59 results about "Signal statistics" patented technology

The invention discloses an intelligent article tracking and identification method and system based on an RFID technology, and the method comprises the steps: collecting an environment noise intensity value, and carrying out the statistics of the number of tags in an effective identification range through a tag response signal; inputting the environmental noise intensity value and the tag number into a power regulation and control model, outputting reader-writer transmitting power, and generating a reader-writer transmitting power instruction; counting a collision time slot number, a successful time slot number and an idle time slot number of the label response signal in a preset time window; dynamically adjusting a frame time slot number Q value according to a proportional relation among the collision time slot number, the success time slot number and the free time slot number; and extracting the EPC code of the successfully identified tag, and deleting the predefined fixed bit field data in the EPC code. According to the method, an environment-data-communication three-in-one optimization system is constructed, the dynamic adaptability, the data transmission efficiency and the data reliability are improved, and the recognition success rate in a high-noise scene is improved.

The invention discloses a flow feature confusion method based on an AI virtual human, and the method comprises the steps: S1, configuring the basic attributes of the virtual human and a preset behavior strategy, and automatically obtaining the fingerprints of Internet access equipment of a user; s2, virtual human environment fingerprint camouflage is carried out by configuring a containerized operation environment and adjusting browser fingerprints; s3, dynamically generating behavior parameters for determining a virtual human online task based on LLM drive of preset parameters; and S4, the virtual human calls a local browser driver on line based on the behavior parameters obtained in the S3, simulates human to access the target site and generates feature traffic. According to the method, the virtual human Agent is driven by the AI, the mixed flow is generated by autonomously acting on the network, the user identification and tracking technology of the network access target based on the flow signal characteristics is effectively interfered, the signal statistics characteristics of the user internet surfing flow can be effectively mixed, and the method is suitable for the network privacy protection requirements in various scenes.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention relates to the technical field of fluorescence scanning, and discloses a fluorescence in-situ hybridization signal processing system, method and equipment and a storage medium. The system comprises an image acquisition subsystem and a signal processing subsystem, wherein the signal processing subsystem comprises a preprocessing module, a signal processing module and a signal statistics module; the image acquisition subsystem is used for scanning the fluorescence in-situ hybridization sample and acquiring a sample image; the preprocessing module is used for preprocessing the sample image to obtain a preprocessed sample image; the signal processing module is used for performing signal processing on the preprocessed sample image by using the optimized target detection model, the optimized segmentation model and the optimized classification model to obtain signal processing data; and the signal statistics module is used for evaluating the signal processing data according to a preset threshold value to obtain a fluorescence in-situ hybridization evaluation result. According to the invention, efficient automation of the whole project of fluorescence in situ hybridization is effectively realized, large-scale data analysis is supported, and the detection efficiency is improved.

Technology is disclosed for an optical receiver. The optical receiver may include an optical and digital signal processing (ODSP) device. The ODSP device may include a photodiode operable to convert an optical signal to an electrical signal; a digital signal processor operable to generate a digital signal based on the electrical signal; a quantization component operable to generate a quantized output signal based on the digital signal; and a processing device. The processing device may be operable to measure one or more signal statistics of the digital signal; and identify one or more quantization thresholds, wherein the one or more quantization thresholds are computed based on the one or more signal statistics.

The present application relates to the technical field of energy storage battery, in particular to a forward-looking energy storage frequency modulation power distribution method and system; the method has a double-time scale structure of minute-level strategy adjustment + second-level real-time control. On the minute-level time scale, an AGC signal statistical feature extraction and prediction framework is constructed to predict the AGC signal characteristics and dynamically adjust and optimize the weight; on the second-level time scale, multi-objective optimization is performed every 2 seconds to distribute the charging and discharging power among the battery units, minimizing the AGC tracking error, battery degradation and SOC deviation. The scheme can significantly reduce the battery degradation cost while maintaining good frequency modulation performance and SOC consistency; by capturing the time statistical characteristics of the AGC signal, the potential impact of the AGC signal on the battery degradation is quantified, that is, the influence of the change trend of the AGC signal on the battery energy storage system is considered, providing forward-looking support for the control strategy of power distribution.

