Method for forming multi-dimensional curve graph by intercepting valid data based on labview

Abstract

The invention discloses a method for forming a multi-dimensional curve graph by intercepting valid data based on labview, and belongs to the technical field of data processing, the method for forming the multi-dimensional curve graph by intercepting valid data based on labview comprises the following specific steps: step 1, dynamic data acquisition: using a DAQmx driving module of LabVIEW to connect data acquisition hardware, the model is NI-9234, and the DAQmx driving module is connected with the DAQmx driving module of LabVIEW, and the DAQmx driving module of LabVIEW is connected with the DAQmx driving module of LabVIEW; the sampling rate is configured to be 10 kHz, the number of channels and the measuring range, and a continuous acquisition mode is started through hardware triggering or software timing. According to the method, compiling is carried out based on a labview language, a loop and judgment statement is added, first-step screening is carried out, needed frequency is screened, frequency indexing is carried out through an index of a one-dimensional array, a numerical value corresponding to the frequency is found out, a two-dimensional array is reformed, circulation is carried out through an XY graph, and a plurality of data curve graphs are generated; the required test result curve graph can be generated only by adding the processing VI at the output end without adding more judgment conditions in the previous program, and compared with a traditional method, the method is more flexible.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a method for forming a multidimensional curve graph by intercepting effective data based on LabVIEW. Background Art

[0002] Radio Frequency Automated Testing refers to the automatic completion of performance parameter testing of radio frequency devices, modules or terminal equipment through integrated software and hardware systems, replacing the traditional manual test method. Its core lies in program-controlled instruments, standardized processes and data intelligence, and is widely used in wireless communications, semiconductors, consumer electronics and other fields.

[0003] In the automated testing of RF components, test data usually contains a large amount of invalid or redundant information (such as non-target frequency band signals, environmental noise, etc.). Traditional methods require complex judgment conditions to be preset at the test front end, resulting in the following problems: Insufficient flexibility: unable to dynamically adapt to changes in test parameters (such as frequency offset, harmonic interference); Inefficiency: Full data processing takes up a lot of computing resources, affecting real-time performance; Lack of visualization: multi-dimensional data (frequency-power-time) is difficult to present intuitively and relies on manual analysis. Existing technologies use fixed threshold filtering, but cannot meet the dynamic requirements of complex RF scenarios. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the disadvantages of the above-mentioned prior art and provide a method for intercepting effective data based on LabVIEW to form a multi-dimensional curve graph.

[0005] The technical solution adopted to solve the above technical problem is: a method for intercepting valid data based on LabVIEW to form a multi-dimensional curve graph, including the following specific steps: Step 1: Dynamic data acquisition: Use LabVIEW's DAQmx driver module to connect the data acquisition hardware, model NI-9234, configure the sampling rate to 10 kHz, the number of channels, and the range, and start the continuous acquisition mode through hardware triggering or software timing; Step 2: Data caching: Ring buffer management: Initialize the buffer first, then monitor the data write / read pointer position in real time. If the remaining space is insufficient, expand the buffer according to the exponential growth strategy; Step 3: Valid data capture: After noise filtering and feature triggering, the data is optimized and then compressed and stored; Step 4: Set the parameter range and judge the data. If the data exceeds the parameter range, the data will be discarded. If the data is within the parameter range, the corresponding data of all columns within the parameter range will be found through the index. Step 5: Combine all the indexed data with the parameters to form a new array and generate a multi-dimensional curve graph.

[0006] Through the above technical solution, there is no need to add more judgment conditions in the previous program. As long as a processing VI is added at the output end, the required test result curve chart can be generated, which is more flexible than the traditional method.

[0007] Furthermore, the collected raw data is stored in real time through a circular buffer, and the noise is removed by combining wavelet transform with sliding window filtering. The valid data segments are dynamically intercepted and the boundaries are extended according to the preset features. The intercepted data is mapped to a multi-dimensional coordinate system and the dynamic curve is rendered by GPU acceleration.

[0008] Through the above technical solution, the accuracy of data collection can be greatly improved, and subsequent processing can be facilitated.

[0009] Furthermore, in the effective data interception step, the trigger conditions include amplitude mutation, frequency component change or user-defined logical expression, and the multidimensional curve graph in step five supports at least three-dimensional data visualization and represents the fourth dimension information through color gradient.

[0010] Through the above technical solutions, data can be visualized and data analysis capabilities can be improved.

[0011] Further, the buffer is initialized according to the sampling rate and expected maximum delay Calculating initial buffer size , and achieve multi-threaded reading and writing synchronization through LabVIEW's queue and notifier to avoid data overwriting and loss.

[0012] Through the above technical solution, data security is improved and data errors are avoided.

