructing a detection model according to the fusion feature set and the dynamic threshold sequence, and outputting to obtain an abnormal mode label; performing time serialization processing on the abnormal mode label, predicting a fault probability in a future time period according to the abnormal mode label subjected to time serialization, and generating a fault prediction result; and performing priority ranking on the fault types in the fault prediction result, and generating a maintenance report according to a priority ranking result. The reliability and practicability of temperature vibration data detection of the dynamic equipment are comprehensively improved, and an efficient solution is provided for health management of the dynamic equipment.

Method and system for analyzing at least one computing system for supply chain vulnerabilities of at least one machine learning model include configuring machine learning model and optionally additional data; decomposing operations of the machine learning model's computational graph into smaller decomposed components; associating properties of each decomposed component with properties of the original operations and associating additional data; detecting the semantic similarity of decomposed component and previously encountered decomposed components; converting decomposed components into a standardized representation; calculating signature of decomposed components; evaluating whether portions of the machine learning model are similar to previously calculated signature; testing for supply chain and model vulnerabilities that exist based on previous signatures; identifying vulnerabilities that persist and correlating defenses; storing the generated signatures, identified vulnerabilities, and identified defenses; and generating report detailing the machine learning model's supply chain, vulnerabilities, and defenses.

Real-time monitoring and alerting of breached security credentials for server-based endpoints is described herein. In various embodiments, a software component (that intercepts outgoing network traffic from a computing device) or a server-based breach monitoring component may receive a request for endpoint data. Credential data may be included in the request. The endpoint and / or the credential data may be compared to credential data linked to a list of compromised endpoints. An alert may be generated for the client device when both a match is detected with credential data of a breach object on the list of compromised endpoints and the breach time field of the breach object is after a previous credentials change for the endpoint. Access to the endpoint may be blocked and / or a change password mechanism may also be displayed to change the user credentials prior to revisiting the endpoint.

The invention discloses an intelligent detection robot system for a valve hall assembly of a power converter station and a detection method. A robot body adopts an overhead six-axis series mechanical arm structure and is suspended in the center of the top of a valve hall; the intelligent path planning module performs optimization by combining a probability path diagram method with an improved A * bidirectional search algorithm, and ensures that the robot has efficient obstacle avoidance capability and an optimal moving track in a complex environment; the point cloud data detection and processing module adopts a multi-sensor fusion technology, combines an ICP algorithm to complete point cloud registration, and generates a three-dimensional model with labels. Compared with traditional manual detection, the system has the advantages that the detection efficiency and safety are improved, the labor cost is remarkably reduced, the development of intelligent maintenance means of electric power facilities is promoted, and technical guarantee is provided for stable operation of a converter station.

The invention discloses a multi-modal abnormal data detection and restoration method and system oriented to a power business scene. The method comprises the following steps: collecting multi-source heterogeneous power data, abstracting a power system into a weighted undirected graph, uniformly mapping the multi-source heterogeneous data into a graph signal, and preprocessing the collected data; extracting spatial features of nodes in a topological structure by adopting a graph convolutional network, and capturing a time dependency relationship in combination with a time sequence encoder; identifying various types of data abnormal points through an abnormal scoring function fusing the time sequence prediction error and the neighborhood consistency; a prediction-reconstruction combined repair strategy is adopted, time sequence prediction and neighborhood diffusion estimation are fused, and a preliminary repair value is generated; a lightweight parameter adapter is introduced, a scene feature vector is used as input, a repair weight and a regularization coefficient are dynamically generated, and a repair strategy is automatically adjusted; and performing physical consistency verification on a data result, wherein the physical consistency verification comprises power injection conservation constraint, voltage amplitude range constraint and time sequence continuity constraint.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on combination of electroencephalogram and millimeter wave radar, and aims to solve the problem that OSA judgment accuracy is insufficient due to complication. The method comprises the following steps: acquiring an electroencephalogram signal of a user based on a wireless EEG sensor, and acquiring a physiological signal of the user by using a millimeter wave radar; performing sleep staging analysis on the electroencephalogram signal, and detecting a micro-awakening event in combination with body movement data; the physiological signals are processed through a physiological signal processing model, physiological signal features of the user are extracted, and an apnea event, a hypopnea event, an arrhythmia event, a blood pressure fluctuation abnormal event and a periodic leg movement event of the user are monitored; calculating each OSA judgment index according to the event; and calculating OSA personalized scores according to the OSA judgment indexes, performing OSA severity grading, and generating an OSA severity grading report for assisting medical staff in diagnosis and treatment scheme formulation.

