A multi-channel interference co-recognition and adaptive filtering method and system

Abstract

The application belongs to the field of robot sensing and precision measurement and control, and discloses a multi-channel interference collaborative identification and adaptive filtering method and system. In the pre-operation stage, the multi-dimensional force sensor multi-channel signals are collected, the power spectrum density of each channel is calculated based on the Welch method, and the candidate interference frequency is identified. The non-maximum suppression is used to eliminate the redundant peak values in the channel, and the frequency cluster center is obtained by using the cluster analysis. The coherence coefficient between the multi-channels is further calculated, and the common interference source frequency set is determined. Based on the set, the bandwidth factor of the cascaded adaptive lattice notch filter is initialized by fuzzy reasoning, the notch filter group is constructed and dynamically adjusted in real-time operation, and the tracking and suppression of the multi-frequency and variable amplitude interference are realized. The application can accurately distinguish the common interference and the channel-specific signal, maintain the phase consistency of each channel, avoid the signal distortion caused by excessive filtering, and improve the measurement accuracy and anti-interference ability of the multi-dimensional force sensor in the complex industrial environment.

Description

Technical Field

[0001] This invention belongs to the field of robot sensing and precision measurement and control, specifically relating to a multi-channel interference collaborative identification and adaptive filtering method and system. Background Technology

[0002] Multidimensional force sensors, as key sensing components in industrial robotics, precision assembly, aerospace, and biomechanics, can simultaneously measure forces (Fx, Fy, Fz) and torques (Mx, My, Mz) in three directions. These sensors detect strain changes in an elastic body under stress and convert the multidimensional force signals into electrical signals. In complex industrial and dynamic operating environments, their output signals are susceptible to various interferences, severely affecting the accuracy of the final decoupling results. These interferences mainly originate from: 1. Environmental mechanical vibration interference: periodic vibrations of the sensor mounting base or platform (such as those caused by motors, engines, and transmission mechanisms), with a relatively fixed frequency but amplitude that may vary with operating conditions; 2. Electrical interference: including power grid frequencies (50Hz / 60Hz) and their harmonic interference, switching power supply noise, and high-frequency switching noise within the data acquisition system; 3. Sensor-load coupling interference: structural modal resonance of the elastic body itself, transient responses excited by dynamic load changes, and crosstalk between measurement channels caused by mechanical coupling.

[0003] To suppress these interferences, existing filtering methods typically process each channel signal independently. This approach has the following drawbacks: First, it lacks an effective method for identifying multi-channel common interference, making it impossible to accurately distinguish between common interference and channel-specific signals. Second, independent filtering of each channel may introduce different phase delays, disrupting the relative relationship between force and torque signals and affecting subsequent calculations and control. Third, it lacks overall optimization, which may lead to some interferences being over-suppressed while others are under-filtered.

[0004] Therefore, it is urgent to study an interference collaborative identification method and an adaptive filtering method for multi-dimensional force sensors to achieve efficient and accurate multi-channel interference suppression. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of independent filtering of multi-dimensional force sensors without cooperative interference identification, phase imbalance and lack of overall filtering optimization in the prior art, and to provide a multi-channel interference cooperative identification and adaptive filtering method and system.

[0006] To achieve the above objectives, the present invention employs the following technical solution: The present invention proposes a multi-channel interference collaborative identification and adaptive filtering method, comprising the following steps: Acquire the differential voltage signal output from the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies; Based on the common set of interference source frequencies, the frequency factor corresponding to each notch frequency point is calculated; based on the interference amplitude corresponding to each notch frequency point, the bandwidth factor of the corresponding adaptive notch filter unit is initialized based on the fuzzy discrimination method. A cascaded adaptive notch filter group is constructed based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its operating mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

[0007] Preferably, the step of identifying interference characteristics of the differential voltage signal and determining the set of common interference source frequencies specifically involves: Acquire the detection signal matrix , Indicates the number of channels. The number of signal points in the sampled signal; Power spectral density of each channel signal was calculated using the Welch method. Identify candidate interference frequency points and amplitudes for each channel; Non-maximum suppression is performed on candidate interference frequency points in each channel to eliminate redundant peaks with similar frequencies within the same channel; Set a frequency interval threshold, collect all candidate frequency points retained from all channels, and obtain the center frequency of different frequency clusters through cluster analysis; For each center frequency, calculate the coherence coefficient between multiple channels, construct a coherence matrix, and determine the set of common interference source frequencies.