A display overdrive control method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring overdrive control reference parameters of multiple display screen partitions, backlight control information, and pixel signal statistical values of multiple dummy partitions for image frames to be displayed (201); according to the overdrive control reference parameters, the backlight control information and the pixel signal statistical values, predicting pixel signal amplitudes when said image frames are displayed (202); determining overdrive coefficients on the basis of mapping relationships between the pixel signal amplitudes and reference overdrive coefficients, and the pixel signal amplitudes (203); on the basis of the overdrive control reference parameters, the pixel signal amplitudes and the overdrive coefficients, predicting partition heat accumulation values (204); performing heat control correction on the overdrive coefficients according to the partition heat accumulation values and the overdrive control reference parameters, so as to obtain target overdrive coefficients (205); and performing overdrive control over a display device by means of the target overdrive coefficients (206). The overdrive duration can be increased without changing the hardware structure of a display device, thereby improving the overdrive effect for the display device.

The embodiment of the present disclosure provides a loudspeaker audio classification method under a plurality of heterogeneous devices, and belongs to the technical field of data identification, and specifically comprises the following steps: step 1, calling a built-in acceleration sensor of a mobile phone to collect an initial vibration signal played by a same on-board loudspeaker; step 2, performing a pretreatment operation on the initial vibration signal; step 3, removing interference signals in the pretreated initial vibration signal to obtain a target vibration signal; step 4, screening signal statistical features highly related to audio content and independent of devices; and step 5, classifying the audio content of the target vibration signal by using an adversarial neural network according to the signal statistical features. Through the scheme of the present disclosure, the efficiency and accuracy of speech recognition are improved.

The present invention provides a method and device for detecting Secure Shell (SSH) traffic in a tunnel with complex bearers. The method includes: Step 1, collecting tunnel mixed traffic and extracting three features of the packet length, arrival time interval, and direction of a specified number of packets in each flow; Step 2, constructing packets with the same direction and consecutive ones into the same burst and building a burst feature sequence; Step 3, mapping each packet in the burst to a frequency domain signal to obtain a mixed frequency domain signal of the burst traffic in the frequency domain representation; Step 4, obtaining the statistical features of the mixed frequency domain signal; Step 5, inputting the statistical features of the mixed frequency domain signal into an encoder-decoder architecture based on a recurrent neural network to obtain a prediction label at the burst level and a prediction label at the flow level, and determining whether there is SSH traffic in the mixed traffic and locating the burst where it is located. This method effectively solves the problem of SSH traffic detection under complex bearers in the tunnel and significantly improves the detection accuracy.

The present invention discloses an OSAHS detection method, system, device and medium. The method includes: obtaining original blood oxygen signal data, performing data preprocessing on the original blood oxygen signal data to obtain blood oxygen signal slice data; using a one-dimensional neural network classifier to analyze the blood oxygen signal slice data to obtain a first-stage classification result; using a classifier based on rule matching to analyze the blood oxygen signal slice data of a target segment to obtain a second-stage classification result. The present invention can achieve the simplicity of signal acquisition and effectively reduce the detection cost only based on the blood oxygen signal. At the same time, the first-stage classification is realized through a learning-based neural network to ensure the generalization of the classification result, and then the second-stage classification is realized by combining the scores of blood oxygen signal statistical indicators to achieve classification refinement and deviation correction, and obtain an accurate classification result. The present invention can achieve accurate and low-cost OSAHS detection and can be widely applied to the field of data detection technology.

