109results about How to "Reduce false positives" patented technology

This invention relates to the field of electricity meter operation monitoring technology, specifically to an intelligent analysis system for electricity meter operation faults based on sensor monitoring. The system includes: an operation status acquisition module, which collects the voltage, current, active power, reactive power, and power factor of the electricity meter and obtains the operation status at each moment; a periodic analysis module, which uses active power as a load parameter and obtains a basic period based on the load parameters within a preset time period, then segments the preset time period to obtain different time periods and obtains the period fit at each moment; and a fault monitoring module, which obtains the comprehensive rationality of the load linkage deviation at each moment; and uses the comprehensive rationality of the load linkage deviation at a moment and the period fit to correct the squared Mahalanobis distance corresponding to the data points composed of voltage, current, reactive power, and power factor at that moment, and uses the corrected squared Mahalanobis distance for fault monitoring. This application can effectively monitor the operation faults of electricity meters.

This invention provides a method for detecting sensor anomalies in nuclear power plants. The method includes: constructing a principal component analysis model, calculating the Q-statistic, and determining a first threshold; training a Bayesian long short-term memory neural network model and calibrating its prediction interval; calculating a second and third threshold based on the predicted value and uncertainty output by the calibrated model; inputting test data into the two models to obtain the Q-statistic, predicted value, and uncertainty, and comparing them with the corresponding thresholds to obtain three judgment results; and making a final judgment on the three results using a logical judgment table. This invention integrates spatiotemporal characteristics and uncertainty assessment, achieving high-precision and high-reliability sensor anomaly detection with only normal operating data, exhibiting high reliability and robustness.

The application discloses a method for anti-electricity stealing based on abnormal connection of user single-phase electric meter, and relates to the technical field of electric power metering, which comprises the following steps: installing sampling units on the incoming line side and the outgoing line side of the user single-phase electric meter, synchronously collecting single-phase voltage, current, neutral line current and temperature and humidity signals of the meter box, and generating synchronous electrical data sequences through standardization and time stamp alignment. Single-phase voltage and current fundamental phase angles are calculated based on voltage and current instantaneous value sequences, and a phase angle difference map is constructed. The effective value of the neutral line current is calculated, and an abnormal flag sequence is generated in combination with a normal load reference range. The environmental temperature and humidity abnormal fluctuation period is extracted. The above-processed features are input into a connection mode recognition model to identify suspected electricity stealing connection mode categories, improve the accuracy of meter connection abnormality recognition, and reduce misjudgment. The method can effectively identify meter connection abnormalities and improve the accuracy of electricity stealing identification.

This invention discloses an AI-based data center operation and maintenance security system, belonging to the field of data center operation and maintenance security technology. It constructs an operation and maintenance intent fingerprint set, combines equipment topology relationships, power connection relationships, and airflow organization relationships to determine the disturbed closed domain, and establishes a counterfactual security twin model based on the disturbed closed domain and a reference equipment group. It performs mapping analysis on multi-source real-time data, extracts interpretable disturbance components, and separates residual abnormal sequences. It achieves anomaly propagation tracking through time-delay coupling relationships, determining the initial source point and propagation chain of the anomaly. Furthermore, it calculates the operation and maintenance intent deviation entropy based on path information entropy and the cross-domain coupling hysteresis gain coefficient based on the frequency domain coherence function, comprehensively evaluates each node within the disturbed closed domain, determines security vulnerability locations, and outputs corresponding handling instructions. This invention can effectively distinguish between operation and maintenance disturbances and abnormal disturbances, achieving accurate anomaly location and proactive intervention, thus improving the security and reliability of data center operation and maintenance.

This invention discloses a maritime rescue method based on improved YOLOv4 target detection, belonging to the field of maritime rescue. It includes: an improved pixel-by-pixel filtering image preprocessing method (de-raining, de-fogging) for processing images captured by a high-definition camera mounted on a drone, converting blurry images into clear images to provide effective data for subsequent image detection; an improved YOLOv4 target detection algorithm for accurately identifying people in the water; and the use of a Feature Pyramid Transformer (FPT) to enhance the semantic extraction capability of images of small floating objects (people awaiting rescue), thereby improving the search and rescue success rate. Adding a Feature Pyramid Transformer (FPT) after the traditional feature pyramid structure enhances the feature map of semantic information for small targets, which is beneficial for detecting small objects at sea.