[0008] Preferably, the non-maximum suppression of candidate interference frequency points for each channel specifically involves: Nonmaximum suppression methods: Candidate interference frequency points identified within a single channel Sort the frequencies in descending order of amplitude value to obtain the frequency sequence. and the corresponding amplitude sequence ,in, N is the total number of candidate interference frequency points identified in this channel; Set the suppression threshold traversal For each frequency point in the channel, eliminate the frequency within the channel.

[0009] Preferably, the step of obtaining the center frequencies of different frequency clusters through cluster analysis specifically involves: By summing all the frequencies retained after non-maximum suppression for each channel, a set of candidate frequency points is obtained. Each frequency point corresponds to an initial cluster centroid, and the initial centroid satisfies... Given a merging threshold Calculate the distance between all pairs of centroids. ,like The centroid of the merger is Delete the two centroids that were merged; iterate the above process repeatedly to complete the merging of all centroids and determine the center frequency of all frequency clusters between channels. in, This is another cluster centroid to be compared.

[0010] Preferably, the step of calculating the coherence coefficients between multiple channels for each center frequency, constructing a coherence matrix, and determining the set of common interference source frequencies specifically involves: For the global frequency vector Calculate the correlation coefficient between multiple channels, for two channel signals. and In frequency coherence coefficient at ;in, for and The cross-power spectral density; for The self-power spectral density; for The self-power spectral density; The coherence coefficients of all frequencies are summed to obtain the total coherence coefficient. Based on the correlation matrix, the common interference frequencies are determined, and the mean correlation coefficient for each frequency point is calculated. ,like If it is determined to be common interference, then the common interference will be identified as the frequency to be eliminated by the filter.

[0011] Preferably, the power spectral density of each channel signal is calculated based on the Welch method. Specifically: For length of Signal Given window length Number of signal overlap points Window sliding step The number of data segments ; For the current segment of frequency domain data Processing is performed to obtain Iterate through all data segments and accumulate each segment. get ; based on and sampling frequency The power spectral density is obtained. ; in, Represents rounding down. .

[0012] Preferably, the initialization of the bandwidth factor of the corresponding adaptive notch filter unit based on the fuzzy discrimination method specifically involves: The initial filter factor is defined as follows: First filter, filter factor is... The second filter has a filter factor of 1. The third filter has a filter factor of 1. The fourth filter has a filter factor of 1. The fifth filter has a filter factor of 1. The membership degrees of different levels are respectively , , , , ; Membership degree

[0013] Calculate the bandwidth factor after inference ; in, for , Indicates the first The magnitude of the power spectral density at each detection frequency; ; It is the maximum value; It is the minimum value; To prevent decimals from being divided by zero.

[0014] Preferably, each notch filter unit updates its bandwidth factor independently according to its operating mode, specifically as follows: Bandwidth factor The adaptive mechanism, based on the output power minimization criterion, defines the error signal. , The iteration format is: ; right Apply boundary constraints: ; in, This is the learning rate.

