Ultra-long-distance rotation speed sensing and measuring method and system based on LoRa signal

Abstract

The invention discloses an ultra-long-distance rotation speed sensing and measuring method and system based on LoRa signals. The method comprises the following steps: sending a LoRa rising modulation signal as a sensing signal through a signal transceiving module, capturing a signal reflected by the signal when the signal encounters a rotating target as sensing original data, and recording the signal during sending as a sensing reference signal; performing signal alignment on the sensing reference signal and the sensing original data through a signal demodulation module, and performing signal demodulation on the sensing original data based on a predefined LoRa descent modulation signal; performing sensing enhancement operation on the demodulated sensing original data through a sensing enhancement module; the specific value of peak energy in the frequency spectrum is calculated through a feature processing module, and different frequencies are automatically distinguished according to a self-adaptive threshold value. According to the invention, the problem that the traditional wireless sensing distance is limited can be solved, and the problem of low sensing upper limit caused by the limited chirp frequency of the LoRa signal itself can be solved.

Description

Technical Field

[0001] The present invention belongs to the field of wireless sensing research, and in particular, relates to a method and system for realizing ultra-long-distance high-frequency rotation speed accurate sensing measurement using LoRa signals, providing a new solution for equipment monitoring and detection in industrial Internet of Things scenarios. Background Art

[0002] Rotation is a fundamental form of motion that is widely present in daily life and industrial scenarios. Measuring its speed is essential for monitoring the health of rotating machinery. To achieve this goal, a variety of rotation speed measurement systems have been developed, which are generally divided into two categories: contact and non-contact.

[0003] Contact systems require contact with the rotating shaft, but contact can cause serious safety issues when dealing with large or high-speed rotating targets. Existing non-contact systems mainly rely on electromagnetic signals, optical signals, or wireless signals to measure rotation speed. Systems based on electromagnetic signals typically use electrostatic effects or Hall effect sensors to detect changes in electromagnetic fields caused by rotation, but their measurement distance is very limited. Optical systems use optical sensors such as cameras and laser receivers to measure rotation speed, but they have strict requirements on lighting conditions and cannot work properly in low-light environments. There are also some systems that use wireless signals such as sound waves and radio frequency signals to measure rotation speed, but they still have limitations in long-distance measurement. When dealing with large mechanical equipment, the limited measurement distance of existing technologies may lead to safety risks. Therefore, in order to reduce these safety risks, it is urgent to develop a rotation speed system that can perform long-distance and accurate measurements.

[0004] In recent years, LoRa signals have shown great potential in ultra-long-distance transmission in the industrial Internet of Things, and there are also studies on using LoRa signals to achieve long-distance perception. For example, LoRa signals can extend the range of breathing perception to 70 meters, demonstrating its potential in long-distance perception. However, due to the low chirp rate of the LoRa signal itself, the existing LoRa-based perception system is only suitable for low-frequency motion perception, rather than high-frequency motion, such as the perception of rotational motion. Summary of the invention

[0005] The purpose of the present invention is to provide an ultra-long-distance rotation speed perception measurement method and system based on LoRa signals, so as to solve the problem of limited traditional wireless perception distance and the low perception problem caused by the limited chirp frequency of the LoRa signal itself, and provide a new solution for equipment monitoring and detection in industrial Internet of Things scenarios.

[0006] In order to achieve the above invention object, the technical solution of the present invention is as follows:

[0007] In a first aspect, a method for sensing and measuring rotation speed over a long distance based on LoRa signals comprises the following steps:

[0008] The LoRa up-modulated signal is sent as a sensing signal through the signal transceiver module, and the signal reflected by the sensing signal encountering the rotating target is captured as the sensing original data, and the signal during transmission is recorded as the sensing reference signal;

[0009] The signal demodulation module performs signal alignment on the sensing reference signal and the sensing raw data, and performs signal demodulation on the sensing raw data based on the predefined LoRa down-modulation signal;

[0010] The perceptual enhancement module performs a perceptual enhancement operation on the demodulated perceptual raw data, wherein the perceptual enhancement operation includes segmenting the received continuous signal, performing frequency domain analysis on each signal segment, and extracting the frequency position and phase information corresponding to the main peak;

[0011] Through the feature processing module, the ratio of the peak energy in the spectrum is calculated according to the signal spectrum after perception enhancement, and different frequencies are automatically distinguished according to the adaptive threshold.