This invention discloses a slurry sensing and adaptive control method for the homogenization process of lithium-ion batteries, relating to the field of lithium-ion battery production technology. The method includes the following steps: obtaining the electrolyte concentration matrix and slurry flow velocity field of the target object; performing differential fusion on the electrolyte concentration matrix and the slurry flow velocity field to generate a local coupling index; performing weighted autoregression calculation on the electrolyte concentration matrix, and correcting the weighted autoregression calculation result based on the local coupling index and the slurry flow velocity field to generate an electrolyte concentration prediction result. This scheme extracts the electrolyte element set at the most recent A time points along the time axis for each grid point and performs weighted autoregression calculation. In the time dimension, it can differentiate the weighting of data according to the importance of different time points, further performing flow field diffusion calculation on the slurry flow velocity field and concentration gradient field, and using the local coupling index to spatially correct the weighted autoregression result.

The application relates to the technical field of intelligent transportation and carbon governance, in particular to a passenger car carbon emission monitoring and regulation system and method based on multi-source spatio-temporal data. The application solves the problem of scattered and inconsistent data by preprocessing and uniformly mapping multi-source spatio-temporal data. A three-dimensional carbon emission calculation model of vehicle-space unit-time period is constructed to realize accurate calculation of single-trip carbon emission of a single vehicle and positioning of high-carbon emission hotspots. A clustering algorithm is used to complete differentiated division of user groups. A dynamic early warning threshold is generated based on a warning threshold prediction model, regional carbon emission targets and historical data to reduce false positives and false negatives. A warning type-policy tool-adaptation object mapping rule library is established, and a regulation strategy is generated by combining early warning signals and real-time data to realize automatic and accurate generation of regulation strategies from problem identification. The model parameter, early warning threshold and mapping rule are optimized by using policy implementation effect data to form a monitoring, early warning, regulation, optimization closed-loop management and realize accurate regulation.

The present application relates to the field of computer vision and industrial detection technology, and particularly relates to a dynamic collaborative rendering industrial surface defect detection method, system, device and medium, the method comprising the following steps: acquiring multi-view point cloud data and high-resolution color image sequences of a defect-free standard part; constructing a high-precision global dense point cloud under an absolute world coordinate system; generating a Mesh model and a 3DGS model under the world coordinate system; online collecting a two-dimensional image of a workpiece to be detected, performing two-dimensional anomaly preliminary screening based on the two-dimensional image, locking a suspected defect area, and extracting a two-dimensional bounding box of the suspected defect area; calculating a global defect probability, dynamically scheduling the Mesh model and / or the 3DGS model based on the global defect probability to render the image to be detected collected in the current frame, and outputting a hybrid rendering reference image; performing consistency checking on the hybrid rendering reference image, and obtaining a final detection result. The method reduces the calculation cost and improves the defect detection accuracy.

This invention belongs to the field of sound-generating device technology, specifically a directional sound-generating device and its manufacturing process. It includes a folding mechanism, a height adjustment mechanism located below the folding mechanism, a rotating mechanism located at the top of the folding mechanism, an angle adjustment mechanism located at the top of the rotating mechanism, and a mounting mechanism located above the angle adjustment mechanism. A directional sound-generating body is fixed above the angle adjustment mechanism via the mounting mechanism. The rotating mechanism includes a fixed base, with a second motor fixedly connected to the left side of the top of the fixed base. A first gear is fixedly connected to the output end of the second motor, and a second gear is meshed with the right side of the first gear. This invention achieves lateral adjustment of the sound-generating device by controlling the rotation of the first gear with the second motor, which in turn drives the second gear to rotate the rotating frame. Vertical adjustment of the sound-generating device is achieved by controlling the up-and-down movement of the mounting frame with a telescopic rod. This solves the problem of traditional sound-generating devices requiring manual adjustment to change the direction of the sound source output, which is labor-intensive.

