6163 results about "Data detection" patented technology

The invention relates to the technical field of battery management, and discloses a state monitoring and predicting method of an intelligent battery pack and related equipment. The method comprises the steps of obtaining original battery pack data and historical operation data of various preset battery pack parameters in a target intelligent battery pack, and performing signal filtering and spectral decomposition on the original battery pack data; abnormal data detection, abnormal correction, multi-mode characteristic decomposition and phase space mapping of the battery pack state are carried out on the initial battery pack characteristic data; extracting corresponding state-of-charge dynamic characteristics in the state evolution characteristics of the battery pack, and performing multilayer mapping and threshold classification on the state evolution characteristics based on historical operation data to obtain a state monitoring result; and obtaining a state transition matrix through state parameter coupling mapping and transition probability calculation, performing state evolution path calculation, and outputting a prediction result. According to the invention, correlation analysis among electrochemical parameters is realized, and reliable evaluation and accurate prediction of the state of the battery pack are enhanced.

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.

Computer-implemented methods and systems for training an adversarial neural network to simulate a stealthy blind false data injection attack on a cyber physical system are provided. Supervised learning is used to generate an initial attack vector, by an adversarial attack generation model, based on inferred grid topology and historical measurements. A final attack vector is generated, by an adversarial verification model, based on a filtered subset of the initial attack vector utilizing a substitute bad data detection threshold, wherein the final attack vector enables creation of a counter measure.

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.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention discloses a text keyword desensitization method, system and equipment based on a large model and a medium, belongs to the technical field of large model and natural language processing, and aims to solve the technical problem of how to automatically identify and desensitize sensitive information, improve the accuracy of sensitive information identification and further improve the text processing efficiency. According to the technical scheme, the method comprises the steps of data collection; preprocessing the data; marking sensitive information; segmenting the data; selecting a basic model; finely adjusting the model; training the fine-tuned large language model, and obtaining and storing the trained fine-tuned large language model; preprocessing the to-be-processed text data; detecting sensitive information, formulating a desensitization strategy and implementing desensitization processing, performing desensitization verification on the text after desensitization processing, and optimizing and improving the fine-tuned large language model according to a verification result; and packaging the optimized and improved fine-tuned large language model into an API (Application Program Interface) service for an application program to call.

The invention relates to the field of building information management and construction progress visualization, in particular to a curtain wall construction progress visualization method and system based on a BIM model, and the method comprises the steps: obtaining the three-dimensional position information and installation state data of a curtain wall unit plate in real time through a sensor and an image collection device, and comparing the three-dimensional position information and installation state data with BIM integrated data, generating and updating a digital twinborn model; analyzing the construction data by using an artificial intelligence model, detecting a construction deviation and generating a construction adjustment suggestion; an optimal path is calculated through a construction path optimization algorithm, construction data are integrated to generate visual feedback, and the construction progress, the installation state and deviation information are displayed in real time through a visual engine, so that the construction monitoring and management efficiency is remarkably improved.

The invention relates to the technical field of point cloud data modeling, and discloses a point cloud data processing and modeling method, system and device for power distribution equipment and a storage medium, and the method comprises the steps: collecting the point cloud data of the power distribution equipment, and constructing a topological relation graph according to the topological relation of the power distribution equipment; semantic segmentation is performed on the point cloud data based on the topological relation graph, a component association model is constructed, and missing data detection is performed in combination with the topological relation graph; and generating a high-precision three-dimensional model based on the component correlation model. According to the method, semantic segmentation is guided through the topological relation graph, the accuracy of point cloud data classification is improved, and the problem of misclassification in a traditional method is avoided. Meanwhile, a part correlation model is generated based on the segmentation result, the geometric and functional connection relation between the devices is comprehensively described, and deep fusion of the device connection information and the three-dimensional geometric model is achieved.

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 an information management method and system for an access control card chip. The method comprises the following steps: providing an anonymous identifier, initializing an authentication process, and laying a foundation for privacy protection and a data link; mutual trust authentication of the access control card and the equipment is completed, and authentication challenge data is provided for follow-up behavior recording and tracing; based on a result and challenge data generated after authentication is completed, generating a complete encryption behavior record and establishing a chained hash structure; storing the encrypted behavior record and the chained hash value locally or synchronizing the encrypted behavior record and the chained hash value to a server to ensure the reliability and traceability of the behavior record; and detecting abnormal behaviors and supporting traceability verification by using complete behavior chain data. Dynamic identity generation, encryption behavior chain storage and a bidirectional authentication mechanism are integrated in the access control card chip, so that the privacy protection capability, the safety and the tracing efficiency of the access control system are remarkably improved.

The invention provides a photovoltaic power station modeling simulation analysis system, and relates to the field of power station modeling simulation. Comprising the steps that a parameter acquisition module collects key data such as voltage, current, temperature and illumination intensity in real time through a sensor and detects the integrity of the data in combination with historical data; the modeling module adopts a segmented modeling mode according to the collected data and the physical topology of the power station, and the transient processes of different stages are respectively ensured, so that the simulation precision is ensured; the simulation module inputs different disturbance parameters based on the model, simulates load and power grid fluctuation through a disturbance generator, analyzes transient response of the system and corrects the model in real time; the multi-scene generation unit is used for automatically generating a simulation scene in an extreme environment by using historical weather and power grid data, and testing the stability of a power station; and the evaluation module generates an evaluation report by analyzing the simulation result, identifies equipment faults, proposes specific suggestions for optimizing operation, and ensures the performance and stability of the power station under complex operation conditions.