The invention discloses a gas-liquid two-phase spiral flow pattern identification method and system based on pressure signal statistical characteristics, and belongs to the technical field of multiphase flow. The method comprises the following steps: denoising a pressure signal through a sliding window method, calculating a mean value and a standard deviation in a window, setting a dynamic threshold value, and replacing an abnormal value with a median; generating a differential pressure signal to eliminate baseline drift; the method comprises the following steps: adaptively selecting a bandwidth based on a Silverman criterion, drawing a kernel density estimation (KDE) curve by adopting an Epanechnikov kernel function, and extracting morphological characteristics; in combination with four time domain parameters of standard deviation, skewness coefficient, variation coefficient and kurtosis, abnormal values are eliminated through a box plot, and a threshold interval is determined; and meanwhile, a flow pattern database is constructed, and flow pattern features are enhanced by using an image sharpening and slice splicing technology. And finally, realizing high-precision flow pattern identification through a KDE curve form and time domain parameter combined criterion. The method has the advantages of being high in signal stability, high in feature identification degree and good in identification robustness, and is suitable for automatic detection and control of complex gas-liquid two-phase spiral flow.

The application discloses a valve tester verification method and system of reverse resistance type IGCT driving function, and relates to the technical field of direct current transmission.The valve control host detects the driving up-sent IGCT forward voltage signal, and the delay input / exit test mode is put into operation.The valve tester is divided into multiple rounds of statistical stages and trigger stages, and the forward voltage signal statistics are realized, so that multiple test function verifications are realized.The valve tester verification method and system of reverse resistance type IGCT driving function are aimed at the characteristics that the IGCT driving energy taking time is long and cannot be automatically turned off, realize the effect that the valve tester identifies test items and matches control time sequences under the condition that the valve tester does not directly communicate with the valve control host or the IGCT driving, and satisfies the test requirements of comprehensive testing of the reverse resistance type IGCT driving performance.

The invention discloses a petroleum drilling big data processing method and system, and belongs to the field of data mining, and the method comprises the following steps: carrying out the wavelet transformation of a petroleum drilling original signal, separating a high-frequency signal from a low-frequency signal, extracting a characteristic frequency band energy ratio, and judging whether noise interference detection exists or not; synchronously analyzing the time sequence / frequency domain correlation between the sensor signal and the environmental parameter, and judging an abnormal source based on a preset threshold value; training a random forest / LSTM model based on historical data, inputting signal statistics and environmental parameters, and outputting an anomaly type; compared with the prior art, the method has the beneficial effects that whether data abnormity is caused by environmental parameters or not is judged by analyzing the time sequence / frequency domain correlation between sensor signals and the environmental parameters, a calibration instruction or shutdown check is triggered when the data abnormity occurs, and a subsequent data correction strategy is synchronously optimized; and safety accidents caused by abnormal data are avoided.

Techniques for an optical receiver are disclosed. The optical receiver may include an optical digital signal processing (ODSP) device. The ODSP device may include: a photodiode operable to convert an optical signal into an electrical signal; a digital signal processor operable to generate a digital signal based on the electrical signal; a quantization component operable to generate a quantized output signal based on the digital signal; and a processing device. The processing device is operable to: measure one or more signal statistics of the digital signal; and identifying one or more quantization thresholds, wherein the one or more quantization thresholds are calculated based on the one or more signal statistics.

The present invention relates to the technical field of conference processing, and particularly relates to a digital conference system with hybrid wired and wireless transmission and a usage method. The system transmits corresponding first audio signals and corresponding second audio signals through a plurality of conference units; a transmission processing unit is used to switch the transmission mode of the conference units to receive the corresponding first audio signals or second audio signals; a statistical unit counts the switching duration of each conference unit during the process of switching the transmission mode; an analysis unit determines whether the switching of the transmission mode of a single conference unit during the conference is qualified based on the switching duration, and, determines whether the switching of the transmission mode of a single conference unit during the conference is qualified according to the switched single audio signal, or determines the reason, generates an instruction based on the reason, and a control unit adjusts the parameters of the corresponding unit or issues a corresponding notification based on the instruction, thereby reducing the duration used for switching during the conference, and thus improving the conference efficiency.