The present application relates to the technical field of aerospace artificial intelligence, and provides a space debris collision risk probability prediction method based on deep learning, which takes ESA Kelvins real rendezvous data as a benchmark, and uses Kessler to generate a synthetic CDM sequence to expand the training sample to improve the coverage of high risk and complex situations; at the same time, a plurality of update records and their associated related events are expressed as a dynamic heterogeneous graph, a dynamic graph neural network is used to learn the law of risk evolution over time and the similarity between events can be optionally used for knowledge transfer, so that the continuous regression prediction result of the final collision risk probability of the rendezvous event can still be stably output under the condition of missing and noise, and the prediction is quickly refreshed when new CDM arrives through the local graph incremental update mechanism, which significantly improves the accuracy, robustness and online availability of risk prediction.

The application relates to the technical field of medical safety, in particular to a patient violence behavior detection method and system based on an intelligent bracelet, which comprises a data acquisition module, a behavior analysis module, an abnormality judgment module and a prewarning execution module. The physiological and motion signals of a patient are collected in real time through multidimensional sensors, a behavior characteristic matrix is generated, a matching degree index is calculated, a violence tendency is analyzed and judged in combination with a threshold interval and time continuity, and graded prewarning and multi-channel notification are realized. The application can significantly improve the recognition accuracy of complex behavior patterns, reduce misjudgment and false positives, ensure that medical staff can respond in time, improve the safety management efficiency, simplify the medical management process, and provide a reliable solution for the whole-process automatic detection and prewarning of patient violence behavior.

This invention relates to the field of boiler pipeline leakage detection technology, specifically to a boiler pipeline leakage detection method and system. The boiler pipeline leakage detection method includes the following steps: real-time acquisition and preprocessing of initial signal data from multiple sensors to generate standard signal data from multiple sensors; designing a separate dual encoder and a symmetric decoder based on the standard signal data from multiple sensors, and constructing a TCAE model; automatically iteratively optimizing the hyperparameters of the TCAE model using Bayesian optimization to obtain an Opt-TCAE model; confirming an adaptive leakage detection threshold; inputting the standard signal data from multiple sensors into the Opt-TCAE model, and calculating the reconstruction error of the standard signal data from multiple sensors in real time, wherein: if the reconstruction error is greater than the adaptive leakage detection threshold and the duration is ≥5 minutes, a leak is determined.

This invention belongs to the field of electrical measurement technology, specifically relating to a method for detecting the lifespan of AC charging piles based on dynamic resistance analysis. The method includes: simultaneously acquiring the contact terminal voltage, charging current, and real-time contact temperature of the AC contactor and performing standardized preprocessing; using physical parameters from each sampling period to obtain an evaluation model including the thermistor coefficient of the contact material, to eliminate thermal interference and determine the contact resistance; combining the change in contact resistance, charging current, and characteristic coefficients to obtain a lifespan degradation index characterizing the energy stress accumulation; determining the output current scaling factor based on the deviation between the lifespan degradation index and the lifespan safety protection threshold, and adjusting the duty cycle of the power switch signal accordingly. This invention solves the technical problems of existing methods being unable to decouple thermal drift and wear loss, and lacking active protection, achieving accurate identification and closed-loop safety management of contactor lifespan degradation.

The invention discloses an industrial control system anomaly detection method based on digital twinborn and reinforcement learning, and aims to solve the problems that in the prior art, abnormal samples are scarce, the false alarm rate is high under a dynamic working condition, and the generalization ability of a single detection model is insufficient. Generating synthetic data by simulating anomalies in the twinborn body, and mixing the synthetic data with normal data to form a training set after space-time and distribution alignment; based on the real-time data, generating a time sequence prediction residual error, a distribution reconstruction error and classification confidence multi-source evidence in parallel; constructing a reinforcement learning environment, constructing a state through multi-source evidence and the like, and guiding an agent to learn a fusion strategy through an asymmetric reward function; and training the intelligent agent to obtain a detection model and performing online deployment. According to the method, the multi-source evidence is adaptively fused through the reinforcement learning agent, so that the robustness, the accuracy and the adaptive capability of anomaly detection under the dynamic working condition are remarkably improved, and missing report and false report are effectively reduced.

