201 results about "Signal processing algorithms" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses an electromechanical equipment health assessment and early warning method based on multi-mode dynamic perception, and belongs to the field of intelligent operation and maintenance of electromechanical equipment. The problems that in the prior art, a single physical quantity cannot comprehensively reflect the equipment state and a traditional signal processing algorithm cannot adapt to the equipment degradation mode change are solved, a panoramic sensing system covering multiple physical fields such as vibration, temperature and noise is constructed through a multi-mode sensor network and a dynamic weight fusion algorithm, and the multi-physical-field multi-physical-field panoramic sensing method is applied to the multi-physical-field multi-physical-field panoramic sensing system. The problem of isolated island of traditional single-dimensional monitoring information is solved; through a physical-depth mixed feature extraction architecture, combining interpretable engineering features with abstract features extracted by a deep neural network to form a health assessment model with mechanism transparency and mode generalization ability; through deep integration of the digital twin platform and the RPA technology, the manual inspection frequency and workload are reduced, the fault recognition accuracy is promoted to increase year by year, and continuously optimized intelligent operation and maintenance ecology is formed.

The invention discloses a self-adaptive rotating baseline direction finding method, device and equipment, a medium and a terminal. Signals are continuously collected through the base line rotatable antenna array; rapidly identifying and outputting environment parameters by using the trained environment evaluation model, and evaluating the environment grade; constructing a dynamic direction-finding angle model by combining the attitude parameters and the motion parameters of the mobile platform, the control parameters of the antenna array and the environmental parameters; according to the environment parameters and the environment grades, a dynamic parameter optimization rule is formulated, and control parameters, direction-finding resolving parameters and signal processing algorithm parameters of the antenna array are adjusted; and based on the environmental parameters, the adjusted control parameters of the antenna array, the adjusted direction-finding resolving parameters and the adjusted adaptive filter parameters, calculating an incoming wave angle by using a dynamic direction-finding angle model. According to the method, high-precision incoming wave signal direction finding is realized in a dynamic environment, and the method is suitable for mobile scenes such as unmanned aerial vehicles and vehicle-mounted communication.

The invention discloses a breath and heartbeat detection method and system based on an FMCW (Frequency Modulated Continuous Wave) radar. The method comprises the following steps: acquiring a continuous wave signal sent by an FMCW millimeter wave radar to detect a target object so as to obtain a reflected echo signal; the echo signals are preprocessed; separating a respiration signal from a heartbeat signal of the preprocessed echo signal based on an EPH multi-stage cooperative signal processing algorithm; and carrying out anomaly correction on the separated instantaneous frequency anomaly data based on a local weighted regression algorithm. Therefore, the vital sign detection accuracy of the radar can be improved under the interference conditions of background noise, signal harmonic waves and the like.

The invention provides a respiratory tract phlegm blockage monitoring method and device, and belongs to the technical field of respiratory tract phlegm blockage monitoring, and the monitoring method specifically comprises the following steps: obtaining respiratory tract multi-mode biophysical signals of a target domain patient and a plurality of source domain patients; the method comprises the following steps: acquiring a target domain signal through a micro sensor array, and constructing an optimal source domain set by using a signal processing algorithm formed by a coding module and a classification module; inputting a target domain signal and the optimal source domain set into a global adapter, extracting shallow layer features through a feature extraction module, and realizing conditional distribution alignment through a global adaptation unit; edge distribution alignment is guided through adversarial training containing a gradient inversion layer; and the discriminator and the global adapter parameters are fixed, and a classifier is trained to identify and evaluate the sputum blockage state. According to the method, the sputum blockage monitoring accuracy is improved through multi-mode signal processing and confrontation training.