[0012] Furthermore, the signal demodulation module performs signal alignment on the sensing reference signal and the sensing original data, and performs signal demodulation on the sensing original data based on a predefined LoRa down-modulation signal, including:

[0013] Perform a cross-correlation operation on the received sensing raw data and the sensing reference signal to obtain the peak value when the two signals reach an aligned state, determine the length of the LoRa symbol according to the position of the peak value, and divide the received signal into independent LoRa symbols;

[0014] The aligned sensing raw data is mixed with the predefined LoRa down-modulation signal to extract the baseband component of the signal, and the mixed signal is low-pass filtered to obtain the demodulated signal.

[0015] Furthermore, the predefined LoRa down modulation signal is a LoRa down modulation signal generated according to preset parameters and matching the received signal, and the preset parameters include one or more of bandwidth, chirp rate, and center frequency.

[0016] Furthermore, the perception enhancement operation includes segmenting the received continuous signal, performing frequency domain analysis on each signal segment, and extracting the frequency position and phase information corresponding to the main peak, including:

[0017] The received continuous signal is segmented according to the specified time window, and the signal segment in each time window will be used as an independent analysis unit;

[0018] Perform fast Fourier transform on each signal segment, convert the time domain signal into the frequency domain signal, obtain the amplitude and phase information of the signal in the frequency domain, and generate the corresponding spectrum diagram;

[0019] The generated spectrum is analyzed to identify the main frequency components and potential noise components in the spectrum, and the frequency position and phase information corresponding to the main peak are extracted.

[0020] Furthermore, after the received continuous signal is segmented according to the specified time window, the method further includes: pre-processing each signal segment using an overlapping segmentation method.

[0021] Furthermore, according to the perceptually enhanced signal spectrum, the ratio of the peak energy in the spectrum is calculated, and different frequencies are automatically distinguished according to the adaptive threshold, including:

[0022] According to the reference signal, a corresponding processing model is selected, and the selection of the processing model depends on the expected range of the target rotation speed;

[0023] If the target rotation speed falls within range 1, determine whether there is harmonic interference in the spectrum. If there is harmonic interference, select the peak with the lowest frequency as the final measurement result. If no harmonic interference is detected, select the frequency corresponding to the peak with the largest energy in the spectrum as the representative frequency of the target rotation speed.

[0024] If the target rotation speed falls within range 2 and two obvious peaks appear in the spectrum, calculate the energy ratio between the highest peak and the surrounding noise, calculate the adaptive threshold based on the energy ratio, and calculate the ratio between the maximum peak energy and the second largest peak energy. If the ratio is greater than the calculated adaptive threshold, select the second largest peak as the final measurement result, otherwise, select the maximum peak as the measurement result; if there is only one obvious peak in the spectrum, directly select the peak as the measurement result of the rotation speed.

[0025] Furthermore, the formula for calculating the adaptive threshold based on the energy ratio is m×10 m , m is the energy ratio.

[0026] Furthermore, the target rotation speed range 1 is a rotation speed less than or equal to 40 rpm; and the target rotation speed range 2 is a rotation speed greater than 40 rpm.

[0027] In the second aspect, an ultra-long distance rotation speed sensing and measuring system based on LoRa signals includes:

[0028] The signal transceiver module is used to send the LoRa up-modulation signal as the sensing signal, and capture the signal reflected by the sensing signal encountering the rotating target as the sensing original data, and record the signal when sending as the sensing reference signal;

[0029] A signal demodulation module, used for signal alignment of the sensing reference signal and the sensing raw data, and signal demodulation of the sensing raw data based on a predefined LoRa down-modulation signal;

[0030] A perception enhancement module is used to perform a perception enhancement operation on the demodulated perception raw data, wherein the perception enhancement operation includes segmenting the received continuous signal, performing frequency domain analysis on each signal segment, and extracting the frequency position and phase information corresponding to the main peak;

[0031] The feature processing module is used to calculate the ratio of peak energy in the spectrum according to the signal spectrum after perception enhancement, and automatically distinguish different frequencies according to the adaptive threshold.

