Anti-interference signal processing method of FPC (Flexible Printed Circuit) sensing module and related equipment

Abstract

The invention relates to an anti-interference signal processing method for an FPC sensing module and related equipment, and the method comprises the following steps: carrying out the time-frequency domain conversion of an original signal, and obtaining a signal time-frequency spectrogram; whether an abnormal interference area exists in the signal time-frequency spectrogram or not is recognized, and if yes, the abnormal interference area is marked; performing feature analysis and classification on the marked abnormal interference region based on the signal time-frequency spectrogram to obtain an interference signal type; performing interference source distribution prediction on the target FPC sensing module based on the interference signal type and an interference signal corresponding to the interference signal type to obtain an interference source feature mapping matrix; and performing anti-interference processing on an interference signal in the original signal based on the interference source feature mapping matrix to obtain a target working signal, thereby solving the technical problems that the interference signal not only affects the precision of the FPC sensing module, but also causes system misjudgment or function failure, and seriously affects the reliability and stability of equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of FPC sensor modules, and in particular to an anti-interference signal processing method of an FPC sensor module and related equipment. Background Art

[0002] FPC (flexible printed circuit board) sensor modules are widely used in modern electronic devices. Their high sensitivity and flexibility make them indispensable in various application scenarios. However, with the complexity of electronic devices and the variability of the environment, FPC sensor modules are facing more and more serious interference problems in practical applications. These interference signals not only affect the accuracy of the sensor, but may also cause system misjudgment or functional failure, seriously affecting the reliability and stability of the equipment.

[0003] At present, the processing methods for interference signals mainly rely on hardware filtering or shielding technology, but these methods have certain limitations. For example, hardware filters are not effective in processing high-frequency and wide-band interference, and increase the cost and complexity of the equipment. In addition, traditional shielding methods cannot completely isolate certain environmental interferences, such as electromagnetic interference (EMI) and radio frequency interference (RFI). The complexity and diversity of these interference signals have prompted researchers to find more efficient and intelligent anti-interference signal processing methods.

[0004] Therefore, it is particularly important to study an algorithm that can intelligently identify and process interference signals. By combining signal processing technology and machine learning algorithms, the anti-interference ability of the FPC sensor module can be improved without increasing hardware costs. However, the existing algorithms still have shortcomings in real-time performance, accuracy, and the identification and classification of different types of interference signals. How to effectively extract interference signal features, classify them, and implement targeted anti-interference processing is a key issue in current research. Summary of the invention

[0005] The main purpose of the present invention is to provide an anti-interference signal processing method and related equipment for an FPC sensor module, which solves the technical problem that the interference signal not only affects the accuracy of the FPC sensor module, but also causes system misjudgment or functional failure, seriously affecting the reliability and stability of the equipment.

[0006] To achieve the above object, the present invention provides an anti-interference signal processing method for an FPC sensor module, comprising the following steps: The original signal of the target FPC sensor module is collected by a preset signal sensor, and the original signal is converted into a time-frequency domain by a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram; By presetting an abnormality recognition model, identifying whether there is an abnormal interference area in the spectrum diagram of the signal, and if so, marking the abnormal interference area; wherein the abnormal interference area represents an interference signal; By using a preset spectral clustering algorithm, characteristic analysis and classification are performed on the marked abnormal interference area based on the signal time-frequency spectrum diagram to obtain the interference signal type; Based on the interference signal type and the interference signal corresponding to the interference signal type, the interference source distribution of the target FPC sensor module is predicted to obtain an interference source feature mapping matrix; Through a preset signal processing technology, anti-interference processing is performed on the interference signal in the original signal based on the interference source feature mapping matrix to obtain a target working signal.

[0007] Furthermore, the original signal is converted into a time-frequency domain by using a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram, including: Performing scale discretization processing on the original signal to obtain discretized signals of different scales; wherein the original signal includes an original electrical signal and an original communication signal; Through the preset Morlet wavelet transform technology, the time-frequency domain conversion and wavelet transform are performed on the discretized signals of different scales to obtain a series of wavelet signals; wherein the series of wavelet signals represent the energy distribution of the original signal in different frequency bands and time windows; Performing dimensionality reduction processing on the wavelet signal to obtain a dimensionality reduced signal; Performing spatial interpolation on the dimension-reduced signal by a preset bilinear interpolation algorithm to obtain an interpolated signal; The interpolated signal is converted into a signal time-frequency spectrum diagram by using a preset grayscale mapping algorithm.

[0008] Furthermore, the method of identifying whether there is an abnormal interference area in the spectrum diagram of the signal by using a preset abnormality identification model, and if so, marking the abnormal interference area, includes: Extracting local binary pattern features from the signal time-frequency spectrum to obtain an LBP feature vector sequence; Based on a preset sliding window, the LBP feature vector sequence is time segmented to obtain a plurality of LBP feature vector subsequences; wherein each of the LBP feature vector subsequences represents a local area of ​​the signal time-frequency spectrum diagram; Performing singular value decomposition on each of the LBP feature vector subsequences to obtain a corresponding singular value vector; wherein the singular value vector is used to reflect the energy distribution characteristics of the LBP feature vector subsequence; By presetting an abnormality recognition model, identifying whether there is an abnormal interference area in the spectrum graph of the signal based on the singular value vector; If it exists, the abnormality degree of the existing abnormal interference area is calculated based on a preset abnormality degree assessment algorithm to obtain a corresponding abnormality score; wherein the higher the abnormality score, the greater the possibility that the interference signal exists in the local area of ​​the signal spectrum diagram; The abnormal interference areas in the signal time-frequency spectrum diagram whose abnormality scores are greater than a preset threshold are combined and sorted, and the abnormal interference areas after the area combination and sorting are marked, wherein the area combination and sorting include removing overlapping areas and merging adjacent areas, and the abnormal interference areas include the start time, duration and frequency range of the interference signal.

[0009] Furthermore, the preset spectral clustering algorithm is used to perform feature analysis and classification on the annotated abnormal interference area based on the signal time-frequency spectrum diagram to obtain the interference signal type, including: Inputting the annotated abnormal interference region into a preset region-signal mapping algorithm to obtain an interference signal corresponding to the annotated abnormal interference region; Extracting energy distribution characteristics of the interference signal in different frequency bands on the signal spectrum diagram through high-order spectrum estimation technology; Constructing a frequency spectrum feature graph based on the energy distribution feature; In the frequency spectrum feature graph, a preset spectrum clustering algorithm is used to analyze the feature similarity of each interference signal to obtain a similar feature distribution result; wherein the similar feature distribution result includes signal frequency concentration, energy density distribution and frequency band correlation coefficient; Performing dimensionality reduction processing on the similar feature distribution result to obtain a similar feature distribution result after dimensionality reduction; The similar feature distribution result after dimensionality reduction is input into a preset support vector machine for classification to obtain the interference signal type.

[0010] Furthermore, the interference source distribution prediction is performed on the target FPC sensor module based on the interference signal type and the interference model corresponding to the interference signal type to obtain an interference source feature mapping matrix, including: Extracting an interference signal corresponding to the interference signal type; Performing high-order Volterra series analysis on the interference signal corresponding to the interference signal type to obtain a nonlinear time-varying interference feature sequence; wherein the nonlinear time-varying interference feature sequence includes amplitude features, phase features and frequency features; Obtain a physical layout distribution map corresponding to the target FPC sensor module, and perform a probability distribution analysis on the physical layout distribution map based on the nonlinear time-varying interference feature sequence using a nonparametric kernel density estimation method to obtain a spatial density of interference source distribution; wherein the spatial density of interference source distribution includes a location probability and an intensity probability of the interference source; Inputting the interference source distribution spatial density into a preset deep belief network to predict the interference source, thereby obtaining a predicted interference source; Adopting an adaptive Kalman particle filter algorithm to perform dynamic trajectory prediction on the predicted interference source, and obtaining the propagation trajectory characteristics of the interference source, including the movement direction characteristics and speed characteristics of the interference source; Performing spatiotemporal correlation analysis on the propagation trajectory characteristics of the interference source to obtain the influence range of the interference source; An interference source characteristic mapping matrix is ​​obtained based on the interference source influence range and the interference signal type; wherein the interference source characteristic mapping matrix includes rows, columns and elements, the rows represent the interference source type, the columns represent the physical location of the FPC sensor module and the elements represent the characteristic intensity of the interference signal.

