64results about How to "Reduce false alarms" patented technology

The invention provides an adaptive CFAR detection method based on scene classification, and the method comprises the steps: carrying out the scene classification of an input SAR image through employing a K-means clustering algorithm, carrying out the target detection of a pure sea area obtained through classification through employing VI-CFAR, and carrying out the CFAR detection of a coastal area through employing a significance model. According to the method, the problems that a traditional CFAR algorithm is high in false alarm rate and unstable in detection performance in port and land complex backgrounds are effectively solved, detection parameters are optimized according to the complexity of different scene backgrounds, and therefore it is ensured that the CFAR algorithm can still maintain the constant false alarm rate in the sea clutter background, and the robustness of target detection is remarkably improved. The ocean area and the land area in the image are effectively distinguished, and the land area is shielded. According to the technical scheme, the problem that a traditional CFAR detection algorithm is interfered by a complex land background when detecting ships on the sea surface is solved.

The invention discloses a water mist resistant algorithm based on an independent photoelectric smoke sensing fire detection alarm, and relates to the technical field of fire detection, the algorithm comprises the following steps: a red and blue light double light source technology is adopted, the wavelength of infrared light is 940nm, the wavelength of blue light is 470nm, and the infrared light and the blue light are respectively transmitted through an infrared transmitting tube and a blue light transmitting tube. By adopting a red and blue light double-light-source technology, utilizing the characteristic that the wavelength of blue light is long and the scattering intensity of particles with small diameters is high and the detection capability of infrared light on particles with medium diameters, non-fire factors such as water mist and dust can be effectively distinguished, the false alarm rate caused by the water mist is remarkably reduced, and the fire hazard detection accuracy is improved. The alarm can more accurately judge the fire situation in a humid environment, false alarm is reduced, after water mist interference is judged, red and blue light continues sampling, the single-chip microcomputer keeps a wakeup state for continuous monitoring, alarm delay caused by water vapor generated at the high temperature of a fire can be effectively prevented, and the reliability of the alarm is improved.

The invention discloses a ground resistance detection method and system for an electrostatic management tester, and relates to the field of ground resistance detection, and the key points of the technical scheme are that Op collection nodes are arranged in parallel on a circuit, voltage data on each node are obtained through a data collection device, and preliminary processing is carried out to obtain an initial data set; an optimization detection algorithm is used for the initial data set, and voltage data are converted into comprehensive direct-current voltage and comprehensive alternating-current voltage; setting an advanced detection threshold value, judging the obtained comprehensive direct current voltage and the comprehensive alternating current voltage with the advanced detection threshold value, performing mode selection according to a judgment result, giving an alarm if the mode is abnormal, and switching to a resistance detection mode and performing voltage measurement if the mode is normal. The device is suitable for detecting grounding resistance by an electrostatic management tester, and high-quality detection is realized.

This invention provides a method and system for remote meter reading and water usage information management for smart water meters. The method mainly includes: receiving raw data frames containing information such as cumulative flow values ​​and collection timestamps, and performing multi-dimensional quality checks to generate collection status identifiers; dividing the water usage interval into valid collection intervals and pending confirmation intervals based on the identifiers; calculating the measured water usage in the valid intervals and extracting abnormal features to generate initial abnormal events; generating a completion sequence for the pending confirmation intervals based on boundary flow values, historical water usage characteristics, and regional balance constraints; determining a supplementary reading control strategy and issuing supplementary reading instructions based on the completion sequence and other information; performing secondary verification of the initial abnormal events based on the supplementary data, and outputting the final abnormal result and meter reading data. This method improves the accuracy of water usage anomaly detection and the reliability of meter reading data through data quality grading, interval hierarchical management, and closed-loop verification of abnormal events.

This utility model relates to a water leakage detection device, comprising a PCB board, two copper foil electrodes disposed on the PCB board, and an electronic control board electrically connected to the two copper foil electrodes. The device is characterized in that the two copper foil electrodes are arranged side-by-side with a gap between them, and the PCB board has a through-hole corresponding to the gap. The advantages of this utility model are that water vapor or small water droplets generated in high-temperature and high-humidity environments are less likely to form on the PCB board, reducing false alarms, improving work efficiency, and the overall structure is simple and easy to install and disassemble.