The invention provides an automatic detection method and system based on a GNSS receiver hardware module, and relates to the technical field of electronic equipment testing, and the method comprises the steps: carrying out the analysis of a preprocessed signal data set, extracting a signal feature boundary based on a convex hull algorithm, and carrying out the detection of the signal feature boundary; calculating key performance indexes such as signal acquisition time, tracking precision, bit error rate and receiving sensitivity; comparing the key performance index with a preset performance threshold value, automatically judging whether the hardware module of the GNSS receiver is qualified or not according to a comparison result, and generating a performance judgment result; and automatically generating a structured detection report based on a performance judgment result, and outputting the report to a specified terminal or a storage position. According to the invention, by simulating GNSS signal excitation, response signal acquisition, a signal processing algorithm and an automatic performance judgment criterion, a scheme for efficiently and fully automatically detecting various key performance indexes of the hardware module of the GNSS receiver is realized.

The invention provides a photoelectric pod tracking method and system for an unmanned aerial vehicle platform. The photoelectric pod tracking method comprises the steps of collecting multi-mode sensor original data of the unmanned aerial vehicle platform in a target scene through a photoelectric pod; performing image processing on the original data of the multi-modal sensor by using a self-adaptive image signal processing algorithm based on a deep reinforcement learning model to obtain an optimized multi-modal image; performing semantic feature extraction on the optimized multi-modal image by using a double-flow neural network to obtain deep multi-semantic association features, and performing fusion processing on the deep multi-semantic association features to obtain a unified feature map in a target scene; based on the unified feature map, using a Transform model to track a target of the unmanned aerial vehicle platform in the target scene to obtain real-time coordinate information of the target; when the target is tracked, self-adaptive image processing is carried out based on the original data of the multi-mode sensor, and stable and efficient tracking of the target in the target scene by the photoelectric pod is facilitated.

The invention discloses a rapid detection method for traumatic brain injury by fusing genetic engineering and quantum dots, and relates to the technical field of traumatic brain injury detection. The method comprises the following steps: acquiring a suspected patient biological sample, extracting biomolecule components, and comparing the biomolecule components with a healthy population biomolecule database to obtain related differential expression molecules. A specifically recognizable recombinant antibody is constructed by genetic engineering, and is connected with a quantum dot through a coupling reaction to prepare a detection probe. The probe and a biological sample are mixed and reacted, and the intensity of a fluorescence signal generated by combination is collected by a fluorescence detection device. A standardized signal value is obtained through signal processing algorithm noise reduction and feature extraction, and a preliminary detection result is determined by combining a bioinformatics database evaluation sample. And finally, according to the differential expression molecule, the standardized signal value and the preliminary result, analyzing a data change trend in a preset time window by using a multivariable statistical model, and generating a diagnosis report. According to the method, various technologies are fused, and a new path is provided for traumatic brain injury detection.

The invention relates to the technical field of battery management, and discloses a lithium battery intelligent charging and discharging management system based on an AI algorithm, and the system comprises a multi-mode synchronous sensing module which is used for synchronously collecting electrical, acoustic and thermodynamic multi-physics field high-frequency signals of a lithium battery in a working process; the multi-modal dynamic fingerprint extraction module is used for extracting a multi-dimensional feature vector capable of representing the internal microscopic state of the battery from the high-frequency signal by applying a signal processing algorithm; the cross validation diagnosis module based on multi-modal fusion is internally provided with a diagnosis model and multi-modal fusion decision logic; the dynamic fingerprint entropy calculation and risk assessment module is used for calculating the complexity of the input fingerprint sequence to quantify the state disorder degree; and the intelligent charging and discharging path planning and preventive intervention control module is used for evaluating the index according to the microscopic degradation mode and the risk evaluation index. Diagnosis accuracy is improved through multi-physical field cross validation, and early risk accumulation is inhibited through an active preventive control strategy.