[0015] This invention proposes a multi-channel interference collaborative identification and adaptive filtering system, comprising: The signal interference identification module is used to acquire the differential voltage signal output by the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies. The parameter initialization module is used to calculate the frequency factor corresponding to each notch frequency point based on the common interference source frequency set; and to initialize the bandwidth factor of the corresponding adaptive notch filter unit based on the interference amplitude corresponding to each notch frequency point using the fuzzy discrimination method. An adaptive filtering module is used to construct a cascaded adaptive notch filter group based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its working mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

[0016] Preferably, the step of identifying interference characteristics of the differential voltage signal and determining the set of common interference source frequencies specifically involves: Acquire the detection signal matrix , Indicates the number of channels. The number of signal points in the sampled signal; Power spectral density of each channel signal was calculated using the Welch method. Identify candidate interference frequency points and amplitudes for each channel; Non-maximum suppression is performed on candidate interference frequency points in each channel to eliminate redundant peaks with similar frequencies within the same channel; Set a frequency interval threshold, collect all candidate frequency points retained from all channels, and obtain the center frequency of different frequency clusters through cluster analysis; For each center frequency, calculate the coherence coefficient between multiple channels, construct a coherence matrix, and determine the set of common interference source frequencies.

[0017] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-channel interference collaborative identification and adaptive filtering method.

[0018] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-channel interference collaborative identification and adaptive filtering method.

[0019] Compared with the prior art, the present invention has the following beneficial effects: This invention proposes a multi-channel interference collaborative identification and adaptive filtering method. First, it acquires the differential voltage signals output from a multi-channel strain gauge bridge. Instead of processing each channel independently, it performs unified interference feature identification on all channel signals, accurately extracting a common set of interference source frequencies. Second, based on the identified common interference source frequency set, it calculates the frequency factor corresponding to each notch filter frequency point. Then, combined with the interference amplitude at each notch filter frequency point, it initializes the bandwidth factor of the adaptive notch filter unit using a fuzzy discrimination method. This avoids the problems of insufficient interference filtering or excessive suppression caused by blindly setting filtering parameters, providing reasonable initial conditions for overall filtering optimization. Finally, based on the notch filter frequency point set and frequency factor, a cascaded adaptive notch filter group is constructed. Each notch filter unit independently updates its bandwidth factor according to its own operating mode. This achieves collaborative filtering of multi-channel interference, avoiding the problems of different phase delays and disruption of signal relative relationships caused by independent filtering of each channel. Furthermore, the adaptive updating of the bandwidth factor achieves overall optimization of the filtering effect, effectively solving the defect of uneven filtering effect in existing methods. Therefore, the method proposed in this invention solves the technical problems of multi-channel independent filtering without cooperative interference identification, phase imbalance, and uneven filtering effect.

[0020] This invention proposes a multi-channel interference collaborative identification and adaptive filtering system. By dividing the system into a signal interference identification module, a parameter initialization module, and an adaptive filtering module, it achieves collaborative identification and adaptive filtering of multi-channel interference. The modular approach ensures that each module is independent, facilitating unified management of all modules. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the multi-channel interference collaborative identification and adaptive filtering method of the present invention.

[0023] Figure 2 This is a schematic diagram of the algorithm flow of the present invention, illustrating the complete process from multi-channel signal acquisition to adaptive filtering.

[0024] Figure 3 The diagram below illustrates the filter structure of the present invention, showing the signal flow graph of the lattice filter structure.

[0025] Figure 4 This is a diagram of the multi-channel interference collaborative identification and adaptive filtering system of the present invention.

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0029] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0030] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 This invention addresses the problems existing in the background technology by proposing a multi-channel interference collaborative identification and adaptive filtering method. Specifically, it involves a signal processing approach for six-dimensional force sensors, based on multi-channel coherence analysis to distinguish between common and characteristic interference, and employing a collaborative strategy for adaptive filtering. This achieves accurate identification of multi-channel common interference frequencies, distinguishing between common interference and channel-specific signals; maintains phase consistency of signals in each channel after filtering; fully utilizes multi-channel information for adaptive filtering; adapts to interference frequency drift and intensity changes; and improves the measurement accuracy and anti-interference capability of the multi-dimensional force sensor. Figure 1 As shown, the method includes the following steps: Step 1: Obtain the differential voltage signal output from the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies. The process of identifying interference characteristics in the differential voltage signal to determine the set of common interference source frequencies specifically involves: Acquire the detection signal matrix , Indicates the number of channels. The number of signal points in the sampled signal; Power spectral density of each channel signal was calculated using the Welch method. Identify candidate interference frequency points and amplitudes for each channel; Non-maximum suppression is performed on candidate interference frequency points in each channel to eliminate redundant peaks with similar frequencies within the same channel; Set a frequency interval threshold, collect all candidate frequency points retained from all channels, and obtain the center frequency of different frequency clusters through cluster analysis; For each center frequency, calculate the coherence coefficient between multiple channels, construct a coherence matrix, and determine the set of common interference source frequencies.

