133 results about "Acquisition time" patented technology

The invention discloses a circuit breaker on-line monitoring edge terminal and a high and low speed synchronous acquisition method. The edge terminal comprises a high-speed acquisition card, a low-speed acquisition card, a time synchronization module and an edge computing host. And the high-speed acquisition card and the low-speed acquisition card are respectively connected with the circuit breaker monitoring sensor and respectively acquire signals at different sampling frequencies. And the time synchronization module realizes time synchronization of the high-speed and low-speed acquisition cards through a trigger signal generation unit and a timestamp alignment unit. The edge computing host comprises a data processing unit, a data storage unit and a data uploading unit and is responsible for filtering, feature extraction, fault diagnosis, historical data storage and data uploading of collected signals. The invention further provides a high and low speed acquisition time synchronization method, high and low frequency signals can be synchronized in real time in a circuit breaker operation event, the accuracy and synchronism of data acquisition are ensured, and therefore the reliability and comprehensiveness of monitoring are improved.

The invention discloses an oil and gas long-distance pipeline automatic control system fault identification method based on deep learning, and the method comprises the following steps: collecting and preprocessing a time sequence, and generating a standardized input sequence; constructing a physical constraint feature set, and generating a physical observable representation; encoding the valve opening instruction time sequence and the pump station frequency instruction time sequence to form a combined input sequence; obtaining Koopman state representation, and generating a linear evolution state sequence; a residual nonlinear compensation structure is introduced to generate residual features, and a comprehensive state sequence is formed; time scale splitting is carried out, and fast, medium-speed and slow time scale feature sequences are decomposed; identifying anomalies and generating a time scale anomaly feature set; leakage energy loss indexes are calculated and fused to form fault mode drift representation; judging the pipeline state, and outputting a fault recognition result. According to the invention, a deep Koopman operator structure is adopted, accurate identification of pipeline faults is realized, and the method has the advantage of high diagnosis reliability.

The invention discloses an early warning method and device for multi-device time sequence data and electronic equipment. The method comprises the steps of determining a time sequence data stream; dividing the time sequence data stream according to the model identifier to obtain a plurality of sub-time sequence data streams, sliding on the sub-time sequence data streams by using the time windows according to a preset step length from the starting timestamps of the sub-time sequence data streams, and determining the time sequence data under the time window which slides each time as a target time sequence data stream; performing timestamp alignment and data filling on time sequence data in the target time sequence data stream to obtain an aligned target time sequence data stream, and predicting a time sequence data predicted value corresponding to the aligned target time sequence data stream in a future time period through a prediction model corresponding to the aligned target time sequence data stream; and determining early warning information corresponding to the target time sequence data stream according to the time sequence data predicted value. The technical problem that the accuracy of an early warning result is affected by data time sequence dislocation caused by inconsistent acquisition time of time sequence data between devices is solved.

The invention discloses a pole piece slitting method, device and equipment and a storage medium, and relates to the technical field of batteries, and the pole piece slitting method comprises the steps that a first signal of the displacement of a slitting assembly in the pole piece slitting equipment in the axial direction of the slitting assembly changing along with the collection moment is obtained, and the axial direction is perpendicular to the pole piece slitting direction of the slitting assembly; based on the first signal, the current state of the slitting assembly is determined; and based on the current state of the slitting assembly, the pole piece slitting equipment is controlled. According to the invention, the first signal that the displacement of the slitting assembly in the axial direction in the pole piece slitting equipment changes along with the collection time can be obtained online in real time, and the current state of the slitting assembly is determined through the signal, so that the pole piece slitting equipment is controlled to be matched with the current state; and the condition that unqualified pole pieces cut by the pole piece cutting equipment flow into the next process can be effectively reduced, so that the performance and the service life of the finally produced battery are improved.

The invention relates to the technical field of power monitoring, in particular to an online monitoring method and system for a power control cabinet. The method comprises the following steps: acquiring a time domain signal, tracking a fundamental wave frequency, adjusting a sampling window, and performing Fourier transform to obtain a harmonic amplitude and a phase; harmonic waves are divided into harmonic wave correlation groups according to association rules, harmonic wave phase consistency coefficients are calculated to weight the total energy in the groups, and weighted harmonic wave energy is obtained; total inter-harmonic energy is calculated, and an inter-harmonic structure factor is obtained based on the spectral entropy. And acquiring a real-time load rate and a change rate, and calculating a synchronous change coefficient and a load factor. And summing the weighted harmonic energy and the corrected inter-harmonic energy to obtain total distortion energy, and obtaining a comprehensive distortion index according to the total distortion energy. According to the scheme, the structured inter-harmonics possibly caused by the early failure of the equipment can be found earlier, and the requirements of early warning and predictive maintenance are met.

