Multi-information field intelligent testing method of intelligent oscilloscope

Abstract

The application discloses a kind of multi-information field intelligent test methods of intelligent oscilloscope, first in real-time file under the signal to be tested is collected and stored in DDR, again through the time-frequency analysis of the collected data in DDR, and based on the trained YOLO neural network, the time-frequency chart is identified, the center frequency and Span parameter of time-frequency event are obtained, the mixing frequency and extraction ratio of down converter are adjusted adaptively according to the parameter;The frequency spectrum of the collected signal is moved to the baseband by down conversion, and the sampling rate is reduced to realize higher frequency resolution.

Description

Technical Field

[0001] This invention belongs to the field of digital oscilloscope technology, and more specifically, relates to a multi-information domain intelligent testing method for intelligent oscilloscopes. Background Technology

[0002] With the rapid development of modern electronic information technology, digital oscilloscopes are measuring signals with increasingly complex and diverse characteristics. Signal bandwidth is widening, transmission rates are rising, and non-stationary characteristics are also rapidly increasing. Simply analyzing signals from the time domain perspective is no longer sufficient; frequency domain and time-frequency domain analysis capabilities are becoming increasingly important. There is a growing demand for measuring instruments that can analyze not only the time domain characteristics of signals but also their characteristics in other domains such as the frequency domain, time-frequency domain, and amplitude domain.

[0003] However, in traditional electronic measurement platforms, the acquisition of high-speed signals often faces limitations in hardware performance, especially during long-term recording, where storage and processing speeds become bottlenecks. At the same time, with the rapid increase in the amount of test signal data, how to effectively store and process this large volume of data has become a challenge. Furthermore, the increasingly complex and diverse characteristics of measurement signals make the introduction of more intelligent and automated technologies imperative to improve the efficiency and accuracy of data analysis. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-information domain intelligent testing method for intelligent oscilloscopes. This method can achieve efficient time-frequency analysis of large amounts of data with a delay of up to a second, and can focus on and perform personalized analysis on information-dense areas containing time-frequency events, thereby improving the accuracy and efficiency of data analysis.

[0005] To achieve the above-mentioned objectives, the present invention provides a multi-information domain intelligent testing method for intelligent oscilloscopes, characterized by comprising the following steps:

[0006] (1) Set the parameters of the digital oscilloscope;

[0007] Input the signal to be tested into the digital oscilloscope, and then adjust the time base and amplitude settings of the digital oscilloscope. Set the time base to the real-time setting and adjust the amplitude setting of the digital oscilloscope so that the amplitude of the signal to be tested is greater than 6 divisions but does not exceed the full screen.

[0008] (2) Acquire the signal to be measured;

[0009] The signal under test is acquired in real time for a duration of T, and then the acquired data is stored in DDR.

[0010] T = D / F s

[0011] Among them, F sWhere is the sampling rate of the digital oscilloscope, and D is the memory depth of DDR;

[0012] (3) Read the acquired data from DDR to generate a two-dimensional time-frequency diagram;

[0013] (4) Train the YOLO network model;

[0014] Each two-dimensional time-frequency image is used as the input to the YOLO network model, and the output is the coordinates (x,y), region (w,h), and signal category of the time-frequency event in the two-dimensional time-frequency image. The trained YOLO network model is obtained through repeated iterative training.

[0015] (5) Intelligent recognition of time-frequency graphs;

[0016] The industrial control computer reads a set of acquired data of length L from the DDR and generates a two-dimensional time-frequency graph;

[0017] The two-dimensional time-frequency plot is input into the trained YOLO network model to obtain the coordinates (x,y), region (w,h), and signal category of the time-frequency event;

[0018] (6) Calculate the center frequency f of the time-frequency event. c and frequency width (Span);

[0019] (7) Update the parameters of the digital oscilloscope;

[0020] The center frequency f c The frequency width (Span) is sent to the down-converter, and the mixing frequency of the down-converter is adjusted to f. c The extraction multiplier is

[0021] (8) The data collected in step (2) is down-converted by the downconverter to shift the spectrum to the baseband, and the sampling rate becomes F'. s =Span; Then perform a 1k-point FFT transformation on the down-converted signal to obtain the signal spectrum. Finally, upload the spectrum to the display and automatically display the analysis view belonging to the signal type. The analysis view includes frequency domain analysis, modulation domain analysis and protocol analysis.