[0011] Further, the performing probability distribution analysis on the physical layout distribution diagram based on the nonlinear time-varying interference feature sequence to obtain the interference source distribution space density includes: Extracting the nonlinear time-varying interference feature sequence to obtain nonlinear dynamic features; wherein the nonlinear dynamic features include the nonlinear coefficient, time constant and frequency response features of the system; Performing multi-scale entropy analysis on the nonlinear dynamic characteristics to obtain complexity characteristics of the interference signal; wherein the complexity characteristics represent the complexity and regularity of the interference signal at different time scales; By using a preset spectrum envelope estimation algorithm, based on the nonlinear dynamic characteristics and the complexity characteristics, spectrum envelope analysis is performed on the physical layout distribution diagram to obtain spectrum envelope distribution; The frequency spectrum envelope distribution is used to perform probability distribution analysis on the physical layout distribution diagram to obtain the interference source distribution space density.

[0012] Furthermore, the anti-interference processing is performed on the interference signal based on the interference source feature mapping matrix by using a preset signal processing technology to obtain a target working signal, including: Performing spatial correlation analysis on the interference signal based on the interference source feature mapping matrix to obtain a position corresponding to the interference signal; At the position, performing notch processing on the interference signal by using a preset adaptive notch filter group and a preset signal processing technology to obtain a notch processed signal; By using a preset signal subspace projection algorithm, a preset signal in the notch processing signal is extracted, and the preset signal is separated from the notch processing signal to obtain a preliminary working signal and a residual interference signal; wherein the preset signal is a signal required by the FPC sensor module when working; Performing signal enhancement on the remaining interference signal to obtain an enhanced signal; Performing frequency selective processing on the enhanced signal to obtain a selective filtering scheme; The selective filtering scheme is used to perform anti-interference signal processing on the remaining interference signal to reduce the contamination of the preliminary working signal by the remaining interference signal, thereby obtaining a target working signal.

[0013] The present invention also provides an anti-interference signal processing device for an FPC sensor module, comprising: A conversion module is used to collect the original signal of the target FPC sensor module through a preset signal sensor, and perform time-frequency domain conversion on the original signal through a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram; An identification module, used to identify whether there is an abnormal interference area in the spectrum diagram of the signal through a preset abnormal identification model, and if so, mark the abnormal interference area; wherein the abnormal interference area represents an interference signal; A classification module, used to perform feature analysis and classification on the annotated abnormal interference area based on the signal time-frequency spectrum diagram by using a preset spectral clustering algorithm to obtain the interference signal type; A prediction module, used to predict the interference source distribution of the target FPC sensor module based on the interference signal type and the interference signal corresponding to the interference signal type, and obtain an interference source feature mapping matrix; The anti-interference module is used to perform anti-interference processing on the interference signal in the original signal based on the interference source feature mapping matrix through a preset signal processing technology to obtain a target working signal.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The anti-interference signal processing method of the FPC sensor module provided by the present invention comprises the following steps: collecting the original signal of the target FPC sensor module through a preset signal sensor, and converting the original signal into time-frequency domain through a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum; identifying whether there is an abnormal interference area in the signal time-frequency spectrum through a preset abnormal recognition model, and if so, marking the abnormal interference area; wherein the abnormal interference area represents an interference signal; performing feature analysis and classification on the marked abnormal interference area based on the signal time-frequency spectrum through a preset spectral clustering algorithm to obtain the interference signal type; and classifying the interference signal type based on the interference signal type and the interference signal type. The interference signal corresponding to the interference signal type predicts the interference source distribution of the target FPC sensor module to obtain the interference source feature mapping matrix; through the preset signal processing technology, the interference signal in the original signal is subjected to anti-interference processing based on the interference source feature mapping matrix to obtain the target working signal. Through the above-mentioned technical means, the interference signal not only affects the accuracy of the FPC sensor module, but also causes system misjudgment or functional failure, which seriously affects the technical problem of the reliability and stability of the equipment. The signal processing technology and the interference source feature mapping matrix are used to perform targeted anti-interference processing on the interference signal in the original signal to obtain a target working signal that is closer to the real one. This processing method not only improves the quality of the signal, but also enhances the beneficial effects of the reliability and stability of the FPC sensor module. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a schematic diagram of the steps of an anti-interference signal processing method of an FPC sensor module in one embodiment of the present invention; Figure 2 It is a structural block diagram of an anti-interference signal processing device of an FPC sensor module in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

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

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of an anti-interference signal processing method of an FPC sensor module in one embodiment of the present invention; In one embodiment of the present invention, a method for processing anti-interference signals of an FPC sensor module is provided, comprising the following steps: Step S1, collecting the original signal of the target FPC sensor module through a preset signal sensor, and converting the original signal into time-frequency domain through a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram.

[0021] Specifically, when implementing the step of "collecting the original signal of the target FPC sensor module through a preset signal sensor, and converting the original signal into the time-frequency domain through the preset Morlet wavelet transform technology to obtain the signal time-frequency spectrum", it is first necessary to use the preset signal sensor to collect the signal of the FPC sensor module. These sensors may be sensor elements integrated on the FPC module, which are specially designed to capture and record various electrical signals generated by the module during operation. Assuming that in a certain industrial automation scenario, the FPC sensor module is used to monitor the vibration of the machine equipment, then the sensor will capture the electrical signal generated by the vibration of the machine. Once the original signal is collected, the preset Morlet wavelet transform technology will be used to process these signals. Morlet wavelet transform is a time-frequency analysis method that can convert the signal in the time domain into the time-frequency domain, thereby obtaining the spectrum information of the signal changing with time. In specific operation, Morlet wavelet transform performs convolution operation on the signal by using Morlet wavelet basis functions with high time resolution and frequency resolution. In this way, a corresponding time-frequency spectrum can be obtained for the signal at each time point. Through this process, the transient characteristics, frequency components and the changes of these components over time in the original signal can be clearly presented. For example, in the above industrial automation scenario, the machine equipment may produce abnormal vibrations due to mechanical failure or external interference during operation. Through the Morlet wavelet transform, we can identify the frequencies and time points corresponding to these abnormal vibrations in the signal time-frequency spectrum. Assuming that the machine equipment has a high-frequency vibration in a certain period of time, this will be displayed as a prominent high-frequency energy peak on the signal time-frequency spectrum, and the time position corresponds to the time point when the vibration occurs. Through this step, we can not only accurately locate the moment when the vibration occurs, but also analyze its frequency characteristics, which is helpful for further fault diagnosis and maintenance plan formulation. It can be seen that the Morlet wavelet transform provides a powerful tool for the time-frequency analysis of the signal, ensuring the effectiveness of the anti-interference signal processing method of the FPC sensor module in practical applications.

[0022] Step S2, using a preset abnormality identification model, identifying whether there is an abnormal interference area in the spectrum diagram of the signal, and if so, marking the abnormal interference area; wherein the abnormal interference area represents an interference signal.