The invention relates to the technical field of multimode signal coverage, and discloses a multimode-based signal coverage amplification system and method. The system comprises a multi-source signal feature acquisition module, a real-time coverage situation generation module, a coverage deviation evaluation module, a resource scheduling module and a beam optimization execution module. The multi-source signal feature acquisition module acquires a multi-dimensional signal backtracking feature set containing first signal intensity distribution and first channel parameters at historical acquisition moments of a target area in a preset scanning period; a real-time coverage situation generation module obtains a real-time coverage situation map containing second signal intensity distribution and second channel parameters; a coverage deviation evaluation module constructs a coverage deviation matrix accordingly; a resource scheduling module retrieves a matched coverage blind area feature set, and when the dispersion reaches the standard, a resource release coefficient is calculated; and the beam optimization execution module executes coverage enhancement operation on the coverage resources according to the coefficient from the storage starting moment, so that the signal coverage quality and the resource utilization efficiency of the target area are effectively improved.

The invention discloses a GIS insulation defect identification method and system, a terminal and a medium, and the method comprises the following steps: employing an adaptive pulse sequence threshold determination method based on a quartile distance, and carrying out the adaptive extraction of a plurality of GIS internal partial discharge pulse signals including HFCT, UHF and optical detection equipment; performing dimensionless energy binarization feature extraction on the HFCT, UHF and AE partial discharge signals within the fixed acquisition time to generate feature vectors; establishing a multi-scale insulation defect partial discharge real-time online incremental database, and automatically adjusting parameters of an incremental support vector machine model by adopting a Bayesian optimization algorithm; and inputting the feature vector into an SVM (Support Vector Machine) model subjected to Bayesian optimization of hyper-parameters, and carrying out multi-parameter partial discharge identification.

The invention discloses an intelligent bracelet integrated with photoelectric volume pulse wave detection and a data processing method thereof, and belongs to the field of intelligent bracelets.The intelligent bracelet comprises a host, a display screen is arranged on the upper surface of the host, an acquisition sensor is arranged on the lower surface of the host, and a control knob is arranged in front of the outer surface of the host; through the detachable structure of the watchband, the mounting groove and the inner groove are formed in the left end and the right end of the host and matched with the clamping block, the pinch bar, the spring and the button of the watchband to form a quick dismounting mechanism, the pinch bar can be driven to be separated from the clamping groove of the clamping block by pressing the button, the watchband can be dismounted by hands without tools, and the operation time is shortened to be within 3 seconds; in order to solve the problem that a traditional watchband is tedious to replace and easy to damage, the method comprises the following steps: in a signal preprocessing stage, combining band-pass filtering and adaptive Kalman filtering, and synchronously introducing motion data of an acceleration sensor and a PPG signal signal-to-noise ratio evaluation mechanism, when SNRlt; and the acquisition time is automatically prolonged or a user is prompted to stabilize the hand at 10dB.

The invention provides a sensor acquisition time synchronization method and system based on ZYNQ. A unified clock source is arranged in the FPGA, and it is guaranteed that no accumulated drift exists in the time difference. Hardware triggering is carried out by adopting an IMU signal as a reference, camera triggering at a precise moment is carried out according to the triggering moment in combination with factors such as exposure time and transmission delay of an image, and it is ensured that IMU data transmitted to a memory and a camera image are from the same real moment. Sensor data are calibrated through a software algorithm, and a result is fed back to the sensor trigger signal offset processing module to form a complete calibration loop. And closed-loop feedback is introduced, so that automatic and self-adaptive high-precision synchronization is realized. The IMU signal is used as a camera image acquisition triggering means, so that the image frame frequency and the IMU frequency are in a multiple relationship, and the parameters of the offset processing module can be modified according to the required frame frequency and the existing IMU frequency in the actual operation process to adjust the frame rate.