[0032] Beneficial effects: The present invention proposes an ultra-long-distance perception measurement method and system based on LoRa signals, which realizes the accurate measurement of ultra-long-distance rotation speed through innovative technical design. First, the perception enhancement module of the present invention significantly improves the sampling rate of the signal, so that it can maintain high accuracy in long-distance perception. Secondly, the feature processing module further optimizes the signal processing process, accurately identifies the target rotation frequency in the spectrum, and effectively eliminates the influence of environmental interference and noise. By integrating the above modules, the present invention not only improves the overall robustness of the system, but also ensures that accurate ultra-long-distance rotation speed measurement can still be achieved in complex environments. Experimental evaluation shows that the method of the present invention can achieve reliable perception within a distance of 50 meters, and keep the average measurement error below 0.69% at various rotation speeds. This breakthrough provides a new solution for equipment monitoring and detection in industrial Internet of Things scenarios, and significantly improves the application potential and practical effectiveness of wireless sensing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0034] Figure 2 It is a schematic diagram of the signal demodulation module flow in the present invention;

[0035] Figure 3 Schematic diagram of the perception enhancement module in the present invention;

[0036] Figure 4 Schematic diagram of the adaptive threshold of the feature processing module in the present invention;

[0037] Figure 5Schematic diagram of measurement error at different rotation speeds in the present invention. DETAILED DESCRIPTION

[0038] The present invention proposes an ultra-long-distance rotation speed sensing and measurement system based on LoRa signals. The system overcomes the limitations of traditional technologies through innovative signal processing methods. The present invention designs four novel signal processing modules. Figure 1 First, the present invention uses a signal transceiver module and a signal demodulation module to preprocess the received signal. Next, the signal-to-noise ratio (SNR) of the signal is further improved through the perception enhancement module, and the sampling rate of perception is significantly improved. Finally, the preprocessed signal is accurately analyzed and calculated through the feature processing module to obtain the rotation speed of the target. Through the close collaboration of these four modules, the present invention can not only realize long-distance and accurate rotation speed perception measurement in the presence of environmental interference and obstacles, but also greatly improve the overall performance and stability of the system.

[0039] (I) Signal transceiver module

[0040] In order to achieve non-contact rotational speed measurement, the present invention sends a LoRa up-modulated signal as a sensing signal through a signal transceiver module. After being sent, this signal will encounter a rotating target and be reflected back. The signal transceiver module then captures these reflected signals as sensing raw data, and records the signal during transmission as a sensing reference signal. These two signal data are then output by the signal transceiver module in preparation for further signal processing and rotational speed calculation. The signal transceiver module includes a data acquisition unit, which is used to record and output raw data and reference signals for subsequent signal alignment and demodulation processing, thereby ensuring the continuity of signal processing and data integrity. This process ensures that the system can accurately sense and measure the rotational speed of the target without direct contact with the target, thereby avoiding the risks and limitations of traditional contact measurement methods.

[0041] According to an embodiment of the present invention, the signal transceiver module includes a USRP (Universal Software Radio Peripheral) device, which generates and sends LoRa up-modulated signals through a flexible software-defined radio platform and receives signals reflected from a target object. The USRP device accurately captures signals through its highly sensitive receiving function and performs preliminary digital processing on the signals to ensure the accuracy of subsequent signal demodulation and feature extraction.

[0042] (II) Signal demodulation module

[0043] In order to reduce the signal attenuation caused by long distance, the received signal needs to be demodulated by LoRa to improve the signal-to-noise ratio (SNR). The present invention proposes a signal demodulation module to achieve this goal. Signal demodulation is a key step in the entire perception system, which directly affects the quality of the signal and the accuracy of subsequent processing. The main function of this module is to improve the signal-to-noise ratio of the signal by accurately aligning, demodulating and filtering the received LoRa signal.