[0022] The objective of this invention is achieved as follows:

[0023] This invention discloses a multi-information domain intelligent testing method for an intelligent oscilloscope. First, the signal under test is acquired in real-time mode and stored in DDR. Then, time-frequency analysis is performed on the acquired data in DDR, and the time-frequency graph is identified based on a trained YOLO neural network to obtain the center frequency and Span parameter of the time-frequency event. Based on the parameter, the mixing frequency and decimation rate of the downconverter are adaptively adjusted. By downconverting, the spectrum of the acquired signal is shifted to the baseband, while the sampling rate is reduced to achieve higher frequency resolution.

[0024] Meanwhile, the multi-information domain intelligent testing method for intelligent oscilloscopes of the present invention also has the following beneficial effects:

[0025] (1) Automatic detection of time-frequency events is achieved by identifying the time-frequency distribution of the data collected in the DDR.

[0026] (2) By automatically adjusting the mixing frequency and decimation rate of the downconverter, the spectrum of the acquired signal is shifted to the baseband to achieve automatic spectrum tracking. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the multi-information domain intelligent testing principle of an intelligent oscilloscope;

[0028] Figure 2 This is a schematic diagram of a two-dimensional time-frequency graph;

[0029] Figure 3 It is a time-frequency event identifier diagram in a two-dimensional time-frequency graph;

[0030] Figure 4 This is a flowchart of the downconverter processing the collected data;

[0031] Figure 5 This is an analytical view of a linear frequency modulated signal. Detailed Implementation

[0032] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0033] Example

[0034] Figure 1 This is a schematic diagram of the multi-information domain intelligent testing principle of an intelligent oscilloscope.

[0035] In this embodiment, as Figure 1 As shown, the present invention provides a multi-information domain intelligent testing method for an intelligent oscilloscope, comprising the following steps:

[0036] (1) Set the parameters of the digital oscilloscope;

[0037] Input the signal to be tested into the digital oscilloscope, and then adjust the time base and amplitude settings of the digital oscilloscope. Set the time base to the real-time setting and adjust the amplitude setting of the digital oscilloscope so that the amplitude of the signal to be tested is greater than 6 divisions but does not exceed the full screen.

[0038] (2) Acquire the signal to be measured;

[0039] The signal under test is acquired in real time for a duration of T, and then the acquired data is stored in DDR.

[0040] T = D / F s

[0041] Among them, F s Where is the sampling rate of the digital oscilloscope, and D is the memory depth of DDR;

[0042] (3) Generate a time-frequency diagram;

[0043] (3.1) Set the parameters for time-frequency analysis, including the width W and height H of the time-frequency plot;

[0044] (3.2) Calculate the data length L and address step S of the industrial control computer reading the acquired data from DDR;

[0045]

[0046] (3.3) The industrial control computer reads data of length L from the DDR with address step S, and reads a total of n sets of data.

[0047] (3.4) Perform FFT transformation on each set of collected data at H points, for a total of W times, to obtain a spectrum matrix of size W*H;

[0048] (3.5) The industrial control computer maps each group of spectrum matrices into a two-dimensional time-frequency diagram, resulting in a total of n two-dimensional time-frequency diagrams;

[0049] (4) Train the YOLO network model;

[0050] Each two-dimensional time-frequency image is used as the input to the YOLO network model, and the output is the coordinates (x,y), region (w,h), and signal category of the time-frequency event in the two-dimensional time-frequency image. The trained YOLO network model is obtained through repeated iterative training.

[0051] (5) Intelligent recognition of time-frequency graphs;

[0052] The industrial control computer reads a set of collected data of length L from the DDR, and then generates a two-dimensional time-frequency diagram according to steps (3.4) and (3.5);

[0053] In this embodiment, the two-dimensional time-frequency diagram is as follows: Figure 2 As shown, the horizontal axis of the time-frequency graph represents time, the vertical axis represents frequency, and the color and brightness represent energy intensity.

[0054] The two-dimensional time-frequency plot is input into the trained YOLO network model to obtain the coordinates (x,y), region (w,h), and signal category of the time-frequency event;

[0055] In this embodiment, as Figure 3 As shown in the figure, the box contains the identified time-frequency events. The coordinates of the lower left corner of the box are (x, y), the width of the box is w, and the height is h.