[0023] Specifically, in the step of "identifying whether there is an abnormal interference area in the signal time spectrum through a preset abnormal identification model, and if so, marking the abnormal interference area; wherein the abnormal interference area represents an interference signal", it is first necessary to establish an abnormal identification model. The model is usually based on machine learning or deep learning algorithms, and learns the characteristic differences between normal working signals and interference signals in the signal time spectrum through training data. In the industrial automation scenario, assuming that the FPC sensor module is used to monitor the vibration of the machine equipment, the abnormal identification model can identify the vibration signal characteristics under normal operating conditions by learning historical data. Once the signal time spectrum is generated, the abnormal identification model will analyze it. The model will scan the entire time spectrum to find areas that do not match the pre-learned normal signal characteristics, which may represent interference signals. Assuming that when the machine equipment is working normally, its vibration signal is mainly concentrated in a certain frequency range, but due to external interference or internal failure, high-frequency or irregular vibrations occur, which will appear as obvious abnormal areas on the time spectrum. The identification model determines whether it is an abnormal interference area by comparing the differences between these areas and the normal signal characteristics. For example, in actual applications, if a machine device experiences a sudden high-frequency vibration at a certain point in time, the anomaly recognition model will identify this high-frequency vibration area as an abnormal interference area by comparing the frequency characteristics of the area with the normal signal characteristics in the historical data. The model will automatically mark these areas on the signal time-frequency spectrum, usually highlighting these abnormal areas through color or other visual marks for subsequent analysis and processing. Through this step, the FPC sensor module can not only identify abnormal interference signals, but also provide precise positioning and marking for subsequent feature analysis and classification, ensuring the effectiveness and pertinence of interference signal processing.

[0024] Step S3, using a preset spectral clustering algorithm, based on the signal time-frequency spectrum diagram, feature analysis and classification are performed on the annotated abnormal interference area to obtain the interference signal type.

[0025] Specifically, in the step of "using a preset spectral clustering algorithm to analyze and classify the marked abnormal interference area based on the signal time-frequency spectrum to obtain the interference signal type", a spectral clustering algorithm needs to be preset first. Spectral clustering is a clustering method based on graph theory, which is particularly suitable for processing data with nonlinear structures. In the industrial automation scenario, it is assumed that the FPC sensor module is used to monitor the vibration of the machine equipment, and the abnormal recognition model has marked the abnormal interference area in the signal time-frequency spectrum. Next, the spectral clustering algorithm will further extract and classify the features based on these marked areas. The spectral clustering algorithm constructs a graph to represent the various abnormal areas in the signal time-frequency spectrum, each area is regarded as a node, and the connection weights between the nodes represent the similarity or distance between them. By spectrally decomposing this graph, the algorithm can find an optimal cutting method to divide the nodes into different groups. Assume that the machine equipment may be subject to different types of interference during operation, such as electromagnetic interference (EMI) or mechanical vibration interference, which will appear as different frequency and time domain characteristics on the time-frequency spectrum. By analyzing the similarities and differences of these features, the spectral clustering algorithm can classify abnormal interference areas into different categories, each of which corresponds to a type of interference signal. For example, if an irregular low-frequency vibration and a high-frequency electromagnetic interference appear during the operation of a machine, the spectral clustering algorithm will classify them into different groups based on their characteristic differences in the time-frequency spectrum. Low-frequency vibrations may be identified as interference caused by mechanical failures, while high-frequency electromagnetic interference may be generated by electrical equipment or nearby electronic equipment. Through this classification, the FPC sensor module can not only identify the presence of interference signals, but also accurately distinguish the type of interference, providing an accurate basis for subsequent interference processing and prevention. This method not only improves the level of intelligence in signal processing, but also provides strong data support for equipment maintenance and optimization.

[0026] Step S4, predicting the interference source distribution of the target FPC sensor module based on the interference signal type and the interference signal corresponding to the interference signal type, to obtain an interference source feature mapping matrix.

[0027] Specifically, in the step of "predicting the distribution of interference sources for the target FPC sensor module based on the interference signal type and the interference signal corresponding to the interference signal type, and obtaining the interference source feature mapping matrix", it is first necessary to use the interference signal type obtained by the spectral clustering algorithm to make a prediction. Assuming that in an industrial automation scenario, the FPC sensor module is used to monitor the vibration of machinery and equipment, and the spectral clustering algorithm has classified different types of interference signals. Next, based on these classification results, we will predict the distribution of interference sources on the FPC sensor module. The prediction process usually involves spatial positioning and intensity analysis of interference signals. By analyzing the characteristics of different types of interference signals, such as frequency, amplitude, duration, etc., the location and impact range of the interference source can be inferred. For example, if the spectral clustering algorithm identifies a low-frequency mechanical vibration interference, then this interference may come from a component of the machinery and equipment, such as a bearing or gear, and the location and operating status of these components can be considered when predicting. Similarly, if a high-frequency electromagnetic interference is identified, this interference may come from nearby electrical equipment or electromagnetic field sources, and the location and distance of the FPC sensor module relative to these interference sources need to be considered when predicting. Based on these analyses, an interference source feature mapping matrix is ​​generated. This matrix not only records the distribution of different types of interference signals on the FPC sensor module, but also includes the characteristics of each interference source, such as the frequency, amplitude, propagation path, etc. of the interference signal. For example, during the operation of the machine equipment, if a high-frequency electromagnetic interference is identified, the interference source may be located in the upper left corner of the FPC sensor module. The frequency of the interference signal is 100MHz and the amplitude is 10dBm. This information will be recorded in the interference source feature mapping matrix, and the possible impact area of ​​the interference source will be marked. In this way, the FPC sensor module can not only identify the type of interference signal, but also predict the distribution of the interference source, providing precise guidance for subsequent anti-interference processing. This not only improves the accuracy of signal processing, but also provides strong data support for equipment optimization and maintenance.

[0028] Step S5, using a preset signal processing technology, based on the interference source feature mapping matrix, performs anti-interference processing on the interference signal in the original signal to obtain a target working signal.

[0029] Specifically, in the step of "using a preset signal processing technology to perform anti-interference processing on the interference signal in the original signal based on the interference source feature mapping matrix to obtain the target working signal", it is first necessary to use the previously generated interference source feature mapping matrix to perform signal processing. In the industrial automation scenario, it is assumed that the FPC sensor module is used to monitor the vibration of the machine equipment. Through the spectral clustering algorithm and the interference source distribution prediction, the type of interference signal and its distribution information on the FPC sensor module have been obtained. Signal processing technology plays a key role here, and its purpose is to remove or weaken the interference signal from the original signal and retain or enhance the target working signal. Based on the interference source feature mapping matrix, the signal processing algorithm can accurately locate the position and characteristics of the interference signal, so as to take targeted processing methods. For example, if the interference signal is high-frequency electromagnetic interference, the signal processing technology may use a filter or an adaptive noise elimination algorithm to suppress these high-frequency components. Assuming that a low-frequency mechanical vibration interference is identified during the operation of the machine equipment, the signal processing algorithm may use frequency domain filtering technology to suppress the signal in this frequency range. In specific operations, signal processing technology may include but is not limited to: time domain filtering, frequency domain filtering, adaptive filtering, waveform matching, blind source separation and other methods. Assume that in an industrial automation scenario, the original signal monitored by the FPC sensor module contains the vibration signal generated by the normal operation of the machine equipment and the electromagnetic interference caused by nearby electrical equipment. The signal processing algorithm will identify the characteristics of electromagnetic interference on the time-frequency spectrum based on the interference source feature mapping matrix, and use appropriate filtering technology to filter out or weaken these interference signals, so as to obtain a target working signal that is closer to the actual machine vibration situation. For example, during the processing process, if a sudden electromagnetic interference is identified, the signal processing algorithm may remove the signal components of the interference frequency band through the frequency domain cutting method to ensure the quality of the target working signal. In this way, the FPC sensor module can effectively suppress interference signals, improve the reliability and stability of the signal, and provide accurate data support for the normal operation and maintenance of the machine equipment.