The present application belongs to the field of aviation manufacturing and intelligent processing technology, and discloses a laminated interface online identification method based on an adaptive statistical discrimination mechanism. The method preprocesses the machining state signal, judges whether to enter the stable drilling machining stage based on the change characteristics of the machining state signal in the sliding time window, and calculates the dynamic mean value of the machining state signal in the stable drilling machining stage. The bilateral cumulative sum statistics are constructed based on the dynamic mean value, and the interface is judged in combination with the first and second judgment thresholds which are adaptively adjusted based on the statistical characteristics of the historical signal. When the preset interface judgment condition is met, it is judged that the machining state at the current drilling position has a sudden change, and the interface position is identified. The method does not need to establish a complex signal model and parameter calibration, can adaptively compensate the slow trend in the machining process, and is suitable for interface online identification under complex working conditions of multi-material laminated structure.

The invention provides a secure shell protocol flow detection method and device for complex bearing in a tunnel, and the method comprises the steps: 1, collecting the mixed flow of the tunnel, and extracting the data packet length, arrival time interval and direction of a specified number of data packets in each flow; 2, constructing continuous data packets in the same direction into the same burst, and constructing a burst feature sequence; step 3, mapping each data packet in the burst into a frequency domain signal to obtain a mixed frequency domain signal of the burst traffic in the frequency domain representation; step 4, obtaining mixed frequency domain signal statistical characteristics; and 5, inputting the mixed frequency domain signal statistical characteristics into an encoder and decoder architecture based on a recurrent neural network to obtain a prediction label of a burst level and a prediction label of a flow level, judging whether the SSH flow exists in the mixed flow or not, and positioning the burst. According to the method, the SSH flow detection problem under the complex bearing of the tunnel is effectively solved, and the detection accuracy is remarkably improved.

The present application provides an integrated monitoring method and device for transmission line fault hazards, which relates to the field of transmission line monitoring technology. The method includes: when a first monitoring cycle is met, collecting traveling wave related parameters and monitoring traveling wave signals; counting baseline traveling wave signals of fault-free line samples that meet the traveling wave related parameters, and calculating a deviation vector; counting the fault triggering probability of the transmission line that meets the deviation vector; when the fault triggering probability is greater than or equal to the fault probability threshold, retrieving the high-frequency fault detection duration of the transmission line that meets the deviation vector, shortening the high-frequency fault detection duration by at least 0.5 times to obtain a second monitoring cycle, and using the second monitoring cycle to replace the first monitoring cycle. The present application can solve the existing problem of difficult balanced configuration of sampling frequencies, achieve the goal of balanced configuration of sampling frequencies, and achieve the technical effect of improving the efficiency of transmission line monitoring.

The invention provides a post-injury rehabilitation tracking system and method based on high-density surface electromyographic signals, and belongs to the technical field of sports medicine and rehabilitation engineering, and the system comprises a synchronous collection module which is used for synchronously collecting the high-density electromyographic signals and joint torque signals; the signal preprocessing module is used for preprocessing the electromyographic signals; the neural decoding module is used for constructing a space-time expansion vector, updating signal statistical characteristics and separating to obtain a discharge sequence of the motion unit; the function evaluation module is used for calculating and optimizing a nerve density index; the rehabilitation decision output module is used for calculating indexes of the indexes and generating personalized training suggestions according to whether the indexes reach the standard or not; and the parameter adjusting module is used for receiving the personalized training suggestions and continuously optimizing the suggestions based on the execution feedback information. And the efficiency, the accuracy and the individuation degree of clinical rehabilitation evaluation and training control are remarkably improved.

The application relates to the technical field of visual detection, and discloses a drowning prevention early warning method and system based on visual detection, which comprises the following steps: acquiring a water surface video and generating an optical flow distribution map; separating a foreground motion region based on optical flow statistical characteristics; extracting reflection noise features for denoising treatment to obtain pure motion data flow; performing time-frequency and extreme value analysis on the pure motion data flow to determine preliminary signs of struggling behavior; performing displacement analysis on multiple frames of images to obtain abnormal behavior tracking results; accumulatively calculating a comprehensive score of a drowning event, and generating an early warning signal when the score exceeds a threshold value; and counting the number of receiving terminals, and triggering an event processing procedure when the number of receiving terminals meets a threshold value. The method can effectively suppress water surface reflection interference and realize high-reliability drowning prevention early warning.