[0031] The non-maximum suppression of candidate interference frequency points in each channel is specifically performed as follows: Nonmaximum suppression methods: Candidate interference frequency points identified within a single channel Sort the frequencies in descending order of amplitude value to obtain the frequency sequence. and the corresponding amplitude sequence ,in, , N This represents the total number of candidate interference frequency points identified within the channel. Set the suppression threshold traversal For each frequency point in the channel, eliminate the frequency within the channel.

[0032] The method of obtaining the center frequencies of different frequency clusters through cluster analysis is as follows: By summing all the frequencies retained after non-maximum suppression for each channel, a set of candidate frequency points is obtained. Each frequency point corresponds to an initial cluster centroid, and the initial centroid satisfies... Given a merging threshold Calculate the distance between all pairs of centroids. ,like The centroid of the merger is Delete the two centroids that were merged; iterate the above process repeatedly to complete the merging of all centroids and determine the center frequency of all frequency clusters between channels. in, This is another cluster centroid to be compared.

[0033] For each center frequency, the coherence coefficients between multiple channels are calculated, a coherence matrix is ​​constructed, and the set of common interference source frequencies is determined, specifically as follows: For the global frequency vector Calculate the correlation coefficient between multiple channels, for two channel signals. and In frequency coherence coefficient at ;in, for and The cross-power spectral density; for The self-power spectral density; for The self-power spectral density; The coherence coefficients of all frequencies are summed to obtain the total coherence coefficient. Based on the correlation matrix, the common interference frequencies are determined, and the mean correlation coefficient for each frequency point is calculated. ,like This is then identified as common interference, and the common interference is determined to be the frequency to be eliminated by the filter; where, To set a threshold.

[0034] The power spectral density of each channel signal is calculated based on the Welch method. Specifically: For length of Signal Segmentation and windowing, given window length Number of signal overlap points Window sliding step The number of data segments ; For the current segment of frequency domain data Processing is performed to obtain Iterate through all data segments and accumulate the frequency domain power of each segment. get ; Based on the sum of frequency domain power of all data segments and sampling frequency The power spectral density is obtained. ; in, Represents rounding down. , For window function correction factors For the window function in the first The value of the point.

[0035] Step 2: Based on the common interference source frequency set, calculate the frequency factor corresponding to each notch frequency point; according to the interference amplitude corresponding to each notch frequency point, initialize the bandwidth factor of the corresponding adaptive notch filter unit based on the fuzzy discrimination method. The initialization of the bandwidth factor of the corresponding adaptive notch filter unit based on the fuzzy discrimination method is specifically as follows: The initial filter factor is defined as follows: First filter, filter factor is... The second filter has a filter factor of 1. The third filter has a filter factor of 1. The fourth filter has a filter factor of 1. The fifth filter has a filter factor of 1. The membership degrees of different levels are respectively , , , , ; Membership degree

[0036] Calculate the bandwidth factor after inference ; in, for , Indicates the first The magnitude of the power spectral density at each detection frequency; To normalize the power spectral density, ; This represents the maximum power spectral density. This represents the minimum power spectral density. To prevent decimals from being divided by zero.

[0037] Step 3: Construct a cascaded adaptive notch filter group based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its working mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

[0038] Each notch filter unit updates its bandwidth factor independently according to its operating mode, specifically as follows: Bandwidth factor The adaptive mechanism, based on the output power minimization criterion, defines the error signal. , The iteration format is: ; right Apply boundary constraints: ; in, For learning rate, The filtered signal This represents the filter factor from the previous time step. This is the minimum value of the filter factor. This represents the maximum value of the filter factor. This represents the filter factor at the current moment.