A system and method for improved endpoint detection and response (EDR) in a cloud computing environment initiates inspection based on data received from a sensor deployed on a workload. The method includes: configuring a resource, deployed in a cloud computing environment, to deploy thereon a sensor, the sensor configured to detect runtime data; detecting a potential cybersecurity threat on the resource based on detected runtime data received from the sensor; and initiating inspection of the resource for the potential cybersecurity threat.

The invention provides a data detection method and system, a detection device, computer equipment and a storage medium, and the method comprises the steps: obtaining a detection signal of each wafer surface in a planetary reaction cavity, and cutting the detection signal based on a trigger signal to obtain a first signal sequence and a second signal sequence; processing the first signal sequence by adopting a classification model to obtain a material type on each wafer; querying a corresponding optical model based on the material type; and performing iterative optimization on the optical model, the first signal sequence and the second signal sequence by adopting a decoupling algorithm to obtain a temperature detection value and a reflectivity detection value. According to the method, the original detection signal is segmented into the independent signal sequences based on the trigger signal, then the material type is determined according to the independent signal sequences, the corresponding optical model is selected according to the material type, and finally the temperature detection value and the reflectivity detection value with high accuracy are obtained through decoupling according to the optical model. Accurate analysis of a single wafer signal is realized, and process repeatability and stability are greatly improved.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: acquiring cable operation log data and cable internal structure design data, extracting a cable structure distribution condition in combination with an operation log, and evaluating cable operation state data; electric power sudden demand characteristics are identified, and then a non-uniform thermal coupling unbalance state is determined; based on the thermal coupling unbalance and the operation state data, detecting the coupling failure condition of the cable multilayer structure, and further judging the structural integrity damage condition; deducing a load stability disintegration situation according to a structure damage and insulation strength reduction trend; further detecting the decline of the quality of transmitted electric energy, analyzing the increase degree of abnormal voltage fluctuation, and identifying the fatigue trend of the metal material of the cable; through the above cable aging diagnosis, the cable aging diagnosis data is output. According to the cable aging diagnosis method and device, cable aging diagnosis is diagnosed, so that cable aging diagnosis is more accurate and efficient.

The present application relates to the technical field of data detection, and discloses an electricity theft behavior detection method, apparatus, and device, and a storage medium. The method comprises: obtaining historical electricity consumption data of an electrical terminal within a first detection period, and assigning a data label to the historical electricity consumption data; performing data preprocessing on missing data in the historical electricity consumption data to obtain preprocessed data; training and testing a preset model on the basis of the preprocessed data and the historical electricity consumption data having the data label to obtain an electricity theft behavior detection model, wherein the preset model is constructed based on OS_CNN technology and AutoXGB technology; and by means of the electricity theft behavior detection model, detecting whether the electrical terminal has an electricity theft behavior. According to the present application, the preset model constructed based on the OS_CNN technology and the AutoXGB technology is trained and tested to obtain the electricity theft behavior detection model, so as to quickly and accurately detect, by means of the electricity theft behavior detection model, whether the electrical terminal has the electricity theft behavior.

The invention provides a nuclear power safety report data extraction method and system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a nuclear power safety report document; carrying out OCR (Optical Character Recognition) and paragraph segmentation on the report document to obtain first text data; performing text semantic understanding on the first text data by adopting a dynamic template matching and semantic driving extraction mechanism to obtain a text feature vector; segmenting pages of the report document and identifying fonts and fonts to generate first format data; adopting a cross-page table reconstruction algorithm to detect table cells of the first format data and splicing a cross-page table to obtain format feature vectors; performing feature alignment on the text feature vector and the format feature vector, and inputting the text feature vector and the format feature vector into a large pre-training language model to obtain a semantic vector; and fusing the text feature vector, the format feature vector and the semantic vector to form a composite expression unit, and intelligently extracting structured data based on the composite expression unit. The semantic entity and structural relationship in the nuclear power report can be effectively identified.