The embodiment of the application provides a text labeling method, system and storage medium, comprising: obtaining a syntax rule set; dividing syntax rules in the syntax rule set into a first rule subset and a second rule subset; obtaining a Chinese text to be labeled; performing sentence division processing on the Chinese text to be labeled to obtain a sentence set; performing word segmentation processing on each initial sentence in the sentence set to obtain a corresponding word set; traversing the syntax rule set, when the word set corresponding to a sentence matches a first verification condition of any syntax rule, generating a candidate syntax set; performing structure verification based on a second verification condition of a syntax candidate to obtain a screening set; when the syntax candidates in the screening set belong to the same rule subset and there is a preset mutual exclusion relationship between the syntax candidates, performing decision verification based on a decision condition corresponding to the rule subset to which the syntax candidate belongs to generate an actual syntax set; and performing syntax labeling on the Chinese text to be labeled based on the actual syntax set.

The present disclosure relates to the technical field of fan monitoring, and particularly relates to a fan group abnormality monitoring method, system, device and storage medium. The method comprises the following steps: collecting running parameters of each fan in a fan group at a sampling time point; dividing the fan group into at least one reference group according to a division dimension; establishing a group statistical benchmark for the running parameters in a target reference group in the at least one reference group; performing horizontal comparison between the group statistical benchmark and the running parameters, establishing a historical benchmark for each fan, performing vertical comparison between the historical benchmark and the running parameters, and obtaining a comparison result; calculating a relative performance degradation index of a target fan based on the comparison result, and obtaining an abnormality monitoring result corresponding to the target fan when the relative performance degradation index is greater than or equal to a preset alarm threshold. The fan abnormality is detected in advance, the maintenance replaces the emergency repair, and the loss caused by unexpected shutdown is reduced.

The application relates to a ship trajectory anomaly detection method and system based on a deep learning model, which comprises the following steps: acquiring sailing trajectory sequence data and corresponding sailing state data; performing geometric feature extraction and motion feature extraction on the sailing trajectory sequence data to obtain a trajectory feature set; inputting the trajectory feature set into a deep learning model to generate spatiotemporal fusion features; based on the spatiotemporal fusion features, constructing a local context window and calculating a local consistency representation; obtaining a state feature vector based on the sailing state data; generating an anomaly detection result through an anomaly determination criterion; in summary, the application acquires sailing trajectory sequence data and sailing state data, extracts spatiotemporal fusion features by using a deep learning model, constructs a local context window to calculate a local consistency representation, and generates an anomaly detection result in combination with a state feature vector, so that the accuracy and interpretability of ship trajectory anomaly detection are effectively improved, and the effects of reducing false positives and false negatives are achieved.

The application relates to the technical field of electric digital data processing, and particularly discloses a product information risk assessment method and system based on a large model, which comprises the following steps: simultaneously scanning source code and products by a scanner to generate a source dependency list and a product component list, performing main name normalization and alignment through a large model, locking a warehouse and a version according to the source dependency list, analyzing original code by a parser to generate a cross-language path logic group, combining the product component list to correct a real package version, converging into a unified intermediate representation, performing reachability and joint analysis on the unified intermediate representation to obtain a risk candidate path set and complete path scoring, finally, according to a preset weight and a running period evidence, modifying the score, screening out a risk path, matching a disposal strategy, and generating executable strategy code by the large model for risk control of a gateway or a service side.

The utility model discloses a broken line alarm structure of high -speed stranding machine belongs to high -speed stranding machine technical field, and it includes frame, and the frame front end surface center place is provided with six first pay -off rack in ring arrangement, and six first pay -off rack front end surface center place all is equipped with pay -off broken line structure, and the pay -off broken line structure inside center place is equipped with broken line alarm structure, and six pay -off broken line structures include six second pay -off rack, and six second pay -off rack is arranged respectively in six first pay -off rack front end surface center place, and six second pay -off rack inside center place all is equipped with first threading groove, in addition, the utility model discloses, improves the accuracy of broken line detection through double protection mechanism, reduces the misreporting situation and can occur, and can stably and orderly pass through each component, better guarantee each strand line synchronization and stability when transmitting, thereby improve the quality of stranding, reduce the broken line failure appearance probability from the root cause that leads to the line confusion and tension uneven.