[0039] The method is described in detail below: like Figure 2As shown, S1 is multi-channel signal interference identification, which identifies common interference frequencies from multi-channel data. This includes: S11, synchronizing multi-channel signals to ensure time alignment and eliminate delay differences; S12, power spectrum estimation and peak detection, calculating the power spectrum of each channel using the Welch method to extract candidate interference frequencies; S13, interference frequency non-maximum suppression, removing the second-largest values ​​and retaining significant peaks (suppressing spurious interference); S14, cluster analysis, clustering the detected frequencies from multiple channels to identify common frequency components; and S15, coherence analysis to determine common interference, calculating the coherence between channels, and confirming the common interference frequencies of each channel. S2 is filter bank parameter determination, calculating filter coefficients based on the identification results. This includes: generating a set of frequency factors from the extracted interference frequencies to determine the notch center frequency; and determining the bandwidth factor corresponding to each frequency through fuzzy inference using the set of filter factors from the extracted interference peaks. Initial values. S3 is for constructing an adaptive synchronous filter bank for real-time filtering processing, including: input is a multi-channel force sensing signal; the processing unit is a second-order lattice structure of an adaptive synchronous lattice notch filter bank, with one notch filter configured for each interference frequency of each channel, parameters... Real-time adaptive adjustment Fixed; the output is a filtered synchronous multi-channel force sensing signal, specifically implemented as follows: S1. The device is powered on and enters the "pre-run" mode. A multi-channel signal acquisition circuit is built based on a high-speed synchronous ADC, which enables it to acquire the differential voltage signal output by the Wheatstone bridge of the multi-dimensional force sensor, and to acquire and identify the interference characteristics of the multi-channel signal.

[0040] Acquire the detection signal matrix , Indicates the number of channels. The number of signal points in the sampled signal; Calculate the power spectral density of the detected signal Specifically, it includes: For length of Signal Given window length The number of overlapping points with the signal is Window sliding step The entire data was divided into Section; among which, Representative: Round down.

[0041] Current segment data dot product window function Then perform FFT calculation (if) Then fill with zeros, if Then cut off, usually ), to obtain the frequency domain data of the current segment. ; The frequency domain data of the current segment Square, that is Iterate through all data segments and accumulate each segment. get ; Will Divide by the energy of the window function (i.e., the sum of squares at each point of the window function) and the sampling frequency. The power spectral density is obtained, i.e.:

[0042] in, .

[0043] Given detection frequency interval For detection frequency points Non-maximum suppression is performed, retaining the point with the largest amplitude and suppressing its neighboring points in the neighborhood to eliminate frequency redundancy issues within the channel.

[0044] The non-maximum suppression algorithm is as follows: frequency point The new frequency sequence is obtained by sorting the frequencies in descending order of amplitude values. and amplitude sequence ,in ; Create a marker array Used to track the processing status of each point and set the suppression threshold. Initialize the reserved frequency point array Used to store the frequency points that are ultimately retained; Traversal For each frequency point, read the marker array. The element ,if If True, it means that the point has been processed, and the current iteration is skipped to continue to the next iteration. If False, obtain the previous frequency point. and corresponding amplitude ,Will Add to array ,Will Setting it to True marks the current point as processed; For all indexes For the point, perform the following judgment: If If True, skip the point; if If False, calculate the distance between the two frequencies. ,like Perform the suppression operation: Setting it to True marks the point as suppressed, preventing it from participating in subsequent processing, thus obtaining a single-channel non-redundant filter frequency.

