Signal processing method and system, engineering machinery, storage medium and program product

Abstract

The invention provides a signal processing method and system, engineering machinery, a storage medium and a program product. The signal processing method comprises the following steps: preprocessing an original signal of a sensor to obtain a preprocessed signal; performing weighted fractional Fourier transform on the preprocessed signal to obtain a signal after weighted fractional Fourier transform; performing spectral analysis on the signal subjected to weighted fractional Fourier transform to obtain spectral components, wherein the spectral components comprise a fundamental frequency component and a harmonic component; based on the fundamental frequency component and the harmonic component, separating an interference signal from the original signal to obtain a useful signal; and a control system for outputting the useful signal to the construction machine.

Description

Technical Field

[0001] The present disclosure relates to the field of engineering machinery control, and in particular to a signal processing method, system, engineering machinery, storage medium and program product. Background Art

[0002] With the development of automation and intelligence in construction machinery, unmanned excavators are increasingly used in mining, construction, environmental remediation and other fields. Unmanned excavators use sensors and control systems to achieve remote or automatic operation, reducing dependence on manual operation and improving work efficiency and safety. In actual applications, the operating environment of the excavator is usually very complex, with various electromagnetic interference, mechanical vibration and noise interference, etc. These interference signals will affect the stability and accuracy of the unmanned control system.

[0003] A variety of anti-interference technologies are used in related technologies. For example, a Kalman filter can be used to filter the signal. The Kalman filter is a recursive filtering algorithm that is widely used in signal processing and estimation. Its main advantage is that it can update the signal estimation in real time and has a good smoothing effect. However, when the Kalman filter processes strong interference signals, the filtering effect will be significantly reduced, especially during the operation of the excavator, which is affected by complex environmental interference and the performance of the Kalman filter is often difficult to meet the requirements. Summary of the invention

[0004] A technical problem solved by the present disclosure is that in the related art, the anti-interference capability of the related signals of the engineering machinery is relatively poor.

[0005] According to one aspect of the present disclosure, there is provided a signal processing method, comprising: performing preprocessing on an original signal of a sensor to obtain a preprocessed signal; performing a weighted fractional Fourier transform on the preprocessed signal to obtain a weighted fractional Fourier transformed signal; performing spectral analysis on the weighted fractional Fourier transformed signal to obtain spectral components, the spectral components including fundamental frequency components and harmonic components; based on the fundamental frequency components and the harmonic components, separating an interference signal from the original signal to obtain a useful signal; and outputting the useful signal to a control system of an engineering machinery.

[0006] In some embodiments, the interference signal corresponds to the harmonic component, and the useful signal corresponds to the fundamental frequency component.

[0007] In some embodiments, performing a weighted fractional Fourier transform on the preprocessed signal to obtain a signal after a weighted fractional Fourier transform, includes: obtaining a transform order of the weighted fractional Fourier transform; calculating a weighting function of the weighted fractional Fourier transform based on the transform order; and performing a weighted fractional Fourier transform on the preprocessed signal based on the weighting function to obtain the signal after a weighted fractional Fourier transform.

[0008] In some embodiments, obtaining the transform order of the weighted fractional Fourier transform includes: based on each transform order of a plurality of transform orders within a predetermined range, simulating the signal transmission quality of the preprocessed signal through a communication quality simulation model to obtain a simulation result, thereby obtaining a plurality of simulation results corresponding to the plurality of transform orders, wherein the channel coding method of the communication quality simulation model adopts the weighted fractional Fourier transform method, and each simulation result includes a bit error rate or a packet loss rate; and selecting the transform order corresponding to the simulation result with the smallest bit error rate or the smallest packet loss rate from the plurality of simulation results as the required transform order.

[0009] In some embodiments, based on the fundamental frequency component and the harmonic component, the interference signal is separated from the original signal to obtain a useful signal, including: dynamically adjusting the threshold of the spectrum analysis through an adaptive threshold method, wherein the threshold includes a first threshold and a second threshold, and the first threshold is smaller than the second threshold; based on the fundamental frequency component and the harmonic component, taking the signal portion of the original signal corresponding to the spectrum components within the range between the first threshold and the second threshold as a useful signal, and taking the signal portion of the original signal corresponding to the spectrum components within the range smaller than the first threshold and the signal portion of the original signal corresponding to the spectrum components within the range greater than the second threshold as interference signals; and removing the interference signal and retaining the useful signal.

[0010] In some embodiments, the signal processing method also includes: for an original signal with a non-stationary interference signal, segmenting the original signal to obtain multiple signal segments, wherein the interference signal of each signal segment is in a stationary state; and performing preprocessing, weighted fractional Fourier transform and spectrum analysis on each signal segment to separate the interference signal of each signal segment from each signal segment, thereby obtaining a useful signal of each signal segment.

[0011] In some embodiments, preprocessing is performed on the raw signal of the sensor, including: performing filtering, denoising and normalization on the raw signal of the sensor.

[0012] In some embodiments, the signal processing method further includes: performing anti-interference processing on the useful signal before outputting the useful signal to the control system of the engineering machinery, wherein the anti-interference processing adopts an adaptive filtering method.

