Spacecraft measurement and control signal real-time anomaly monitoring and diagnosis method based on KNN algorithm

Abstract

The invention relates to a spacecraft measurement and control signal real-time abnormity monitoring and diagnosis method based on a KNN algorithm. The method comprises the following steps: establishing a signal simulation model library and a measurement and control radio frequency abnormal signal frequency spectrum library; performing modal decomposition on the abnormal fault signals in the measurement and control radio frequency abnormal signal frequency spectrum library to obtain characteristic energy information of the abnormal signals in each modal; performing real-time anomaly monitoring on the sampled data, and obtaining spectrum characteristics of each mode of an abnormal signal by using a variational mode decomposition algorithm; calculating an energy characteristic by using the spectrum characteristic of each mode of the abnormal signal in the sampling data; and inputting the feature energy into a KNN classifier, and performing fault classification on the abnormal signals based on a preset K value to complete fault diagnosis. According to the method and the device, the problems that a tester is difficult to find an anomaly in time and troubleshooting traceability, anomaly positioning and fault diagnosis are difficult are solved, the anomaly traceability accuracy and timeliness are realized, the working efficiency of the tester is greatly improved, and the waste of human resources is reduced.

Description

Technical Field

[0001] The present invention relates to the field of spacecraft testing technology, and in particular to a real-time abnormal monitoring and diagnosis method for spacecraft measurement and control signals based on a KNN algorithm. Background Art

[0002] During the testing of spacecraft measurement and control subsystems, abnormal phenomena are difficult to reproduce and trace. Testers have a strong demand for real-time discovery and real-time processing of measurement and control signal abnormalities. At the same time, improving the reliability of abnormal analysis and elimination and reducing the manpower and material costs of abnormal analysis have become urgent issues to be solved in spacecraft testing.

[0003] The underlying principles of the measurement and control subsystem are relatively complicated, the test equipment is highly complex, and the number of test equipment is large. When an abnormal problem occurs in the measurement and control system, personnel often simulate the state at the test site and wait for the problem to recur to find the source of the abnormal problem. The troubleshooting process takes a relatively long time for test personnel and delays the progress of spacecraft R&D and testing. At the same time, the diagnosis of abnormal measurement and control signals requires strong experience and knowledge and skilled equipment operation capabilities. Figure 1 As shown, the traditional abnormal troubleshooting method is relatively cumbersome and relies heavily on testers with rich model experience, which poses certain challenges to the universality and efficiency of abnormal diagnosis and troubleshooting.

[0004] The current abnormal diagnosis has the following inconveniences:

[0005] (1) Abnormal problems are difficult to detect and discover in real time, which makes it difficult to trace the source of abnormalities;

[0006] (2) While waiting for the problem to recur, testers who are more skilled in equipment operation and have rich experience in fault diagnosis are required to monitor the problem around the clock, which consumes a lot of manpower and energy;

[0007] (3) When the problem recurs, testers and single-machine developers are required to analyze it collectively, resulting in low work efficiency. Summary of the invention

[0008] In order to solve the technical problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a real-time anomaly monitoring and diagnosis method for spacecraft measurement and control signals based on the KNN algorithm, which solves the problem that it is difficult for testers to detect anomalies in time and to troubleshoot, locate anomalies, and diagnose faults.

[0009] To achieve the above-mentioned purpose, the present invention provides a method for real-time abnormal monitoring and diagnosis of spacecraft measurement and control signals based on a KNN algorithm, comprising the following steps:

[0010] Step S1, establishing a signal simulation model library and a spectrum library for measuring and controlling abnormal radio frequency signals;

[0011] Step S2, performing modal decomposition on the abnormal fault signal in the measurement and control radio frequency abnormal signal spectrum library to obtain characteristic energy information of the abnormal signal under each mode;

[0012] Step S3, performing real-time abnormal monitoring on the sampled data, and obtaining the frequency spectrum characteristics of each mode of the abnormal signal using a variational mode decomposition algorithm;

[0013] Step S4, calculating energy characteristics using the frequency spectrum characteristics of each mode of the abnormal signal in the sampled data;

[0014] Step S5: input the characteristic energy into the KNN classifier, classify the abnormal signal into faults based on the preset K value, and complete the fault diagnosis.

[0015] According to a technical solution of the present invention, in step S1, based on prior knowledge, the frequency spectrum of abnormal measurement and control signals in existing spacecraft model missions is sorted out, and its mathematical model and characteristic modal information are obtained through simulation to establish a signal simulation model library and a measurement and control radio frequency abnormal signal spectrum library;

[0016] Among them, the spectrum of abnormal radio frequency signals for measurement and control includes at least close-frequency power suppression and close-frequency intermodulation interference signals under USB, UXB and UHF systems.