[0013] Furthermore, the noise filtering includes wavelet transform denoising, selecting the Daubechies wavelet basis, performing a three-layer decomposition on the original signal, filtering out high-frequency noise components by a soft threshold method, and reconstructing the signal. The noise filtering also includes a sliding window mean filter, performing window smoothing on the reconstructed signal in the time domain, and dynamically adjusting the window length according to the signal characteristics. The specific dynamic adjustment formula is as follows: Where W is the window length, is the sampling rate; Wavelet denoising focuses on global noise suppression, while sliding windows process local fluctuations. The two are weightedly fused, with the wavelet result accounting for 70% and the mean accounting for 30%, to enhance robustness.

[0014] Furthermore, the feature trigger mechanism includes amplitude mutation detection and calculation of the root mean square (RMS) of the signal in the sliding window. If the RMS of five consecutive windows exceeds the threshold, it is determined to be a valid segment. At the same time, the signal spectrum is calculated in real time through the FFT module of LabVIEW. If the energy proportion of a specific frequency band of 500-1000 Hz exceeds the set value and lasts for 1.5-2 seconds, interception is triggered.

[0015] Furthermore, the boundary optimization includes extending the data within the 5% range at both ends of the trigger point forward and backward after the trigger interception, and the expansion logic is: Backtracking: Search 5-7 points forward from the trigger point index to ensure that potential rising edges are included; Backward extension: If the subsequent data does not trigger a new event within 2 seconds, the redundant part will be automatically truncated; The original data segments are marked by timestamps to preserve contextual information.

[0016] Furthermore, the data compression storage includes normalizing the intercepted data segments, specifically converting them into 16-bit integers, generating a coding table by counting symbol frequencies, and implementing compression using the "OpenG" toolkit of LabVIEW.

[0017] The beneficial effects of the present invention are as follows: the present invention is written based on the labview language, and a loop plus a judgment statement is added to perform the first step of screening, screening the required frequency, indexing the frequency through the index of the 1-dimensional array, finding the numerical value corresponding to the frequency, reorganizing the 2-dimensional array, and looping through the XY graph to generate multiple data curve graphs. There is no need to add more judgment conditions to the previous program. As long as a processing VI is added at the output end, the required test result curve graph can be generated. Compared with the traditional method, it is more flexible, can dynamically adapt to changes in test parameters, greatly improves efficiency, does not require complex judgment conditions, and at the same time visualizes the data through the data curve graph, which is convenient for data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a block diagram of the judgment program part in the VI program of the present invention; Figure 3 It is a block diagram of the frequency index portion corresponding to the test result required in the VI program of the present invention; Figure 4 It is a block diagram of the curve graph generated in the VI program of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1-Figure 4 As shown, the method of this embodiment of intercepting valid data based on LabVIEW to form a multi-dimensional curve graph includes the following specific steps: Step 1: Dynamic data acquisition: Use LabVIEW's DAQmx driver module to connect the data acquisition hardware, model NI-9234, configure the sampling rate to 10 kHz, the number of channels, and the range, and start the continuous acquisition mode through hardware triggering or software timing; Step 2: Data caching: Ring buffer management: Initialize the buffer first, then monitor the data write / read pointer position in real time. If the remaining space is insufficient, expand the buffer according to the exponential growth strategy; Step 3: Valid data capture: After noise filtering and feature triggering, the data is optimized and then compressed and stored; Step 4: Set the parameter range and judge the data. If the data exceeds the parameter range, the data will be discarded. If the data is within the parameter range, the corresponding data of all columns within the parameter range will be found through the index. Step 5: Combine all the indexed data with the parameters to form a new array and generate a multi-dimensional curve graph. There is no need to add more judgment conditions in the previous program. Just add a processing VI at the output end to generate the required test result curve graph, which is more flexible than the traditional method.

[0021] The collected raw data is stored in real time in a circular buffer, and noise is removed by combining wavelet transform with sliding window filtering. The valid data segments are dynamically intercepted and the boundaries are expanded according to preset features. The intercepted data is mapped to a multi-dimensional coordinate system and the dynamic curve is rendered by GPU acceleration. This can greatly improve the accuracy of data during collection and facilitate subsequent processing.

[0022] In the effective data interception step, the trigger conditions include amplitude mutation, frequency component change or user-defined logical expression. The multidimensional curve chart in step five supports at least three-dimensional data visualization and represents the fourth dimension information through color gradient, which can visualize the data and improve data analysis capabilities.

[0023] The buffer is initialized according to the sampling rate and expected maximum delay Calculating initial buffer size And through LabVIEW's queue (Queue) and notifier (Notifier) ​​to achieve multi-threaded reading and writing synchronization, avoid data overwriting and loss, improve data security, and avoid data errors.