[0013] In some embodiments, the signal processing method further includes: obtaining a signal collected by the sensor before performing preprocessing on the original signal of the sensor, wherein the sensor is disposed on the engineering machinery; and performing preliminary processing on the signal collected by the sensor to obtain the original signal of the sensor, wherein the preliminary processing includes analog-to-digital conversion processing and data buffering processing.

[0014] According to another aspect of the present disclosure, a signal processing system is provided, including: a preprocessing module, used to perform preprocessing on the original signal of the sensor to obtain a preprocessed signal; a weighted fractional Fourier transform processing module, used to perform a weighted fractional Fourier transform on the preprocessed signal to obtain a signal after the weighted fractional Fourier transform; an interference signal separation module, used to perform spectral analysis on the signal after the weighted fractional Fourier transform to obtain spectral components, the spectral components including fundamental frequency components and harmonic components, and based on the fundamental frequency components and the harmonic components, separate the interference signal from the original signal to obtain a useful signal; and a signal output module, used to output the useful signal to the control system of the engineering machinery.

[0015] According to another aspect of the present disclosure, a signal processing system is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute the aforementioned signal processing method based on instructions stored in the memory.

[0016] According to another aspect of the present disclosure, there is provided a construction machine, comprising: the signal processing system as described above.

[0017] In some embodiments, the engineering machine is an unmanned excavator.

[0018] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored. When the computer instructions are executed by a processor, the signal processing method as described above is implemented.

[0019] According to another aspect of the present disclosure, a computer program product is provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the signal processing method as described above is implemented.

[0020] In the above method, a weighted fractional Fourier transform is performed on the preprocessed signal, and then a spectrum analysis is performed on the signal after the weighted fractional Fourier transform. Based on the spectrum analysis result, the interference signal is separated from the original signal to obtain a useful signal, which can improve the anti-interference ability of the relevant signals of the engineering machinery.

[0021] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0023] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0024] Figure 1 is a flowchart illustrating a signal processing method according to some embodiments of the present disclosure;

[0025] Figure 2 is a block diagram showing a physical implementation of a discrete sequence WFRFT according to some embodiments of the present disclosure;

[0026] Figure 3 is a block diagram schematically illustrating a structure of a signal processing system according to some embodiments of the present disclosure;

[0027] Figure 4 is a block diagram schematically showing a structure of a signal processing system according to some other embodiments of the present disclosure;

[0028] Figure 5 is a block diagram schematically showing a structure of a signal processing system according to some other embodiments of the present disclosure;

[0029] Figure 6 is a block diagram schematically showing a structure of a signal processing system according to some other embodiments of the present disclosure. DETAILED DESCRIPTION

[0030] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0031] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0032] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0033] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0034] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0035] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0036] The inventors of the present disclosure have found that in the actual operation of engineering machinery (e.g., unmanned excavators), the complexity of interference signals is mainly manifested in the following aspects:

[0037] (1) Multi-source interference: There are many sources of interference during the operation of construction machinery, including electromagnetic interference, mechanical vibration, environmental noise, etc. These interference sources may act on the sensors and control systems of the construction machinery at the same time, resulting in a decrease in signal quality.

[0038] (2) Non-stationarity: The frequency and amplitude of the interference signal change over time, which has non-stationarity characteristics. This makes it difficult for the frequency domain filtering technology in related technologies to effectively filter out these interference signals.

[0039] (3) Complex spectral characteristics: The spectral characteristics of interference signals are complex and may contain multiple frequency components and harmonic components, which poses a great challenge to signal processing.

[0040] In conclusion, in the related technologies, the anti-interference capability of the related signals of the engineering machinery is relatively poor.

[0041] In view of this, an embodiment of the present disclosure provides a signal processing method to improve the anti-interference capability of related signals of engineering machinery.

[0042] Figure 1 is a flow chart showing a signal processing method according to some embodiments of the present disclosure. Figure 1 As shown, the signal processing method includes steps S102 to S108.

[0043] In step S102, preprocessing is performed on the original signal of the sensor to obtain a preprocessed signal.

[0044] For example, step S102 includes: performing filtering, denoising and normalizing on the original signal of the sensor. Here, the preprocessing step performs filtering, denoising and normalizing on the collected original signal to remove high-frequency noise and low-frequency drift to ensure the clarity and stability of the signal.

[0045] (1) Filtering processing.

[0046] For example, a low-pass filter is used to remove high-frequency noise in the signal, and a high-pass filter is used to remove low-frequency drift in the signal. For complex interference environments, a band-pass filter can also be used to filter specific frequency bands.

[0047] (2) Denoising processing.

[0048] For example, use a median filter to remove impulse noise from a signal:

[0049] g(x,y)=med{f(xm,yn),m,n∈w}, (1)

[0050] Among them, f(x, y) is the original data (for example, the original image), g(x, y) is the processed data (for example, the processed image), m and n represent the size of the two-dimensional processing window, which is generally an odd-numbered area, and w represents the maximum value of the two-dimensional processing window that can be obtained by m and n.

[0051] For example, wavelet denoising method is used to remove noise components in the signal through multi-scale analysis:

[0052]

[0053] Among them, Ψ(x) is the wavelet mother function, ψ a,b (x) is a continuous wavelet function that depends on (a, b), a is a non-zero real number, and b is the time-shift center.

[0054] (3) Normalization.

