HPLC and HRF dual-mode communication fault detection method and system

Abstract

The invention relates to an HPLC and HRF dual-mode communication fault detection method and system. The method comprises the following steps that original receiving signals of an HPLC channel and an HRF channel of an HPLC and HRF dual-mode communication module are collected and preprocessed; a composite coherence analysis model is constructed, and the composite coherence of the two channels is calculated through the preprocessed original receiving signals; constructing a composite channel difference model, and calculating the channel difference of the two channels through the preprocessed original received signals; constructing an instantaneous phase difference measurement model, and calculating the instantaneous phase difference of the two channels through the preprocessed original receiving signals; and integrating the composite coherence, the channel difference and the instantaneous phase difference of the two channels to obtain a fault index of the HPLC and HRF dual-mode communication module, presetting a fault index threshold, and when the fault index reaches the preset fault index threshold, judging that the HPLC and HRF dual-mode communication module has a fault.

Description

Technical Field

[0001] The present invention relates to a method and system for fault detection of HPLC and HRF dual - mode communication, belonging to the field of communication technology. Background Art

[0002] In modern communication systems, fault detection has always been a key link to ensure communication quality and stable operation of the system. With the development of communication technology towards high - speed and multi - modal directions, the limitations of traditional single - mode communication fault detection methods in complex scenarios have gradually emerged. Especially in a dual - mode system composed of high - voltage power line high - speed carrier communication (HPLC) and high - frequency radio frequency communication (HRF), the signal transmission environment has characteristics such as multipath interference, noise interference, and non - stationary characteristics, making the fault detection task more complex.

[0003] In traditional PLC and radio frequency communication systems, fault detection technologies mainly focus on the following aspects: Time - domain analysis method: It mainly uses indicators such as signal energy and instantaneous change rate to detect faults, but it is often difficult to capture the subtle non - stationary changes of signals and is easily interfered by noise.

[0004] Frequency - domain analysis method: It extracts spectral features through Fourier transform or wavelet transform, and has a certain inhibitory effect on multipath fading and frequency - selective fading. However, single - domain frequency - domain analysis is difficult to take into account the time - domain change information.

[0005] Detection method based on statistical characteristics: It relies on historical data statistical models and uses statistics such as mean, variance, and correlation to judge the fault state. Although it has good effects in stable channels, its detection accuracy is not high in dynamic and variable environments.

[0006] For a dual - mode system, due to the significant differences in signal propagation, signal - to - noise ratio, interference characteristics, etc. between the HPLC and HRF channels, single - domain or single - model fault detection methods often cannot fully capture the heterogeneous information between the two channels, easily leading to misjudgment or missed judgment. Summary of the Invention

[0007] In order to solve the problems existing in the above - mentioned prior art, the present invention proposes a method and system for fault detection of HPLC and HRF dual - mode communication.

[0008] The technical solution of the present invention is as follows: On the one hand, the present invention provides a method for fault detection of HPLC and HRF dual - mode communication, including the following steps: Collect the original received signals of the HPLC channel and the HRF channel of the HPLC and HRF dual - mode communication module and pre - process the original received signals; Construct a composite coherence analysis model, and calculate the composite coherence of the HPLC channel and the HRF channel through the preprocessed original received signal; Construct a composite channel difference model, and calculate the channel difference between the HPLC channel and the HRF channel through the preprocessed original received signal; Construct an instantaneous phase difference measurement model, and calculate the instantaneous phase difference between the HPLC channel and the HRF channel through the preprocessed original received signal; Integrate the composite coherence, channel difference, and instantaneous phase difference of the HPLC channel and the HRF channel to obtain the fault index of the HPLC and HRF dual-mode communication module. Preset the fault index threshold. When the fault index reaches the preset fault index threshold, it is determined that the HPLC and HRF dual-mode communication module fails.

[0009] As a preferred embodiment of the present invention, the specific steps for preprocessing the original received signal are as follows: Filter and denoise the original received signals of the collected HPLC channel and HRF channel; Calculate the signal energy of the HPLC channel within a fixed time period, as shown in the following formula: ; Where: represents the signal energy of the HPLC channel at moment; represents the filtered and denoised signal of the HPLC channel within a fixed time period; represents the starting moment; represents the fractional differential operator; Calculate the signal energy of the HRF channel within a fixed time period, as shown in the following formula: ; Where: represents the signal energy of the HRF channel at moment; represents the filtered and denoised signal of the HRF channel within a fixed time period; represents the integration variable.