This invention discloses a cross-modal data processing system for the safe operation of hydrogen refueling stations, including a raw data acquisition and processing module, a full-variable safety scanning module, a physical relationship coupling diagnosis module, an adaptive operating condition clustering module, a question-answer pair construction module, a hydrogen refueling station time-series command data acquisition module, and a fault type identification module. By constructing a three-layer semantic enhancement logic and model adaptation strategy, this invention effectively solves the technical problems in the prior art, such as the lack of supervision signals in the raw data of hydrogen refueling stations, the difficulty in identifying hidden faults under complex operating conditions, and the difficulty in adapting heterogeneous feature space models.

The application provides a cerebral apoplexy postoperative sign monitoring system, which is fused with a nerve motor function evaluation wristband and a head-mounted brain electrical impedance dynamic imaging device, and breaks through the blind area of postoperative monitoring. The wristband device collects upper limb electromyographic signals and joint motion mechanics parameters through a microneedle electrode array and a distributed piezoresistive sensor, drives a neural network to output a standardized motor score based on time-frequency domain features containing electromyography, joint angle, trajectory smoothness and grip force change rate, and is related to a clinical Fugl-Meyer scale; the imaging device adopts a multi-ring electrode array and an improved D-bar algorithm to reconstruct a brain electrical conductivity distribution map, constructs a multi-level decision tree early warning model by extracting impedance change rate Delta Z, spatial gradient entropy SGE and hemisphere asymmetry Asym, and realizes brain edema detection. Through multi-modal data fusion and minute-level response, the system solves the fragmentation problem of postoperative monitoring and provides precise decision support for cerebral apoplexy rehabilitation.

This application relates to the field of intelligent monitoring and alarm technology, providing a remote monitoring method and system for archive storage rooms based on a 3D panoramic model. This solves the problems of inaccurate anomaly location, unintuitive alarms, and high false alarm rates in existing technologies. The method includes: creating a 3D panoramic model of the target archive storage room; acquiring multi-dimensional real-time data; inputting the multi-dimensional real-time data into a preset collaborative analysis unit to analyze environmental, security sensing, and access control data to obtain analysis results; generating a target anomaly event when the analysis results meet preset anomaly triggering conditions; dynamically binding the target anomaly event to the 3D panoramic model, locating and highlighting the archive equipment or storage area associated with the anomaly event, and triggering a remote alarm corresponding to the anomaly event type based on the binding result. The technical solution provided by this application achieves accurate full-process location and intelligent alarm for archive storage room anomalies.

This invention relates to the field of cable defect identification technology, solving the technical problems of low accuracy in cable defect identification caused by gradient diffusion and feature degradation in existing deep networks, which lead to loss of edge details and difficulty in effectively extracting true weak thermal deformation features. Specifically, it relates to a method for identifying surface thermal deformation defects in extruded insulated low-voltage fire-resistant cables. The method acquires a visual image of the cable surface in the extrusion cooling state and a one-dimensional temporal physical signal, preprocesses the surface visual image to obtain a standard image, and uses a weighted gradient edge detection algorithm to construct templates in multiple directions to perform edge detection on the standard image. This invention reduces the probability of network parameter combinations getting trapped in local optima during model training, lowers false positive and false negative rates, improves the classification accuracy of cable surface thermal deformation defects, and meets the high-precision and high-robust defect detection requirements of extruded insulated low-voltage fire-resistant cables.

The application relates to the technical field of radar electronic countermeasure, in particular to a radar deceptive jamming dragging success rate monitoring method and system; the method comprises the following steps: obtaining target observation track data recorded by a radar and real track data of the target; for any moment in the jamming attempt process, judging whether the jamming dragging at the moment is effective; for any moment, in response to the effective jamming dragging at the moment, confirming the moment as a jamming moment; for any jamming attempt, counting the duration that the distance deviation corresponding to the effective jamming moment in the operation is greater than the spatial error threshold as a continuous duration, and in response to the continuous duration being greater than a preset duration threshold, considering that the jamming attempt dragging is successful; counting the number of times of dragging success in a preset evaluation period, and taking the proportion of the number of times of dragging success to the total number of jamming attempts in the evaluation period as the dragging success rate. The application has the effect of improving the monitoring accuracy of the dragging success rate.