The application discloses a radar target stable tracking method under a shielding environment, and comprises the following steps: a northeast two-dimensional coordinate system is established, and a target ship state is mapped into a state vector; a Kalman filter is used for prediction, a residual error and an innovation covariance matrix are calculated in combination with actual observation, and an elliptical threshold is constructed; if the observation falls into the threshold, the state is updated; otherwise, the observation is judged as a mismatched observation, and short-time missed detection prediction is performed; a sliding time window is set, when the number of mismatched observations in the window reaches a threshold, shielding judgment is performed in combination with the geometric relationship among the predicted positions of the ship and the target and the boundary of a static obstacle; if the condition is met, a shielding maintaining mode is entered, and the target track is continuously estimated by adjusting filter parameters or a predicted track; when a plurality of continuous observation frames fall into the threshold and the geometric shielding is removed, normal tracking is recovered through smooth transition. The application can effectively solve the problem of tracking interruption caused by the shielding of a radar target, and improve the continuity and stability of tracking under a shielding environment.

The present disclosure provides an axle temperature early warning monitoring method and device, electronic equipment and storage medium, which can be applied to the field of rail transit technology. The axle temperature early warning monitoring method comprises: analyzing an axle temperature data set from a sensor to obtain temperature rise rate data; determining a time interval in which the first temperature rise rate changes to the second temperature rise rate according to the temperature rise rate data; in the case that the length of the time interval is less than or equal to a preset threshold, determining the axle temperature data corresponding to the time interval in the axle temperature data set as jump data; and performing early warning monitoring according to other axle temperature data in the axle temperature data set except the jump data.

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.

This application provides an apparatus and method for capturing multimode signals with fused communication and navigation, relating to the field of signal capture technology. The apparatus includes: a configurable differential correlation unit, whose configurable parameters include an enable signal and differential parameters; a configurable PMF unit, whose configurable parameters include an enable signal and correlation accumulation parameters; a configurable FFT unit, whose configurable parameters include an enable signal and the number of FFT points; a result generation unit, whose configurable parameters include a feature threshold; and, if its configurable parameters also include interpolation parameters, performing interpolation estimation on the frequency calculation result based on the interpolation parameters; and a scheduling control unit, used for parameter configuration and scheduling control of the configurable differential correlation unit, the configurable PMF unit, the configurable FFT unit, and the result generation unit. The implementation provided in this application can achieve different signal capture processes through parameter configuration in different application scenarios.

The invention relates to the technical field of vehicle control, and discloses a vehicle vibration monitoring method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining a motion signal of each sensing channel; calculating to obtain an acceleration vector modulus length and an acceleration modulus length change rate based on the first motion signal, and calculating to obtain an angular velocity vector modulus length and an angular velocity modulus length change rate based on the second motion signal; generating a first preliminary vibration metric value based on the acceleration vector modulus length and the acceleration modulus length change rate, and generating a second preliminary vibration metric value based on the angular velocity vector modulus length and the angular velocity modulus length change rate; performing time sequence integration on the first preliminary vibration metric value and the second preliminary vibration metric value in the historical acquisition period by adopting a sliding window mechanism to obtain a first smooth vibration metric value and a second smooth vibration metric value; and performing fusion processing on the first smooth vibration metric value and the second smooth vibration metric value to obtain a target vibration metric value, and determining whether an effective vibration event occurs based on the target vibration metric value.