[0010] As a preferred embodiment of the present invention, the composite coherence analysis model is specifically as shown in the following formula: ; Where: represents the composite coherence at moment; represents the size of the frequency interval of the original received signal; represents the lowest frequency point of the frequency interval; represents the highest frequency point of the frequency interval; Represents the exponentially weighted coefficient within the time window; Represents the scaling coefficient for adjusting the frequency axis; Represents the deviation sensitivity coefficient; Represents At time The channel coherence coefficient of the ; Where: Represents the time-frequency representation of the corresponding channel after normalization, ; Represents the conjugate operation; The calculation formula for the time-frequency representation of the corresponding channel after normalization is: ; Where: Represents the natural constant; Represents the imaginary unit, ; Represents the filtered and noise-reduced signal within a fixed time period of the corresponding channel, .

[0011] As a preferred embodiment of the present invention, the composite channel difference model is optimized for parameters through a machine learning model, specifically as shown in the following formula: ; Where: Represents The channel difference at time , Respectively represent the weight factors of different terms; Represents the adjustment coefficient; Represents the original receiving signal acquisition period; Represents the signal attenuation factor; Represents the total number of signal modulation types; Represents the Modulation component of the th signal modulation type;

[0012] As a preferred embodiment of the present invention, the instantaneous phase difference measurement model is specifically as shown in the following formula: ; Where: Represents The instantaneous phase difference at time Represents the signal energy proportion coefficient of the HPLC channel; Represents the signal energy proportion coefficient of the HRF channel; Represents the instantaneous phase information of the HPLC channel at time; represents the instantaneous phase information of the HRF channel at the moment; represents the channel phase difference control coefficient; represents the scaling factor.

[0013] As a preferred embodiment of the present invention, the calculation formula of the fault index of the HPLC and HRF dual-mode communication module is: ; where: represents the fault index of the HPLC and HRF dual-mode communication module at the moment; , , respectively represent the weight indexes of composite coherence, channel difference, and instantaneous phase difference.

[0014] On the other hand, the present invention also provides an HPLC and HRF dual-mode communication fault detection system, including a data acquisition module, a composite coherence analysis module, a channel difference analysis module, an instantaneous phase difference measurement module, and a fault detection module; The data acquisition module is used to collect the original received signals of the HPLC channel and the HRF channel of the HPLC and HRF dual-mode communication module and preprocess the original received signals; The composite coherence analysis module is used to construct a composite coherence analysis model and calculate the composite coherence of the HPLC channel and the HRF channel through the preprocessed original received signals; The channel difference analysis module is used to construct a composite channel difference model and calculate the channel difference of the HPLC channel and the HRF channel through the preprocessed original received signals; The instantaneous phase difference measurement module is used to construct an instantaneous phase difference measurement model and calculate the instantaneous phase difference of the HPLC channel and the HRF channel through the preprocessed original received signals; The fault detection module is used to synthesize the composite coherence, channel difference, and instantaneous phase difference of the HPLC channel and the HRF channel to obtain the fault index of the HPLC and HRF dual-mode communication module, preset a fault index threshold, and when the fault index reaches the preset fault index threshold, it is determined that the HPLC and HRF dual-mode communication module fails.

[0015] On yet another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0016] On the other hand, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0017] The present invention has the following beneficial effects: 1. The present invention introduces a composite channel difference model, which can keenly reflect the differences in energy and dynamic characteristics between the HPLC and HRF channels, so as to more accurately distinguish normal and faulty states. By integrating machine learning to optimize the model parameters, the accuracy and robustness are improved.

[0018] 2. Make full use of the complementary characteristics of the two channels in the HPLC and HRF dual-mode communication system, and realize fault detection through multi-dimensional index comprehensive decision-making. This not only improves the reliability of fault detection, but also can quickly respond when detecting abnormalities, ensuring the continuous and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] It should be understood that the step numbers used herein are only for convenient description and do not limit the execution order of the steps.