[0055] The signal is amplitude normalized so that the signal amplitude is within a standard range for easy subsequent processing. The signal is time normalized so that the sampling frequency of the signal is consistent to ensure the synchronization of the data of each sensor.

[0056] In step S104, a weighted fractional Fourier transform (WFRFT) is performed on the preprocessed signal to obtain a signal after weighted fractional Fourier transform.

[0057] Weighted fractional Fourier transform is an improved Fourier transform method that can flexibly convert between frequency domain and time domain and adjust weights according to the characteristics of the signal, thereby achieving effective processing of non-stationary signals.

[0058] Principle of weighted fractional Fourier transform: Fractional Fourier transform (FrFT) is a generalized form of Fourier transform, which can be regarded as an arbitrary angle rotation between the time domain and the frequency domain. Weighted fractional Fourier transform (WFRFT) introduces a weighting function based on FrFT, further enhancing the flexibility and adaptability of the transform. The kernel function K(α;u,t) of WFRFT can be expressed as

[0059]

[0060] Among them, α is the transformation order, t represents time, u represents the input signal, and ω0, ω1, ω2 and ω3 are weighting coefficients.

[0061] By performing a classical FrFT transform of order α on the signal f(t) in the square integrable space, the definition of WFRFT (i.e., the weighting function) can be described as

[0062]

[0063] In the formula, is the α-order WFRFT operator, f(t), F(t), f(-t), and F(-t) are the results of f(t) after 0 to 3 discrete Fourier transforms, respectively, and f(t) is the signal to be transformed. is the weight coefficient corresponding to each state function, which is defined as

[0064]

[0065] Among them, for the third term on the right side of the equal sign in the above formula (5), when the index is "+", it means that the WFRFT representation axis rotates counterclockwise around the origin, and when the index is "-", it means that the WFRFT representation axis rotates clockwise around the origin. The weighting coefficients defined in the two ways are conjugate to each other and rotate in opposite directions. α is the transformation order.

[0066] Here, the process of WFRFT transformation can be referred to Figure 2 shown. Figure 2 is a block diagram showing the physical implementation of a discrete sequence WFRFT according to some embodiments of the present disclosure. Figure 2 The detailed structure of the weighted fractional Fourier transform processing module is shown, including the related WFRFT algorithm unit and its working principle.

[0067] like Figure 2As shown, a serial data stream is input, and the serial data stream becomes a parallel data stream after conversion by the serial-to-parallel data conversion module. The transformation order α is input to the coefficient generation module to generate weighting coefficients ω0, ω1, ω2 and ω3. The data stream f(t) corresponds to the weighting coefficient ω0. The data stream f(t) becomes f(-t) after passing through the inversion module, and f(-t) corresponds to the weighting coefficient ω2. The data stream f(t) becomes F(t) after passing through the DFT (Discrete Fourier Transform) module, and F(t) corresponds to the weighting coefficient ω. F(t) becomes F(-t) after passing through the inversion module, and F(-t) corresponds to the weighting coefficient ω3. Then, the data stream ω0f(t)+ω1F(t)+ω2f(-t)+ω3F(-t) is obtained by addition. The data stream is converted by the parallel-to-serial data conversion module to obtain a serial data stream, and then the serial data stream is output. This completes the WFRFT transformation.

[0068] In some embodiments, step S104 includes: obtaining a transform order of a weighted fractional Fourier transform; calculating a weighting function of the weighted fractional Fourier transform based on the transform order (i.e., the weighting function (4) described above); and performing a weighted fractional Fourier transform on the preprocessed signal based on the weighting function to obtain a signal after the weighted fractional Fourier transform. In this embodiment, by obtaining a suitable transform order, a corresponding weighting function can be obtained, so that a weighted fractional Fourier transform can be performed on the preprocessed signal based on the weighting function, thereby realizing the operation of performing a weighted fractional Fourier transform on the preprocessed signal.

[0069] Of course, in order to be able to perform weighted fractional Fourier transform on the preprocessed signal, a suitable transform order needs to be obtained.

[0070] In some embodiments, obtaining a transform order of a weighted fractional Fourier transform includes: based on each transform order of a plurality of transform orders within a predetermined range, simulating the signal transmission quality of a preprocessed signal through a communication quality simulation model to obtain a simulation result, thereby obtaining a plurality of simulation results corresponding to the plurality of transform orders, wherein the channel coding method of the communication quality simulation model adopts a weighted fractional Fourier transform method, and each simulation result includes a bit error rate or a packet loss rate; and selecting a transform order corresponding to a simulation result with a minimum bit error rate or a minimum packet loss rate from the plurality of simulation results as the required transform order.

[0071] In the above embodiment, the communication quality simulation model can adopt the communication quality simulation model framework known to those skilled in the art, but the channel coding method of the communication quality simulation model adopts the weighted fractional Fourier transform method, so as to select the appropriate transformation order for the weighted fractional Fourier transform according to the simulation result. The transformation order corresponding to the simulation result is determined by selecting the simulation result with the minimum bit error rate or the minimum packet loss rate from multiple simulation results, that is, the required appropriate transformation order. In this way, the acquisition of the transformation order is realized, so that it is convenient to perform the weighted fractional Fourier transform on the preprocessed signal.