The invention relates to the field of hydrogen storage device alarm, in particular to a leakage-proof alarm device for a hydrogen storage device, which comprises a base, a vertical beam and the like, a vertical beam is fixedly connected to the base. The suspension beam and corresponding parts on the suspension beam are controlled through the DD motor to actively avoid obstacles, collision of main parts of the alarm is reduced, the alarm is prevented from being damaged, the horn-shaped top cover and the fan blades are arranged, blown air is more concentrated on the upper portion of the top cover, and therefore the detection result of the gas sensitive sensor is more accurate, and the safety of the alarm is improved. The detected gas enters the hollow vertical beam through the gas guide pipe and is finally discharged into air from the gas hole I below the vertical beam, the discharged hydrogen is far away from the position of the gas sensitive sensor, the false alarm condition is reduced, the density of the hydrogen is smaller than that of the air, so that the discharged hydrogen quickly floats upwards and is fully mixed with the air in the floating-up process, and the detection accuracy is improved. And the hydrogen concentration is further diluted, so that threats caused by hydrogen explosion are reduced.

The embodiment of the invention provides a switch fan fault determination method and device, electronic equipment and a storage medium. The switch fan fault determination method comprises the steps that the fan rotating speed of a fan and temperature data corresponding to a plurality of functional components are obtained through a simple network management protocol; determining reference temperature data according to the correlation coefficient of the fan rotating speed and the temperature data; determining predicted temperature data according to the rotating speed of the fan; and determining switch fan fault information based on the reference temperature data and the predicted temperature data. According to the embodiment of the invention, fault false alarms can be reduced, and the timeliness and adaptability of fault determination are improved.

The invention discloses an anti-counterfeiting and anti-channel conflict monitoring method based on anti-counterfeiting graph codes and space-time sequence reconstruction, belongs to the technical field of anti-counterfeiting traceability and supply chain management, and can solve the problems that traditional static anti-counterfeiting is easy to copy and channel conflict monitoring is lagged. The method comprises the following steps: receiving a code scanning verification request initiated by a user terminal, and obtaining a mapping relation between unique identification information of a target product and product factory information; analyzing the unique identification information, and collecting spatio-temporal data corresponding to the code scanning verification request; constructing a spatio-temporal trajectory sequence to be detected; inputting the spatio-temporal trajectory sequence into a preset spatio-temporal sequence reconstruction model, and outputting a reconstruction sequence by performing feature compression and deconstruction on the spatio-temporal trajectory sequence; and calculating a difference metric value between the space-time trajectory sequence and the reconstruction sequence, and if the difference metric value exceeds a dynamic threshold value, judging that the product circulation is abnormal.

The invention relates to an output end detection system and method based on intelligent temperature cabinet control, and the system comprises a collection module which is connected to a load access point and is used for collecting circuit parameters and temperature parameters of the load access point; the data processing module is in signal connection with the acquisition module and is used for receiving the circuit parameters and the temperature parameters and analyzing the safety risk of the load connection point based on the circuit parameters and the temperature parameters; the driving module is connected to the load connection point and is used for controlling the on-off of the load connection point, and the driving module is in signal connection with the data processing module; and the data processing module controls the on-off of the load connection point through the driving module according to the safety risk of the load connection point. The method is applied to the system. The acquisition module and the driving module are connected to the load connection point, on-off can be controlled according to the condition of the load connection point, an effective monitoring and protection effect can be achieved, and the temperature cabinet control circuit is suitable for temperature cabinet equipment.

The invention belongs to the technical field of intelligent traffic detection and intelligent logistics supervision, and provides a track-level dynamic overrun detection method and system for a large transport vehicle, and the technical scheme is that the method comprises the steps: carrying out the cross-frame target association and tracking based on an output single-frame clustering result, and estimating the motion state of the vehicle based on Kalman filtering; transforming and aligning historical multi-frame point cloud clusters of the same target to a current frame coordinate system by using the motion state, and carrying out superposition and fusion to generate a track-level holographic point cloud cluster; geometric feature vectors are extracted from the obtained track-level holographic point cloud clusters, and vehicle types are recognized and obtained in combination with the geometric feature vectors and the trained classification model; and for the identified large transport vehicle target, carrying out adaptive slicing on the track-level holographic point cloud cluster of the large transport vehicle target along the height direction, fitting a vehicle body edge straight line in each slice to calculate the size, and combining the obtained weight data to complete dynamic overrun judgment according to a set standard. And high-precision and high-robustness over-limit detection of the large transport vehicle is realized.

