Signal interference suppression methods, devices, storage media and electronic equipment

Abstract

This application discloses a signal anti-interference method, apparatus, storage medium, and electronic device. Relating to the field of signal processing, the method includes: acquiring a target signal during surgery, wherein the target signal includes at least the magnetic navigation signal corresponding to the surgical instrument and interference signals in the surgical environment where the surgical instrument is located; dividing the target signal into multiple signal segments based on a time window; and processing the multiple signal segments using a signal processing model to obtain an anti-interference target signal. The signal processing model uses an attention mechanism to assign weights to feature vectors in the feature vector set corresponding to the signal segment, where the weights characterize the importance of the feature vectors in the anti-interference target signal. This application solves the problem of poor anti-interference performance in related technologies that use filters to filter interference signals when acquiring position signals generated by magnetic navigation systems.

Description

Technical Field

[0001] This application relates to the field of signal processing, and more specifically, to a signal anti-interference method, apparatus, storage medium, and electronic device. Background Technology

[0002] In simple terms, surgical magnetic navigation dynamic signal anti-interference involves processing the dynamic position signals generated by the magnetic navigation system during surgery (such as foot and ankle surgery) to resist external interference and ensure signal accuracy and stability. This approach is of paramount value and significance. During surgery, the magnetic navigation system provides surgeons with precise surgical positioning and guidance, enabling them to operate more accurately and reducing surgical trauma and complications. Dynamic signal anti-interference technology is crucial for ensuring the normal operation of the magnetic navigation system. Only by ensuring the signal is undisturbed can the magnetic navigation system accurately reflect the actual situation at the surgical site, providing strong support for the success of the surgery.

[0003] Currently, surgical magnetic navigation dynamic signal anti-interference technology typically employs hardware filtering, adding filters to the signal transmission lines of the magnetic navigation system to filter out external interference signals. However, this method can only filter interference signals of specific frequencies and is ineffective against complex and variable interference signals, resulting in poor anti-interference performance.

[0004] There is currently no effective solution to the aforementioned problems in the relevant technologies. Summary of the Invention

[0005] The main objective of this application is to provide a signal anti-interference method, device, storage medium, and electronic device to solve the problem that related technologies use filters to filter interference signals when acquiring position signals generated by magnetic navigation systems, resulting in poor anti-interference performance.

[0006] To achieve the above objectives, according to one aspect of this application, a signal anti-interference method is provided. The method includes: acquiring a target signal during surgery, wherein the target signal includes at least a magnetic navigation signal corresponding to the surgical instrument and interference signals in the surgical environment where the surgical instrument is located; dividing the target signal into multiple signal segments based on a time window; and processing the multiple signal segments using a signal processing model to obtain an anti-interference target signal, wherein the signal processing model uses an attention mechanism to assign weights to feature vectors in the feature vector set corresponding to the signal segment, and the weights characterize the importance of the feature vectors in the anti-interference target signal.

[0007] Optionally, the signal anti-interference method further includes: for each signal segment, performing feature extraction on the signal in the signal segment to obtain a set of feature vectors; determining the weight of the feature vectors based on the similarity between the feature vectors in the feature vector set and the query vector of the attention mechanism; determining the signal feature vector of the signal segment based on the feature vectors in the feature vector set and the weight of the feature vectors; and determining the target signal for anti-interference based on the signal feature vectors of multiple signal segments.

[0008] Optionally, the signal anti-interference method further includes: extracting features from the magnetic navigation signal in the signal segment to obtain a first feature; extracting features from the interference signal in the signal segment to obtain a second feature; and fusing the first feature and the second feature to obtain a feature vector set.

[0009] Optionally, the signal anti-interference method further includes: calculating the similarity between the feature vectors in the feature vector set and the query vector; normalizing the similarity of each feature vector in the feature vector set to obtain the weight corresponding to each feature vector.

[0010] Optionally, the signal anti-interference method further includes: selecting target feature vectors from the feature vector set, wherein the target feature vectors are feature vectors with weights greater than or equal to a weight threshold; and determining signal feature vectors based on the target feature vectors and their weights.

[0011] Optionally, the signal anti-interference method further includes: processing multiple signal segments through a signal processing model to obtain an initial anti-interference signal; calculating the signal deviation based on the initial anti-interference signal and the posture data of the surgical instrument using a preset signal deviation calculation formula; and determining the target signal for anti-interference based on the signal deviation and the initial anti-interference signal.

[0012] Optionally, the signal anti-interference method further includes: correcting the initial anti-interference signal based on the signal deviation to obtain a corrected initial anti-interference signal; and processing the corrected initial anti-interference signal through a Kalman filter to obtain the target signal for anti-interference.

[0013] To achieve the above objectives, according to another aspect of this application, a signal anti-interference device is provided. The device includes: a data acquisition module for acquiring target signals during surgery, wherein the target signals include at least the magnetic navigation signal corresponding to the surgical instrument and interference signals in the surgical environment where the surgical instrument is located; a segmentation module for dividing the target signals into multiple signal segments based on a time window; and a processing module for processing the multiple signal segments using a signal processing model to obtain an anti-interference target signal, wherein the signal processing model uses an attention mechanism to assign weights to feature vectors in the feature vector set corresponding to the signal segments, and the weights characterize the importance of the feature vectors in the anti-interference target signal.