The invention relates to the technical field of power equipment monitoring, in particular to an intelligent electric energy meter operation state abnormity monitoring management method. Obtaining a fluctuation characteristic value according to local data at any moment in the load data sequence and the corresponding fitting data; filtering according to the fluctuation characteristic value to obtain a smooth load sequence; obtaining different load time periods according to the smooth load sequence, carrying out clustering, and obtaining a power utilization fluctuation value according to fluctuation characteristic values at all moments in a time period cluster; acquiring an acquisition time period according to the time period distribution characteristics of the time period cluster; the relative fluctuation degree is obtained according to the electricity utilization fluctuation values of the user and other users in the collection time period and the distance characteristics of the electric energy meter; and obtaining a frequency adjustment coefficient according to the load data of the user, the temperature sequence and the relative fluctuation degree. The monitoring data of the electric energy meter are adaptively acquired according to the frequency adjustment coefficient, and the monitoring accuracy of potential faults of the electric energy meter is improved while the storage pressure of the monitoring data is reduced.

The invention provides an echo signal processing method, an integrated circuit chip and an ultrasonic flowmeter, and is suitable for the technical field of echo signal processing. The method comprises the following steps: acquiring an echo signal, and controlling an analog-to-digital converter to acquire the echo signal based on an acquisition starting moment and a preset acquisition time interval of each acquisition period to obtain N pieces of acquisition data; based on a predetermined storage rule, storing the collected data of each collection period until the collected data of the M collection periods are stored to corresponding positions of the cache region; and arranging the collected data in sequence based on the positions of the collected data of the M collection periods in the cache region to obtain a reduced waveform. The echo signal processing method provided by the embodiment of the invention can be suitable for a built-in analog-to-digital converter to carry out data acquisition, and the problems that an external high-speed analog-to-digital converter has high requirements on circuit design and high cost, and signal interference exists in high-speed transmission are solved.

The invention discloses a high-frequency electrotome negative plate impedance detection method and system and a high-frequency electrotome, and the detection method comprises the steps: an impedance detection signal preprocessing step: determining an acquisition time interval of an impedance detection signal according to a current working mode and a corresponding optimal acquisition time interval; only the impedance detection signals in the acquisition time interval are collected; the acquisition time interval is one or more carrier periods in which the signal-to-noise ratio of the impedance detection signal is higher than the average value of the signal-to-noise ratio in a unit modulation period T under the current working mode; and the optimized impedance detection signal is filtered and amplified and then is input into an impedance calculation module to calculate and obtain a real-time impedance value of the negative plate of the high-frequency electrotome. Interference is filtered out by monitoring information such as power and impedance intervals when the high-frequency electrotome works and combining noise waveform characteristics of different working modes.

The invention provides a non-contact life signal processing method in a strong clutter environment, and belongs to the field of data processing.The method comprises the steps that extreme points in a signal data sequence of a user and first and last signal data are recorded as to-be-corrected data; obtaining an amplitude adjustment parameter and a slope adjustment parameter of each piece of to-be-corrected data according to a numerical value difference and an acquisition time difference between each piece of to-be-corrected data and the to-be-corrected data on the left side and the right side of the to-be-corrected data; and combining the numerical value of the to-be-corrected data to obtain a corrected value of the to-be-corrected data, thereby obtaining upper and lower envelope lines. The invention aims to solve the problem that the signal data of a user is processed by an EMD (empirical mode decomposition) algorithm due to the fact that an upper envelope line and a lower envelope line which are obtained through extreme points in a signal data sequence are greatly influenced by noise because the noise in an environment where the user is located has a great influence on the signal data of the user. And the accuracy of the obtained heart rate signal and respiration signal is low.

The invention relates to the technical field of plastic pipe cutting, and provides a multi-angle band saw operation control method and system.The method comprises the steps that vibration data, current data and rotor angles at different collection moments are collected in the plastic pipe cutting process, and the target collection moment is marked; constructing a vibration sequence, a current sequence and a rotor angle sequence at a target acquisition moment; determining a cutting misalignment degree, a cutting misalignment degree sequence and a cutting misalignment degree predicted value sequence at the target acquisition moment, and determining a notch form offset degree at the target acquisition moment according to the difference between the cutting misalignment degree sequence at the target acquisition moment and the cutting misalignment degree predicted value sequence; and an integral gain adjustment value at the target acquisition moment is calculated, and real-time adjustment of operation of the multi-angle band saw for plastic pipe cutting is achieved in combination with the rotor angle at the target acquisition moment. According to the method, the integral gain is adaptively valued, and the cutting track offset is reduced.