The application relates to the technical field of hardware equipment quality tracing, and discloses a hardware full-life-cycle quality tracing method and system based on IoT, which comprises the following steps: acquiring multi-dimensional real-time data flow and arranging the same into a real-time running data set; extracting distribution characteristics based on a historical data archive to obtain a benchmark reference space; projecting the real-time data to calculate a deviation quantitative value, correcting environmental deviation when the deviation quantitative value exceeds a threshold value to obtain a deviation result; extracting degradation characteristics to match historical trajectories to locate a degradation process; performing time sequence analysis to predict residual life to obtain a failure time window; and generating and arranging maintenance intervention instructions into a scheduling instruction set. The method realizes hardware full-life-cycle quality tracing and early abnormality early warning, improves maintenance accuracy, and reduces fault loss.

This invention belongs to the field of railway intelligent fire protection and pipeline multi-source leakage detection technology, and discloses a comprehensive intelligent leak detection and maintenance system and method for railway fire protection pipeline networks. The invention adopts a three-level hardware architecture: a station-level perception layer, a station-section-level regional aggregation layer, and a group-company-level global control layer, with a supporting execution linkage layer. The perception layer deploys IEEE1588PTP sub-millisecond synchronous multimodal acquisition units to simultaneously acquire pressure, buried acoustic signatures, and inlet / outlet flow time-series signals. The edge end incorporates convolutional FastICA blind source separation, BO-LightGBM leak identification, multi-source topology adaptive fusion positioning, and flow steady-state dual-criteria modules, dynamically adjusting the positioning dimension weights, using dual-condition judgment to reduce false alarms, and is equipped with differentiated maintenance processes for track maintenance windows and train operations. This invention is adaptable to railway scenarios with strong interference and multi-branch pipeline networks, achieving a micro-leakage identification accuracy of ≥97.4%, a normal positioning error of 0.91m, and local operation compliance with railway network security standards at the edge end. It can be widely applied to online monitoring of buried fire protection pipeline networks in various railway stations.

The present application belongs to the technical field of radar signal processing, and particularly relates to an indoor fan interference distinguishing method based on millimeter wave radar signal autocorrelation. In order to overcome the problem of poor generalization performance of the current energy threshold method and convolutional neural network method in the field of indoor personnel detection, the present application provides an indoor head-shaking fan and human target distinguishing method based on autocorrelation function. The specific operation is to calculate the maximum correlation coefficient value of radar echo under different distance gates to determine the signal periodicity, and to distinguish human signals and fan signals by using the signal periodicity. The method can be applied to personnel detection methods in different indoor scenes, and can effectively improve the anti-interference ability of the radar system to the head-shaking fan.

The invention relates to a method for detecting a loosening fault of a hoop of a speed sensor of a passenger train, and relates to the method for detecting the loosening fault of the hoop of the speed sensor of the passenger train. The objective of the invention is to solve the problems of low accuracy, more false alarms and low generalization ability of speed sensor clamp loosening fault detection based on an original convolution detection network. The method comprises the following steps: acquiring railway carriage images to obtain a training set picture set; constructing a speed sensor clamp loosening fault detection network model; obtaining a trained speed sensor clamp loosening fault detection network model; performing speed sensor clamp loosening fault detection on the to-be-detected passenger train image based on the trained speed sensor clamp loosening fault detection network model, and if no fault exists, continuing to detect the to-be-detected passenger train image; and if a fault exists, an alarm needs to be given. The method is applied to the field of passenger train fault detection.

The application relates to the technical field of crack detection, and discloses a cement pouring surface crack detection method and system, which comprises the following steps: a high-frequency sound wave is emitted to a cement pouring surface structure by using an ultrasonic instrument; when the sound wave meets the cement pouring surface, a reflection is generated to form an echo; the echo is captured by a receiver; and whether the cement pouring surface has cracks and the depth of the cracks is identified according to the time difference of the echo. The application continuously captures crack echoes of a preset data detection amount, carries out peak amplitude sorting and difference value calculation on all captured echo segment signals, and further judges whether there is an abnormal echo fluctuation by comparing the fluctuation amplitudes, so that the double judgment mechanism can more effectively exclude invalid scattering and redundant reflection signals caused by rough cement pouring surfaces and other factors, and the risk of misjudgment or false crack alarm is greatly reduced.