The invention provides a hard and brittle material subsurface damage mechanism signal classification method based on an acoustic emission technology. The method comprises the following steps: firstly, acquiring an acoustic emission original signal and surface appearance of the hard and brittle material through grinding tests at various grinding speeds; then, carrying out preprocessing and time-frequency domain feature extraction on the acoustic emission signals by using a wavelet packet signal processing algorithm and short-time Fourier transform; finally, distinguishing a micro-crack signal, a radial crack signal and a transverse crack signal from the characteristic space by combining the damage characteristics and acoustic emission characteristic parameters of the hard and brittle material, and obtaining a characteristic frequency band range corresponding to each crack damage mode; a hard and brittle material subsurface damage mechanism signal classification model is constructed, and a theoretical basis is provided for monitoring of grinding subsurface damage of hard and brittle materials under complex working conditions. According to the method, the damage mechanism of the hard and brittle material can be quickly identified, the subsequent online evaluation of the subsurface damage of the hard and brittle material is realized, and the technical problem of non-destructive accurate monitoring in the grinding process is solved.

The invention relates to the technical field of high-voltage circuit breaker monitoring, and discloses a high-voltage circuit breaker contact wear intelligent online detection and fault prediction system. The system comprises a contact wear online detection module, a data acquisition and processing module, a wear degree intelligent analysis module, a fault risk prediction module, an alarm and output module, a data storage and management module and a user interaction module. When online detection is carried out on the wear state of the contact of the high-voltage circuit breaker, electrical, mechanical and thermal parameters of the contact are synchronously acquired through a multi-sensor fusion technology, and adaptive filtering and optimization are carried out on an original detection signal by adopting a wavelet transform and Kalman filtering multi-stage signal processing algorithm; influences of electric arcs and electromagnetic noise instantaneous strong interference on detection signals can be suppressed, the accuracy of calculation of contact resistance and stroke key parameters of the contact is guaranteed, the accuracy of contact abrasion state evaluation is improved, and evaluation errors caused by signal interference are reduced.

The invention discloses a method and a system for diagnosing defects of a sheath grounding system under resonance voltage resistance of a power transmission cable. The method comprises the following steps: installing a sheath grounding current acquisition system; sequentially carrying out main insulation resonance voltage withstanding tests on three phases of the power transmission cable, and synchronously recording a grounding current waveform of a metal sheath by adopting a current acquisition system when each phase is subjected to voltage withstanding; processing the grounding current waveform by adopting a signal processing algorithm, and extracting a resonance component; and constructing a defect diagnosis matrix according to the resonance component, calculating a deviation between the defect diagnosis matrix and a standard matrix as a defect characterization value of the cable sheath grounding system, and obtaining a defect diagnosis conclusion of the sheath grounding system under the resonance withstand voltage of the power transmission cable. According to the method, the grounding current waveform of the metal sheath is synchronously acquired and analyzed in the main insulation resonance voltage withstand test of the three-phase cable, the resonance component is extracted, accurate identification of the defect is realized based on deviation calculation of the defect diagnosis matrix, and the detection sensitivity and the diagnosis reliability of the latent defect of the sheath grounding system before the cable is put into operation are improved.

The invention discloses a brain-computer interface-based closed-loop transcranial magnetic stimulation treatment system and method, and the system comprises an electroencephalogram monitoring module which is used for collecting an electroencephalogram signal of a subject in real time; the transcranial magnetic stimulation module is used for transmitting pulse stimulation to a brain target spot of a subject; the control module is in communication connection with the electroencephalogram monitoring module and the transcranial magnetic stimulation module, and the control module is configured to receive the electroencephalogram signals output by the electroencephalogram monitoring module, preprocess the electroencephalogram signals by adopting a signal processing algorithm to remove electromagnetic interference, and send the preprocessed electroencephalogram signals to the transcranial magnetic stimulation module; the signal processing algorithm comprises but is not limited to fast continuous wavelet transform (FCWT) and fast independent component analysis; according to the method, a closed-loop monitoring mechanism is adopted, electroencephalogram oscillation is recognized in real time, stimulation is dynamically triggered, the stimulation opportunity is accurately matched with the brain excitation state, and the treatment pertinence is improved; the signals are preprocessed through FCWT and an algorithm, electromagnetic interference and artifacts are effectively removed, and the accuracy of electroencephalogram oscillation feature extraction is guaranteed.