The invention discloses a residual current real-time remote protection method based on edge calculation, which relates to the technical field of power failure protection, and comprises the following steps: executing time sorting and differential calculation based on residual current signal data acquired by current sensors deployed on power distribution branches and corresponding acquisition timestamps; generating a residual current signal record containing the time interval value and the amplitude difference value; extracting a signal arrival delay value and a time jitter value of each record according to the residual current signal record and a locally stored model version number, and fitting the two types of data to generate a time drift compensation parameter; performing version consistency comparison based on the time drift compensation parameter and a cloud model version number returned by the cloud interface; according to the method, a synchronous verification and arbitration updating mechanism of the local and cloud residual current anomaly judgment result is constructed in an edge computing environment, so that the consistency of the judgment result can still be kept under the conditions that the network is unstable and the model is frequently updated.

The invention relates to the technical field of image communication, in particular to an active tracking control method of a high-precision PTZ camera. The method comprises the following steps: acquiring a Rocker Cmd object; the speed values of all the shafts are judged based on a Rocker Cmd object, and corrected rocker input values are obtained; performing protocol mapping on the corrected rocker input value, and caching the corrected rocker input value to obtain an instruction queue; removing invalid stop instructions in the instruction queue to obtain an optimized continuous control instruction sequence; and performing acquisition time and direction judgment on the optimized continuous control instruction sequence to obtain a corrected control instruction set. Through intelligent processing, protocol mapping and caching, logic analysis and state feedback of rocker input signals, high-precision, smooth, reliable and durable closed-loop control of the PTZ camera is realized, and the control error-tolerant rate is remarkably improved.

The application discloses a method, device and equipment for identifying a movement time point of a surface electromyogram signal and a medium. The method comprises the following steps: acquiring a to-be-detected force platform signal corresponding to a to-be-detected surface electromyogram signal; wherein the acquisition time points of the to-be-detected surface electromyogram signal and the to-be-detected force platform signal are synchronous; determining a positive slope cumulative function according to the to-be-detected force platform signal, and performing first-order derivation on the positive slope cumulative function to obtain a positive slope change rate function; determining a movement time point of a movement event corresponding to the to-be-detected surface electromyogram signal according to the positive slope change rate function and a movement change rate threshold; wherein the independent variable and the dependent variable of the positive slope cumulative function are the acquisition time point of the to-be-detected force platform signal and the cumulative positive slope respectively, and the movement time point is a starting time point or an ending time point. The embodiment of the application solves the problem that the traditional identification method is susceptible to noise interference of the surface electromyogram signal, and improves the identification accuracy of the movement time point.

The invention discloses a signal testing method and device and a storage medium, and relates to the technical field of communication. The method comprises the following steps: acquiring target video information and signal intensity information in a target tunnel; extracting N target tags from the target video information, and determining first acquisition time and position information of each target tag to obtain N pieces of first acquisition time and N pieces of position information; determining second acquisition time of S signal intensities from the signal intensity information to obtain S second acquisition time; and based on the N first acquisition times and the S second acquisition times, according to a target algorithm and the S signal intensities, determining the signal intensities matched with the N pieces of position information corresponding to the N target labels. According to the invention, the technical problem in the prior art that the signal intensity in the tunnel is difficult to accurately identify due to inaccurate position information matching in the communication signal test in the tunnel is solved.

The invention relates to the technical field of power plant auxiliary equipment, in particular to a power plant auxiliary equipment vibration fault diagnosis method and system based on deep learning. Comprises: establishing a fault database of power plant auxiliary equipment; setting a fault diagnosis model and a plurality of equipment points based on the fault database, and acquiring a monitoring data packet of each monitoring point according to a preset acquisition time node; judging whether an early warning instruction is generated or not according to all the monitoring data packets and the fault diagnosis model; the method comprises the following steps of: performing structural analysis on the power plant auxiliary equipment, setting a plurality of equipment points, constructing an abnormal signal set of each equipment point by utilizing a deep learning technology, setting a corresponding processing sub-model and a global diagnosis model according to the abnormal signal set, and analyzing a time sequence association relationship among characteristic sub-signals in a single abnormal signal set to obtain a diagnosis result of the abnormal signal set. And the corresponding processing sub-model is constructed, so that the processing efficiency of the monitoring data of each equipment point and the screening efficiency of the fault signal are improved, and the fault diagnosis efficiency of the power plant auxiliary equipment is improved.