[0017] According to a technical solution of the present invention, in step S3, real-time abnormality monitoring of the sampled data specifically includes:

[0018] Step S31, presetting the signal sliding window length N;

[0019] Step S32: Perform real-time data calculation using the ML-CFAR algorithm based on the sampled data in the window;

[0020] Step S33, according to the detection threshold and threshold multiplication factor pre-set by the algorithm, real-time detection of whether the sampled data is abnormal;

[0021] Step S34: When an abnormality in the measurement and control signal is detected, an alarm message appears on the user interface.

[0022] According to a technical solution of the present invention, in step S3, the frequency spectrum characteristics of each mode of the abnormal signal are obtained by using a variational mode decomposition algorithm, which specifically includes:

[0023] Step S35, performing VMD decomposition on the abnormal signal to obtain different BIMF components, where the different BIMF components are multiple different modal signals of the abnormal signal;

[0024] Step S36: for any modal signal function u k, find the marginal spectrum through Hilbert transform; use exponential correction to move the spectrum of the modal function to the estimated center frequency; demodulate the signal through Gaussian smoothing to obtain the bandwidth of each modal function;

[0025] Step S37: Display the BIMF components to the host computer in real time through a graphical user interface and store them regularly.

[0026] According to a technical solution of the present invention, the step S4 specifically includes:

[0027] Step S41: Set the number of sampling points N in advance according to the carrier frequency, calculate the signal power of the multiple different modal signals decomposed in step S3, and obtain the energy of each modal

[0028] Step S42, summing the power of each modal signal to obtain the modal total energy E;

[0029] Step S43: Calculate the energy characteristics of each mode

[0030] According to a technical solution of the present invention, the step S5 specifically includes:

[0031] Step S51, inputting the calculation result in step S4 as an input condition into the KNN classifier;

[0032] Step S52: for the abnormal fault signal samples in the abnormal signal knowledge base, perform Euclidean distance calculation on K nearest neighbors with similar characteristics;

[0033] Step S53: sort the calculated distance values, and obtain the fault type according to the sorting result.

[0034] According to one aspect of the present invention, there is provided an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes a real-time abnormality monitoring and diagnosis method for spacecraft measurement and control signals based on a KNN algorithm as described in any one of the above technical solutions.

[0035] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions. When the computer instructions are executed by a processor, a real-time abnormality monitoring and diagnosis method for spacecraft measurement and control signals based on a KNN algorithm as described in any one of the above technical solutions is implemented.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention proposes a real-time abnormal monitoring and diagnosis method for spacecraft measurement and control signals based on the KNN algorithm. By establishing a signal simulation model library and a measurement and control radio frequency abnormal signal spectrum library, the abnormal fault signal in the measurement and control radio frequency abnormal signal spectrum library is modally decomposed to obtain the characteristic energy information of the abnormal signal under each mode, and then the sampled data is monitored for real-time abnormality, and the spectrum characteristics of each mode of the abnormal signal are obtained by using the variational mode decomposition algorithm. Then, the energy characteristics are calculated using the spectrum characteristics of each mode of the abnormal signal in the sampled data, and finally the characteristic energy is input into the KNN classifier. Based on the preset K value, the abnormal signal is fault-classified to complete the fault diagnosis. For the wired and wireless test scenarios commonly used at this stage, the measurement and control signal abnormality caused by changes in equipment status and environmental conditions or external interference signals can be accurately monitored during the test process, which solves the problem that it is difficult for testers to find abnormalities in time and troubleshooting, abnormal location, and fault diagnosis are difficult, and the accuracy and timeliness of abnormal tracing are achieved, which greatly improves the work efficiency of testers and reduces the waste of human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 Schematic representation of the traditional test model diagram;

[0040] Figure 2 A flowchart of a real-time signal anomaly monitoring algorithm according to an embodiment of the present invention is schematically shown;

[0041] Figure 3 A schematic diagram of a flow chart of abnormal signal modal decomposition according to an embodiment of the present invention is shown;

[0042] Figure 4 A flowchart of abnormal signal classification according to an embodiment of the present invention is schematically shown;

[0043] Figure 5 The overall flow chart of a method for real-time abnormality monitoring and diagnosis of spacecraft measurement and control signals based on a KNN algorithm according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0044] The description of the embodiments of this specification should be combined with the corresponding drawings, which should be considered as part of the complete specification. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated for simplification or convenience. Furthermore, the parts of each structure in the drawings will be described separately. It is worth noting that the elements not shown in the drawings or not described in words are in a form known to ordinary technicians in the relevant technical field.