[0030] In a specific embodiment, the time-frequency domain conversion of the original signal is performed by using a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram, including: Performing scale discretization processing on the original signal to obtain discretized signals of different scales; wherein the original signal includes an original electrical signal and an original communication signal; Through the preset Morlet wavelet transform technology, the time-frequency domain conversion and wavelet transform are performed on the discretized signals of different scales to obtain a series of wavelet signals; wherein the series of wavelet signals represent the energy distribution of the original signal in different frequency bands and time windows; Performing dimensionality reduction processing on the wavelet signal to obtain a dimensionality reduced signal; Performing spatial interpolation on the dimension-reduced signal by a preset bilinear interpolation algorithm to obtain an interpolated signal; The interpolated signal is converted into a signal time-frequency spectrum diagram by using a preset grayscale mapping algorithm.

[0031] Specifically, in the step of "converting the original signal into the time-frequency domain by using the preset Morlet wavelet transform technology to obtain the signal time-frequency spectrum", the original signal needs to be discretized first. In the industrial automation scenario, assuming that the FPC sensor module is used to monitor the vibration of the machine equipment, the original signal may include electrical signals and communication signals generated when the machine is working. The purpose of the discretization process is to convert the continuous time signal into a series of discrete scale signals. Specifically, the original signal is sampled according to the preset scale, for example, different time window lengths or sampling frequencies are selected to obtain discretized signals of different scales. Assuming that when the machine equipment is operating normally, the frequency components of the vibration signal are mainly concentrated in the low frequency band, while when a fault occurs, high-frequency components may appear. Through discretization, the signal characteristics in these different frequency ranges can be better captured. Next, the scale discretized signals are subjected to wavelet transform and time-frequency domain conversion by using the preset Morlet wavelet transform technology. Morlet wavelet transform is a time-frequency analysis method that uses a special wavelet basis function (i.e., Morlet wavelet) to perform convolution operations on the signal to obtain a series of wavelet signals. These wavelet signals represent the energy distribution of the original signal in different frequency bands and time windows. For example, during the operation of a machine, if a sudden high-frequency vibration occurs, the Morlet wavelet transform can clearly show the duration and frequency position of this high-frequency vibration on the time-frequency spectrum, providing a basis for subsequent abnormality identification. After obtaining the wavelet signals, these signals need to be processed for dimensionality reduction. The purpose of dimensionality reduction is to simplify the data structure, reduce computational complexity, and retain key information. In industrial automation scenarios, the FPC sensor module may generate a large amount of vibration data, and dimensionality reduction processing can compress this data from a high-dimensional space to a low-dimensional space. For example, principal component analysis (PCA) or other dimensionality reduction algorithms can be used to extract the main components of the wavelet signal, reduce redundant information, and ensure the efficiency and accuracy of subsequent processing. After dimensionality reduction processing, the preset bilinear interpolation algorithm is used to perform spatial interpolation on the reduced-dimensional signal. Bilinear interpolation is an interpolation technique that interpolates signals in two-dimensional space to generate a more detailed interpolated signal. In the application of FPC sensor modules, interpolation technology can help restore the details of the signal on the time-frequency spectrum, making the boundaries of the abnormal interference area clearer. For example, if a machine device has a short high-frequency vibration at a certain point in time, the time and frequency range of the vibration can be more accurately located through interpolation processing. Finally, the interpolated signal is converted into a signal time-frequency spectrum through a preset grayscale mapping algorithm. The grayscale mapping algorithm maps the energy distribution of the signal to the grayscale level to generate an intuitive image representation.In the industrial automation scenario, if an irregular vibration occurs during the operation of a machine, the grayscale mapping algorithm can display this vibration as different grayscale areas on the time-frequency spectrum. The brightness indicates the energy intensity of the vibration, and the color change represents the change in frequency. In this way, operators can intuitively observe the vibration of the machine at different time points, identify the characteristics of the abnormal interference area, and provide visual assistance for subsequent fault diagnosis and maintenance. For example, in actual applications, if a high-frequency vibration occurs in a machine within a certain period of time, after Morlet wavelet transform, dimensionality reduction processing and interpolation, a high-brightness area will be displayed on the generated signal time-frequency spectrum, representing the time and frequency position of this high-frequency vibration. Through this processing, the FPC sensor module can not only identify abnormal interference signals, but also provide accurate positioning and marking for subsequent feature analysis and classification, ensuring the effectiveness and pertinence of interference signal processing.

[0032] In a specific embodiment, the method of identifying whether there is an abnormal interference area in the spectrum diagram of the signal by using a preset abnormality identification model, and if so, marking the abnormal interference area, includes: Extracting local binary pattern features from the signal time-frequency spectrum to obtain an LBP feature vector sequence; Based on a preset sliding window, the LBP feature vector sequence is time segmented to obtain a plurality of LBP feature vector subsequences; wherein each of the LBP feature vector subsequences represents a local area of ​​the signal time-frequency spectrum diagram; Performing singular value decomposition on each of the LBP feature vector subsequences to obtain a corresponding singular value vector; wherein the singular value vector is used to reflect the energy distribution characteristics of the LBP feature vector subsequence; By presetting an abnormality recognition model, identifying whether there is an abnormal interference area in the spectrum graph of the signal based on the singular value vector; If it exists, the abnormality degree of the existing abnormal interference area is calculated based on a preset abnormality degree assessment algorithm to obtain a corresponding abnormality score; wherein the higher the abnormality score, the greater the possibility that the interference signal exists in the local area of ​​the signal spectrum diagram; The abnormal interference areas in the signal time-frequency spectrum diagram whose abnormality scores are greater than a preset threshold are combined and sorted, and the abnormal interference areas after the area combination and sorting are marked, wherein the area combination and sorting include removing overlapping areas and merging adjacent areas, and the abnormal interference areas include the start time, duration and frequency range of the interference signal.