The invention provides a high-precision time-keeping low-phase-noise time synchronization device, and belongs to the technical field of time synchronization. The system comprises a power supply module, a satellite time service module, a frequency source module, a clock synchronization module, a frequency shunt module, a communication interface module and a Bluetooth module. Wherein the power supply module adopts a dual-power-supply redundant circuit design, and after one power supply breaks down, the other power supply can be automatically switched; the satellite time service module adopts a single Beidou signal baseband signal processing algorithm and an anti-cheating design, so that the safety of equipment is improved; the frequency source module comprises a constant-temperature crystal oscillator and a rubidium atomic clock, and high-time-keeping and low-phase-noise signal output is achieved in the mode that the crystal oscillator and the atomic clock are complementary. According to the invention, the problem that the conventional time synchronization equipment cannot have high time keeping precision and low phase noise of output signals at the same time is solved, a high-steady-state low-phase-noise 10MHz frequency standard signal, a 1PPS time service signal and an NTP network time service signal can be provided, and time service is provided for various terminals or servers.

The invention relates to the technical field of health monitoring equipment, in particular to a high-precision multi-dimensional health monitoring body fat scale which comprises a pressure sensor used for accurately measuring the weight of a user; the bioelectrical impedance sensor is used for measuring electrical impedance values of different parts of the human body; the millimeter-wave radar module integrates a millimeter-wave radar transmitter and a millimeter-wave radar receiver and is used for monitoring the heart rate and the respiratory rate of the user in a non-contact manner; the main control chip is used for receiving signals transmitted by the pressure sensor, the bioelectrical impedance sensor and the millimeter-wave radar module, operating an improved BIA algorithm, a millimeter-wave radar signal processing algorithm and a multi-sensor data fusion algorithm, and processing and analyzing health data; the display module is used for displaying body weight, body fat rate, heart rate, respiratory rate and other health data; and the power supply module provides a stable power supply for each component. The body fat scale solves the technical problems that an existing body fat scale is insufficient in measurement precision and stability, and multi-dimensional data monitoring is limited.

The invention discloses a classification training method and system for sound spitting speed and definition of clarinet, and relates to the technical field of clarinet training. The rhythm signal of the player is captured in real time through the high-precision audio sensor, the rhythm speed is dynamically adjusted based on the preset threshold value so as to provide self-adaptive training difficulty, and real-time monitoring data is seamlessly integrated to the intelligent feedback system for generation of a subsequent evaluation report, so that the training efficiency is improved. It is ensured that a primary trainee can establish a stable rhythm basis step by step, and human error interference is reduced; a high-precision microphone is adopted to capture playing audios in real time, key feature parameters are automatically extracted through an embedded signal processing algorithm, a training scheme is optimized in combination with historical records, and it is ensured that a player can visually recognize improvement points and improve the performance ability in complex music segments; the breath efficiency index is calculated through an embedded algorithm, and the output of the audio recording and analyzing module is combined to generate personalized breath optimization suggestions, so that the breath control precision in playing is improved.

The invention provides a DC system fault positioning method based on an improved variable frequency signal injection method, and the method comprises the steps: firstly, injecting AC signals of different frequencies into a bus of a DC system, and collecting the response current of each branch after the AC signals are injected; then, screening the response current through a signal processing algorithm to obtain a response characteristic quantity, and calculating a response ratio of each branch according to the response characteristic quantity; and finally, based on the response ratio of each branch, judging the branch with the fault through a numerical comparison method. According to the method, the frequency conversion signals are injected at the two ends, branch feedback detection and corresponding signal algorithms are combined, and the feedback result is processed by utilizing ratios, so that the operation steps are simplified, the requirements of detection equipment are reduced, the influence of interference signals on the final judgment result can be reduced through algorithm combination processing, and the overall detection accuracy of the method is improved.