[0045] Given detection frequency interval Cluster analysis is performed on all detected frequency points after non-maximum suppression, retaining the point with the largest amplitude and suppressing its neighboring points to eliminate frequency redundancy within the channel. The cluster analysis algorithm is as follows: Summarize all frequencies retained by each channel after non-maximum suppression. The initial centroid of each frequency point is itself. ; Calculate the distance between all pairs of centroids ; like The centroid of the merger is Remove the centroid from the set of centroids , ,Add to This completes the merging of all centroids, thereby determining the center frequencies of all frequency clusters across all channels. .

[0046] Furthermore, for the global frequency vector Given frequency interval ,right Each frequency Calculate the six channel signals in the frequency band. Internal coherence :

[0047] in: , and The cross-power spectral density; The self-power spectral density; The self-power spectral density; Constructing frequency points coherence coefficient matrix ,as follows:

[0048] Calculate the mean of each off-diagonal element. , This is then identified as common interference, and the common interference frequency is determined as the frequency to be eliminated by the filter. This process is repeated to identify all common interference frequency points. .

[0049] S2. Determine the initial parameters of the filter: The parameters of the filter include the frequency factor. and bandwidth factor , The calculation method is as follows:

[0050] in: This is the signal sampling frequency.

[0051] Bandwidth factor The following was determined through fuzzy reasoning: right Convert dB units to ,Right now: ; Then, through the amplitude normalization point, that is:

[0052] in, The maximum value (determined from accumulated historical data); The minimum value (determined by accumulated historical data); To prevent decimals from being divided by zero.

[0053] The initial filter factor is defined as having 5 intensity levels, namely: Minimal Filter (parameter: Filtering factor: Small filter (parameters are:) Filtering factor: ), medium filter (parameters are: Filtering factor: ), deep filtering (parameters are: Filtering factor: ), Extremely deep filtering (parameters are: Filtering factor: ), determine the membership degree of different levels. , , , , The algorithm is as follows:

[0054] in, for , Indicates the first The magnitude of the power spectral density at each detection frequency; Calculate the filter factor after inference ,in: .

[0055] S3. Construct a cascaded adaptive lattice filter bank to eliminate common interference frequencies determined in the previous steps.

[0056] The adaptive mechanism of a single-stage lattice filter is as follows: like Figure 3 The diagram shown is the signal computation flow graph for the lattice filter. Input ports: After being scaled by a gain factor of 1 / 2, the signal enters the system; ⊕ is an adder used for 5 summing nodes to perform signal superposition. Two unit delay units constitute the storage node for the second-order difference equation; This is the filter factor, which controls the notch bandwidth; The notch frequency factor determines the notch center frequency; -1 is the reflection coefficient of the lattice structure, forming a cross-coupled feedback path; Output port: The output from the first adder on the left is used as the residual signal after notch filtering. Forward path: The signal is input from the left and passes sequentially through the α gain stage, ... The β-gain stage transmits to the delay unit to the right; feedback path: the output of the delay unit is fed back to the previous adder via a cross path (coefficient-1), forming a recursive structure. The specific forward propagation calculation of the filter is as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] in, to This represents the intermediate state of the filter at the current moment. The signal to be filtered. The state variable is the one from the previous moment. This refers to the state variable from the previous moment.

[0067] Based on the output power minimization criterion, the error signal is defined. ; Calculate the gradient. ; LMS-based update ,Right now:

[0068] in, The learning rate; To ensure the stability of the IIR lattice filter, Apply boundary constraints:

[0069] Example 2 This invention proposes a multi-channel interference collaborative identification and adaptive filtering system, such as... Figure 4 As shown, it includes: The signal interference identification module is used to acquire the differential voltage signal output by the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies. The parameter initialization module is used to calculate the frequency factor corresponding to each notch frequency point based on the common interference source frequency set; and to initialize the bandwidth factor of the corresponding adaptive notch filter unit based on the interference amplitude corresponding to each notch frequency point using the fuzzy discrimination method. An adaptive filtering module is used to construct a cascaded adaptive notch filter group based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its working mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