[0033] Specifically, in the step of "identifying whether there is an abnormal interference area in the signal time-frequency spectrum by a preset abnormal identification model, and if so, marking the abnormal interference area", it is first necessary to extract the local binary pattern (LBP) feature of the signal time-frequency spectrum. In the industrial automation scenario, it is assumed that the FPC sensor module is used to monitor the vibration of the machine equipment, and the signal time-frequency spectrum shows the vibration frequency and energy distribution of the machine equipment in different time periods. LBP feature extraction is an image processing technology that generates a series of feature vector sequences by comparing the values ​​of each pixel with its surrounding neighboring pixels. These feature vectors can capture the texture information of the local area in the signal time-frequency spectrum. For example, if an irregular vibration occurs in the machine equipment during operation, LBP feature extraction can identify the characteristic difference of this vibration in the time-frequency spectrum. Next, these LBP feature vector sequences are time segmented based on a preset sliding window. The sliding window is a moving average or segmentation method that slides a fixed-size time window on the signal time-frequency spectrum to obtain multiple LBP feature vector subsequences. Each subsequence represents a local area of ​​the signal time-frequency spectrum, allowing detailed analysis of different time periods of the signal. For example, during the operation of a machine, a sliding window can capture vibration features at different time points, thereby identifying possible abnormal vibration areas. Perform singular value decomposition (SVD) on each LBP feature vector subsequence to obtain the corresponding singular value vector. Singular value decomposition is a matrix decomposition method used to reflect the energy distribution characteristics of the LBP feature vector subsequence. Through SVD, the local features of the signal can be compressed into several main singular values, which reflect the concentration of signal energy. For example, if the vibration signal in a certain period of time appears as a high energy concentration area on the time-frequency spectrum, then through singular value decomposition, a high singular value can be obtained, indicating that there may be abnormal interference in this area. By presetting an abnormal recognition model, based on these singular value vectors, it is identified whether there is an abnormal interference area in the signal time-frequency spectrum. The abnormal recognition model may be a classifier based on machine learning, which learns the characteristic differences between normal working signals and interference signals through training data. For example, if the vibration signal of a machine is mainly concentrated in the low frequency band when it is operating normally, and when it fails or is disturbed, the signal will have high-frequency components. The abnormal recognition model can identify these abnormal features by comparing the singular value vectors. If abnormal interference areas are identified, the abnormality level of these areas needs to be calculated based on the preset abnormality level assessment algorithm to obtain the corresponding abnormality score. The higher the abnormality score, the greater the possibility of interference signals existing in the local area. For example, during the operation of a machine, if a sudden high-frequency vibration is identified, the abnormality level assessment algorithm can calculate a high abnormality score based on its duration, frequency range and other characteristics, indicating that this area needs special attention.Finally, the abnormal interference areas with abnormal scores greater than the preset threshold are combined and sorted, and the combined and sorted abnormal interference areas are marked. Area combination and sorting include removing overlapping areas and merging adjacent areas to ensure that the marked abnormal areas are accurate and coherent. When marking the abnormal interference areas, it is necessary to record the start time, duration and frequency range of the interference signal. For example, in an industrial automation scenario, if it is identified that the machine equipment continues to vibrate abnormally within a certain period of time, this information will be marked on the signal time-frequency spectrum and provided to the operator or maintenance personnel as a basis for fault diagnosis and maintenance. Through this step, the FPC sensor module can not only identify abnormal interference signals, but also accurately locate and mark the characteristics of these signals, providing a clear goal for subsequent interference processing. This method not only improves the level of intelligence in signal processing, but also provides strong data support for equipment maintenance and optimization.

[0034] In a specific embodiment, the preset spectral clustering algorithm is used to perform feature analysis and classification on the annotated abnormal interference area based on the signal time-frequency spectrum diagram to obtain the interference signal type, including: Inputting the annotated abnormal interference region into a preset region-signal mapping algorithm to obtain an interference signal corresponding to the annotated abnormal interference region; Extracting energy distribution characteristics of the interference signal in different frequency bands on the signal spectrum diagram through high-order spectrum estimation technology; Constructing a frequency spectrum feature graph based on the energy distribution feature; In the frequency spectrum feature graph, a preset spectrum clustering algorithm is used to analyze the feature similarity of each interference signal to obtain a similar feature distribution result; wherein the similar feature distribution result includes signal frequency concentration, energy density distribution and frequency band correlation coefficient; Performing dimensionality reduction processing on the similar feature distribution result to obtain a similar feature distribution result after dimensionality reduction; The similar feature distribution result after dimensionality reduction is input into a preset support vector machine for classification to obtain the interference signal type.

[0035] Specifically, in the step of "using a preset spectral clustering algorithm to analyze and classify the marked abnormal interference regions based on the signal time-frequency spectrum to obtain the interference signal type", the marked abnormal interference regions need to be input into the preset region-signal mapping algorithm to obtain the interference signals corresponding to these regions. In the industrial automation scenario, it is assumed that the FPC sensor module is used to monitor the vibration of the machine equipment, and the abnormal recognition model has marked the abnormal interference regions on the signal time-frequency spectrum. The region-signal mapping algorithm converts these regions into corresponding interference signals by analyzing the time domain and frequency domain characteristics. For example, if a high-frequency vibration occurs in the machine equipment at a certain point in time, the region-signal mapping algorithm can extract the specific manifestation of this high-frequency vibration on the time-frequency spectrum. Next, the high-order spectrum estimation technology is used to extract the energy distribution characteristics of these interference signals in different frequency bands. High-order spectrum estimation is a signal processing technology used to analyze the nonlinear characteristics of signals. It can identify the energy concentration of interference signals in different frequency bands. For example, during the operation of a machine, if a low-frequency mechanical vibration interference occurs, the high-order spectrum estimation technology can analyze the energy distribution of this vibration in the low-frequency band, providing a basis for subsequent feature analysis. Based on these energy distribution features, a spectrum feature graph is constructed. The spectrum feature graph visualizes the energy distribution of different frequency bands. For example, if a machine is subjected to electromagnetic interference during operation, this interference may appear as a high-energy peak in the high-frequency band. The spectrum feature graph will clearly display these features, providing an intuitive reference for subsequent analysis and classification. In the spectrum feature graph, a preset spectrum clustering algorithm is used to analyze the feature similarity of each interference signal. Spectral clustering is a clustering method based on graph theory, which is suitable for processing data with nonlinear structures. It constructs a graph and regards each interference signal as a node. The connection weights between nodes represent the similarity or distance between them. For example, in an industrial automation scenario, if the machine is subjected to two main types of interference signals, one is low-frequency mechanical vibration interference and the other is high-frequency electromagnetic interference, the spectrum clustering algorithm will classify these signals into different groups based on their feature differences in the spectrum feature graph. Through analysis, similar feature distribution results are obtained. The similar feature distribution results include signal frequency concentration, energy density distribution and frequency band correlation coefficient. For example, low-frequency vibration interference may be manifested as high frequency concentration and energy density distribution in the low frequency band, while high-frequency electromagnetic interference has obvious energy concentration and frequency band correlation in the high frequency band. Next, these similar feature distribution results are processed by dimensionality reduction. The purpose of dimensionality reduction is to simplify the data structure, reduce the computational complexity, and retain key information. For example, the main components in the similar feature distribution results can be extracted through principal component analysis (PCA) or other dimensionality reduction algorithms to ensure the efficiency and accuracy of subsequent processing. The similar feature distribution results after dimensionality reduction are input into the preset support vector machine (SVM) for classification.Support vector machine is a powerful classification algorithm that learns the characteristics of data, finds the optimal decision boundary, and separates different types of interference signals. For example, during the operation of machinery and equipment, if a sudden electromagnetic interference is identified, the support vector machine can classify it as an electromagnetic interference type based on its characteristics on the spectrum feature map. Through this step, the FPC sensor module can not only identify abnormal interference signals, but also accurately distinguish the type of interference. For example, if a low-frequency mechanical vibration interference and a high-frequency electromagnetic interference occur during the operation of the machinery and equipment, through the spectral clustering algorithm and support vector machine classification, the FPC sensor module can classify these interference signals as mechanical fault interference and electromagnetic interference, respectively, providing an accurate basis for subsequent interference processing and prevention. This method not only improves the level of intelligence in signal processing, but also provides strong data support for equipment maintenance and optimization.

[0036] In a specific embodiment, the interference source distribution prediction of the target FPC sensor module based on the interference signal type and the interference model corresponding to the interference signal type is performed to obtain an interference source feature mapping matrix, including: Extracting an interference signal corresponding to the interference signal type; Performing high-order Volterra series analysis on the interference signal corresponding to the interference signal type to obtain a nonlinear time-varying interference feature sequence; wherein the nonlinear time-varying interference feature sequence includes amplitude features, phase features and frequency features; Obtain a physical layout distribution map corresponding to the target FPC sensor module, and perform a probability distribution analysis on the physical layout distribution map based on the nonlinear time-varying interference feature sequence using a nonparametric kernel density estimation method to obtain a spatial density of interference source distribution; wherein the spatial density of interference source distribution includes a location probability and an intensity probability of the interference source; Inputting the interference source distribution spatial density into a preset deep belief network to predict the interference source, thereby obtaining a predicted interference source; Adopting an adaptive Kalman particle filter algorithm to perform dynamic trajectory prediction on the predicted interference source, and obtaining the propagation trajectory characteristics of the interference source, including the movement direction characteristics and speed characteristics of the interference source; Performing spatiotemporal correlation analysis on the propagation trajectory characteristics of the interference source to obtain the influence range of the interference source; An interference source characteristic mapping matrix is ​​obtained based on the interference source influence range and the interference signal type; wherein the interference source characteristic mapping matrix includes rows, columns and elements, the rows represent the interference source type, the columns represent the physical location of the FPC sensor module and the elements represent the characteristic intensity of the interference signal.