[0014] Optionally, the processing module further includes: a feature extraction submodule, used to extract features from the signal in each signal segment to obtain a set of feature vectors; a first determination submodule, used to determine the weights of the feature vectors based on the similarity between the feature vectors in the feature vector set and the query vector of the attention mechanism; and a second determination submodule, used to determine the signal feature vectors of the signal segment based on the feature vectors in the feature vector set and the weights of the feature vectors, and to determine the anti-interference target signal based on the signal feature vectors of multiple signal segments.

[0015] Optionally, the feature extraction submodule further includes: a first feature extraction unit for extracting features from the magnetic navigation signal in the signal segment to obtain a first feature; a second feature extraction unit for extracting features from the interference signal in the signal segment to obtain a second feature; and a first processing unit for fusing the first feature and the second feature to obtain a feature vector set.

[0016] Optionally, the first determining submodule further includes: a calculation unit for calculating the similarity between the feature vectors in the feature vector set and the query vector; and a second processing unit for normalizing the similarity of each feature vector in the feature vector set to obtain the weight corresponding to each feature vector.

[0017] Optionally, the second determining submodule further includes: a filtering unit, used to filter out target feature vectors from the feature vector set, wherein the target feature vectors are feature vectors with weights greater than or equal to a weight threshold; and a first determining unit, used to determine signal feature vectors based on the target feature vectors and their weights.

[0018] Optionally, the processing module further includes: a processing submodule, used to process multiple signal segments through a signal processing model to obtain an initial anti-interference signal; a calculation submodule, used to calculate the signal deviation based on the initial anti-interference signal and the posture data of the surgical instrument using a preset signal deviation calculation formula; and a third determination submodule, used to determine the target signal for anti-interference based on the signal deviation and the initial anti-interference signal.

[0019] Optionally, the third determining submodule further includes: a correction unit, used to correct the initial anti-interference signal based on the signal deviation to obtain the corrected initial anti-interference signal; and a third processing unit, used to process the corrected initial anti-interference signal through a Kalman filter to obtain the anti-interference target signal.

[0020] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the above-described signal anti-interference method.

[0021] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described signal anti-interference method during runtime.

[0022] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the above-described signal anti-interference method.

[0023] In this embodiment, by using a signal processing model to process multiple signal segments, and during the processing, using an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segments, it is possible to specifically enhance the effective features in the signal related to surgical positioning and guidance, suppress interference features, thereby improving the filtering effect of interference signals, improving the signal processing quality, and thus improving the anti-interference effect of the anti-interference target signal output by the model.

[0024] Therefore, the method provided in this application achieves the goal of using the attention mechanism to assign weights to signal features in order to suppress interference features, thereby improving the anti-interference effect of the signal. It also solves the technical problem that related technologies use filters to filter interference signals when collecting position signals generated by magnetic navigation systems, resulting in poor anti-interference effect. Attached Figure Description

[0025] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0026] Figure 1 This is a hardware structure block diagram of a computer terminal provided according to an embodiment of this application;

[0027] Figure 2 This is a flowchart of a signal anti-interference method provided according to an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a signal anti-interference method provided according to an embodiment of this application;

[0029] Figure 4 This is a schematic diagram of a signal anti-interference device provided according to an embodiment of this application;

[0030] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0034] Example 1

[0035] According to an embodiment of this application, an embodiment of a signal anti-interference method is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1A hardware block diagram of a computer terminal (or mobile device) for implementing a signal interference suppression method is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor (MCU) or a field-programmable gate array (FPGA), etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output (I / O) interface, a Universal Serial Bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0037] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0038] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the signal anti-interference method in the embodiments of this application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned signal anti-interference method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0039] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0040] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0041] Under the aforementioned operating environment, this application provides the following: Figure 2 The signal anti-interference method shown. Figure 2 This is a flowchart of a signal anti-interference method according to Embodiment 1 of this application.

[0042] Step S201: Collect target signals during the surgical process, wherein the target signals include at least the magnetic navigation signals corresponding to the surgical instruments and interference signals in the surgical environment where the surgical instruments are located.

[0043] Optionally, electronic devices, application systems, servers, and other devices can be used as the execution subject of this application. In this embodiment, the target processing system is used as the execution subject to execute the above-mentioned signal anti-interference method.

[0044] In an optional embodiment, the aforementioned surgery refers to a surgery that requires the use of a magnetic navigation system, such as foot or ankle surgery. The target processing system can acquire the dynamic position signal (i.e., magnetic navigation signal) of the magnetic navigation system during surgery, and simultaneously acquire multi-source interference signals from electromagnetic devices, metal instruments, and other sources within the surgical environment where the surgical instruments are located, in order to obtain the target signal.