[0044] First, the signal needs to be aligned and each LoRa symbol needs to be accurately extracted for subsequent demodulation. If the signal alignment is not performed, the signal will not only retain the influence of chirp spread spectrum modulation, but also introduce new frequency interference, thereby affecting the extraction of signal features. After signal alignment, the influence of chirp spread spectrum modulation (CSS) will be completely eliminated, and the system will be able to extract pure signal features. Further, a cross-correlation operation is performed on the received signal and the reference signal. When the signals are aligned, the cross-correlation operation produces a peak whose horizontal axis represents the starting position of the first LoRa symbol. The signal demodulation module will track the position of each peak and divide the received signal into independent LoRa symbols according to the symbol length. For each LoRa symbol in the received signal, the present invention performs a de-chirp operation. The signal energy within the entire frequency domain bandwidth can be concentrated on a specific frequency, thereby solving the problem of low signal-to-noise ratio and providing a stable signal for processing. The processing logic of the signal demodulation module is as follows Figure 2 The dechirped signal will be further processed in subsequent modules to extract signal features and calculate the rotation speed.

[0045] According to an embodiment of the present invention, the signal demodulation module is integrated into a computer terminal equipped with a high-performance digital signal processor (DSP) and dedicated software for performing signal alignment, demodulation and energy concentration processing. By aligning the reference signal with the original data, the phase offset is eliminated, and the signal is demodulated using the predefined LoRa down-modulation signal to improve the signal-to-noise ratio, so that the stability and reliability of the long-distance perception signal are significantly improved.

[0046] Specifically, the specific processing logic of the signal demodulation module of the present invention is as follows:

[0047] Step 1: Signal alignment. First, the received signal is cross-correlated with the reference signal. Through the cross-correlation operation, when the two signals reach an aligned state, an obvious peak is generated in the result. The position of this peak identifies the starting point of the first LoRa symbol. The present invention uses the position of the peak to track the alignment state of the signal and divides the received signal into independent LoRa symbols according to the length of each LoRa symbol.

[0048] Step 2: LoRa down-modulation signal generation. According to the preset parameters (such as bandwidth, chirp rate, center frequency, etc.), a LoRa down-modulation signal matching the received signal is generated. This generated down-modulation signal will be used as a reference signal for demodulation and is ready for demodulation operation with the aligned original data.

[0049] Step 3: Signal demodulation. The aligned raw data is mixed with the generated LoRa down-modulation signal (i.e., the two are multiplied) to extract the baseband component of the signal. Next, the mixed signal is low-pass filtered to remove the high-frequency component and retain the low-frequency part, thereby obtaining the demodulated signal.

[0050] 3. Perception Enhancement Module

[0051] Traditional LoRa sensing methods face significant limitations when dealing with high-frequency motion because the sampling rate of the extracted signal features does not meet the requirements of the Nyquist sampling theorem. The Nyquist sampling theorem stipulates that in order to accurately reconstruct the signal, the sampling rate must be at least twice the highest frequency component in the signal. If the sampling rate is lower than this standard, it will cause an aliasing effect on the signal, making it impossible to correctly capture and interpret the information of high-frequency motion, affecting subsequent measurement calculations.

[0052] A relatively direct solution to this problem is to increase the sampling rate by reducing the spreading factor of the LoRa signal or increasing its bandwidth. The spreading factor is a key parameter in the modulation of the LoRa signal. A lower spreading factor can increase the data transmission rate, thereby indirectly increasing the sampling rate. Increasing the bandwidth directly expands the frequency domain range that the signal can cover, allowing high-frequency components to be captured more clearly.

[0053] However, it is not easy to implement this solution. First, such adjustments require specially designed LoRa chips to support flexible parameter configuration. In addition, although this method can increase the sampling rate, when extracting phase information directly from the original de-chirped signal, a lot of noise is often introduced into the signal characteristics. This is because the original signal not only contains information about the target motion, but also contains environmental noise, interference and other non-correlated signal components. When these noises are mixed with the useful signal, the quality and reliability of the signal will be seriously affected, making subsequent signal processing and feature extraction more difficult, and may even lead to erroneous perception results. Therefore, although increasing the sampling rate seems to be an effective solution, it faces technical and cost challenges in practical applications, and may also cause new problems, such as an increase in signal noise. These factors together limit the scope of application of traditional LoRa sensing methods in high-frequency motion detection, prompting the applicant and the inventor to explore new solutions to overcome these technical obstacles.