[0045] The description of the embodiments herein and any reference to directions and orientations are only for the convenience of description and are not to be construed as any limitation on the scope of protection of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination, and the present invention is not particularly limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0046] like Figures 2 to 5 As shown, a real-time abnormal monitoring and diagnosis method of spacecraft measurement and control signals based on the KNN algorithm of the present invention comprises the following steps:

[0047] Step S1, establishing a signal simulation model library and a spectrum library for measuring and controlling abnormal radio frequency signals;

[0048] Based on prior knowledge, the spectrum of abnormal measurement and control signals in existing spacecraft models and missions is sorted out, and its mathematical model and characteristic modal information are obtained through simulation, and a signal simulation model library and a spectrum library of abnormal measurement and control RF signals are established;

[0049] Among them, the spectrum of abnormal radio frequency signals for measurement and control includes at least close-frequency power suppression and close-frequency intermodulation interference signals under USB, UXB and UHF systems.

[0050] Based on prior knowledge and rich experience in spacecraft testing, we construct a signal simulation model library and a spectrum library of abnormal measurement and control RF signals, combining theory with practice. The model library and spectrum library obtained based on the accumulated spacecraft measurement and control abnormal problems and conventional abnormal interference signals are highly representative and accurate.

[0051] Step S2, performing modal decomposition on the abnormal fault signal in the measurement and control radio frequency abnormal signal spectrum library to obtain characteristic energy information of the abnormal signal under each mode;

[0052] Step S3, performing real-time abnormal monitoring on the sampled data, and obtaining the frequency spectrum characteristics of each mode of the abnormal signal using a variational mode decomposition algorithm;

[0053] Among them, real-time abnormal monitoring of sampling data includes:

[0054] Step S31, presetting the signal sliding window length N;

[0055] Step S32: Perform real-time data calculation using the ML-CFAR algorithm based on the sampled data in the window;

[0056] Step S33, according to the detection threshold and threshold multiplication factor pre-set by the algorithm, real-time detection of whether the sampled data is abnormal;

[0057] Step S34: When an abnormality in the measurement and control signal is detected, an alarm message appears on the user interface.

[0058] In order to solve the problem of real-time abnormal monitoring, the present invention performs sliding window ML-CFAR (mean-like constant false alarm algorithm) calculation on the sampled data (the window period can be flexibly set according to the hardware computing performance and the calculation accuracy required by the user), and can autonomously monitor the time domain signal through the time domain signal constant false alarm calculation. When the measurement and control signal power jitters or changes abnormally, the abnormal signal time domain data and spectrum information can be saved and screenshot and an alarm signal can be issued to prompt the test personnel;

[0059] like Figure 2 As shown, the monitored signal is sampled in real time, and according to the power window K set in advance, the background power around the detection unit is calculated by sliding the mean CFAR (Z = mean (X + Y)), and according to the threshold product factor α set in advance, the Neyman-Pearson criterion likelihood ratio test can be used to make the following judgment: D>T = αZ, that is, when the sampling value of the unit to be detected is greater than the detection threshold, it is judged that there may be other signals in the unit, otherwise it is judged that no target appears.

[0060] Through the real-time anomaly detection algorithm, the measurement and control signal anomalies are autonomously monitored, effectively improving the timeliness of testers' anomaly discovery.

[0061] The variational mode decomposition algorithm is used to obtain the spectral characteristics of each mode of the abnormal signal, including:

[0062] Step S35, performing VMD decomposition on the abnormal signal to obtain different BIMF components, where the different BIMF components are multiple different modal signals of the abnormal signal;

[0063] Step S36: for any modal signal function u k , find the marginal spectrum through Hilbert transform; use exponential correction to move the spectrum of the modal function to the estimated center frequency; demodulate the signal through Gaussian smoothing to obtain the bandwidth of each modal function;

[0064] Step S37: Display the BIMF components to the host computer in real time through a graphical user interface and store them regularly.