[0045] For example, to acquire dynamic position signals from a magnetic navigation system, positioning sensors are installed at key locations within the system (e.g., specific positions on surgical instruments). These sensors possess high sensitivity and rapid response capabilities to capture dynamic positional changes of the surgical instruments during surgery. Magnetic navigation signals are used to represent the position of the surgical contact point relative to the lesion; for example, the position of the part of the surgical instrument used for contact with the lesion relative to the lesion. The magnetic navigation signals acquired by the positioning sensors placed on the surgical instruments are the magnetic navigation signals corresponding to the surgical instruments. The aforementioned sensors convert the acquired analog signals into digital signals via an analog-to-digital converter, and then transmit them to a data acquisition card via a dedicated data transmission line. The data acquisition card performs preliminary signal processing, such as filtering and amplification, to improve signal quality, and then transmits the processed data to a processing terminal (e.g., a target processing system) for further analysis.

[0046] For collecting multi-source interference signals from electromagnetic devices, metal instruments, and other sources in the surgical environment, multiple electromagnetic interference sensors can be strategically distributed within the surgical area. These sensors can cover electromagnetic interference signals across different frequency bands to comprehensively capture interference in the surgical environment. The sensors then perform analog-to-digital conversion and preliminary processing on the collected interference signals before transmitting them to a processing terminal (e.g., a target processing system).

[0047] Step S202: Divide the target signal into multiple signal segments based on the time window.

[0048] In an optional embodiment, after obtaining the target signal, the target signal is continuously divided into multiple signal segments with a time window length (e.g., 10 milliseconds) as the step size. Optionally, the multiple signal segments are formed by continuously dividing the signal along the time dimension with a fixed window length to maintain the continuity and change information of the signal in the time dimension.

[0049] In another optional embodiment, after obtaining the target signal, the target signal is first filtered and preprocessed to remove high-frequency noise, and then the filtered target signal is divided based on a time window to obtain multiple signal segments.

[0050] For example, filtering preprocessing can employ a low-pass filter, which can be a Butterworth filter. The cutoff frequency and order are selected based on the signal frequency distribution and interference characteristics. The principle of a low-pass filter is based on the frequency characteristics of the signal, allowing signal components below the cutoff frequency to pass through while suppressing high-frequency components above the cutoff frequency. The frequency response characteristics of a Butterworth filter can be described by the following formula:

[0051] ;

[0052] in, This represents the frequency effect function of the filter. Indicates the angular frequency of the signal. Indicates the cutoff angular frequency. This indicates the filter's order. By appropriately selecting the cutoff frequency and order, high-frequency noise can be effectively filtered out while retaining useful low-frequency signal components. In practice, the filter parameters can be determined based on the frequency distribution of the original signal and the characteristics of the interfering signal.

[0053] After filtering preprocessing, the filtered target signal can be segmented into a continuous sequence of signal windows along the time dimension; each signal window is also a signal segment. Optionally, a fixed time window length T can be set. Starting from the beginning of the filtered target signal, signal segments are sequentially extracted at intervals of T to form continuous signal windows. Each signal window contains the magnetic navigation signal and interference signal within the corresponding time range. The choice of window length T is crucial. If T is too small, it may not contain enough signal information; if T is too large, the signal characteristics within the window may become too complex, which is not conducive to subsequent analysis. To ensure that the signal characteristics of the magnetic navigation signal and the interference signal can be accurately extracted, an appropriate time window length T can be determined based on the signal change rate and the required accuracy of the analysis.

[0054] Step S203: Process multiple signal segments using a signal processing model to obtain the anti-interference target signal. The signal processing model uses an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segment. The weights represent the importance of the feature vectors in the anti-interference target signal.

[0055] Optionally, the signal processing model can be a neural network model, in which an attention mechanism is employed.

[0056] The target processing system can input multiple signal segments into the signal processing model, and process the multiple signal segments through the signal processing model to obtain the anti-interference target signal.

[0057] In an optional embodiment, an attention mechanism is used to calculate the association weights between effective navigation features and interfering features (i.e., the weights assigned to the feature vectors mentioned above), enhancing the weight ratio of effective features. The attention mechanism calculates a similarity score by performing a dot product operation between the learnable query vector and the feature vectors in the feature vector set corresponding to the signal segment, and then normalizes the result using a softmax function to obtain the association weights. Based on the weights output by the attention module, the feature vectors in the feature vector set corresponding to the signal segment are weighted and fused, thus obtaining an anti-interference target signal based on the weighted fused features from multiple signal segments. The attention mechanism acts like an intelligent "focuser," automatically focusing on important parts of the signal and ignoring unimportant interfering information. In the application of anti-interference for dynamic signals in surgical magnetic navigation, the attention mechanism analyzes the acquired signal point by point, assigning different weights to each signal point based on the signal's features and contextual information. Important signal points related to surgical positioning and guidance are given higher weights, while interfering signal points are given lower weights. Then, the signals are weighted and summed according to these weights to obtain a purer and more accurate signal.

[0058] The attention mechanism is highly adaptable, capable of automatically identifying and processing various complex and changing interference signals without requiring design for specific frequencies like hardware filtering. Secondly, this method boasts high processing speed, enabling real-time processing of dynamic signals and ensuring both real-time performance and accuracy. Furthermore, the attention mechanism possesses self-learning capabilities, gradually improving its ability to identify and process interference signals through continuous learning.