[0054] To solve this problem, the inventor of this application proposed a perception enhancement module that processes the received signal in segments and performs a fast Fourier transform on each signal segment to concentrate the signal energy on a specific frequency in the frequency domain. Thereafter, phase information is extracted from each signal segment as the signal feature of the segment. The principle diagram of the perception enhancement module is shown in FIG. Figure 3 These improvements enable the LoRa system to reliably detect and measure high-frequency rotational motion in complex environments, providing higher accuracy and robustness for practical applications.

[0055] According to an embodiment of the present invention, the perception enhancement module is connected to a computer terminal, and realizes real-time transmission and processing of large-capacity data through a high-speed data interface (such as USB 3.0 or PCIe). The module uses a specific algorithm to process LoRa signals, significantly improves the sampling rate of signal perception, breaks through the inherent chirp frequency limit of LoRa signals, thereby improving the system's perception of high-frequency rotational motion, making it suitable for a wider range of industrial application scenarios.

[0056] The main function of the perception enhancement module in the present invention is to achieve accurate signal feature extraction through a series of signal processing steps, while effectively eliminating environmental noise and other interference factors. The design and implementation of this module not only improves the system's sensitivity to rotational motion, but also ensures that the system can still maintain stable and accurate performance under changing environmental conditions.

[0057] The specific implementation logic of the perception enhancement module is as follows:

[0058] Step 1: Signal segmentation. The received continuous signal is segmented according to a certain time window. The signal segment in each time window will be used as an independent analysis unit. In order to avoid spectrum leakage, the overlapping segmentation method is used to pre-process each signal segment (such as windowing) to reduce the influence of spectrum edge effects and ensure the accuracy of subsequent frequency domain analysis.

[0059] Step 2: Frequency domain analysis. Perform a fast Fourier transform (FFT) on each preprocessed signal segment to convert the time domain signal into a frequency domain signal. Through FFT, the amplitude and phase information of the signal in the frequency domain is obtained to generate the corresponding spectrum. Perform a preliminary analysis on the generated spectrum to identify the main frequency components and potential noise components in the spectrum in preparation for subsequent peak extraction.

[0060] Step 3: Frequency domain peak phase extraction. Extract the frequency position and phase information corresponding to the main peak. At the same time, use the extracted phase information to further analyze the phase change characteristics of the signal to provide accurate phase data for subsequent rotation speed calculation. The phase information of these peaks is used as a key feature and passed to the feature processing module for high-precision rotation speed calculation.

[0061] This method is not limited to the LoRa signal type and is applicable to any LoRa chip and further improves the perceived signal-to-noise ratio.

[0062] (IV) Feature processing module

[0063] According to the findings of the current named inventors during the research process, the chirp frequency of the LoRa signal itself will cause significant interference effects in the spectrum, which is not only strong but also difficult to ignore. We call this frequency the "perception boundary frequency". Specifically, the perception boundary frequency is determined by the inherent characteristics of the LoRa signal. It is a fixed frequency value and is directly determined by the parameters in the signal modulation process. Although this frequency is fixed in theory and can be clearly calculated and identified, it is not simple to deal with this problem in actual operation. Directly filtering out the signal component corresponding to this perception boundary frequency seems to be an ideal solution, but this approach will bring a series of new problems. First, since the chirp modulation of the LoRa signal is a process of gradually changing the frequency, filtering out the perception boundary frequency may cause mis-filtering of other important frequency components, thereby affecting the integrity of the overall signal. Secondly, the existence of the perception boundary frequency is intertwined with other signal characteristics, and simple filtering processing is easy to destroy the phase information and spectrum structure of the signal, which directly affects the accuracy and stability of the final measurement results.