[0037] Specifically, in the step of "predicting the interference source distribution of the target FPC sensor module based on the interference signal type and the interference model corresponding to the interference signal type to obtain the interference source feature mapping matrix", it is first necessary to extract the interference signal corresponding to the interference signal type. In the industrial automation scenario, it is assumed that the FPC sensor module is used to monitor the vibration of the machine equipment. Different types of interference signals have been identified through spectral clustering algorithm and support vector machine classification. After extracting these signals, they need to be analyzed more deeply. The interference signal corresponding to the interference signal type is analyzed by high-order Volterra series to obtain a nonlinear time-varying interference feature sequence. High-order Volterra series is a nonlinear system analysis method that can describe the dynamic behavior of signals in nonlinear systems. It extracts the amplitude characteristics, phase characteristics and frequency characteristics of the interference signal through a series of convolution integrals. For example, if the machine equipment is disturbed by a low-frequency mechanical vibration during operation, Volterra series analysis can reveal the nonlinear changes of this vibration signal at different time points. Next, the physical layout distribution map corresponding to the target FPC sensor module is obtained, and the probability distribution analysis of the physical layout distribution map is performed based on the nonlinear time-varying interference feature sequence by the nonparametric kernel density estimation method. Nonparametric kernel density estimation is a statistical method used to estimate the probability density function of data. It calculates the probability density of each position by placing a kernel function around the data point, thereby obtaining the distribution space density of the interference source on the FPC sensor module. For example, in the operation of the machine equipment, if an electromagnetic interference is identified, the nonparametric kernel density estimation can help predict the possible location and intensity distribution of this interference source. The spatial density of the interference source distribution is input into the preset deep belief network for interference source prediction. The deep belief network is a deep learning model that is good at processing complex nonlinear data. By learning the characteristics of the spatial density of the interference source distribution, the deep belief network can predict the location and intensity of the interference source. For example, if the machine equipment is interfered with within a certain period of time, the deep belief network can identify that the interference source may come from a specific location of the equipment. The adaptive Kalman particle filter algorithm is used to predict the dynamic trajectory of the predicted interference source. Kalman particle filter is a fusion filtering technology that combines the advantages of Kalman filter and particle filter, and is suitable for dynamic trajectory prediction of nonlinear and non-Gaussian systems. It provides a dynamic prediction model by tracking the motion direction characteristics and speed characteristics of the interference source. For example, during the operation of a machine, if an interference signal is identified to be moving, the adaptive Kalman particle filter algorithm can predict its future motion trajectory. The propagation trajectory characteristics of the interference source are analyzed in time and space to obtain the impact range of the interference source. The time and space correlation analysis analyzes the physical area that may be affected by the interference source by considering its motion trajectory and time distribution.For example, if the interference source appears on the left side of the FPC sensor module and moves to the right side, the spatiotemporal correlation analysis can determine the area affected during the entire movement process. Finally, the interference source feature mapping matrix is ​​obtained based on the interference source impact range and interference signal type. The interference source feature mapping matrix is ​​a two-dimensional matrix, in which the rows represent the interference source type, the columns represent the physical location of the FPC sensor module, and the elements represent the characteristic intensity of the interference signal. For example, if the machine equipment is subject to low-frequency mechanical vibration interference and high-frequency electromagnetic interference during operation, the interference source feature mapping matrix will record the distribution and intensity of these interference signals on the FPC sensor module, providing an intuitive view to help understand the impact of the interference source on the equipment. Through this step, the FPC sensor module can not only identify abnormal interference signals, but also predict the distribution, dynamic trajectory and impact range of the interference source, providing an accurate basis for subsequent interference processing and prevention. This method not only improves the intelligence level of signal processing, but also provides strong data support for equipment maintenance and optimization.

[0038] In a specific embodiment, the performing probability distribution analysis on the physical layout distribution diagram based on the nonlinear time-varying interference feature sequence to obtain the interference source distribution space density includes: Extracting the nonlinear time-varying interference feature sequence to obtain nonlinear dynamic features; wherein the nonlinear dynamic features include the nonlinear coefficient, time constant and frequency response features of the system; Performing multi-scale entropy analysis on the nonlinear dynamic characteristics to obtain complexity characteristics of the interference signal; wherein the complexity characteristics represent the complexity and regularity of the interference signal at different time scales; By using a preset spectrum envelope estimation algorithm, based on the nonlinear dynamic characteristics and the complexity characteristics, spectrum envelope analysis is performed on the physical layout distribution diagram to obtain spectrum envelope distribution; The frequency spectrum envelope distribution is used to perform probability distribution analysis on the physical layout distribution diagram to obtain the interference source distribution space density.

[0039] Specifically, in the step of "performing a probability distribution analysis on the physical layout distribution diagram based on the nonlinear time-varying interference feature sequence to obtain the spatial density of the interference source distribution", it is first necessary to extract features from the nonlinear time-varying interference feature sequence. The nonlinear time-varying interference feature sequence has obtained the amplitude characteristics, phase characteristics and frequency characteristics of the interference signal through high-order Volterra series analysis, and these characteristics reflect the changes of the interference signal at different time points. For example, in an industrial automation scenario, if the FPC sensor module detects that the machine equipment is subjected to a low-frequency mechanical vibration interference during operation, the nonlinear coefficient, time constant and frequency response characteristics of the vibration signal at different time points can be obtained through feature extraction. Then, these nonlinear dynamic features are subjected to multi-scale entropy analysis. Multi-scale entropy analysis is a nonlinear signal processing method used to evaluate the complexity and regularity of signals. Through this analysis, the complexity characteristics of the interference signal at different time scales can be obtained. For example, if the machine equipment is subjected to a high-frequency electromagnetic interference during operation, the multi-scale entropy analysis can reveal the complexity changes of the interference signal at different time scales and help understand the essential characteristics of the interference. Through the preset spectrum envelope estimation algorithm, the spectrum envelope analysis of the physical layout distribution map is performed based on nonlinear dynamic characteristics and complexity characteristics. Spectrum envelope analysis is a signal processing technology that can reveal the distribution of the spectrum characteristics of the signal by performing envelope processing on the spectrum of the signal. For example, during the operation of the machine equipment, if a sudden vibration interference is identified, the spectrum envelope analysis can display the envelope shape of the interference signal on the spectrum, providing an intuitive view to help locate the interference source. The probability distribution analysis of the physical layout distribution map is performed using the spectrum envelope distribution to obtain the spatial density of the interference source distribution. The probability distribution analysis maps the spectrum envelope distribution to the physical layout distribution map through the non-parametric kernel density estimation method, and calculates the location probability and intensity probability of the interference source on the FPC sensor module. For example, if the machine equipment is subjected to a low-frequency mechanical vibration interference during operation, the spectrum envelope analysis can identify that the interference is more obvious in a specific area of ​​the equipment, and the non-parametric kernel density estimation can further calculate the distribution of the interference source on the FPC sensor module. Through this process, the FPC sensor module can not only identify abnormal interference signals, but also predict the distribution of interference sources on the equipment. For example, if a machine is subject to a sudden high-frequency electromagnetic interference during operation, the FPC sensor module can identify the nonlinear characteristics and complexity characteristics of the interference signal through feature extraction, multi-scale entropy analysis and spectrum envelope analysis, and then determine the possible location and intensity distribution of the interference source through spectrum envelope distribution and probability distribution analysis, providing an accurate basis for subsequent interference processing and prevention. This method not only improves the intelligence level of signal processing, but also provides strong data support for equipment maintenance and optimization.For example, in industrial automation scenarios, the FPC sensor module can identify abnormal vibrations of machinery and equipment during operation, and through a series of nonlinear signal processing technologies, provide a reliable vibration monitoring data to ensure the operating stability and safety of the equipment, reduce false alarms or missed alarms caused by interference signals, and enhance the intelligent monitoring capabilities of the equipment.