[0070] Example 3 Please see Figure 5 As shown, the present invention also provides an electronic device 100 for a multi-channel interference collaborative identification and adaptive filtering method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0071] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the multi-channel interference collaborative identification and adaptive filtering method described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0072] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0073] The memory 101 in the electronic device 100 stores multiple instructions to implement a multi-channel interference collaborative identification and adaptive filtering method, and the processor 102 can execute the multiple instructions to achieve the following: Acquire the differential voltage signal output from the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies; Based on the common set of interference source frequencies, the frequency factor corresponding to each notch frequency point is calculated; based on the interference amplitude corresponding to each notch frequency point, the bandwidth factor of the corresponding adaptive notch filter unit is initialized based on the fuzzy discrimination method. A cascaded adaptive notch filter group is constructed based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its operating mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

[0074] Example 4 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0075] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0077] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0078] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0079] Therefore, this invention proposes a multi-channel interference collaborative identification and adaptive filtering method for multi-dimensional force sensors. This method includes: in the pre-running phase, acquiring multi-channel signals from the multi-dimensional force sensor using a high-speed synchronous ADC; calculating the power spectral density of each channel based on the Welch method to identify candidate interference frequencies; eliminating redundant peaks within channels through non-maximum suppression and obtaining frequency cluster centers using cluster analysis; further calculating the coherence coefficients between multiple channels to determine the common interference source frequency set. Based on this set, initializing the bandwidth factor of the cascaded adaptive lattice notch filter through fuzzy inference, constructing and dynamically adjusting the notch filter group during real-time operation to achieve tracking and suppression of multi-frequency, variable-amplitude interference. This invention can accurately distinguish between common interference and channel-specific signals, maintain phase consistency across channels, avoid signal distortion caused by over-filtering, and significantly improve the measurement accuracy and anti-interference capability of multi-dimensional force sensors in complex industrial environments. The advantages of this invention are as follows: 1) adaptive identification of multiple interference frequencies in industrial settings; 2) adaptive adjustment of the filtering depth of each filter according to the difference in interference intensity; 3) ensuring filtering effect while avoiding signal distortion caused by over-filtering.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-channel interference collaborative identification and adaptive filtering method, characterized in that, Includes the following steps: Acquire the differential voltage signal output from the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies; Based on the common set of interference source frequencies, the frequency factor corresponding to each notch frequency point is calculated; based on the interference amplitude corresponding to each notch frequency point, the bandwidth factor of the corresponding adaptive notch filter unit is initialized based on the fuzzy discrimination method. A cascaded adaptive notch filter group is constructed based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its operating mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

2. The multi-channel interference collaborative identification and adaptive filtering method according to claim 1, characterized in that, The process of identifying interference characteristics in the differential voltage signal to determine the set of common interference source frequencies specifically involves: Acquire the detection signal matrix , Indicates the number of channels. The number of signal points in the sampled signal; Power spectral density of each channel signal was calculated using the Welch method. Identify candidate interference frequency points and amplitudes for each channel; Non-maximum suppression is performed on candidate interference frequency points in each channel to eliminate redundant peaks with similar frequencies within the same channel; Set a frequency interval threshold, collect all candidate frequency points retained from all channels, and obtain the center frequency of different frequency clusters through cluster analysis; For each center frequency, calculate the coherence coefficient between multiple channels, construct a coherence matrix, and determine the set of common interference source frequencies.

3. The multi-channel interference collaborative identification and adaptive filtering method according to claim 2, characterized in that, The non-maximum suppression of candidate interference frequency points in each channel is specifically performed as follows: Nonmaximum suppression methods: Candidate interference frequency points identified within a single channel Sort the frequencies in descending order of amplitude value to obtain the frequency sequence. and the corresponding amplitude sequence ,in, , N This represents the total number of candidate interference frequency points identified within the channel. Set the suppression threshold traversal For each frequency point in the channel, eliminate the frequency within the channel.