The invention relates to the technical field of electronic device detection and intelligent repair, in particular to a Russian circuit board failure analysis method. N types of synchronous scanning are carried out on the plating layer of the tested circuit board, and acquisition timestamps and space coordinates are marked; aligning the scanning images in various forms according to the acquisition timestamps and the space coordinates to obtain a multi-modal data set; key material characteristic parameters of the Russian circuit board are extracted as priori knowledge; the method comprises the following steps: constructing an N-channel Transform encoder, and designing different encoder structures for each channel; constructing a failure feature library, and determining circuit board failure modes and key features corresponding to each failure mode; performing similarity calculation on the multi-modal data set, the priori knowledge and the failure feature library by using an N-channel Transform encoder; after Softmax normalization, the confidence of each failure mode is output, and a 32-dimensional confidence vector is formed; and judging the main failure mode of the current coating according to the maximum value in the confidence vector.

The disclosure relates to time of flight calculations for a lidar transceiver. Example embodiments include a method of calculating time of flight of a lidar signal, the method comprising: i) transmitting a light pulse to start (502) an acquisition time period having a plurality of successive time portions; ii) for each successive time portion, if a photon is received (503): iia) recording (505) a time at which the photon is received; iib) updating (504) a histogram to record a cumulative count of photons received during the time portion; iic) calculating (505) a time difference between the time at which the photon is received relative to a reference point of the time portion; iid) updating (506) a cumulative total of the calculated time difference for the time portion; iii) repeating steps i) to iid) for a succession of N transmitted light pulses; iv) identifying (509) a peak in a time portion of the histogram; and v) estimating (510) a time of flight from the cumulative total of the calculated time difference for the time portion of the histogram containing the identified peak.

The embodiment of the invention provides a vehicle end data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: analyzing vehicle end structural data, and obtaining a plurality of collection signal frames of each collection signal, and the collection time and collection value of each collection signal frame; according to each acquisition time of each acquisition signal frame in each acquisition signal, comparing two acquisition values of any two adjacent acquisition signal frames of each acquisition signal, and generating analysis data of each acquisition signal frame of each acquisition signal; and obtaining an analysis data packet of the vehicle end structured data according to the analysis data of each acquisition signal frame of each acquisition signal. Therefore, the invention provides an efficient data compression processing scheme, the data transmission quantity and the transmission time consumption of the vehicle end structured data in the transmission process can be reduced, and the data storage space is reduced.

The invention provides a data processing method and system, electronic equipment, a storage medium and a robot, and relates to the field of data processing. The method is applied to a processing device, and comprises the following steps: under the condition that first acquisition data reported by a first acquisition device is received, sending an acquisition trigger signal to a second acquisition device; under the condition that second collection data reported by the second collection device is received, the collection time of the second collection data is determined based on the sending time of the collection trigger signal, the single collection duration of the second collection device and the generation time of the second collection data carried by the second collection data, the acquisition time of the first acquisition data is synchronized with the acquisition time of the second acquisition data. According to the method, collection time synchronization of different collection devices is realized.

The present application relates to a kind of suspension bridge pin joint type sling rotation angle measurement method, comprising the following steps: S1, selecting the two places of sling of suspension bridge around lower pin shaft rotation site are carried out transverse marking and longitudinal marking;S2, the marked part of rotation site is videoed and frame is extracted, and the image sequence after frame extraction is obtained;S3, the image gray scale of image sequence after frame extraction is carried out using self-defined single channel method;S4, after the pre-processing of gray-scale image, all straight lines of image are identified using Hough transform, and the long straight line located at the edge of transverse marking and longitudinal marking is extracted;S5, the angle between long straight line respectively located at the edge of transverse marking and longitudinal marking is calculated, and the angle value of sling around pin shaft rotation with time variation in acquisition time period is obtained.The method proposed in the present application can realize the measurement of sling around pin shaft rotation angle, and provide reliable data basis for sling rotation law and related damage mechanism research.