[0022] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless otherwise clearly specified in the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0023] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0024] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] Embodiment 1: See Figure 1, an HPLC and HRF dual-mode communication fault detection method, comprising the following steps: Collect the original received signals of the HPLC channel and the HRF channel of the HPLC and HRF dual-mode communication module, and preprocess the original received signals; Construct a composite coherence analysis model, and calculate the composite coherence of the HPLC channel and the HRF channel through the preprocessed original received signals; Construct a composite channel difference model, and calculate the channel difference between the HPLC channel and the HRF channel through the preprocessed original received signals; Construct an instantaneous phase difference measurement model, and calculate the instantaneous phase difference between the HPLC channel and the HRF channel through the preprocessed original received signals; Integrate the composite coherence, channel difference, and instantaneous phase difference of the HPLC channel and the HRF channel to obtain the fault index of the HPLC and HRF dual-mode communication module. Preset the fault index threshold. When the fault index reaches the preset fault index threshold, it is determined that the HPLC and HRF dual-mode communication module fails, and a warning message is sent.

[0026] As a preferred implementation manner of this embodiment, the specific steps for preprocessing the original received signals are as follows: Perform filtering and noise reduction processing on the original received signals of the HPLC channel and the HRF channel collected; Calculate the signal energy of the HPLC channel within a fixed time period, as shown in the following formula: ; Where: Represents the signal energy of the HPLC channel at Time; Represents the filtered and noise-reduced signal of the HPLC channel within a fixed time period; Represents the starting time; Represents the fractional differential operator; Calculate the signal energy of the HRF channel within a fixed time period, as shown in the following formula: ; Where: Represents the signal energy of the HRF channel at Time; Represents the integral operation of the filtered and noise-reduced signal of the HRF channel within a fixed time period; Represents the integration variable, which is used to perform integral calculation on the filtered and noise-reduced signal Within the given time window ; The approximate expression of the fractional differential operator is: ; Wherein: represents the order of differentiation. When , it corresponds to the traditional first-order differentiation. When takes a non-integer value, fractional-order differentiation is obtained, which can describe the more subtle change characteristics of the signal; represents the sampling step of the sampling signal. A smaller can obtain higher precision, but the computational complexity will also increase accordingly; represents the dummy index variable in the summation, which is used to successively accumulate and consider the sampling points from the current time going back in the past every . When , it represents the current time . represents time, and so on; represents the generalized binomial coefficient. When takes a non-integer value, the traditional definition of the binomial coefficient is no longer applicable, and it needs to be extended with the generalized binomial coefficient. This coefficient determines the weight of each delayed sampling point , and the weight will change with the increase of , reflecting the importance of historical information in fractional-order differentiation.

[0027] As a preferred implementation manner of this embodiment, the composite coherence analysis model is specifically shown as the following formula: ; Wherein: represents the composite coherence at time represents the size of the frequency interval of the original received signal; represents the lowest frequency point of the frequency interval; represents the highest frequency point of the frequency interval; represents the exponential weighting coefficient within the time window; represents the scaling coefficient for adjusting the frequency axis, dynamically adjusting the scaling effect of the frequency axis in the time-frequency representation, so that when the actual spectral center of the signal shifts, a representation consistent with the reference frequency can still be obtained; represents the deviation sensitivity coefficient; represents the channel coherence coefficient at the th frequency point at time ; Wherein: represents the time-frequency representation corresponding to the channel normalization, ; represents the conjugate operation; The calculation formula for the time-frequency representation corresponding to channel normalization is as follows: ; Where: represents the natural constant; represents the imaginary unit, ; represents the filtered and noise-reduced signal within a fixed time period of the corresponding channel, ; When is relatively large, as increases, will decrease faster, that is, the weights of samples farther from the current time decrease rapidly; Assume that it is desired that after a time , the weight drops to 50% of the original, that is, there is , and solving gives , thus confirming the value of ; The calculation formula for the scaling coefficient for adjusting the frequency axis is , represents the reference frequency, usually the center frequency expected in system design or application; Assume that after the signal undergoes short-time Fourier transform or other time-frequency analysis methods, a complex representation is obtained within the frequency band of interest . Define the energy-weighted average frequency within the time as: ; When the energy of the signal is mainly concentrated at a position lower than the reference frequency , at this time , which is equivalent to magnifying the actually measured frequency value, so as to stretch the frequency axis in the time-frequency representation to align it with the reference frequency, and vice versa is equivalent to compressing the frequency axis; In order to avoid sudden changes in caused by instantaneous noise or signal fluctuations, an exponential smoothing strategy can be adopted to smoothly update .