[0064] In order to minimize the impact of the boundary frequency on the measurement results, the present invention divides the entire speed range into two parts: Range 1 (rotation speed less than or equal to 40 rpm) and Range 2 (rotation speed greater than 40 rpm). Specifically, the present invention selects a LoRa signal with a boundary frequency close to 60 Hz to measure the rotation speed in Range 1, and selects a LoRa signal with a boundary frequency close to 30 Hz to measure the rotation speed in Range 2. The purpose of this is to ensure that the boundary frequency does not appear in the corresponding measurement range.

[0065] The present invention proposes a feature processing module to achieve high-precision measurement and calculation. The core principle of this module is to calculate the ratio of peak energy based on spectrum analysis, and determine the physical meaning of the peak in the spectrum according to an adaptive threshold. After experimental analysis, it is found that there are mainly three key frequencies that may appear in the spectrum: target rotation frequency, perception boundary frequency, and harmonic frequencies of these frequencies. In order to accurately distinguish these three frequencies, the feature processing module will analyze the spectrum of the transmitted signal and determine the appropriate processing mode according to the characteristics of these frequency components, so as to accurately calculate the target rotation frequency.

[0066] In order to improve the accuracy of differentiation, the module sets adaptive thresholds. These thresholds are dynamically adjusted according to the actual environment and signal characteristics, so that the target frequency and interference frequency in the spectrum can still be effectively distinguished under different measurement conditions. In this way, the feature processing module can accurately identify and calculate the rotation frequency in a complex signal environment, ensuring the accuracy and reliability of the measurement. In order to prove the rationality and reliability of the setting of the adaptive threshold, we measured the rotation speed of the target (10cm long propeller) in the range of 1-15m, and collected a total of 100 sets of data to evaluate the role of the adaptive threshold. Figure 4 The figure shows the recognition results after applying the adaptive threshold. The horizontal axis represents the ratio of the highest peak in the frequency domain to the noise (i.e. the peak reference), the vertical axis represents the ratio of the highest peak in the frequency domain to the second highest peak (i.e. the peak ratio), the purple dotted line represents the adaptive threshold after calculation, the blue dot represents the correct selection of the highest peak in the frequency domain as the calculation result, the yellow dot represents the correct selection of the second highest peak in the frequency domain as the calculation result, and the red dot represents the wrong recognition result. Figure 4 It can be seen that only 4 sets of data out of 100 have calculation errors, and these 4 sets of data are all measured at a test distance of 15m. This is because the reflection signal of the small target is too weak when the distance is too far. This test can prove the rationality and reliability of the adaptive threshold setting.

[0067] According to an embodiment of the present invention, the feature processing module is integrated in a computer terminal and uses an advanced spectrum analysis tool to perform a comprehensive frequency domain analysis on the captured signal. The module automatically distinguishes different frequencies by calculating the ratio of peak energy in the spectrum and according to an adaptive threshold. In this way, the system can accurately identify the target rotation speed in a complex signal environment and ensure high accuracy and reliability of the measurement.

[0068] The present invention implements a feature processing module to perform accurate rotational speed calculation. The core of this module is to identify the physical meaning of different frequency peaks in the spectrum through precise spectrum analysis, and accurately determine the peak corresponding to the target rotation frequency, thereby achieving high-precision rotational speed measurement. The design of the feature processing module not only focuses on identifying the main frequency components in the spectrum, but also takes into account the complex interference and noise factors that may exist in the signal. This process not only improves the accuracy of rotational speed measurement, but also ensures the reliability of the system in various industrial application scenarios.

[0069] The specific processing logic of the feature processing module is as follows:

[0070] Step 1: Spectrum analysis and processing model selection. According to the reference signal, select the corresponding processing model. The selection of the processing model depends on the expected range of the target rotation speed. Different rotation speed ranges may correspond to different frequency distribution characteristics, and the selection of the processing model will directly affect the subsequent rotation speed calculation. In order to predetermine the approximate target rotation speed range, a LoRa signal with a boundary frequency close to 60Hz can be used to determine whether the target rotation frequency is in range 1 (preset low-frequency range). If the measurement results show obvious periodicity and the frequency domain characteristics show obvious peak information, the rotation speed of the target is in range 1 (preset low-frequency range). Otherwise, the rotation speed of the target is judged to be in range 2 (preset high-frequency range).