[0059] In this embodiment, by using a signal processing model to process multiple signal segments, and during the processing, using an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segments, it is possible to specifically enhance the effective features in the signal related to surgical positioning and guidance, suppress interference features, thereby improving the filtering effect of interference signals, improving the signal processing quality, and thus improving the anti-interference effect of the anti-interference target signal output by the model.

[0060] Therefore, the method provided in this application achieves the goal of using the attention mechanism to assign weights to signal features in order to suppress interference features, thereby improving the anti-interference effect of the signal. It also solves the technical problem that related technologies use filters to filter interference signals when collecting position signals generated by magnetic navigation systems, resulting in poor anti-interference effect.

[0061] Optionally, in the signal anti-interference method provided in this application embodiment, multiple signal segments are processed by a signal processing model to obtain the target signal for anti-interference, including: for each signal segment, feature extraction is performed on the signal in the signal segment to obtain a set of feature vectors; the weight of the feature vector is determined based on the similarity between the feature vector in the feature vector set and the query vector of the attention mechanism; the signal feature vector of the signal segment is determined based on the feature vector in the feature vector set and the weight of the feature vector; and the target signal for anti-interference is determined based on the signal feature vectors of multiple signal segments.

[0062] In an optional embodiment, the signal processing model includes a feature extraction layer. After multiple signal segments are input into the signal processing model, the feature extraction layer extracts features from each signal segment to obtain a set of feature vectors. The set of feature vectors includes effective navigation features and interference features.

[0063] Optionally, the query vector q in the attention mechanism is a learnable vector, which is learned by the model during the training process of the signal processing model. The attention mechanism can assign weights by calculating the similarity between features. For example, the learnable query vector q can be compared with each vector in the feature vector set. Perform dot product operations on each feature vector to obtain the similarity score for each feature vector. Therefore, the weights of the feature vectors are determined based on this similarity score.

[0064] After determining the weights, the model can perform a weighted summation of the feature vectors in the feature vector set based on the calculated weights of each feature vector to obtain the signal feature vector of the signal segment. For each signal segment, the aforementioned steps can be performed to obtain a signal feature vector set containing the signal feature vectors of multiple signal segments. Finally, the signal feature vectors of all signal segments can be combined in chronological order to form a continuous anti-interference target signal.

[0065] It should be noted that by calculating the similarity between the feature vector and the query vector and assigning a weight to each feature vector, the model can focus more attention on signal features closely related to the surgical navigation task, while reducing the contribution of irrelevant or interfering features. By performing weighted summation based on the weights, the influence of interfering signals can be effectively suppressed, highlighting the effective magnetic navigation signal features, thereby improving the anti-interference effect.

[0066] Optionally, in the signal anti-interference method provided in the embodiments of this application, feature extraction is performed on the signal in the signal segment to obtain a feature vector set, including: feature extraction of the magnetic navigation signal in the signal segment to obtain a first feature; feature extraction of the interference signal in the signal segment to obtain a second feature; and feature fusion of the first feature and the second feature to obtain a feature vector set.

[0067] Optionally, the magnetic navigation signal can be feature-extracted using the feature extraction layer in the model to obtain the first feature, and the interference signal can be feature-extracted using the same layer to obtain the second feature. For example, the signal can be converted to a frequency domain representation, and the spectral features can be extracted using techniques such as Fourier transform. Furthermore, statistical features of the signal, such as mean and standard deviation, as well as dynamic change features, such as signal gradient and second derivative, can be calculated to reflect the signal's trend over time. The first feature may include at least one feature vector, and the second feature may include at least one feature vector.

[0068] After obtaining the first feature and the second feature, feature fusion is performed on the first feature and the second feature to obtain a set of feature vectors. For example, feature fusion can refer to concatenating the first feature and the second feature.

[0069] It should be noted that the above method enables the accurate determination of the feature vector set.

[0070] Optionally, in the signal anti-interference method provided in this application embodiment, the weight of the feature vector is determined based on the similarity between the feature vector in the feature vector set and the query vector of the attention mechanism, including: calculating the similarity between the feature vector in the feature vector set and the query vector; and normalizing the similarity of each feature vector in the feature vector set to obtain the weight corresponding to each feature vector.

[0071] Optionally, use a learnable query vector q with each vector in the feature vector set. Perform dot product operations on each feature vector to obtain the similarity score for each feature vector. .

[0072] After obtaining the similarity scores, the model can normalize the similarity of the feature vectors using the softmax function to obtain the weights corresponding to each feature vector. For example, normalization can be achieved using the following formula:

[0073] ;

[0074] in, Let represent the weight corresponding to the i-th eigenvector, and n represent the total number of eigenvectors in the eigenvector set to which the i-th eigenvector belongs. After normalization, the sum of the weights of eigenvectors in the same eigenvector set is 1.