This invention provides a high-sensitivity satellite navigation signal acquisition method. It utilizes an improved acquisition algorithm based on FFT coefficient compensation and block code Doppler compensation to acquire satellite navigation signals, obtaining the acquisition result. This invention effectively reduces the impact of Doppler frequency offset during long coherent acquisition times through coefficient-compensated FFT operations and the block code Doppler compensation algorithm. When calculating the FFT spectrum, only the spectral lines at specified positions need to be calculated, effectively shortening the calculation time, improving acquisition efficiency, and achieving high-sensitivity acquisition of weak signals.

The invention provides a coal quality detection system, method and equipment based on near-infrared coupling TRLIBS and a medium, and the system comprises a transmission module which is used for conveying a coal sample and inputting an operation state signal to a synchronous control module; the TRLIBS detection module is used for exciting a coal sample to generate plasma, collecting a time-resolved laser-induced breakdown spectrum of element characteristics and inputting the time-resolved laser-induced breakdown spectrum into the data processing module; the near-infrared detection module is used for acquiring a near-infrared absorption spectrum of a molecular functional group of the coal sample and inputting the near-infrared absorption spectrum into the data processing module; the synchronous control module is used for receiving the running state signal of the transmission module, sending an asynchronous delay control synchronous trigger signal and controlling an acquisition time sequence; and the data processing module is used for receiving, fusing and processing the TRLIBS spectrum and the near-infrared absorption spectrum, and outputting a coal quality parameter detection result, so that the problems of incomplete parameter coverage, poor multi-module synchronization performance and low detection precision of the coal quality online detection device are solved.

The invention discloses a real-time audio and video end-to-end delay statistical method based on two-way time synchronization, and particularly relates to the technical field of real-time audio and video communication and network performance measurement, which comprises the following steps: S1, time synchronization: a plug flow client and a pull flow client respectively calculate clock skew with an SFU server through two-way message exchange; the method comprises the following steps: S1, a flow pushing end converts an acquisition timestamp and deviation into SFU reference time, and the SFU reference time and stage time consumption metadata are embedded into SEI information to be sent along with a video stream, S3, an SFU server forwards the video stream containing the SEI information, and S4, delay calculation and decomposition: a flow pulling end analyzes the SEI information, calculates total end-to-end delay in combination with the deviation of the flow pulling end, and decomposes the time consumption of each stage. Through high-precision time synchronization and time data losslessly carried by SEI information, non-intrusive, automatic, full-link-covered and decomposable end-to-end delay accurate statistics is realized, and the bottleneck problems that a traditional method is high in invasiveness, low in precision and incapable of positioning are effectively solved.

This invention claims protection for a MEMS gyroscope installation error calibration method based on a large-range autocollimator, comprising the following steps: Step 1, designing an autocollimator with an angle measurement range of ±9° using the reflection angle sensitivity modulation mechanism of a non-standard corner cone mirror (HCCCR); Step 2, establishing an evaluation benchmark for three-axis MEMS gyroscope measurement data by orthogonally combining two large-range autocollimators; Step 3, establishing a common data acquisition serial port between the autocollimator camera and the IMU host computer, and achieving dimensional unification and time synchronization between the autocollimator and MEMS gyroscope output readings by integrating the MEMS gyroscope angular rate measurement readings with time; Step 4, calibrating the MEMS gyroscope installation error coefficient using a traditional precision angular rate output turntable and conducting a comparative experiment. The experiment shows that the relative deviation of the IMU output after compensation by the two calibration methods is less than 0.3%.

The application discloses a high-sensitivity fast acquisition method and system in a star-ground dynamic environment, and the method comprises the following steps: S1, a transmitting end transmits a communication signal conforming to a target communication frequency, and a receiving end receives the communication signal; S2, a plurality of local signals with different frequency points are generated in a frequency search interval through a local parallel digital control oscillator (NCO) group, and FFT-IFFT acquisition determination is performed on the communication signal; S3, if the acquisition determination fails, the frequency search interval is adjusted, and the step S2 is performed again; if the acquisition determination fails in two adjacent frequency search intervals, sideband interval estimation is performed on the two adjacent frequency search intervals, and FFT-IFFT acquisition determination is performed according to the sideband interval estimation result; the step is repeated until the acquisition determination succeeds. Under the conditions that the detection probability is more than 99%, the minimum carrier-to-noise ratio is 34.66 dB, and the minimum signal-to-noise ratio is 16 dB, the receiver sensitivity of-130 dBm can be realized, the total Doppler frequency offset acquisition range can reach 140 kHz, and the acquisition time can be as low as 0.8 seconds.