[0072] For example, the predetermined range is [-1, 1]. For example, multiple transform orders α can be determined stepwise from the predetermined range with a step size of 0.1, and then a suitable transform order is obtained from the multiple transform orders α through the above process.

[0073] Of course, those skilled in the art will appreciate that the above-mentioned predetermined range is only exemplary, and the scope of the present disclosure is not limited thereto. Those skilled in the art can determine the predetermined range as needed.

[0074] As described above, a suitable transform order α can be selected. A suitable transform order can be selected according to the characteristics of the signal and the spectrum distribution. After selecting a suitable transform order, the fractional kernel function K(α;u,t) is calculated. Next, the preprocessed signal is transformed by WFRFT to obtain the representation of the signal in the current time-frequency domain.

[0075] In some embodiments, the weighting function may be adjusted according to the spectrum characteristics of the signal to highlight the useful signal and suppress the interference signal.

[0076] Assume that the parameters of the WFRFT used for information exchange between the source node and the destination node are not obtained by the eavesdropping node. The estimation result of the source data symbol sequence by the eavesdropping node is composed of a weighted combination of four sub-signals f(t), F(t), f(-t), and F(-t). When the difference Δα between the actual transformation order and the predicted transformation order ≠ 0, the separated signals interfere with each other. Therefore, only part of the signal energy from the source node is collected by the eavesdropping node for information content interception, while the remaining signal energy exists in the form of an interference signal. The implementation of WFRFT will not affect the signal reception of the target node. Under this system, the security performance of the WFRFT system comes from the equivalent mutual interference within its signal:

[0077] (1) All the energy of the source node's transmitted signal received by the destination receiver is used to recover the communication content, and the implementation of WFRFT will not cause any performance loss.

[0078] (2) The receiving performance of the eavesdropping node is suppressed. Only a small part of the signal energy sent by the source node is leaked to the eavesdropping node for content interception, and the remaining signal energy exists in the form of "self-interference noise".

[0079] In step S106, spectrum analysis is performed on the signal after weighted fractional Fourier transformation to obtain spectrum components, where the spectrum components include fundamental frequency components and harmonic components.

[0080] Here, the signal after WFRFT transformation is subjected to spectrum analysis to identify the fundamental frequency component (i.e., the main frequency component) and the harmonic component. In this way, the frequency range of the useful signal and the interference signal can be determined according to the spectrum distribution.

[0081] In step S108, based on the fundamental frequency component and the harmonic component, the interference signal is separated from the original signal to obtain a useful signal.

[0082] The interference signal corresponds to the harmonic component, and the useful signal corresponds to the fundamental frequency component. Therefore, the frequency domain feature extraction algorithm can be used to identify the frequency component and harmonic component of the interference signal. In this way, the interference signal can be separated from the original signal, and the remaining signal is the useful signal.

[0083] In some embodiments, step S108 includes: dynamically adjusting the threshold of spectrum analysis by an adaptive threshold method, wherein the threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold; based on the fundamental frequency component and the harmonic component, taking the signal portion of the original signal corresponding to the spectrum component within the range between the first threshold and the second threshold as a useful signal, taking the signal portion of the original signal corresponding to the spectrum component within the range less than the first threshold and the signal portion of the original signal corresponding to the spectrum component within the range greater than the second threshold as an interference signal; and removing the interference signal and retaining the useful signal. In this way, by using the adaptive threshold method, the interference signal can be separated from the original signal by dynamically adjusting the threshold of the spectrum analysis, and the recognition accuracy of the interference signal can be improved.

[0084] The above steps S106 and S108 realize the separation and extraction of interference signals. Based on the WFRFT results, by analyzing the characteristics of the signal in each frequency domain, the frequency components and harmonic components of the interference signal are identified and extracted, and separated from the original signal, so that a useful signal can be obtained.

[0085] In step S110 , the useful signal is output to the control system of the construction machine.

[0086] For example, useful signals can be output to the control system of an unmanned excavator for subsequent operation and control.

[0087] In the process of outputting useful signals to the control system of construction machinery, the signal format conversion can be performed on the useful signals. By converting the format of the useful signals, their compatibility with the control system is ensured. According to the requirements of the control system, the signals are encoded and modulated for easy transmission and processing.

[0088] During signal transmission, wired or wireless transmission methods can be used to transmit the processed signals to the control system of the engineering machinery (for example, an unmanned excavator), thereby improving the reliability and real-time performance of signal transmission and reducing possible signal loss or delay during the transmission process.

[0089] The output signal can be checked to improve the integrity and accuracy of the signal. For example, checksums and redundancy detection techniques can be used to detect and correct errors that may occur during transmission.

[0090] So far, a signal processing method according to some embodiments of the present disclosure is provided. The signal processing method includes: performing preprocessing on the original signal of the sensor to obtain a preprocessed signal; performing a weighted fractional Fourier transform on the preprocessed signal to obtain a signal after a weighted fractional Fourier transform; performing spectrum analysis on the signal after the weighted fractional Fourier transform to obtain spectrum components, the spectrum components include fundamental frequency components and harmonic components; based on the fundamental frequency components and harmonic components, separating the interference signal from the original signal to obtain a useful signal; and outputting the useful signal to the control system of the engineering machinery. In this method, a weighted fractional Fourier transform is performed on the preprocessed signal, and then a spectrum analysis is performed on the signal after the weighted fractional Fourier transform, and based on the spectrum analysis result, the interference signal is separated from the original signal to obtain a useful signal, which can improve the anti-interference ability of the relevant signals of the engineering machinery.