[0065] When the ML-CFAR algorithm is used to analyze the time domain information and find anomalies, the abnormal data is further analyzed in time and frequency, and the high-resolution VMD (variational mode decomposition) algorithm is used to perform modal decomposition on the abnormal signal, and further derive the spectral characteristics of the abnormal signal under different modes (each principal component). Figure 3 As shown, the abnormal signal is sampled and Hilbert transform is performed to obtain the analytical signal of the abnormal signal Modulate the modal signal to baseband The variational problem is constructed and solved to obtain the characteristic information of each modal signal. By performing variational modal decomposition analysis on abnormal signals, multi-dimensional signal information characteristics can be provided.

[0066] Step S4, calculating the energy characteristics using the frequency spectrum characteristics of each mode of the abnormal signal in the sampled data, specifically includes:

[0067] Step S41: Set the number of sampling points N in advance according to the carrier frequency, calculate the signal power of the multiple different modal signals decomposed in step S3, and obtain the energy of each modal

[0068] Step S42, summing the power of each modal signal to obtain the modal total energy E;

[0069] Step S43: Calculate the energy characteristics of each mode

[0070] Step S5: input the characteristic energy into the KNN classifier, classify the abnormal signal into faults based on the preset K value, and complete the fault diagnosis, which specifically includes:

[0071] Step S51, inputting the calculation result in step S4 as an input condition into the KNN classifier;

[0072] Step S52: for the abnormal fault signal samples in the abnormal signal knowledge base, perform Euclidean distance calculation on K nearest neighbors with similar characteristics;

[0073] Step S53: sort the calculated distance values, and obtain the fault type according to the sorting result.

[0074] The acquired feature energy is input into the KNN classifier. According to the K value set in advance, the Euclidean distance is calculated with the feature energy information in the abnormal signal knowledge base, and the abnormal signals are sorted according to the distance size, and the fault classification is performed to achieve the purpose of abnormal signal fault diagnosis.

[0075] For abnormal signal classification, Figure 4As shown in the figure, first, an abnormal knowledge experience base is established for the abnormal signal spectrum and mathematical model based on the spacecraft test experience and fault cases; secondly, the K value of the number of K neighbors is pre-set; thirdly, the Euclidean distance between the abnormal modal signal characteristic value and the knowledge experience base is calculated; then, the calculated distance values ​​are sorted; finally, the abnormality is diagnosed and classified according to the sorting results. By using the KNN algorithm to analyze the abnormal signal characteristic mode and the abnormal signal modal information of the fault knowledge base, the current fault type can be located and diagnosed.

[0076] like Figure 5 As shown, the overall process of a real-time abnormal monitoring and diagnosis method for spacecraft measurement and control signals based on the KNN algorithm of the present invention includes three parts: an initial construction stage, a real-time data acquisition and processing stage, and a comparative analysis stage. Among them, the initial construction stage includes: collecting and establishing fault signal samples; performing VMD decomposition on the sample signals; establishing a fault signal modal experience knowledge base; the real-time data acquisition and processing stage includes: real-time sampling of measurement and control signals; performing CFAR time domain detection on the sampled signals; discovering fault signals and performing VMD; the comparative analysis stage includes: setting the signal bandwidth and K nearest neighbor value and performing KNN fault comparison to determine the fault type.

[0077] The fault types include at least co-frequency power suppression interference and close-frequency intermodulation interference.

[0078] According to one aspect of the present invention, there is provided an electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes a real-time abnormality monitoring and diagnosis method for spacecraft measurement and control signals based on the KNN algorithm, such as any one of the above technical solutions.

[0079] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions. When the computer instructions are executed by a processor, a real-time abnormality monitoring and diagnosis method for spacecraft measurement and control signals based on a KNN algorithm as described in any of the above technical solutions is implemented.

[0080] Computer-readable storage media may include any medium capable of storing or transmitting information. Examples of computer-readable storage media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. Code segments may be downloaded via computer networks such as the Internet, intranets, etc.

[0081] The present invention discloses a real-time abnormal monitoring and diagnosis method for spacecraft measurement and control signals based on a KNN algorithm. By establishing a signal simulation model library and a measurement and control radio frequency abnormal signal spectrum library, the abnormal fault signal in the measurement and control radio frequency abnormal signal spectrum library is modally decomposed to obtain the characteristic energy information of the abnormal signal under each mode, and then the sampled data is monitored for abnormality in real time. The spectrum characteristics of each mode of the abnormal signal are obtained by using a variational mode decomposition algorithm. Then, the energy characteristics are calculated by using the spectrum characteristics of each mode of the abnormal signal in the sampled data. Finally, the characteristic energy is input into a KNN classifier, and the abnormal signal is classified into faults based on a preset K value to complete fault diagnosis. For the wired and wireless test scenarios commonly used at this stage, the measurement and control signal abnormalities caused by changes in equipment status and environmental conditions or external interference signals can be accurately monitored during the test process, which solves the problem that it is difficult for testers to find abnormalities in time and troubleshooting, abnormal location, and fault diagnosis are difficult, and the accuracy and timeliness of abnormal tracing are achieved, which greatly improves the work efficiency of testers and reduces the waste of human resources.