[0075] In an optional embodiment, after determining the weights corresponding to the feature vectors, the signal feature vectors of the signal segment can be determined based on the following formula. :

[0076] ;

[0077] In this process, effective navigation features, due to their high similarity to the query vector, receive a larger weight, while interference features have a relatively smaller weight, thus enhancing the weight ratio of effective features. After obtaining the signal feature vector, it can be reconstructed into signal form through a fully connected layer in the model to obtain an anti-interference target signal, which can then be used for subsequent navigation tasks to improve navigation accuracy and robustness. In an optional embodiment, a fully connected layer can be omitted, and other model structures can be used to process the signal feature vector to convert it back into signal form. For example, other model structures could be transposed convolutional layers, decoders, etc.

[0078] It should be noted that normalization processing enables fair comparison of all similarity scores after they are converted into weights. This allows for more intelligent identification and highlighting of the feature vectors in the signal that are most relevant to the anti-interference task, while suppressing non-critical or interfering information, thereby effectively improving the anti-interference effect.

[0079] Optionally, in the signal anti-interference method provided in this application embodiment, determining the signal feature vector of a signal segment based on the feature vectors in the feature vector set and the weights of the feature vectors includes: selecting target feature vectors from the feature vector set, wherein the target feature vectors are feature vectors with weights greater than or equal to a weight threshold; and determining the signal feature vectors according to the target feature vectors and their weights.

[0080] Optionally, the weight threshold can be a preset value. The weight threshold is used to eliminate interference components with weights below the threshold, thus selecting feature vectors that contribute significantly to the signal segment. For example, when the weight of a feature vector is greater than or equal to the weight threshold, it is identified as a target feature vector and retained for the construction of the signal feature vector; conversely, when the weight of a feature vector is below this threshold, it is considered an interference component and removed from the fusion process. This effectively reduces the impact of interference on the navigation signal. After determining the target feature vectors, the selected target feature vectors and their corresponding weights are weighted and summed to obtain the signal feature vector.

[0081] For example, the above process can be represented by the following formula:

[0082] ;

[0083] in, For indicator functions, when hour, ,otherwise, .

[0084] In an optional embodiment, the target processing system can train a signal processing model by: acquiring a training sample set, wherein the training samples in the training sample set are multiple sample signal segments corresponding to sample signals (including sample magnetic navigation signals and sample interference signals) during the sample surgical process, and the true labels of the training samples are anti-interference sample signals; training an initial signal processing model using the training sample set to obtain the signal processing model. During training, the signal processing model learns the query vector q.

[0085] It should be noted that by using weighted thresholds to remove specific signal features, noise and interference components in the signal are effectively removed, thereby further improving the signal's anti-interference effect.

[0086] Optionally, in the signal anti-interference method provided in this application embodiment, multiple signal segments are processed by a signal processing model to obtain the target signal for anti-interference, including: processing multiple signal segments by a signal processing model to obtain an initial anti-interference signal; calculating the signal deviation based on the initial anti-interference signal and the posture data of the surgical instrument using a preset signal deviation calculation formula; and determining the target signal for anti-interference based on the signal deviation and the initial anti-interference signal.

[0087] In an optional embodiment, the signal output by the signal processing model is directly used as the target signal for anti-interference.

[0088] In another optional embodiment, the signal output by the signal processing model is used as the initial anti-interference signal. In this case, the target processing system can acquire the posture data of the surgical instrument in real time, and then correct the initial anti-interference signal output by the model based on the posture data of the surgical instrument to obtain the anti-interference target signal. The aforementioned posture data is used to represent the position information of the surgical instrument itself.

[0089] Optionally, attitude data can be acquired using an Inertial Measurement Unit (IMU). For example, when acquiring real-time attitude data of surgical instruments, an IMU, consisting of an accelerometer, gyroscope, and magnetometer, is installed on the surgical instrument. These sensors measure information such as the instrument's acceleration, angular velocity, and magnetic field strength, and the attitude data of the surgical instrument is calculated using the following formula:

[0090] ;

[0091] in, This represents the orientation matrix of the surgical instruments at time t. It is the angular velocity at time t. It is a time interval. and These are the attitude matrices calculated by the accelerometer and magnetometer, respectively. and This is the corresponding gain matrix. The calculated attitude data is transmitted to a computing terminal (e.g., a target processing system) to achieve synchronous acquisition with the dynamic position signal and multi-source interference signals of the magnetic navigation system.

[0092] In an optional embodiment, the initial interference signal is assumed to be... Real-time posture data of surgical instruments can be represented as a vector. ,in These represent three dimensions of attitude information. The target processing system can define the following matching function (i.e., the signal deviation calculation formula):

[0093] ;

[0094] Where M(S(t), P(t)) represents the signal deviation between the initial anti-interference signal and the attitude data. and These are weighting coefficients. and It is a mapping function determined based on actual physical relationships. The target processing system can substitute the initial anti-interference signal and the posture data of the surgical instruments into the above formula to calculate the signal deviation caused by residual interference.

[0095] After determining the signal deviation, the initial anti-interference signal is corrected based on the signal deviation to obtain the target anti-interference signal.

[0096] It should be noted that by using the signal deviation calculation formula to quantify the deviation between the signal and the actual posture of the surgical instrument, and by making real-time corrections to the signal based on these deviations, the signal anti-interference effect can be further improved.