[0091] In the related art, the interference signals in the operating environment of engineering machinery are often non-stationary and complex, which makes it difficult for frequency domain filtering technology to meet the needs.

[0092] In view of this, the signal processing method in some embodiments of the present disclosure may also include: for an original signal with a non-stationary interference signal, segmenting the original signal to obtain multiple signal segments, wherein the interference signal of each signal segment is in a stable state; and performing preprocessing, weighted fractional Fourier transform and spectrum analysis on each signal segment to separate the interference signal of each signal segment from each signal segment, thereby obtaining a useful signal of each signal segment. In this embodiment, for a non-stationary interference signal, a segmented analysis method is adopted, and the signal is segmented and then subjected to WFRFT transformation and spectrum analysis to improve the separation effect.

[0093] It should be noted that when the original signal with a non-stationary interference signal is segmented, the interference signal of each segmented signal can be in a basically stable state, but not limited to an absolute stable state, as long as it is convenient for subsequent WFRFT transformation and spectrum analysis.

[0094] In some embodiments, the signal processing method further includes: before outputting the useful signal to the control system of the engineering machinery, performing anti-interference processing on the useful signal, wherein the anti-interference processing adopts an adaptive filtering method. In this way, anti-interference processing of the useful signal is achieved. The processed useful signal is output to the control system of the engineering machinery for subsequent operation and control. After the anti-interference processing, the output signal has higher stability and accuracy, which helps to improve the operating performance and safety of the engineering machinery.

[0095] In the anti-interference processing, the separated interference signal is filtered and suppressed, which can improve the integrity and accuracy of the useful signal. Adaptive filtering technology can be used to dynamically adjust and process the interference signal.

[0096] For example, an adaptive filter can be used to dynamically adjust the filter parameters according to the changes in the interference signal to optimize the filtering effect. Typical adaptive filtering algorithms include LMS (Least Mean Squares) algorithm or RLS (Recursive Least Squares) algorithm. The adaptive filter can automatically adjust the filter parameters according to environmental changes and has a certain anti-interference ability.

[0097] In the anti-interference processing, the weight and filter coefficient of the adaptive filter can be adjusted by real-time monitoring of the frequency and amplitude of the interference signal, and the appropriate filter structure and parameters can be selected according to the spectrum characteristics of the interference signal.

[0098] For the interference signals that have been identified and separated, the use of filters to suppress them can improve the integrity of the useful signal. For complex interference signals, a combination of multiple filtering methods, such as adaptive filtering and frequency domain filtering, can improve the interference suppression effect.

[0099] In some embodiments, the signal processing method may further include: obtaining a signal collected by a sensor before performing preprocessing on the original signal of the sensor, wherein the sensor is disposed on an engineering machine; and performing preliminary processing on the signal collected by the sensor to obtain the original signal of the sensor, wherein the preliminary processing includes analog-to-digital conversion processing and data buffering processing.

[0100] Signal acquisition is the basis of the entire system. Multiple sensors are used to collect signals generated during the operation of construction machinery (for example, excavators). These sensors are arranged in different parts of the excavator to fully obtain signals about the equipment's operating status and the surrounding environment.

[0101] For example, the sensors used may include at least one of an acceleration sensor, a gyroscope, a position sensor, and an environmental sensor. The acceleration sensor is used to measure the acceleration changes of various parts of the engineering machinery (e.g., an excavator) and capture mechanical vibration signals. The gyroscope is used to measure the angular velocity and posture changes of the engineering machinery (e.g., an excavator) and capture rotational motion signals. The position sensor is used to measure the position changes of various parts of the engineering machinery (e.g., an excavator) and capture movement signals. The environmental sensor is used to measure parameters such as electromagnetic interference, temperature, humidity, etc. in the environment and capture external interference signals. In addition, the sensors used may also include cameras, height sensors, angle sensors, etc.

[0102] Taking an excavator as an example, the above sensors can be installed in key parts of the excavator, such as the boom, bucket, and cockpit. For example, an acceleration sensor, position sensor, gyroscope, environmental sensor, and camera can be installed on the cockpit, a height sensor and an angle sensor can be installed on the bucket, and an angle sensor can be installed on the boom. The sensor collects various signals generated during the operation of the excavator in real time and transmits the signals to the information processor; the information processor performs preliminary processing on the sensor data, such as analog-to-digital conversion, data buffering, etc. This realizes the collection of the original signal of the sensor.

[0103] Figure 3 Schematically shows a structural block diagram of a signal processing system according to some embodiments of the present disclosure. Figure 3 As shown, the signal processing system includes a pre-processing module 302 , a weighted fractional Fourier transform processing module 304 , an interference signal separation module 306 and a signal output module 308 .

[0104] The preprocessing module 302 is used to perform preprocessing on the original signal of the sensor to obtain a preprocessed signal.

[0105] In some embodiments, the preprocessing module 302 is used to perform filtering, denoising and normalization processing on the original signal of the sensor. For example, the preprocessing module includes a filter, a noise reducer and a normalization processor, etc., which are used to preprocess the collected original signal. Through preprocessing, high-frequency noise and low-frequency drift are removed to improve the clarity and stability of the signal.