The present application relates to the technical field of physical testing, in particular to an ultrasonic flaw detection method, system and equipment for isostatic pressing graphite finished products, the method comprising: determining local fluctuation synchronization features of a target position based on ultrasonic time domain signals of the isostatic pressing graphite finished product at the target position and its spatiotemporal neighborhood positions; determining multi-scale scattering correlation features of the target position by using mutual information between adjacent scale component signal features of the ultrasonic time domain signals; determining scattering events based on local signal-to-noise ratios of the ultrasonic time domain signals, determining event state transition probability matrices based on scattering event features, and obtaining sequence signal statistical evolution features of the target position; determining a comprehensive structure continuity parameter of the target position by combining the above multiple features of the target position, and obtaining a flaw detection result by adjusting separation constraint criteria of ICA based on the comprehensive structure continuity parameter. Through the technical scheme of the present application, substantial improvement in accuracy and reliability of graphite material defect detection is achieved.

This invention discloses a method and system for predicting industrial equipment faults based on deep learning, belonging to the field of fault diagnosis and prediction technology. The method includes: constructing an equipment topology graph model, abstracting sensor data channels as graph nodes, and constructing an adjacency matrix based on physical mechanical connections and signal statistical correlations to characterize stress transmission and vibration coupling paths; extracting spatial feature vectors through a multi-layer graph convolutional neural network in a parallel architecture, and simultaneously extracting temporal feature vectors through a multi-head self-attention mechanism; concatenating the spatiotemporal features and extracting equipment health feature vectors through a nonlinear mapping module containing parameterized linear rectifier units; outputting the posterior probability of the fault and triggering an early warning. The system includes a topology graph model construction module, a parallel feature extraction module, a feature fusion and mapping module, and a fault prediction output module. This invention can achieve highly sensitive identification and early warning of early faults in industrial equipment.

The invention discloses a partial discharge identification method and device based on multi-dimensional waveform feature extraction, and belongs to the technical field of state monitoring and intelligent diagnosis of high-voltage electrical equipment, and the method comprises the steps: obtaining the voltage and current waveform data of a transformer under the impact voltage, and carrying out the preprocessing of the voltage and current waveform data, voltage and current waveform preprocessing data are obtained; original signal statistical characteristics, partial discharge event characteristics and voltage structure characteristics are extracted; standardized features are obtained through standardized processing; constructing a neural network model based on the standardized features; training the neural network model by using a labeled sample set to obtain a trained neural network model; and inputting a signal acquired in real time into the trained neural network module for calculation so as to realize identification of the partial discharge signal. According to the invention, the features are trained and classified by using the multilayer perceptron neural network, and automatic identification of different discharge stages is realized.

The invention discloses a flow feature confusion method based on an AI virtual human, and the method comprises the steps: S1, configuring the basic attributes of the virtual human and a preset behavior strategy, and automatically obtaining the fingerprints of Internet access equipment of a user; s2, virtual human environment fingerprint camouflage is carried out by configuring a containerized operation environment and adjusting browser fingerprints; s3, dynamically generating behavior parameters for determining a virtual human online task based on LLM drive of preset parameters; and S4, the virtual human calls a local browser driver on line based on the behavior parameters obtained in the S3, simulates human to access the target site and generates feature traffic. According to the method, the virtual human Agent is driven by the AI, the mixed flow is generated by autonomously acting on the network, the user identification and tracking technology of the network access target based on the flow signal characteristics is effectively interfered, the signal statistics characteristics of the user internet surfing flow can be effectively mixed, and the method is suitable for the network privacy protection requirements in various scenes.

A FDSS parameter configuration method includes obtaining, by a User Equipment (UE), first information, and dynamically switching or configuring, by the UE, an FDSS-related parameter based on the first information. The FDSS-related parameter is a parameter used for performing FDSS-based uplink transmission. The first information includes at least one of the following: an association relationship between the FDSS-related parameter and a first parameter, the first parameter being a parameter used for performing uplink transmission; a first indication information, wherein the first indication information is used for indicating the FDSS-related parameter that needs to be dynamically switched or configured; or a signal measurement value or a signal statistics value.