[0024] The noise filtering includes wavelet transform denoising, selecting Daubechies wavelet basis, performing 3-layer decomposition on the original signal, filtering out high-frequency noise components by soft threshold method, and reconstructing the signal. The noise filtering also includes sliding window mean filtering, performing window smoothing on the reconstructed signal in the time domain, and dynamically adjusting the window length according to the signal characteristics. The specific dynamic adjustment formula is as follows: Where W is the window length, is the sampling rate; Wavelet denoising focuses on global noise suppression, while sliding windows process local fluctuations. The two are weightedly fused, with the wavelet result accounting for 70% and the mean accounting for 30%, to enhance robustness.

[0025] The feature trigger mechanism includes amplitude mutation detection and calculation of the root mean square (RMS) of the signal in the sliding window. If the RMS of five consecutive windows exceeds the threshold, it is determined to be a valid segment. At the same time, the signal spectrum is calculated in real time through the FFT module of LabVIEW. If the energy proportion in the frequency band of 500-1000 Hz exceeds the set value and lasts for 1.5-2 seconds, interception is triggered.

[0026] The boundary optimization includes extending the data within the 5% range at both ends of the trigger point forward and backward after the trigger interception, and the expansion logic is: Backtracking: Search 5-7 points forward from the trigger point index to ensure that potential rising edges are included; Backward extension: If the subsequent data does not trigger a new event within 2 seconds, the redundant part will be automatically truncated; The original data segments are marked by timestamps to preserve contextual information.

[0027] The data compression storage includes normalizing the intercepted data segments, specifically converting them into 16-bit integers, generating a coding table by counting symbol frequencies, and realizing compression using the "OpenG" toolkit of LabVIEW.

[0028] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for intercepting valid data based on LabVIEW to form a multidimensional curve graph, characterized in that: The specific steps include: Step 1: Dynamic data acquisition: Use LabVIEW's DAQmx driver module to connect the data acquisition hardware, model NI-9234, configure the sampling rate to 10 kHz, the number of channels, and the range, and start the continuous acquisition mode through hardware triggering or software timing; Step 2: Data caching: Ring buffer management: Initialize the buffer first, then monitor the data write / read pointer position in real time. If the remaining space is insufficient, expand the buffer according to the exponential growth strategy; Step 3: Valid data capture: After noise filtering and feature triggering, the data is optimized and then compressed and stored; Step 4: Set the parameter range and judge the data. If the data exceeds the parameter range, the data will be discarded. If the data is within the parameter range, the corresponding data of all columns within the parameter range will be found through the index. Step 5: Combine all the indexed data with the parameters to form a new array and generate a multi-dimensional curve graph.

2. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: The collected raw data is stored in real time through a circular buffer, and noise is removed by combining wavelet transform with sliding window filtering. The valid data segment is dynamically intercepted and the boundary is expanded according to the preset features. The intercepted data is mapped to a multi-dimensional coordinate system and the dynamic curve is rendered by GPU acceleration.

3. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: In the effective data interception step, the triggering conditions include amplitude mutation, frequency component change or user-defined logical expression, and the multidimensional curve graph in step five supports at least three-dimensional data visualization and represents the fourth dimensional information through color gradient.

4. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: The buffer is initialized according to the sampling rate and expected maximum delay Calculating initial buffer size , and achieve multi-threaded reading and writing synchronization through LabVIEW's queue and notifier to avoid data overwriting and loss.

5. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: The noise filtering includes wavelet transform denoising, selecting Daubechies wavelet basis, performing 3-layer decomposition on the original signal, filtering out high-frequency noise components by soft threshold method, and reconstructing the signal. The noise filtering also includes sliding window mean filtering, performing window smoothing on the reconstructed signal in the time domain, and dynamically adjusting the window length according to the signal characteristics. The specific dynamic adjustment formula is as follows: Where W is the window length, is the sampling rate; Wavelet denoising focuses on global noise suppression, while sliding windows process local fluctuations. The two are weightedly fused, with the wavelet result accounting for 70% and the mean accounting for 30%, to enhance robustness.

6. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: The feature trigger mechanism includes amplitude mutation detection and calculation of the root mean square (RMS) of the signal in the sliding window. If the RMS of five consecutive windows exceeds the threshold, it is determined to be a valid segment. At the same time, the signal spectrum is calculated in real time through the FFT module of LabVIEW. If the energy proportion in the frequency band of 500-1000 Hz exceeds the set value and lasts for 1.5-2 seconds, interception is triggered.

7. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: The boundary optimization includes extending the data within the 5% range at both ends of the trigger point forward and backward after the trigger interception, and the expansion logic is: Backtracking: Search 5-7 points forward from the trigger point index to ensure that potential rising edges are included; Backward extension: If the subsequent data does not trigger a new event within 2 seconds, the redundant part will be automatically truncated; The original data segments are marked by timestamps to preserve contextual information.

8. The method for forming a multidimensional curve graph based on LabVIEW intercepting valid data according to claim 1, characterized in that: The data compression storage includes normalizing the intercepted data segments, specifically converting them into 16-bit integers, generating a coding table by counting symbol frequencies, and implementing compression using the "OpenG" toolkit of LabVIEW.

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