4. The multi-channel interference collaborative identification and adaptive filtering method according to claim 2, characterized in that, The method of obtaining the center frequencies of different frequency clusters through cluster analysis is as follows: By summing all the frequencies retained after non-maximum suppression for each channel, a set of candidate frequency points is obtained. Each frequency point corresponds to an initial cluster centroid, and the initial centroid satisfies... Given a merging threshold Calculate the distance between all pairs of centroids. ,like The centroid of the merger is Delete the two centroids that were merged; iterate the above process repeatedly to complete the merging of all centroids and determine the center frequency of all frequency clusters between channels. in, This is another cluster centroid to be compared.

5. The multi-channel interference collaborative identification and adaptive filtering method according to claim 2, characterized in that, For each center frequency, the coherence coefficients between multiple channels are calculated, a coherence matrix is ​​constructed, and the set of common interference source frequencies is determined, specifically as follows: For the global frequency vector Calculate the correlation coefficient between multiple channels, for two channel signals. and In frequency coherence coefficient at ;in, for and The cross-power spectral density; for The self-power spectral density; for The self-power spectral density; The coherence coefficients of all frequencies are summed to obtain the total coherence coefficient. Based on the correlation matrix, the common interference frequencies are determined, and the mean correlation coefficient for each frequency point is calculated. ,like This is then identified as common interference, and the common interference is determined to be the frequency to be eliminated by the filter; where, To set a threshold.

6. The multi-channel interference collaborative identification and adaptive filtering method according to claim 2, characterized in that, The power spectral density of each channel signal is calculated based on the Welch method. Specifically: For length of Signal Segmentation and windowing, given window length Number of signal overlap points Window sliding step The number of data segments ; For the current segment of frequency domain data Processing is performed to obtain Iterate through all data segments and accumulate the frequency domain power of each segment. get ; Based on the sum of frequency domain power of all data segments and sampling frequency The power spectral density is obtained. ; in, Represents rounding down. , For window function correction factors For the window function in the first The value of the point.

7. The multi-channel interference collaborative identification and adaptive filtering method according to claim 1, characterized in that, The initialization of the bandwidth factor of the corresponding adaptive notch filter unit based on the fuzzy discrimination method is specifically as follows: The initial filter factor is defined as follows: First filter, filter factor is... ; The second filter has a filter factor of . ; The third filter has a filter factor of [value missing]. ; The fourth filter has a filter factor of 1. ; The fifth filter has a filter factor of 1. The membership degrees of different levels are respectively , , , , ; Membership degree Calculate the bandwidth factor after inference ; in, for , Indicates the first The magnitude of the power spectral density at each detection frequency; To normalize the power spectral density, ; This represents the maximum power spectral density. This represents the minimum power spectral density. To prevent decimals from being divided by zero.

8. The multi-channel interference collaborative identification and adaptive filtering method according to claim 1, characterized in that, Each notch filter unit updates its bandwidth factor independently according to its operating mode, specifically as follows: Bandwidth factor The adaptive mechanism, based on the output power minimization criterion, defines the error signal. , The iteration format is: ; right Apply boundary constraints: ; in, For learning rate, The filtered signal This represents the filter factor from the previous time step. This is the minimum value of the filter factor. This represents the maximum value of the filter factor. This represents the filter factor at the current moment.

9. A multi-channel interference collaborative identification and adaptive filtering system, characterized in that, include: The signal interference identification module is used to acquire the differential voltage signal output by the multi-channel strain detection bridge, identify the interference characteristics of the differential voltage signal, and determine the set of common interference source frequencies. The parameter initialization module is used to calculate the frequency factor corresponding to each notch frequency point based on the common interference source frequency set; and to initialize the bandwidth factor of the corresponding adaptive notch filter unit based on the interference amplitude corresponding to each notch frequency point using the fuzzy discrimination method. An adaptive filtering module is used to construct a cascaded adaptive notch filter group based on the notch filter frequency point set and frequency factor. Each notch filter unit independently updates its bandwidth factor according to its working mode, thereby realizing the collaborative identification and adaptive filtering of multi-channel interference.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-channel interference collaborative identification and adaptive filtering method as described in any one of claims 1 to 8.

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