[0056] (6) Calculate the center frequency f of the time-frequency event. c and frequency width (Span);

[0057]

[0058] (7) Update the parameters of the digital oscilloscope;

[0059] The center frequency f c The frequency width (Span) is sent to the down-converter, and the mixing frequency of the down-converter is adjusted to f. c The extraction multiplier is

[0060] (8) The data collected in step (2) is down-converted by the downconverter to shift the spectrum to the baseband, and the sampling rate becomes F'. s =Span; In this embodiment, the process of the down-converter processing the acquired data is as follows: Figure 4 As shown, a baseband signal with a reduced sampling rate is obtained through mixing and decimation. Finally, a 1k-point FFT transformation is performed on the down-converted baseband signal to obtain the signal spectrum. The spectrum is then uploaded to the display, and an analysis view belonging to the signal type is automatically displayed. The analysis view includes frequency domain analysis, modulation domain analysis, and protocol analysis.

[0061] In this embodiment, taking a linear frequency modulated (LFM) signal as an example, the center frequency of the time-frequency event is identified as 100MHz, and the frequency bandwidth is 100MHz. Then, the mixing frequency and decimation rate of the downconverter are updated. After the LFM signal is processed in step (8), the spectrum of the signal is obtained, and then the spectrum is uploaded to the display for display. At the same time, the analysis view of the LFM signal is displayed, such as... Figure 5 As shown, the top figure is the time-frequency diagram of the linear frequency modulated (LFM) signal, the middle figure is the spectrum diagram of the LFM signal, and the bottom figure is the time-domain waveform of the LFM signal.

[0062] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A multi-information domain intelligent testing method for an intelligent oscilloscope, characterized in that, Includes the following steps: (1) Set the parameters of the digital oscilloscope; Input the signal to be tested into the digital oscilloscope, and then adjust the time base and amplitude settings of the digital oscilloscope. Set the time base to the real-time setting and adjust the amplitude setting of the digital oscilloscope so that the amplitude of the signal to be tested is greater than 6 divisions but does not exceed the full screen. (2) Acquire the signal to be measured; The signal under test is acquired in real time for a duration of T, and then the acquired data is stored in DDR. T=D / F s Among them, F s Where is the sampling rate of the digital oscilloscope, and D is the memory depth of DDR; (3) Read the acquired data from DDR to generate a two-dimensional time-frequency diagram; (4) Train the YOLO network model; Each two-dimensional time-frequency image is used as the input to the YOLO network model, and the output is the coordinates (x,y), region (w,h), and signal category of the time-frequency event in the two-dimensional time-frequency image. The trained YOLO network model is obtained through repeated iterative training. (5) Intelligent recognition of time-frequency graphs; The industrial control computer reads a set of acquired data of length L from the DDR and generates a two-dimensional time-frequency graph; The two-dimensional time-frequency plot is input into the trained YOLO network model to obtain the coordinates (x,y), region (w,h), and signal category of the time-frequency event; (6) Calculate the center frequency f of the time-frequency event. c and frequency width (Span); (7) Update the parameters of the digital oscilloscope; The center frequency f c The frequency width (Span) is sent to the down-converter, and the mixing frequency of the down-converter is adjusted to f. c The extraction multiplier is (8) The data collected in step (2) is down-converted by the downconverter to shift the spectrum to the baseband, and the sampling rate becomes F'. s =Span; Then perform a 1k-point FFT transformation on the down-converted signal to obtain the signal spectrum. Finally, upload the spectrum to the display and automatically display the analysis view belonging to the signal type. The analysis view includes frequency domain analysis, modulation domain analysis and protocol analysis.

2. The multi-information domain intelligent testing method for an intelligent oscilloscope according to claim 1, characterized in that, The method for generating a two-dimensional time-frequency diagram by reading the collected data in step (3) is as follows: (2.1) Set the parameters for time-frequency analysis, including the width W and height H of the time-frequency plot; (2.2) Calculate the data length L and address step S of the industrial control computer reading the acquired data from DDR; (2.3) The industrial control computer reads data of length L from the DDR with address step S, and reads a total of n sets of data. (2.4) Perform FFT transformation on each set of collected data at H points, for a total of W times, to obtain a spectrum matrix of size W*H; (2.5) The industrial control computer maps each group of spectrum matrices into a two-dimensional time-frequency diagram, resulting in a total of n two-dimensional time-frequency diagrams.

3. The multi-information domain intelligent testing method for an intelligent oscilloscope according to claim 1, characterized in that, The center frequency f of the time-frequency event c The formula for calculating the frequency bandwidth (Span) is:

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