The application discloses a hyperspectral image anomaly detection method, device and medium, relates to the technical field of hyperspectral image processing, and comprises the following steps: acquiring a hyperspectral image, performing preliminary detection on the hyperspectral image to obtain a preliminary detection result, replacing the pixel value of each target pixel point in the hyperspectral image and the pixel value of a neighborhood pixel point to obtain an initial background image, taking the initial background image as input, training an anti-similarity optimization coding network under the constraints of reconstruction loss and anti-similarity loss to obtain a trained model, and reconstructing the initial background image by using the trained model to obtain a reconstructed background image, calculating the difference between the hyperspectral image and the reconstructed background image to obtain a hyperspectral anomaly detection image. The application can significantly improve the accuracy and robustness of target detection, and better cope with detection requirements in various complex scenes.

This invention relates to a method for detecting the edge of weak moving infrared targets based on frame difference local contrast. The method identifies the main false alarm sources—high-brightness cloud edges, blind flash noise, and the inter-frame radiometric characteristics differences of the target. It enhances the target edge through three-frame temporal difference and an improved local contrast calculation method, removing false alarm sources such as flash noise, high-brightness background, and background edges. Then, based on the generated saliency map, threshold segmentation is used to obtain the target edge detection result, and finally, the original image is retrieved to obtain the final accurate target location. This invention addresses the difficulty in simultaneously meeting the requirements of detection rate and false alarm rate for weak infrared targets under complex dynamic backgrounds in practical engineering. Experimental results show that the proposed algorithm has high stability, effectively reducing the number of false alarms and improving target detectability while ensuring detection efficiency.

This invention relates to the field of industrial system condition monitoring and intelligent early warning technology, specifically disclosing a risk early warning method and system for the cooling circuit of a liquid-cooled charging cable. The method involves collecting and preprocessing multi-dimensional time-series data composed of liquid circuit, electrical, and environmental parameters. Based on a combination of K-means clustering and rule-based judgment, five operating conditions are identified: standby, low power, high-power fast charging, peak overcharging, and derating operation. A Kalman filter-ARX model or a TCN-Transformer fusion model is then applied according to the operating condition for time-series prediction. Based on the predicted residual sequence, four trend features are extracted: cumulative residual value, growth rate, multivariate consistency, and confidence interval deviation. The final risk score is obtained by dynamically weighting the data according to the operating condition and combining it with the confidence coefficient of the abnormal duration. Finally, early warning levels are classified, and graded charging power linkage control commands are output.

The application discloses an unmanned ship underwater target intelligent identification method based on an imaging sonar, and relates to the fields of unmanned operation platforms and target identification. The application is aimed at the requirements of unmanned ship navigation and tasks, considers the task characteristics of the unmanned ship, and specifically adopts a forward-looking sonar to perform target ranging positioning and accurate identification, and based on machine learning and, realizes thorough water area perception, target detection and accurate identification for underwater targets. The application can improve the underwater target identification performance of the unmanned ship.

This invention provides a method for identifying ground faults in distribution network lines based on a time-series large model, comprising: Step S1: data acquisition and spatiotemporal alignment; Step S2: multi-scale sliding window segmentation; Step S3: calculation of physical enhancement features; Step S4: construction of a training sample set; Step S5: construction of a time-series large model with physical information enhancement; Step S6: model training; Step S7: real-time acquisition and identification of ground faults. This invention achieves high-precision, low-false-alarm ground fault detection, accurately identifies fault types and faulty lines, and effectively solves the identification challenges of extreme scenarios such as intermittent arc grounding and high-resistance grounding.