[0106] The weighted fractional Fourier transform processing module 304 is used to perform a weighted fractional Fourier transform on the preprocessed signal to obtain a signal after a weighted fractional Fourier transform.

[0107] For example, the weighted fractional Fourier transform processing module 304 includes a WFRFT algorithm unit, which is used to perform frequency domain transform processing on the preprocessed signal. This module can flexibly adjust the transform weights and effectively process non-stationary signals according to the characteristics of the signal. Through WFRFT processing, the representation of the signal in the time-frequency domain is obtained, which provides a basis for the subsequent separation and extraction of interference signals.

[0108] In some embodiments, the weighted fractional Fourier transform processing module is used to obtain the transformation order of the weighted fractional Fourier transform, calculate the weighting function of the weighted fractional Fourier transform based on the transformation order, and perform a weighted fractional Fourier transform on the preprocessed signal based on the weighting function to obtain a signal after the weighted fractional Fourier transform.

[0109] In some embodiments, a weighted fractional Fourier transform processing module is used to: based on each transform order of multiple transform orders within a predetermined range, simulate the signal transmission quality of the preprocessed signal through a communication quality simulation model to obtain a simulation result, thereby obtaining multiple simulation results corresponding to the multiple transform orders, wherein the channel coding method of the communication quality simulation model adopts a weighted fractional Fourier transform method, and each simulation result includes a bit error rate or a packet loss rate; and select the transform order corresponding to the simulation result with the smallest bit error rate or the smallest packet loss rate from the multiple simulation results as the required transform order.

[0110] The interference signal separation module 306 is used to perform spectrum analysis on the signal after the weighted fractional Fourier transform to obtain spectrum components, the spectrum components include fundamental frequency components and harmonic components, and based on the fundamental frequency components and harmonic components, separate the interference signal from the original signal to obtain a useful signal. For example, the interference signal corresponds to the harmonic component, and the useful signal corresponds to the fundamental frequency component.

[0111] For example, the interference signal separation module 306 includes a signal separation algorithm unit for separating and extracting useful signals and interference signals. The module identifies and extracts the frequency components and harmonic components of the interference signal by analyzing the characteristics of the signal in each frequency domain, and separates them from the original signal. The separated interference signal will be further processed, while the useful signal will be retained and output.

[0112] In some embodiments, the interference signal separation module is used to: dynamically adjust the threshold of spectrum analysis through an adaptive threshold method, wherein the threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold; based on the fundamental frequency component and the harmonic component, take the signal portion of the original signal corresponding to the spectrum components within the range between the first threshold and the second threshold as a useful signal, and take the signal portion of the original signal corresponding to the spectrum components within the range less than the first threshold and the signal portion of the original signal corresponding to the spectrum components within the range greater than the second threshold as interference signals; and remove the interference signal and retain the useful signal.

[0113] The signal output module 308 is used to output useful signals to the control system of the engineering machinery.

[0114] For example, the signal output module is used to output useful signals to the control system of an unmanned excavator.

[0115] For example, the signal output module includes a signal format converter and a signal verification unit. The signal output module can perform format conversion and encoding processing on the output signal according to the needs of the control system to improve the compatibility of the signal with the control system.

[0116] So far, a signal processing system according to some embodiments of the present disclosure is provided. The signal processing system includes: a preprocessing module, which is used to perform preprocessing on the original signal of the sensor to obtain a preprocessed signal; a weighted fractional Fourier transform processing module, which is used to perform a weighted fractional Fourier transform on the preprocessed signal to obtain a signal after the weighted fractional Fourier transform; an interference signal separation module, which is used to perform spectrum analysis on the signal after the weighted fractional Fourier transform to obtain spectrum components, the spectrum components include fundamental frequency components and harmonic components, and based on the fundamental frequency components and harmonic components, separate the interference signal from the original signal to obtain a useful signal; and a signal output module, which is used to output the useful signal to the control system of the engineering machinery. The signal processing system can improve the anti-interference ability of the relevant signals of the engineering machinery.

[0117] Figure 4 Schematically shows a structural block diagram of a signal processing system according to some other embodiments of the present disclosure. Figure 4 As shown, the signal processing system includes a pre-processing module 302 , a weighted fractional Fourier transform processing module 304 , an interference signal separation module 306 and a signal output module 308 .

[0118] In some embodiments, Figure 4 As shown, the signal processing system further includes an anti-interference processing module 412. The anti-interference processing module 412 is used to perform anti-interference processing on the useful signal, wherein the anti-interference processing adopts an adaptive filtering method.

[0119] For example, the anti-interference processing module 412 includes a filtering unit and a suppression unit, which are used to filter and suppress interference signals.

[0120] For another example, the anti-interference processing module may also include an adaptive filter and a filter parameter adjustment unit. The anti-interference processing module uses adaptive filtering technology to dynamically adjust and process interference signals, thereby minimizing the impact of interference on useful signals. Through adaptive filtering, the system can automatically adjust the filter parameters according to the changes in the interference signal, thereby improving the effectiveness and real-time performance of the anti-interference processing. After the anti-interference processing, the subsequent output signal has higher stability and accuracy, which can significantly improve the operating performance and safety of the construction machinery.