The invention discloses an intelligent operation system for aquaculture management, which relates to the field of aquaculture and comprises a water quality detection module for detecting water quality data in an aquaculture unit, a meteorological detection module for detecting meteorological data of the position of the aquaculture unit, and an ultrasonic detection module for detecting quantity data of fishes in the aquaculture unit, the image detection module detects weight data and growth stages of fish in the breeding unit, and the analysis control module analyzes standard water quality and standard feed feeding amount in the breeding unit through an AI analysis model according to meteorological data, quantity data, weight data and growth stages; the analysis control module compares the difference between the water quality data at the current moment and the standard water quality, controls the oxygenation equipment to oxygenate and controls the water treatment equipment to change water; according to the standard feed feeding amount, a control signal is sent to control feeding system equipment to feed. According to the invention, powerful support is provided for modernized development of aquaculture, and more efficient, real-time and intelligent aquaculture management is realized.

The invention discloses a photovoltaic system abnormal data detection method and device, and relates to an abnormal detection technology based on a multi-head self-attention mechanism fusion variational auto-encoder and a bidirectional long short-term memory network (BiLSTM). Abnormal detection of electrical parameters (including DC voltage and current and AC voltage and current), environment parameters (relative humidity, wind direction and wind speed) and system parameters (including inverter error codes, inverter environment temperature difference, module environment temperature difference and the like) in a photovoltaic system is realized. The invention discloses a photovoltaic system abnormal data detection method. The method comprises the following steps: (1) photovoltaic system data acquisition and data processing; (2) encoding time sequence data of the photovoltaic system; (3) data sampling and data feature enhancement; (4) data decoding operation; and (5) detecting an abnormal value of the photovoltaic system.

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.

The invention discloses an online public opinion research, judgment, analysis, classification and grading early warning method and system, and the method comprises the steps: constructing a malicious public opinion data detection and judgment model, and carrying out the iterative training of a pre-training neural network through large-scale online public opinion historical data, so as to achieve the fine adjustment and optimization of the model; after malicious public opinion data are detected, fine-grained classification of malicious public opinions is realized through deep semantic comprehension and multi-dimensional text structure analysis by adopting BERT and ERNIE deep learning models and combining semantic role labeling and dependency syntactic analysis technologies; a malicious public opinion data grading and distinguishing model is constructed, deep correlation between public opinion events is captured through multi-layer convolution operation, and the recognition capacity for public opinion sensitivity and urgency degree is improved. According to the invention, malicious public opinion data related to a specific industry field can be efficiently and accurately identified and classified, and the malicious public opinion data is subjected to severity grading, so that the accuracy and the real-time performance of network public opinion management are improved.

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 provides a PSI5-based data detection method and system, and the method comprises the steps: receiving a data frame transmitted by a sensor through a peripheral sensor interface PSI5 of each ECU, and enabling the format of the data frame to be defined through a PSI5 protocol; and each ECU inputs the data passing through the cyclic redundancy check into the data frame anomaly prediction model, identifies normal and abnormal data modes, and sends the data frame identified as the normal mode to a main control unit in the automobile according to the time slot allocated by the dynamic time slot. Through the method and the corresponding system, the precision, the efficiency and the safety of data collection and processing in the automobile can be effectively improved.

The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance management system and method based on artificial intelligence, and the system comprises an operation baseline modeling module, a resource consumption prediction module, an automatic resource allocation module, a data correlation analysis module, an abnormal data detection module and a fault path analysis module. According to the method, accurate data support is provided for resource management through real-time monitoring of operation data and baseline performance modeling, resource demands are analyzed and predicted in combination with a time sequence, shortage and excess in resource configuration are identified, automatic allocation of resources is achieved, optimal matching between resource supply and demands is ensured, and the optimal resource allocation efficiency is improved through construction of a dependency graph. The interaction between the data stream and the application is analyzed, abnormal data detection and fault path analysis are helped, fault problems are found in real time, reasons are identified, the downtime of the system is shortened, the continuity of services and the integrity of data are ensured, and the automation degree and the intelligent level of operation and maintenance management are improved.

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 invention discloses a valve fault data detection method and system, and relates to the technical field of industrial automation control and fault diagnose.The method comprises the steps that historical data of operation of a valve are obtained, the collected historical data are analyzed through a deep learning method, and time sequence features are extracted; a fault scoring model is established according to the extracted time sequence features, the fault of the valve is warned in advance, and meanwhile the state of the valve is monitored in real time through the latest real-time data flow; and the prediction result of the fault scoring model is verified through the real-time data flow, and the valve abnormity scoring standard is dynamically adjusted according to the real-time data and the output of the fault scoring model. According to the method, the fault prediction model is constructed by collecting the time sequence data of the valve rod displacement and combining the deep learning technology, the valve operation state can be monitored in real time, abnormal features can be accurately recognized, and delay and errors of manual detection are avoided.

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.