[0071] Step 2: Signal processing mode 1. If the target rotation speed falls within range 1 (preset low frequency range), the system will first determine whether there is harmonic interference in the spectrum. Harmonic interference usually appears as multiple peaks with frequencies that are integer multiples of the fundamental frequency. If harmonic interference exists, the system will select the fundamental frequency signal (i.e., the peak with the lowest frequency) as the final measurement result to avoid the influence of harmonic interference on the rotation speed calculation. If no harmonic interference is detected in the spectrum, the system will select the frequency corresponding to the peak with the largest energy in the spectrum as the representative frequency of the target rotation speed.

[0072] Step 3: Signal processing mode 2. If the target rotation speed falls within range 2 (preset high frequency range) and two obvious peaks appear in the spectrum, the system will further analyze the relationship between these peaks. First, the energy ratio between the highest peak and the surrounding noise is calculated, denoted as m. Using the energy ratio m, the adaptive threshold is calculated, and the threshold calculation formula is m×10 mThis is to determine which peak in the spectrum is more likely to correspond to the actual rotation speed. Next, the ratio between the maximum peak energy and the second largest peak energy is calculated. If this ratio is greater than the calculated adaptive threshold, the system will select the second largest peak as the final measurement result, believing that it is closer to the actual rotation speed; otherwise, the maximum peak is selected as the measurement result. If there is only one obvious peak in the spectrum, the system will directly select this peak as the measurement result of the rotation speed because it has no other competing frequency components.

[0073] Step 4: Output calculation results. After completing the above processing, the system uses the frequency corresponding to the selected peak as the final rotation speed calculation result. The rotation speed result is fed back to the user or other system modules for equipment status monitoring or further processing and analysis.

[0074] This feature processing method enables the system to find the changes caused by the target rotation in the frequency domain and accurately extract them. It solves the problem that the LoRa frequency and the target rotation speed coexist in the spectrum, allowing the system to accurately sense and measure the rotation speed of the target.

[0075] In the system, the computer terminal is equipped with a high-performance processor (such as a multi-core CPU or GPU) and a large-capacity memory to support real-time processing and storage of large-scale data. The terminal is also equipped with a graphical user interface (GUI) that displays the measurement results of the rotation speed and related spectrum analysis information in real time through intuitive visualization tools, thereby providing clear and accurate feedback to the operator and supporting efficient monitoring and fault diagnosis of equipment in the industrial Internet of Things scenario.

[0076] In order to verify the performance of the method of the present invention, an experimental test was conducted. In the embodiment, the measurement performance of the system for different rotation speeds (100RPM-5100RPM) was tested at a test distance of 6m. The test results are as follows: Figure 5 As shown. It can be seen that the measurement error of the system of the present invention for different rotation speeds is less than 1.8% (MAPE represents the mean absolute percentage error). At the same time, by utilizing different signal processing modes, the system of the present invention can realize the measurement of a wide range of rotation speeds. Experimental tests have shown that the present invention can solve the problem of limited traditional wireless perception distance, and also breaks through the problem of low perception upper limit caused by the limited chirp frequency of the LoRa signal itself. The present invention can ultimately use the LoRa signal to realize the precise perception and measurement of ultra-long-distance high-frequency rotation speeds, providing a new solution for equipment monitoring and detection in industrial Internet of Things scenarios.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for sensing and measuring rotation speed over a long distance based on LoRa signals, characterized in that: The following steps are involved: The LoRa up-modulated signal is sent as a sensing signal through the signal transceiver module, and the signal reflected by the sensing signal encountering the rotating target is captured as the sensing original data, and the signal during transmission is recorded as the sensing reference signal; The signal demodulation module performs signal alignment on the sensing reference signal and the sensing raw data, and performs signal demodulation on the sensing raw data based on the predefined LoRa down-modulation signal; The perceptual enhancement module performs a perceptual enhancement operation on the demodulated perceptual raw data, wherein the perceptual enhancement operation includes segmenting the received continuous signal, performing frequency domain analysis on each signal segment, and extracting the frequency position and phase information corresponding to the main peak; Through the feature processing module, the ratio of the peak energy in the spectrum is calculated according to the signal spectrum after perception enhancement, and different frequencies are automatically distinguished according to the adaptive threshold.