The invention discloses a broadband oscillation active damping method, system and device based on MMC sub-module cluster collaboration and a storage medium, and relates to the technical field of power system stability control, and the method comprises the steps: collecting the electrical quantity of a power grid in real time, and recognizing a broadband oscillation mode and characteristic parameters through a signal processing algorithm; equivalent circuit parameters of the programmable virtual resonator are calculated, and the number of sub-modules of a sub-module cluster is determined in combination with the real-time states of the sub-modules of the modular multilevel converter; generating a control instruction according to the parameter and the oscillation component, and adjusting a sub-module pulse width modulation signal; oscillation amplitude attenuation, a damping ratio and a sub-module state are continuously monitored, adjustment is performed according to an evaluation result, and safe operation is ensured; the method can accurately suppress power grid oscillation, improves the utilization rate of the MMC device, saves the cost, is high in self-adaption, guarantees the reliable operation of the system, reduces the loss, is easy to expand and maintain due to the modular design, and improves the stability of the power system.

The invention relates to the field of chip clamps, in particular to a hearing aid output limit adjusting method, which comprises an adjusting system, and the adjusting system comprises a hardware module and a software module. The hardware module comprises a DSP module, a collection module, a power amplifier module and a power supply module. The software module comprises a signal processing module, a user interface module and a system management module; the DSP module is used for realizing a complex signal processing algorithm and analyzing sound signals in real time through cooperation of an efficient digital signal processor and an artificial intelligence algorithm, and the method is used through modular connection, collects and analyzes user hearing signals in real time, dynamically adjusts output limitation in combination with hearing conditions and environmental factors of a user, and improves the user experience. In this way, comfort of the user in various hearing scenes can be guaranteed, hearing of the user can be effectively protected, perfect balance between the amplification effect and the comfort of the user is achieved, habits of the user are conveniently recorded in cooperation with the DSP module and the system management module, and user experience is improved.

The invention belongs to the field of signal processing algorithms, and provides a phase loss sparse low-rank signal recovery method based on convex programming. According to the method, through convex relaxation and anchor regression, a convex optimization frame based on an original parameter space is provided for the first time aiming at the problem of simultaneous sparse low-rank phase loss matrix signal recovery, the frame is used for recovering a simultaneous sparse low-rank matrix signal from phase loss observation data, and the frame works in an original space domain of an unknown variable. And aiming at the convex frame, a low-complexity alternating direction multiplier minimum method is developed. The sparse low-rank phase loss signal recovery method is a first sparse low-rank phase loss matrix signal recovery method based on convex optimization and in an original parameter space, and the calculation complexity is very low.

The invention discloses a positioning error compensation method and device for a UWB single base station positioning system, and relates to the technical field of wireless positioning, and the method comprises the steps: taking a single base station as a center, determining an equilateral triangle according to the wavelength of a UWB signal, and setting a UWB antenna at each vertex of the equilateral triangle, so as to form an equilateral triangle array antenna; calculating the arrival angles of the received signals by different UWB antennas by using the phase differences of the UWB signals arriving at the UWB antennas through a signal processing algorithm and combining the geometrical relationship of the equilateral triangle array antennas; constructing an equation set in combination with triangular constraint conditions; solving the equation set by using a weighted least square method to obtain initial position information of the positioning tag; and smoothing the initial position information by using Kalman filtering to obtain the position information of the positioning tag. According to the invention, the special antenna array structure is constructed on the single base station, and the information carried by the UWB signal is fully mined by using the triangulation positioning principle, so that the positioning precision is improved.