[0097] Optionally, in the signal anti-interference method provided in the embodiments of this application, determining the target signal for anti-interference based on the signal deviation and the initial anti-interference signal includes: correcting the initial anti-interference signal based on the signal deviation to obtain a corrected initial anti-interference signal; and processing the corrected initial anti-interference signal through a Kalman filter to obtain the target signal for anti-interference.

[0098] Optionally, after determining the signal deviation, the initial anti-interference signal is corrected based on the signal deviation to obtain a corrected initial anti-interference signal. For example, a gain adjustment strategy based on the deviation value can be used to correct the initial anti-interference signal.

[0099] After obtaining the corrected initial anti-interference signal, a second correction can be performed on the initial anti-interference signal using a Kalman filter to obtain the anti-interference target signal. Optionally, the target processing system can acquire a preset initial estimate and initial error covariance; perform an estimation calculation on the magnetic navigation signal based on the preset initial estimate and initial error covariance using a Kalman filter to obtain a first estimate; calculate the Kalman gain of the first estimate to obtain a Kalman gain value; and calculate the anti-interference target signal based on the Kalman gain value, the first estimate, and the corrected initial anti-interference signal.

[0100] For example, the input data for a Kalman filter includes: the corrected initial anti-interference signal (as an observation). The data includes real-time attitude data of the surgical instruments (provided by the IMU) and the state transition model. The data to be predicted is the state vector of the actual navigation signal. This includes position, velocity, etc. The specific implementation steps of a Kalman filter can be as follows:

[0101] First, initialize the filter: set the initial state estimate. And error covariance matrix .

[0102] Secondly, state prediction (prediction step): predict the state at the next moment based on the system model (i.e., predict the first estimate): Prediction error covariance: In one optional embodiment, .in Here is the state transition matrix. For control input (such as attitude data acquired by IMU). Let be the process noise covariance.

[0103] Then, state observation (update step): obtain the actual observation values. (i.e., the corrected initial anti-interference signal), calculate the observation residuals: in This is the observation matrix.

[0104] Finally, state correction:

[0105] Calculate the Kalman gain: .

[0106] Updated state estimate: .

[0107] Update error covariance: ,in To observe the noise covariance.

[0108] Final output This is the target signal for interference suppression. This process is repeated cyclically to achieve real-time interference suppression and signal correction. The interference-suppressed target signal obtained after the above processing can more accurately reflect the actual position and orientation of surgical instruments, providing reliable support for surgical operations.

[0109] It should be noted that by using a Kalman filter to perform secondary correction on the signal, the signal quality can be further optimized and the signal's anti-interference effect can be improved.

[0110] In an optional embodiment, Figure 3 This is a schematic diagram of a signal anti-interference method provided according to an embodiment of this application. Figure 3 An optional application process of this embodiment will be described. For example... Figure 3 As shown, the target processing system synchronously acquires multi-source signals, including magnetic navigation signals, interference signals, and surgical instrument attitude data, during the surgical procedure, and performs filtering preprocessing on the acquired signals. Then, a time window is used to segment the interference signals and magnetic navigation signals, resulting in multiple signal segments. The attention mechanism of the signal processing model is used to calculate the weights of the feature vectors in the feature vector set corresponding to each signal segment, and target feature vectors are selected from the feature vector set based on weight thresholds to eliminate interference components and retain effective features. Subsequently, the selected target feature vectors and their weights are weighted and fused to obtain the signal feature vectors of the signal segments, and the initial anti-interference signal is determined based on the signal feature vectors of multiple signal segments.

[0111] like Figure 3 As shown, after obtaining the initial anti-interference signal, the signal deviation is calculated by combining the posture data of the surgical instruments. Based on the signal deviation, the initial anti-interference signal is corrected for the first time to obtain the corrected initial anti-interference signal. Then, a Kalman filter is used to correct the corrected initial anti-interference signal for the second time to obtain the target anti-interference signal.

[0112] Therefore, the method provided in this application achieves the goal of using the attention mechanism to assign weights to signal features in order to suppress interference features, thereby improving the anti-interference effect of the signal. It also solves the technical problem that related technologies use filters to filter interference signals when collecting position signals generated by magnetic navigation systems, resulting in poor anti-interference effect.

[0113] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0114] Example 2

[0115] This application also provides a signal anti-interference device. It should be noted that the signal anti-interference device of this application can be used to execute the signal anti-interference method provided in this application. The signal anti-interference device provided in this application is described below.

[0116] According to an embodiment of this application, an apparatus for implementing the above-described signal anti-interference method is also provided, such as... Figure 4 As shown, the device includes:

[0117] The acquisition module 401 is used to acquire target signals during the surgical process, wherein the target signals include at least the magnetic navigation signal corresponding to the surgical instrument and the interference signal in the surgical environment where the surgical instrument is located;

[0118] The segmentation module 402 is used to divide the target signal into multiple signal segments based on a time window;

[0119] The processing module 403 is used to process multiple signal segments through a signal processing model to obtain an anti-interference target signal. The signal processing model uses an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segments. The weights represent the importance of the feature vectors in the anti-interference target signal.