The application discloses a dengue fever prediction and early warning system based on heterogeneous data fusion and a graph neural network, and relates to the technical field of infectious disease prediction and early warning. The data acquisition module is used for acquiring case data, meteorological data, remote sensing vegetation index data, population flow data and social media sentiment data. The dengue fever prediction and early warning system based on heterogeneous data fusion and the graph neural network effectively improves the accuracy and precision of dengue fever epidemic prediction by integrating multi-source heterogeneous data and constructing a dynamic multi-dimensional graph structure for spatio-temporal coupling prediction. The use of a graph attention network and a time convolution network captures complex spatial dependence and temporal dynamics, and the use of a parallel model cross-verification and arbitration mechanism enhances the reliability of the prediction results, improves the sensitivity and stability of risk identification, and reduces false positives.

This utility model discloses a temperature measurement and monitoring device for the lower guide bearing of a hydro-generator, relating to the field of condition monitoring and fault diagnosis technology for large-scale hydropower equipment. It includes a bearing base, multiple bearing supports, a tray, a lower guide bearing, a distributed temperature sensing layer, and intelligent acquisition units. It also includes an external monitoring host and a reliable data transmission network module. The reliable data transmission network module is a highly interference-resistant communication link connecting each intelligent acquisition unit to the monitoring host. This design can significantly improve monitoring accuracy and coverage. By densely deploying monitoring points to capture localized overheating, it completely solves the problem of missed detections caused by sparse temperature measurement, truly reflecting the overall temperature field of the bearing and bearing. Furthermore, it reduces the number of fragile long analog signal lines, and the sensors and communication links designed to withstand harsh environments greatly reduce the risk of false alarms and missed detections caused by sensor failure and signal interference.

The application discloses an industrial internet high-risk software identification system and method, and particularly relates to the technical field of industrial internet security, and comprises an industrial software gene map automatic construction module, a business context-aware dynamic risk assessment module and a map-associated precise disposal strategy recommendation module; through multi-dimensional feature acquisition and fusion technology, a unique digital identity is generated for software assets, precise identification is realized; the real security risk of software assets is dynamically quantified by comprehensively considering vulnerability severity, business criticality and network exposure; based on asset relationship map and game analysis, the optimal disposal strategy with the least impact on business is generated and recommended for high-risk software. The application solves the problems of inaccurate industrial software identification, risk assessment out of business context and lack of intelligent decision support for disposal measures in the prior art, realizes automatic closed-loop management from identification, evaluation to disposal, and significantly improves the security protection capability of the industrial internet.

The application provides an emergency landing blocking method and device for a fixed-wing unmanned aerial vehicle, effectively solves the problem of poor blocking effect of the blocking technology of the fusion flexible blocking net and the electromagnetic brake, which leads to serious threat to landing safety. The method comprises the following steps: an intelligent cooperative control unit generates an emergency starting instruction to an executing mechanism according to a plurality of flight state signals of the fixed-wing unmanned aerial vehicle monitored by a state sensing module in real time; the executing mechanism executes a blocking strategy to block the fixed-wing unmanned aerial vehicle; a blocking signal of the executing mechanism in the blocking process and a landing signal of the fixed-wing unmanned aerial vehicle in the emergency landing process are acquired in real time, and the intelligent cooperative control unit generates an adjustment instruction in real time; the state data of the fixed-wing unmanned aerial vehicle after the executing mechanism executes the adjustment instruction are monitored in real time, it is determined that the emergency landing blocking is completed, and a reset operation is performed.

The application discloses an electrical automation equipment fault diagnosis method based on artificial intelligence, and particularly relates to the technical field of equipment fault diagnosis, and the method comprises the following steps: collecting multi-source electrical signals, suppressing noise through adaptive morphological filtering of a particle swarm optimization, performing bispectrum analysis on the filtered signals to extract energy peak values, frequency pairs and bispectrum entropy as preliminary features, obtaining a feature vector F1 through Relief-F screening and PCA dimension reduction; aligning F1 with equipment operating conditions, control instructions and static semantic information in time and space, constructing a sequence and adding sinusoidal position coding, obtaining a joint feature tensor F2 through a Transformer encoder to fuse multi-modal data; and constructing a fault mechanism knowledge graph containing fault phenomenon, reason and location entities, wherein the application can extract early weak fault features from strong noise, fuse multi-source information to reduce false positives, and assist operation and maintenance personnel to quickly and accurately repair through an interpretable reasoning process.