The invention discloses a wireless air interface abnormal data processing system, and the system comprises a wireless network information collection and analysis module which is used for collecting wireless network communication data and carrying out the analysis of the data, and obtaining the analysis data; the abnormal event and behavior detection module is used for carrying out feature extraction according to the analysis data so as to detect abnormal events and behaviors; the wireless network attack identification module is used for identifying denial of service attack, replay attack and camouflage attack in the wireless network; and the abnormal behavior reasoning module is used for carrying out behavior intention feature modeling according to the collected behavior features so as to predict a target behavior intention. According to the wireless air interface abnormal data processing system, a feature extraction method based on information entropy and a detection method based on a time sequence diagram are adopted, signal statistics of all time periods is regarded as a random process, features in a wireless network can be rapidly and effectively extracted, and abnormal events and behaviors can be detected.

The embodiment of the invention provides a crowd counting method and device, electronic equipment and a readable storage medium, and relates to the technical field of wireless communication, a signal group from an AP is received, and the signal group comprises a timestamp, the signal strength of a detection request frame, a random hardware address and an AP number; performing signal statistical processing on the signal groups of which the timestamps are located in the same preset time period to obtain the number of random hardware addresses of N APs and the number of random hardware addresses of each AP; determining the number of people in each sensing area according to the target total number of people, the number of random hardware addresses of the N APs and the number of random hardware addresses of each AP; therefore, since the signal groups in the same preset time period are subjected to signal statistical processing, the number of random hardware addresses of the N APs and the number of random hardware addresses of each AP are obtained, and the number of people in each sensing area can be obtained according to the number of target total people, the number of random hardware addresses of the N APs and the number of random hardware addresses of each AP.

The invention provides a dose determination method and device based on a time sequence signal, equipment and a storage medium, and relates to the field of physics and computers. The method comprises the following steps: acquiring a plurality of sample CT images and determining dose distribution information and nuclide distribution information of each sample CT image; for each sample CT image, generating a PET accumulated signal image corresponding to each signal statistics window based on nuclide distribution information; training the first initial model by taking the sample CT image and the PET accumulated signal image corresponding to the sample CT image as a first training data set to obtain a first target model; training a second initial model by taking the dose distribution information, an output result of the first target model and part of training data in the first training data set as a second training set to obtain a second target model; and sequentially inputting the target CT image and the PET accumulated signal image of the target time window into the first target model and the second target model to obtain dose information output by the second target model.

The invention provides a pedestrian classification and identification method based on signal statistical characteristics, and the method comprises the steps: obtaining a pedestrian sample data set which is radar detection data and comprises different types of pedestrian samples and corresponding label information; performing micro-Doppler feature extraction on each pedestrian sample in the pedestrian sample data set to obtain signal statistical features of different types of pedestrian samples; determining signal statistical feature ranges of different types of pedestrians based on the signal statistical features of the different types of pedestrian samples, and storing the signal statistical feature ranges of the different types of pedestrians; and carrying out classification identification on the to-be-detected pedestrian based on the stored signal statistical feature range.

The invention relates to the field of pipeline leakage detection, and discloses a digital leakage listening detection system based on stepless frequency and multi-band bandwidth adjustment, the system comprises a signal acquisition module, a user adjustment module, a main control module, a display module, an audio output module and a power supply module, and the system constructs a real-time simulation auditory sense and asynchronous digital vision dual-channel architecture; the core of the method is that a main control module calculates a leakage signal steady-state index by fusing an energy stability index representing signal energy time stability and a statistical randomness index representing signal statistical randomness in an asynchronous digital visual path; the index provides an objective and quantitative visual navigation basis for operators, and continuous leakage signals and transient and periodic interference can be effectively distinguished. According to the invention, auditory experience and objective data analysis of operators are considered, and the accuracy, reliability and efficiency of leakage detection are remarkably improved.