[0121] In some embodiments, Figure 4 As shown, the signal processing system further includes a signal acquisition module 410. The signal acquisition module 410 is used to obtain the signal collected by the sensor and perform preliminary processing on the signal collected by the sensor to obtain the original signal of the sensor, wherein the sensor is set on the engineering machinery, and the preliminary processing includes analog-to-digital conversion processing and data buffering processing.

[0122] For example, the signal acquisition module 410 includes a plurality of sensors, such as an acceleration sensor, a gyroscope, a position sensor, and an environmental sensor. The signal acquisition module 410 also includes an information processor. The sensor collects various signals generated during the operation of the engineering machinery (e.g., an excavator) in real time and transmits the signals to the information processor of the signal acquisition module.

[0123] In some embodiments, the signal processing system further includes a segment processing module (not shown in the figure). The segment processing module is used to segment the original signal with a non-stationary interference signal to obtain multiple segments of signals, wherein the interference signal of each segment of the signal is in a stable state, and each segment of the signal is respectively subjected to preprocessing, weighted fractional Fourier transform and spectrum analysis to separate the interference signal of each segment of the signal from each segment of the signal, thereby obtaining a useful signal of each segment of the signal.

[0124] like Figure 4 The workflow of the signal processing system shown includes the following steps (1) to (6).

[0125] (1) Signal acquisition. The sensor collects various signals generated during the operation of the excavator in real time and transmits the signals to the information processor of the signal acquisition module. The information processor performs preliminary processing on the sensor data, such as analog-to-digital conversion and data buffering.

[0126] For example, the method and system of the present disclosure may also adopt multi-sensor data fusion technology. Multi-sensor data fusion technology can comprehensively obtain the operating status and environmental signals of engineering machinery (for example, unmanned excavators), and provide a more comprehensive signal processing basis. Through multi-sensor data fusion, interference signals can be better identified and separated, and the accuracy and robustness of signal processing can be improved. Data fusion methods may include weighted averaging, Kalman filtering, or Bayesian estimation, etc., and appropriate data fusion methods are selected according to specific application requirements.

[0127] (2) Preprocessing. The preprocessing module filters, denoises, and normalizes the collected raw signals. Through preprocessing, high-frequency noise and low-frequency drift are removed, improving the clarity and stability of the signal.

[0128] (3) Weighted Fractional Fourier Transform Processing. The weighted fractional Fourier transform processing module performs WFRFT processing on the preprocessed signal. Through WFRFT processing, the representation of the signal in the time-frequency domain is obtained, which provides a basis for the subsequent separation and extraction of interference signals.

[0129] (4) Interference signal separation and extraction. Based on the WFRFT results, the interference signal separation module analyzes the characteristics of the signal in each frequency domain, identifies and extracts the frequency components and harmonic components of the interference signal, and separates them from the original signal.

[0130] (5) Anti-interference processing. The anti-interference processing module filters and suppresses the separated interference signals to improve the integrity and accuracy of the useful signals. This module can use adaptive filtering technology to dynamically adjust and process the interference signals, thereby minimizing the impact of interference on the useful signals.

[0131] (6) Signal output. The signal output module outputs the processed useful signal to the control system of the construction machinery (for example, an unmanned excavator). After anti-interference processing, the output signal has higher stability and accuracy, which can significantly improve the operating performance and safety of the excavator.

[0132] The embodiments of the present disclosure provide an anti-interference signal processing method and system based on weighted fractional Fourier transform, which can improve the operational performance and safety of engineering machinery (e.g., unmanned excavators) in complex environments. With the development and popularization of unmanned control technology, the application of engineering machinery such as excavators in various complex environments will become more and more extensive. The method and system of the present disclosure can improve the anti-interference ability and signal processing accuracy of the unmanned control system, improve its stability and reliability in complex environments, and thus promote the further development and application of unmanned control technology in the field of engineering machinery.

[0133] Figure 5Schematically shows a structural block diagram of a signal processing system according to some other embodiments of the present disclosure. The signal processing system includes a memory 510 and a processor 520. Among them:

[0134] The memory 510 can be a disk, a flash memory, or any other non-volatile storage medium. The memory is used to store Figure 1 and / or Figure 2 The instructions in the corresponding embodiment.

[0135] The processor 520 is coupled to the memory 510 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller. The processor 520 is used to execute instructions stored in the memory, which can improve the anti-interference ability of related signals of the engineering machinery.

[0136] In some embodiments, it is also possible to Figure 6 As shown, the signal processing system 600 includes a memory 610 and a processor 620. The processor 620 is coupled to the memory 610 via a BUS 630. The signal processing system 600 can also be connected to an external storage device 650 via a storage interface 640 to call external data, and can also be connected to a network or another computer system (not shown) via a network interface 660, which will not be described in detail here.

[0137] In this embodiment, the anti-interference capability of the related signals of the engineering machinery can be improved by storing data instructions in the memory and processing the instructions through the processor.

[0138] In some embodiments of the present disclosure, there is further provided an engineering machine, comprising: the signal processing system as described above (for example, Figure 3 , Figure 4 , Figure 5 or Figure 6 For example, the engineering machinery is an unmanned excavator or the like.