[0120] In this embodiment, by using a signal processing model to process multiple signal segments, and during the processing, using an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segments, it is possible to specifically enhance the effective features in the signal related to surgical positioning and guidance, suppress interference features, thereby improving the filtering effect of interference signals, improving the signal processing quality, and thus improving the anti-interference effect of the anti-interference target signal output by the model.

[0121] Therefore, the method provided in this application achieves the goal of using the attention mechanism to assign weights to signal features in order to suppress interference features, thereby improving the anti-interference effect of the signal. It also solves the technical problem that related technologies use filters to filter interference signals when collecting position signals generated by magnetic navigation systems, resulting in poor anti-interference effect.

[0122] Optionally, in the signal anti-interference device provided in this application embodiment, the processing module further includes: a feature extraction submodule, used to extract features from the signal in each signal segment to obtain a set of feature vectors; a first determination submodule, used to determine the weight of the feature vectors based on the similarity between the feature vectors in the feature vector set and the query vector of the attention mechanism; and a second determination submodule, used to determine the signal feature vector of the signal segment based on the feature vectors in the feature vector set and the weight of the feature vectors, and to determine the target signal for anti-interference based on the signal feature vectors of multiple signal segments.

[0123] Optionally, in the signal anti-interference device provided in this application embodiment, the feature extraction submodule further includes: a first feature extraction unit, used to extract features from the magnetic navigation signal in the signal segment to obtain a first feature; a second feature extraction unit, used to extract features from the interference signal in the signal segment to obtain a second feature; and a first processing unit, used to fuse the first feature and the second feature to obtain a feature vector set.

[0124] Optionally, in the signal anti-interference device provided in the embodiments of this application, the first determining submodule further includes: a calculation unit, used to calculate the similarity between the feature vectors in the feature vector set and the query vector; and a second processing unit, used to normalize the similarity of each feature vector in the feature vector set to obtain the weight corresponding to each feature vector.

[0125] Optionally, in the signal anti-interference device provided in the embodiments of this application, the second determining submodule further includes: a filtering unit, used to filter out target feature vectors from the feature vector set, wherein the target feature vector is a feature vector with a weight greater than or equal to a weight threshold; and a first determining unit, used to determine the signal feature vector based on the target feature vector and the weight of the target feature vector.

[0126] Optionally, in the signal anti-interference device provided in this application embodiment, the processing module further includes: a processing submodule, used to process multiple signal segments through a signal processing model to obtain an initial anti-interference signal; a calculation submodule, used to calculate the signal deviation based on the initial anti-interference signal and the posture data of the surgical instrument using a preset signal deviation calculation formula; and a third determination submodule, used to determine the target signal for anti-interference based on the signal deviation and the initial anti-interference signal.

[0127] Optionally, in the signal anti-interference device provided in the embodiments of this application, the third determining submodule further includes: a correction unit, used to correct the initial anti-interference signal based on the signal deviation to obtain the corrected initial anti-interference signal; and a third processing unit, used to process the corrected initial anti-interference signal through a Kalman filter to obtain the anti-interference target signal.

[0128] It should be noted that the acquisition module 401, the division module 402, and the processing module 403 mentioned above correspond to steps S201 to S203 in Embodiment 1. The three modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0129] Example 3

[0130] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0131] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0132] The processor can access the information and application programs stored in the memory via the transmission device to perform the following steps: acquiring target signals during the surgical process, wherein the target signals include at least the magnetic navigation signals corresponding to the surgical instruments and interference signals in the surgical environment where the surgical instruments are located; dividing the target signals into multiple signal segments based on a time window; and processing the multiple signal segments through a signal processing model to obtain an anti-interference target signal, wherein the signal processing model uses an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segment, and the weights characterize the importance of the feature vectors in the anti-interference target signal.

[0133] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: for each signal segment, extract features from the signal in the signal segment to obtain a set of feature vectors; determine the weight of the feature vectors based on the similarity between the feature vectors in the feature vector set and the query vector of the attention mechanism; determine the signal feature vector of the signal segment based on the feature vectors in the feature vector set and the weight of the feature vectors; and determine the anti-interference target signal based on the signal feature vectors of multiple signal segments.

[0134] The processor can also call the information and application program stored in the memory through the transmission device to perform the following steps: extracting features from the magnetic navigation signal in the signal segment to obtain a first feature; extracting features from the interference signal in the signal segment to obtain a second feature; and fusing the first feature and the second feature to obtain a feature vector set.

[0135] The processor can also call the information and application program stored in the memory through the transmission device to perform the following steps: calculate the similarity between the feature vectors in the feature vector set and the query vector; normalize the similarity of each feature vector in the feature vector set to obtain the weight corresponding to each feature vector.

[0136] The processor can also call the information and application program stored in the memory through the transmission device to perform the following steps: selecting target feature vectors from the feature vector set, wherein the target feature vectors are feature vectors with weights greater than or equal to a weight threshold; and determining signal feature vectors based on the target feature vectors and their weights.

[0137] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: process multiple signal segments through a signal processing model to obtain an initial anti-interference signal; calculate the signal deviation based on the initial anti-interference signal and the posture data of the surgical instruments using a preset signal deviation calculation formula; and determine the target signal for anti-interference based on the signal deviation and the initial anti-interference signal.