[0040] In a specific embodiment, the anti-interference processing is performed on the interference signal based on the interference source feature mapping matrix by a preset signal processing technology to obtain a target working signal, including: Performing spatial correlation analysis on the interference signal based on the interference source feature mapping matrix to obtain a position corresponding to the interference signal; At the position, performing notch processing on the interference signal by using a preset adaptive notch filter group and a preset signal processing technology to obtain a notch processed signal; By using a preset signal subspace projection algorithm, a preset signal in the notch processing signal is extracted, and the preset signal is separated from the notch processing signal to obtain a preliminary working signal and a residual interference signal; wherein the preset signal is a signal required by the FPC sensor module when working; Performing signal enhancement on the remaining interference signal to obtain an enhanced signal; Performing frequency selective processing on the enhanced signal to obtain a selective filtering scheme; The selective filtering scheme is used to perform anti-interference signal processing on the remaining interference signal to reduce the contamination of the preliminary working signal by the remaining interference signal, thereby obtaining a target working signal.

[0041] Specifically, in the step of "performing anti-interference processing on the interference signal based on the interference source feature mapping matrix through a preset signal processing technology to obtain a target working signal", it is first necessary to perform spatial correlation analysis on the interference signal based on the interference source feature mapping matrix. The interference source feature mapping matrix has recorded the distribution of different types of interference signals on the FPC sensor module. Through spatial correlation analysis, the source of these interference signals can be accurately located. For example, in an industrial automation scenario, if the FPC sensor module detects that a low-frequency mechanical vibration interference occurs during the operation of the machine equipment, the spatial correlation analysis can identify that the interference signal is mainly concentrated in a specific area of ​​the equipment. After identifying the location of the interference signal, these interference signals are notched through a preset adaptive notch filter group and a preset signal processing technology. The adaptive notch filter is a dynamic signal processor that can automatically adjust the notch frequency according to the change of the input signal, thereby effectively suppressing the interference signal within a specific frequency range. For example, if a high-frequency electromagnetic interference is identified, the adaptive notch filter can perform notch processing on the signal for this frequency band to generate a notch processing signal. Next, the preset signal subspace projection algorithm is used to extract the preset signal in the notch processing signal, and the preset signal is separated from the notch processing signal to obtain the preliminary working signal and the remaining interference signal. In the industrial automation scenario, the preset signal refers to the vibration signal required by the machine equipment when it is working normally. The signal subspace projection algorithm can separate the interference signal from the normal working signal. For example, if the normal working signal of the machine equipment is mainly concentrated in the low frequency band, and the interference signal is in the high frequency band, then the signal subspace projection algorithm can extract the signal in the low frequency band as the preliminary working signal and identify the remaining high frequency signal as the remaining interference signal. The remaining interference signal is subjected to signal enhancement processing. Signal enhancement is a signal processing technology used to increase the strength of useful signals and reduce the influence of noise and interference. For example, through adaptive noise elimination technology or frequency domain filtering, the useful components in the remaining interference signal can be enhanced to obtain an enhanced signal. Next, the enhanced signal is subjected to frequency selective processing to obtain a selective filtering scheme. Frequency selective processing selectively suppresses or enhances signal components within a specific frequency range by analyzing the spectral characteristics of the enhanced signal. For example, if there are still some low-frequency mechanical vibration interferences in the enhanced signal, the frequency selective processing can design a filtering scheme for these low-frequency interferences. Finally, the selective filtering scheme is used to perform anti-interference signal processing on the remaining interference signal to reduce the contamination of the preliminary working signal by the remaining interference signal and obtain the target working signal. Through a carefully designed filtering scheme, the harmful components in the remaining interference signal will be weakened or removed, making the preliminary working signal purer.For example, if the remaining interference signal contains the noise of the environment around the machine equipment, the selective filtering scheme will effectively suppress these noise components, so that the final target working signal reflects the actual working status of the machine equipment as much as possible. Through this step, the FPC sensor module can not only identify abnormal interference signals, but also effectively process these interferences through a series of signal processing technologies, thereby obtaining a target working signal that is closer to the actual vibration of the machine equipment. This method not only improves the intelligence level of signal processing, but also provides accurate data support for the normal operation and maintenance of the equipment. For example, in industrial automation scenarios, the FPC sensor module can identify abnormal vibrations of the machine equipment during operation, and through anti-interference processing, provide a reliable vibration monitoring data to ensure the stability and safety of the equipment's operation.

[0042] The above describes the anti-interference signal processing method of the FPC sensor module in the embodiment of the present invention. The following describes the anti-interference signal processing device of the FPC sensor module in the embodiment of the present invention. Figure 2 , an embodiment of the anti-interference signal processing device of the FPC sensor module in the embodiment of the present invention includes: The conversion module 21 is used to collect the original signal of the target FPC sensor module through a preset signal sensor, and perform time-frequency domain conversion on the original signal through a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram; The identification module 22 is used to identify whether there is an abnormal interference area in the signal spectrum diagram through a preset abnormal identification model, and if so, mark the abnormal interference area; wherein the abnormal interference area represents an interference signal; The classification module 23 is used to perform feature analysis and classification on the marked abnormal interference area based on the signal time-frequency spectrum diagram by using a preset spectral clustering algorithm to obtain the interference signal type; A prediction module 24 is used to predict the interference source distribution of the target FPC sensor module based on the interference signal type and the interference signal corresponding to the interference signal type to obtain an interference source feature mapping matrix; The anti-interference module 25 is used to perform anti-interference processing on the interference signal in the original signal based on the interference source feature mapping matrix through a preset signal processing technology to obtain a target working signal.

[0043] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0044] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0046] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0048] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An anti-interference signal processing method for an FPC sensor module, characterized in that: The following steps are involved: The original signal of the target FPC sensor module is collected by a preset signal sensor, and the original signal is converted into a time-frequency domain by a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram; By presetting an abnormality recognition model, identifying whether there is an abnormal interference area in the spectrum diagram of the signal, and if so, marking the abnormal interference area; wherein the abnormal interference area represents an interference signal; By using a preset spectral clustering algorithm, characteristic analysis and classification are performed on the marked abnormal interference area based on the signal time-frequency spectrum diagram to obtain the interference signal type; Based on the interference signal type and the interference signal corresponding to the interference signal type, the interference source distribution of the target FPC sensor module is predicted to obtain an interference source feature mapping matrix; Through a preset signal processing technology, anti-interference processing is performed on the interference signal in the original signal based on the interference source feature mapping matrix to obtain a target working signal.