The utility model discloses a radio frequency chip test experiment device, and relates to the field of radio frequency chip test experiments. Comprising a first signal input end, a third change-over switch, a radio frequency transmitting chip circuit, a radio frequency transmitting end, a radio frequency receiving chip circuit, a radio frequency receiving end, a fourth change-over switch, a second output test end, a first change-over switch, a first digital source meter, a second change-over switch, a second digital source meter and a liquid crystal control circuit. The radio frequency chip test experimental device can optimize the test process, reduce the test time and labor cost, improve the test accuracy and reliability, accurately control the transmission and reception of test signals by using a digital source meter, and adopt an advanced signal processing algorithm, so that the test accuracy and reliability are improved, and the test efficiency is improved. According to the radio frequency chip testing device, the problem of signal interference during chip testing can be effectively reduced, multiple functions needed by radio frequency chip testing are integrated into one compact device through integrated design, and testing convenience and flexibility are remarkably improved.

The invention relates to the technical field of medical health, in particular to an epilepsy auxiliary evaluation method and device based on heart rate variability, equipment and a medium. The method comprises the following steps: acquiring at least two target physiological states in a current acquisition combination, wherein the target physiological states comprise a resting state and one or two stimulation states; based on the target physiological state, electrocardiosignals of the subject are collected; based on a preset signal processing algorithm, heart rate variability analysis is conducted on each group of electrocardiosignals, a corresponding resting state HRV feature set and at least one stimulation state HRV feature set are obtained, and each HRV feature set comprises a plurality of feature items; determining a group of target feature items suitable for the current collection combination according to the independent judgment efficiency, the correlation judgment efficiency and / or the global judgment efficiency of each feature item on epilepsy; and generating a screening report for assisting epilepsy risk assessment based on the target feature item. And the operation efficiency is improved while the evaluation accuracy is guaranteed.

This invention provides an automated testing method and system for GNSS receiver hardware modules, relating to the field of electronic equipment testing technology. The method includes: parsing a preprocessed signal dataset and extracting signal feature boundaries based on a convex hull algorithm, calculating key performance indicators such as signal acquisition time, tracking accuracy, bit error rate, and receiver sensitivity; comparing these key performance indicators with preset performance thresholds, automatically determining whether the GNSS receiver hardware module is qualified based on the comparison results, and generating a performance judgment result; and automatically generating a structured test report based on the performance judgment result, and outputting the report to a designated terminal or storage location. This invention achieves an efficient and fully automated solution for testing various key performance indicators of GNSS receiver hardware modules by simulating GNSS signal excitation, response signal acquisition, signal processing algorithms, and automated performance judgment criteria.

The invention discloses a gas specific heat ratio measuring device and a gas specific heat ratio measuring method, particularly relates to the technical field of gas specific heat ratio measurement, and solves the technical problems of low measuring precision, inconvenience in operation, poor safety and narrow application range of a traditional gas specific heat ratio measuring experimental device in the prior art. According to the technical scheme, an air pressure sensor measures changes of air pressure and temperature in a bottle, and data can be displayed in real time by being connected with a computer; by optimizing sensor design and a signal processing algorithm, the measuring device can respond to air temperature and pressure changes more quickly; the stepping motor is used for controlling opening and closing of the piston, and errors caused by human factors are reduced; the change of the air specific heat ratio at different temperatures can be explored by changing the temperature; according to the invention, intelligent and automatic measurement is realized, errors caused by manual operation are reduced, and data change visualization in the whole experiment process is provided, so that students can intuitively analyze the change of each physical quantity.

This invention relates to a method and apparatus for ecoacoustic monitoring of macrobenthic animals in mangrove areas, belonging to the field of ecological monitoring technology. Based on acoustic propagation characteristic data in mangrove areas, this invention deploys acoustic equipment to monitor and identify functional groups of macrobenthic animals, statistically analyzes the relative abundance of these functional groups, and finally assesses the acoustic diversity of macrobenthic animals based on the analysis results. By optimizing the deployment of acoustic sensors, developing efficient acoustic signal processing algorithms, and integrating environmental parameter monitoring, this invention achieves long-term, continuous, and non-invasive monitoring of macrobenthic animals in mangrove areas, providing scientific basis and technical support for the health assessment of mangrove ecosystems and biodiversity conservation.