This invention relates to the field of power communication technology, and particularly to a method, apparatus, and storage medium for fault detection in communication links. The method includes: extracting signal quality features and residual distortion based on a signal eye diagram; extracting distortion event parameters based on the residual distortion to construct distortion impact features; determining application-layer experience quality features based on webpage full load time and RTP packet loss rate, and generating comprehensive link degradation features by combining them with bit error rate metrics; collecting terminal information, constructing a link congestion retransmission factor, determining link status, and adjusting the comprehensive link degradation features according to the link status; generating a graded early warning index based on the corrected degradation features and distortion impact features, and executing early warnings and dynamically adjusting monitoring strategies accordingly. This invention effectively improves the sensitivity of communication link fault detection.

The application provides an implementation method, system and device of an integrated monitoring operation and maintenance platform and a storage medium. The method comprises the following steps: collecting state data and performance data of heterogeneous facilities; performing cleaning and standardization processing on the state data and the performance data to obtain standard operation data, and storing the standard operation data into a database; analyzing the standard operation data to find abnormalities and trigger unified alarms; generating operation and maintenance to-be-done tasks according to the abnormalities, and visually displaying the standard operation data and the abnormalities. The disclosure adopts a layered decoupling architecture of data collection and integration, analysis and alarm, visualization and operation and maintenance management, breaks the information barriers among the heterogeneous facilities, realizes unified cleaning and standardization storage of multi-protocol operation data, reduces false alarms caused by cascading faults of underlying devices by using alarm compression and suppression mechanisms, shortens the average fault discovery time and the average repair time, reduces the operation cost of team cross-system maintenance, and improves the management efficiency and the anti-risk ability of the information technology infrastructure.

The invention discloses a feeding machine bottom belt slipping detection method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a first signal; determining a working condition state according to the first signal; matching the detection strategy according to the working condition state to obtain a first strategy; and performing slip detection according to the first strategy. According to the method, the current working condition of the material transportation system is determined according to the obtained first signal, the corresponding detection strategy is matched according to the obtained working condition, and slip detection is performed on the material transportation system according to the obtained detection strategy, so that multi-device signals and the detection strategy can be combined; when a plurality of associated signals continuously appear at the same time according to a specific fault logic mode, an alarm is triggered, so that the occurrence of false alarms is greatly reduced.

The utility model provides a pipeline valve chamber monitoring, alarming and closing system, and relates to the technical field of pipelines, the system comprises a pressure acquisition module, a human-computer interface module, a plurality of calculation modules and a plurality of output modules; the pressure acquisition module is connected with a valve chamber in the pipeline and is used for acquiring the pressure of the valve chamber; the man-machine interface module is used for selecting a target alarm condition based on the number of the pressure acquisition modules; the plurality of calculation modules are connected with the pressure acquisition module and the human-computer interface module and are used for calculating the pressure drop rate of the valve chamber according to the pressure; determining whether the pressure drop rate meets a target alarm condition or not; and the output modules are connected with the calculation modules and used for outputting a valve closing instruction when the pressure drop rate meets the target alarm condition. Thus, on one hand, the user can select the target alarm condition, the user demand is met, and the user experience is improved; and on the other hand, the selected target alarm condition can reduce the occurrence of false alarm and false turn-off.

The utility model discloses an intelligent monitoring device for a fire hydrant, which relates to the technical field of fire-fighting equipment protection and comprises a push-type switch and a wireless transmission module used for transmitting on-off states of the push-type switch to a monitoring terminal. The pressing end of the pressing type switch abuts against a valve of the fire hydrant in a power-on state, and the fixed end of the pressing type switch is detachably connected to the fire hydrant through a connecting piece. The pressing end of the pressing type switch is kept to abut against the valve of the fire hydrant through the connecting piece, the pressing type switch is in an on state, when water is taken illegally, the connecting piece needs to be detached, at the moment, the pressing type switch loses retaining force and is bounced off to be in an off state, and the wireless transmission module transmits information that the pressing type switch is off to the monitoring terminal. Workers can know that the fire hydrant has illegal operation at present, monitoring of the fire hydrant is achieved, the monitoring effect is improved, and meanwhile the input cost is reduced.