2. The method according to claim 1, characterized in that: The signal demodulation module performs signal alignment on the sensing reference signal and the sensing raw data, and performs signal demodulation on the sensing raw data based on the predefined LoRa down-modulation signal, including: Perform a cross-correlation operation on the received sensing raw data and the sensing reference signal to obtain the peak value when the two signals reach an aligned state, determine the length of the LoRa symbol according to the position of the peak value, and divide the received signal into independent LoRa symbols; The aligned sensing raw data is mixed with the predefined LoRa down-modulation signal to extract the baseband component of the signal, and the mixed signal is low-pass filtered to obtain the demodulated signal.

3. The method according to claim 2, characterized in that The predefined LoRa down modulation signal is a LoRa down modulation signal that matches the received signal and is generated according to preset parameters, and the preset parameters include one or more of bandwidth, chirp rate, and center frequency.

4. The method according to claim 1, characterized in that The perception enhancement operation includes segmenting the received continuous signal, performing frequency domain analysis on each signal segment, and extracting the frequency position and phase information corresponding to the main peak, including: The received continuous signal is segmented according to the specified time window, and the signal segment in each time window will be used as an independent analysis unit; Perform fast Fourier transform on each signal segment, convert the time domain signal into the frequency domain signal, obtain the amplitude and phase information of the signal in the frequency domain, and generate the corresponding spectrum diagram; The generated spectrum is analyzed to identify the main frequency components and potential noise components in the spectrum, and the frequency position and phase information corresponding to the main peak are extracted.

5. The method according to claim 4, characterized in that After the received continuous signal is segmented according to the specified time window, the method further includes: pre-processing each signal segment using an overlapping segmentation method.

6. The method according to claim 1, characterized in that According to the perceptually enhanced signal spectrum, the ratio of the peak energy in the spectrum is calculated, and different frequencies are automatically distinguished according to the adaptive threshold, including: According to the reference signal, a corresponding processing model is selected, and the selection of the processing model depends on the expected range of the target rotation speed; If the target rotation speed falls within range 1, determine whether there is harmonic interference in the spectrum. If there is harmonic interference, select the peak with the lowest frequency as the final measurement result. If no harmonic interference is detected, select the frequency corresponding to the peak with the largest energy in the spectrum as the representative frequency of the target rotation speed. If the target rotation speed falls within range 2 and two obvious peaks appear in the spectrum, calculate the energy ratio between the highest peak and the surrounding noise, calculate the adaptive threshold based on the energy ratio, and calculate the ratio between the maximum peak energy and the second largest peak energy. If the ratio is greater than the calculated adaptive threshold, select the second largest peak as the final measurement result, otherwise, select the maximum peak as the measurement result; if there is only one obvious peak in the spectrum, directly select the peak as the measurement result of the rotation speed.

7. The method according to claim 6, characterized in that The formula for calculating the adaptive threshold based on the energy ratio is m×10 m , m is the energy ratio.

8. The method according to claim 6, characterized in that The target rotation speed range 1 is a rotation speed less than or equal to 40 rpm; the target rotation speed range 2 is a rotation speed greater than 40 rpm.

9. An ultra-long distance rotation speed sensing and measurement system based on LoRa signal, characterized in that: include: The signal transceiver module is used to send the LoRa up-modulation signal as the sensing signal, and capture the signal reflected by the sensing signal encountering the rotating target as the sensing original data, and record the signal when sending as the sensing reference signal; A signal demodulation module, used for signal alignment of the sensing reference signal and the sensing raw data, and signal demodulation of the sensing raw data based on a predefined LoRa down-modulation signal; A perception enhancement module is used to perform a perception enhancement operation on the demodulated perception raw data, wherein the perception enhancement operation includes segmenting the received continuous signal, performing frequency domain analysis on each signal segment, and extracting the frequency position and phase information corresponding to the main peak; The feature processing module is used to calculate the ratio of peak energy in the spectrum according to the signal spectrum after perception enhancement, and automatically distinguish different frequencies according to the adaptive threshold.

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