2. The anti-interference signal processing method of the FPC sensor module according to claim 1, characterized in that: The method of converting the original signal into a time-frequency domain by using a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram includes: Performing scale discretization processing on the original signal to obtain discretized signals of different scales; wherein the original signal includes an original electrical signal and an original communication signal; Through the preset Morlet wavelet transform technology, the time-frequency domain conversion and wavelet transform are performed on the discretized signals of different scales to obtain a series of wavelet signals; wherein the series of wavelet signals represent the energy distribution of the original signal in different frequency bands and time windows; Performing dimensionality reduction processing on the wavelet signal to obtain a dimensionality reduced signal; Performing spatial interpolation on the dimension-reduced signal by a preset bilinear interpolation algorithm to obtain an interpolated signal; The interpolated signal is converted into a signal time-frequency spectrum diagram by using a preset grayscale mapping algorithm.

3. The anti-interference signal processing method of the FPC sensor module according to claim 1, characterized in that: The method of identifying whether there is an abnormal interference area in the signal spectrum diagram by using a preset abnormality identification model, and if so, marking the abnormal interference area, includes: Extracting local binary pattern features from the signal time-frequency spectrum to obtain an LBP feature vector sequence; Based on a preset sliding window, the LBP feature vector sequence is time segmented to obtain a plurality of LBP feature vector subsequences; wherein each of the LBP feature vector subsequences represents a local area of ​​the signal time-frequency spectrum diagram; Performing singular value decomposition on each of the LBP feature vector subsequences to obtain a corresponding singular value vector; wherein the singular value vector is used to reflect the energy distribution characteristics of the LBP feature vector subsequence; By presetting an abnormality recognition model, identifying whether there is an abnormal interference area in the spectrum graph of the signal based on the singular value vector; If it exists, the abnormality degree of the existing abnormal interference area is calculated based on a preset abnormality degree assessment algorithm to obtain a corresponding abnormality score; wherein the higher the abnormality score, the greater the possibility that the interference signal exists in the local area of ​​the signal spectrum diagram; The abnormal interference areas in the signal time-frequency spectrum diagram whose abnormality scores are greater than a preset threshold are combined and sorted, and the abnormal interference areas after the area combination and sorting are marked, wherein the area combination and sorting include removing overlapping areas and merging adjacent areas, and the abnormal interference areas include the start time, duration and frequency range of the interference signal.

4. The anti-interference signal processing method of the FPC sensor module according to claim 1, characterized in that: The preset spectral clustering algorithm is used to perform feature analysis and classification on the annotated abnormal interference area based on the signal time-frequency spectrum diagram to obtain the interference signal type, including: Inputting the annotated abnormal interference region into a preset region-signal mapping algorithm to obtain an interference signal corresponding to the annotated abnormal interference region; Extracting energy distribution characteristics of the interference signal in different frequency bands on the signal spectrum diagram through high-order spectrum estimation technology; Constructing a frequency spectrum feature graph based on the energy distribution feature; In the frequency spectrum feature graph, a preset spectrum clustering algorithm is used to analyze the feature similarity of each interference signal to obtain a similar feature distribution result; wherein the similar feature distribution result includes signal frequency concentration, energy density distribution and frequency band correlation coefficient; Performing dimensionality reduction processing on the similar feature distribution result to obtain a similar feature distribution result after dimensionality reduction; The similar feature distribution result after dimensionality reduction is input into a preset support vector machine for classification to obtain the interference signal type.

5. The anti-interference signal processing method of the FPC sensor module according to claim 1, characterized in that: The interference source distribution prediction is performed on the target FPC sensor module based on the interference signal type and the interference model corresponding to the interference signal type to obtain an interference source feature mapping matrix, including: Extracting an interference signal corresponding to the interference signal type; Performing high-order Volterra series analysis on the interference signal corresponding to the interference signal type to obtain a nonlinear time-varying interference feature sequence; wherein the nonlinear time-varying interference feature sequence includes amplitude features, phase features and frequency features; Obtain a physical layout distribution map corresponding to the target FPC sensor module, and perform a probability distribution analysis on the physical layout distribution map based on the nonlinear time-varying interference feature sequence using a nonparametric kernel density estimation method to obtain a spatial density of interference source distribution; wherein the spatial density of interference source distribution includes a location probability and an intensity probability of the interference source; Inputting the interference source distribution spatial density into a preset deep belief network to predict the interference source, thereby obtaining a predicted interference source; Adopting an adaptive Kalman particle filter algorithm to perform dynamic trajectory prediction on the predicted interference source, and obtaining the propagation trajectory characteristics of the interference source, including the movement direction characteristics and speed characteristics of the interference source; Performing spatiotemporal correlation analysis on the propagation trajectory characteristics of the interference source to obtain the influence range of the interference source; An interference source characteristic mapping matrix is ​​obtained based on the interference source influence range and the interference signal type; wherein the interference source characteristic mapping matrix includes rows, columns and elements, the rows represent the interference source type, the columns represent the physical location of the FPC sensor module and the elements represent the characteristic intensity of the interference signal.

6. The anti-interference signal processing method of the FPC sensor module according to claim 5, characterized in that: The performing probability distribution analysis on the physical layout distribution diagram based on the nonlinear time-varying interference feature sequence to obtain the interference source distribution space density includes: Extracting the nonlinear time-varying interference feature sequence to obtain nonlinear dynamic features; wherein the nonlinear dynamic features include the nonlinear coefficient, time constant and frequency response features of the system; Performing multi-scale entropy analysis on the nonlinear dynamic characteristics to obtain complexity characteristics of the interference signal; wherein the complexity characteristics represent the complexity and regularity of the interference signal at different time scales; By using a preset spectrum envelope estimation algorithm, based on the nonlinear dynamic characteristics and the complexity characteristics, spectrum envelope analysis is performed on the physical layout distribution diagram to obtain spectrum envelope distribution; The frequency spectrum envelope distribution is used to perform probability distribution analysis on the physical layout distribution diagram to obtain the interference source distribution space density.

7. The anti-interference signal processing method of the FPC sensor module according to claim 1, characterized in that: The method of performing anti-interference processing on the interference signal based on the interference source feature mapping matrix by using a preset signal processing technology to obtain a target working signal includes: Performing spatial correlation analysis on the interference signal based on the interference source feature mapping matrix to obtain a position corresponding to the interference signal; At the position, performing notch processing on the interference signal by using a preset adaptive notch filter group and a preset signal processing technology to obtain a notch processed signal; By using a preset signal subspace projection algorithm, a preset signal in the notch processing signal is extracted, and the preset signal is separated from the notch processing signal to obtain a preliminary working signal and a residual interference signal; wherein the preset signal is a signal required by the FPC sensor module when working; Performing signal enhancement on the remaining interference signal to obtain an enhanced signal; Performing frequency selective processing on the enhanced signal to obtain a selective filtering scheme; The selective filtering scheme is used to perform anti-interference signal processing on the remaining interference signal to reduce the contamination of the preliminary working signal by the remaining interference signal, thereby obtaining a target working signal.

8. An anti-interference signal processing device for an FPC sensor module, characterized in that: include: A conversion module is used to collect the original signal of the target FPC sensor module through a preset signal sensor, and perform time-frequency domain conversion on the original signal through a preset Morlet wavelet transform technology to obtain a signal time-frequency spectrum diagram; An identification module, used to identify whether there is an abnormal interference area in the spectrum diagram of the signal through a preset abnormal identification model, and if so, mark the abnormal interference area; wherein the abnormal interference area represents an interference signal; A classification module, used to perform feature analysis and classification on the annotated abnormal interference area based on the signal time-frequency spectrum diagram by using a preset spectral clustering algorithm to obtain the interference signal type; A prediction module, used to predict the interference source distribution of the target FPC sensor module based on the interference signal type and the interference signal corresponding to the interference signal type, and obtain an interference source feature mapping matrix; The anti-interference module is used to perform anti-interference processing on the interference signal in the original signal based on the interference source feature mapping matrix through a preset signal processing technology to obtain a target working signal.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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