[0139] In some embodiments, the present disclosure further provides a computer-readable storage medium (eg, a non-transitory computer-readable storage medium) having computer program instructions stored thereon, which are executed by a processor to implement Figure 1 and / or Figure 2 The steps of the method in the corresponding embodiment. Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] In some embodiments of the present disclosure, a computer program product is further provided. The computer program product includes a computer program or instructions. When the computer program or instructions are executed by a processor, the signal processing method as described above is implemented.

[0144] In some embodiments of the present disclosure, a computer program is further provided, comprising: instructions, which, when executed by a processor, cause the processor to perform the signal processing method as described above.

[0145] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0146] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A signal processing method, comprising: performing preprocessing on the original signal of the sensor to obtain a preprocessed signal; Performing a weighted fractional Fourier transform on the preprocessed signal to obtain a weighted fractional Fourier transformed signal; Performing spectrum analysis on the signal after weighted fractional Fourier transformation to obtain spectrum components, wherein the spectrum components include fundamental frequency components and harmonic components; Based on the fundamental frequency component and the harmonic component, separating the interference signal from the original signal to obtain a useful signal; and The useful signal is output to a control system of the construction machinery.

2. The signal processing method according to claim 1, wherein: The interference signal corresponds to the harmonic component, and the useful signal corresponds to the fundamental frequency component.

3. The signal processing method according to claim 1 or 2, wherein: Performing a weighted fractional Fourier transform on the preprocessed signal to obtain a weighted fractional Fourier transformed signal, comprising: Obtaining a transform order of the weighted fractional Fourier transform; Calculating a weighting function of the weighted fractional Fourier transform based on the transform order; and A weighted fractional Fourier transform is performed on the preprocessed signal based on the weighting function to obtain the weighted fractional Fourier transformed signal.

4. The signal processing method according to claim 3, wherein: Obtaining the transform order of the weighted fractional Fourier transform, comprising: Based on each transform order of a plurality of transform orders within a predetermined range, a signal transmission quality of the preprocessed signal is simulated by a communication quality simulation model to obtain a simulation result, thereby obtaining a plurality of simulation results corresponding to the plurality of transform orders, wherein the channel coding method of the communication quality simulation model adopts the weighted fractional Fourier transform method, and each simulation result includes a bit error rate or a packet loss rate; and The transformation order corresponding to the simulation result with the smallest bit error rate or the smallest packet loss rate is selected from the multiple simulation results as the required transformation order.

5. The signal processing method according to claim 1, wherein: Based on the fundamental frequency component and the harmonic component, separating the interference signal from the original signal to obtain a useful signal includes: Dynamically adjust the threshold of spectrum analysis by an adaptive threshold method, wherein the threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold; Based on the fundamental frequency component and the harmonic component, taking the signal portion of the original signal corresponding to the spectral components within the range between the first threshold and the second threshold as a useful signal, and taking the signal portion of the original signal corresponding to the spectral components within the range smaller than the first threshold and the signal portion of the original signal corresponding to the spectral components within the range larger than the second threshold as interference signals; and The interference signal is removed and the useful signal is retained.

6. The signal processing method according to claim 1, further comprising: For an original signal with a non-stationary interference signal, segmenting the original signal to obtain multiple segments of signals, wherein the interference signal of each segment of the signal is in a stationary state; and Preprocessing, weighted fractional Fourier transform and spectrum analysis are performed on each signal segment to separate the interference signal of each signal segment from the interference signal of each signal segment, so as to obtain the useful signal of each signal segment.

7. The signal processing method according to claim 1, wherein: Perform preprocessing on the raw signal from the sensor, including: The raw signal from the sensor is filtered, denoised and normalized.

8. The signal processing method according to claim 1, further comprising: Before the useful signal is output to the control system of the engineering machinery, anti-interference processing is performed on the useful signal, wherein the anti-interference processing adopts an adaptive filtering method.

9. The signal processing method according to claim 1, further comprising: Before performing preprocessing on the original signal of the sensor, obtaining a signal collected by the sensor, wherein the sensor is disposed on the engineering machine; and The signal collected by the sensor is preliminarily processed to obtain the original signal of the sensor, wherein the preliminarily processing includes analog-to-digital conversion processing and data buffering processing.

10. A signal processing system comprising: A preprocessing module, used for performing preprocessing on the original signal of the sensor to obtain a preprocessed signal; A weighted fractional Fourier transform processing module, used for performing a weighted fractional Fourier transform on the preprocessed signal to obtain a signal after a weighted fractional Fourier transform; An interference signal separation module is used to perform spectrum analysis on the signal after the weighted fractional Fourier transform to obtain spectrum components, wherein the spectrum components include fundamental frequency components and harmonic components, and to separate the interference signal from the original signal based on the fundamental frequency components and the harmonic components to obtain a useful signal; and The signal output module is used to output the useful signal to the control system of the engineering machinery.

11. A signal processing system comprising: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the signal processing method according to any one of claims 1 to 9 based on instructions stored in the memory.

12. An engineering machine comprising: A signal processing system as claimed in claim 10 or 11.

13. The construction machine according to claim 12, wherein: The engineering machinery is an unmanned excavator.

14. A computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the signal processing method according to any one of claims 1 to 9.

15. A computer program product, comprising a computer program or instructions, which implement the signal processing method according to any one of claims 1 to 9 when executed by a processor.

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