This invention belongs to the field of periodic signal parameter estimation, and discloses a method, device, and medium for estimating the periodicity of a signal based on off-grid. The method includes: Step 1, constructing a cost function; Step 2, performing p=0 iterations to initialize the fundamental frequency f of the periodic signal; Step 3, performing the (p+1)th iteration, substituting the estimated value of f from the p-th iteration into J(f,u), and optimizing u to obtain an estimated value of u; Step 4, substituting the estimated value of u from the (p+1)th iteration into J(f,u), and using Newton's method to optimize the fundamental frequency f; Step 5, obtaining the period estimate from the (p+1)th iteration; Step 6, repeating steps 3-5 until the number of iterations P. This invention can be applied to practical communication or signal processing hardware systems to solve the problem of inaccurate period estimation caused by crystal oscillator frequency drift in the hardware system, thereby improving the performance of subsequent communication or signal processing algorithms.

The invention relates to a ZYNQ-based multichannel AD9361 controller and a control method, and relates to the technical field of digital signal processing. Comprising an ARM processing system and a programmable logic system, and a controller module and a signal processing algorithm module are arranged in the programmable logic system; the controller module comprises an AD9361 controller 1 arranged in the ARM processing system, and an AD9361 controller 2, an arbiter, an AXI controller and a data distributor which are arranged in the programmable logic system, and further comprises independent SPI interfaces and independent GPIO interfaces which are in one-to-one correspondence with the AD9361 chips; the AD9361 controller 1 and the AD9361 controller 2 apply for a data transmission control right to the arbiter according to a control demand; each independent SPI interface and each independent GPIO interface are respectively connected with an SPI pin and a GPIO pin of the corresponding AD9361 chip; the ARM processing system and the programmable logic system are interconnected through an AXI bus. The circuit can be externally connected with a plurality of AD9361 chips, and supports multi-channel parallel control.

The application provides a lightning stroke positioning system and method with big data intelligent analysis, and is applied to the field of data processing applications. The application collects multidimensional data, including monitoring, historical lightning strikes, real-time weather, etc., around lightning stroke positioning and power grid risk research and judgment. The monitoring data is processed by a single-chip microcomputer, and the power grid equipment parameters include line insulation level, etc. The original lightning stroke signal is preprocessed by a signal processing algorithm to obtain a data set, then an AI big data model combined with a neural network is used to analyze and fuse data to predict lightning strikes, and power outage research and judgment rules are established combined with electric field data and power grid parameters. The real-time power grid data is used to verify and judge the power-off state. Subsequent synchronous analysis results are presented, time synchronization is ensured by Beidou + dual-mode backup and delay compensation, maintenance data is generated by regular calibration, and finally the maintenance data is processed, related system data is stored, and results are generated.

The invention discloses an asphalt pavement compaction uniformity evaluation method based on a ground penetrating radar, and relates to the field of data detection, and the method comprises the following steps: 1, obtaining a uniformity parameter set through a ground penetrating radar sensor through a signal processing algorithm, comparing the uniformity parameter set with a preset uniformity attribute index threshold value, and determining the uniformity attribute index threshold value; judging whether an event exceeding a threshold value or a low threshold value exists or not, when an event not meeting the threshold value is detected, identifying the amplitude of data violating the threshold value, and preliminarily classifying the compaction uniformity state of the current pavement as normal, light anomaly or heavy anomaly; 2, constructing a state evaluation model based on a deep learning algorithm; according to the method, the compaction uniformity variation trend of the pavement in a future period can be predicted, factors causing abnormity can be accurately identified, targeted maintenance suggestions are provided, the cost effectiveness of a repair scheme is improved, real-time monitoring and data sharing are provided, the pavement management efficiency is improved, and the service life of the pavement is prolonged.