[0138] The processor can also call the information and application program stored in the memory through the transmission device to perform the following steps: correcting the initial anti-interference signal based on the signal deviation to obtain the corrected initial anti-interference signal; and processing the corrected initial anti-interference signal through a Kalman filter to obtain the target anti-interference signal.

[0139] In this embodiment, by using a signal processing model to process multiple signal segments, and during the processing, using an attention mechanism to assign weights to the feature vectors in the feature vector set corresponding to the signal segments, it is possible to specifically enhance the effective features in the signal related to surgical positioning and guidance, suppress interference features, thereby improving the filtering effect of interference signals, improving the signal processing quality, and thus improving the anti-interference effect of the anti-interference target signal output by the model.

[0140] Therefore, the method provided in this application achieves the goal of using the attention mechanism to assign weights to signal features in order to suppress interference features, thereby improving the anti-interference effect of the signal. It also solves the technical problem that related technologies use filters to filter interference signals when collecting position signals generated by magnetic navigation systems, resulting in poor anti-interference effect.

[0141] Those skilled in the art will understand that Figure 5 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 5 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 5 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 5 The different configurations shown.

[0142] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0143] Example 4

[0144] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the signal anti-interference method provided in Embodiment 1.

[0145] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0146] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing signal anti-interference method steps.

[0147] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0148] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0153] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A signal anti-interference method, characterized in that, include: Collect target signals during the surgical process, wherein the target signals include at least the magnetic navigation signals corresponding to the surgical instruments and interference signals in the surgical environment where the surgical instruments are located; The target signal is divided into multiple signal segments based on a time window; The signal processing model processes the multiple signal segments to obtain an anti-interference target signal. The signal processing model uses an attention mechanism to assign weights to feature vectors in the feature vector set corresponding to the signal segments. The weights represent the importance of the feature vectors in the anti-interference target signal. The weights of the feature vectors are determined based on the similarity between the feature vectors in the feature vector set and the query vector of the attention mechanism. The query vector is learned by the model during the training process of the signal processing model. The multiple signal segments are processed using a signal processing model to obtain an anti-interference target signal, including: The signal processing model is used to process the multiple signal segments to obtain an initial anti-interference signal. The signal deviation is calculated using a preset signal deviation calculation formula based on the initial anti-interference signal and the posture data of the surgical instruments. The target signal for anti-interference is determined based on the signal deviation and the initial anti-interference signal.

2. The method according to claim 1, characterized in that, The multiple signal segments are processed using a signal processing model to obtain an anti-interference target signal, including: For each signal segment, feature extraction is performed on the signal in the signal segment to obtain a set of feature vectors; The weights of the feature vectors are determined based on the similarity between the feature vectors in the feature vector set and the query vectors of the attention mechanism. The signal feature vector of the signal segment is determined based on the feature vectors in the feature vector set and the weights of the feature vectors, and the anti-interference target signal is determined based on the signal feature vectors of the multiple signal segments.

3. The method according to claim 2, characterized in that, Feature extraction is performed on the signal in the signal segment to obtain a set of feature vectors, including: Feature extraction is performed on the magnetic navigation signal in the signal segment to obtain the first feature; The interference signal in the signal segment is subjected to feature extraction to obtain the second feature; The first feature and the second feature are fused to obtain the feature vector set.

4. The method according to claim 2, characterized in that, The weights of the feature vectors are determined based on the similarity between the feature vectors in the feature vector set and the query vector of the attention mechanism, including: Calculate the similarity between the feature vectors in the feature vector set and the query vector; The similarity of each feature vector in the feature vector set is normalized to obtain the weight corresponding to each feature vector.

5. The method according to claim 2, characterized in that, Determining the signal feature vector of the signal segment based on the feature vectors in the feature vector set and the weights of the feature vectors includes: Select target feature vectors from the set of feature vectors, wherein the target feature vectors are feature vectors whose weights are greater than or equal to a weight threshold; The signal feature vector is determined based on the target feature vector and its weight.

6. The method according to claim 1, characterized in that, Determining the target signal for anti-interference based on the signal deviation and the initial anti-interference signal includes: The initial anti-interference signal is corrected based on the signal deviation to obtain the corrected initial anti-interference signal; The modified initial anti-interference signal is processed by a Kalman filter to obtain the target anti-interference signal.

7. A signal anti-interference device, characterized in that, For performing the method according to any one of claims 1 to 6, comprising: The acquisition module is used to acquire target signals during the surgical process, wherein the target signals include at least the magnetic navigation signal corresponding to the surgical instrument and the interference signal in the surgical environment where the surgical instrument is located; A segmentation module is used to divide the target signal into multiple signal segments based on a time window; The processing module is used to process the multiple signal segments through a signal processing model to obtain an anti-interference target signal. The signal processing model uses an attention mechanism to assign weights to feature vectors in the feature vector set corresponding to the signal segments. The weights represent the importance of the feature vectors in the anti-interference target signal.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the signal anti-interference method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program executes the signal anti-interference method according to any one of claims 1 to 6 when it runs.

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