[0082] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0083] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] It should also be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0086] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A real-time abnormal monitoring and diagnosis method for spacecraft measurement and control signals based on KNN algorithm, characterized in that: The following steps are involved: Step S1, establishing a signal simulation model library and a spectrum library for measuring and controlling abnormal radio frequency signals; Step S2, performing modal decomposition on the abnormal fault signal in the measurement and control radio frequency abnormal signal spectrum library to obtain characteristic energy information of the abnormal signal under each mode; Step S3, performing real-time abnormal monitoring on the sampled data, and obtaining the frequency spectrum characteristics of each mode of the abnormal signal using a variational mode decomposition algorithm; Step S4, calculating energy characteristics using the frequency spectrum characteristics of each mode of the abnormal signal in the sampled data; Step S5: input the characteristic energy into the KNN classifier, classify the abnormal signal into faults based on the preset K value, and complete the fault diagnosis.

2. The method for real-time abnormal monitoring and diagnosis of spacecraft measurement and control signals based on the KNN algorithm according to claim 1 is characterized in that: In the step S1, based on prior knowledge, the frequency spectrum of abnormal measurement and control signals in existing spacecraft model missions is sorted out, and its mathematical model and characteristic modal information are obtained through simulation, and a signal simulation model library and a measurement and control radio frequency abnormal signal spectrum library are established; Among them, the spectrum of abnormal radio frequency signals for measurement and control includes at least close-frequency power suppression and close-frequency intermodulation interference signals under USB, UXB and UHF systems.

3. The method for real-time abnormal monitoring and diagnosis of spacecraft measurement and control signals based on the KNN algorithm according to claim 1 is characterized in that: In step S3, real-time abnormality monitoring of the sampled data is performed, specifically including: Step S31, presetting the signal sliding window length N; Step S32: Perform real-time data calculation using the ML-CFAR algorithm based on the sampled data in the window; Step S33, according to the detection threshold and threshold multiplication factor pre-set by the algorithm, real-time detection of whether the sampled data is abnormal; Step S34: When an abnormality in the measurement and control signal is detected, an alarm message appears on the user interface.

4. The method for real-time abnormal monitoring and diagnosis of spacecraft measurement and control signals based on the KNN algorithm according to claim 3 is characterized in that: In step S3, the frequency spectrum characteristics of each mode of the abnormal signal are obtained by using the variational mode decomposition algorithm, which specifically includes: Step S35, performing VMD decomposition on the abnormal signal to obtain different BIMF components, where the different BIMF components are multiple different modal signals of the abnormal signal; Step S36: for any modal signal function u k , find the marginal spectrum through Hilbert transform; use exponential correction to move the spectrum of the modal function to the estimated center frequency; demodulate the signal through Gaussian smoothing to obtain the bandwidth of each modal function; Step S37: Display the BIMF components to the host computer in real time through a graphical user interface and store them regularly.

5. The method for real-time abnormal monitoring and diagnosis of spacecraft measurement and control signals based on the KNN algorithm according to claim 4 is characterized in that: The step S4 specifically includes: Step S41: Set the number of sampling points N in advance according to the carrier frequency, calculate the signal power of the multiple different modal signals decomposed in step S3, and obtain the energy of each modal Step S42, summing the power of each modal signal to obtain the modal total energy E; Step S43: Calculate the energy characteristics of each mode 6. The method for real-time abnormal monitoring and diagnosis of spacecraft measurement and control signals based on the KNN algorithm according to claim 5 is characterized in that: The step S5 specifically includes: Step S51, inputting the calculation result in step S4 as an input condition into the KNN classifier; Step S52: for the abnormal fault signal samples in the abnormal signal knowledge base, perform Euclidean distance calculation on K nearest neighbors with similar characteristics; Step S53: sort the calculated distance values, and obtain the fault type according to the sorting result.

7. An electronic device, characterized in that: include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device executes the real-time abnormality monitoring and diagnosis method of spacecraft measurement and control signals based on the KNN algorithm as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, implement the real-time abnormality monitoring and diagnosis method for spacecraft measurement and control signals based on the KNN algorithm as described in any one of claims 1 to 6.

Images