[0028] As a preferred implementation manner of this embodiment, the composite channel difference model is optimized for parameters through a machine learning model, specifically as shown in the following formula: ; Where: represents the channel difference at the moment ; , respectively represent the weight factors of different terms; represents an adjustment coefficient used to prevent the denominator from being zero; represents the acquisition period of the original received signal; represents the signal attenuation factor; represents the total number of signal modulation types; represents the modulation component of the When the power line is used as the transmission medium, it usually faces multipath effects, frequency-selective fading, and noise interference. To improve the data transmission rate and anti-interference ability in such a complex environment, the HPLC system generally tends to adopt OFDM modulation, QAM (such as 16-QAM, 64-QAM, etc.), or PSK modulation; The HRF channel generally has a more complex environment, but may have a relatively wide bandwidth and high-frequency resources. The choice of its modulation method depends on specific application requirements. In some cases, PSK (such as QPSK, 8-PSK) or QAM modulation may be adopted to balance the anti-interference performance while ensuring the transmission rate. In some low-speed or high-reliability cases, FSK modulation can also be selected; represents the frequency adjustment coefficient, which controls the oscillation period or frequency characteristics of each modulation component within this window; The composite channel difference model can be constructed using regression models such as support vector regression (SVR), random forest regression, or deep neural networks (such as multi-layer perceptron MLP or recurrent neural network RNN / LSTM commonly used in time series prediction). It is trained for the 、 、 、 and other parameter tuning, and the optimal parameter estimation value at the current moment is output through the machine learning model; Use the labeled data for offline training to optimize the mean square error of the machine learning model. After training, cross-validation is used to ensure that the model has good generalization ability.

[0029] As a preferred implementation manner of this embodiment, the instantaneous phase difference measurement model is specifically shown as the following formula: ; Where: represents the instantaneous phase difference at represents the signal energy proportion coefficient of the HPLC channel; represents the signal energy proportion coefficient of the HRF channel; Since the amplitude scales of signals in different channels may vary 、 are respectively used to adjust the proportion of signal energy; represents the instantaneous phase information of the HPLC channel at moment; represents the instantaneous phase information of the HRF channel at moment; represents the channel phase difference control coefficient. When it is necessary to enhance the sensitivity to subtle phase difference changes, , and when it is necessary to compress the phase difference range to reduce the influence of noise, select ; represents the scaling factor, which is adaptively adjusted according to the average energy of the signal over a past period of time. Specifically: , where: represents the integration window length, represents an adjustable constant parameter used to balance the influence of the fixed bias and the energy mean; As a preferred implementation manner of this embodiment, the calculation formula for the fault index of the HPLC and HRF dual-mode communication module is: ; where: represents the fault index of the HPLC and HRF dual-mode communication module at moment; , , respectively represent the weight indices of the composite coherence, channel difference, and instantaneous phase difference, which are used to control the specific weights of each item.

[0030] Embodiment 2: An HPLC and HRF dual-mode communication fault detection system includes a data acquisition module, a composite coherence analysis module, a channel difference analysis module, an instantaneous phase difference measurement module, and a fault detection module; The data acquisition module is used to collect the original received signals of the HPLC channel and the HRF channel of the HPLC and HRF dual-mode communication module and preprocess the original received signals; The composite coherence analysis module is used to construct a composite coherence analysis model and calculate the composite coherence of the HPLC channel and the HRF channel through the preprocessed original received signals; The channel difference analysis module is used to construct a composite channel difference model and calculate the channel difference of the HPLC channel and the HRF channel through the preprocessed original received signals; The instantaneous phase difference measurement module is used to construct an instantaneous phase difference measurement model and calculate the instantaneous phase difference of the HPLC channel and the HRF channel through the preprocessed original received signals; The fault detection module is used to obtain the fault indicators of the HPLC and HRF dual-mode communication module by synthesizing the composite coherence, channel difference, and instantaneous phase difference of the HPLC channel and the HRF channel, preset the fault indicator threshold, and when the fault indicator reaches the preset fault indicator threshold, it is determined that the HPLC and HRF dual-mode communication module fails.

[0031] This system is used to implement the method in the first embodiment, which will not be elaborated here.

[0032] Embodiment 3: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0033] Embodiment 4: This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method described in any embodiment of the present invention.

[0034] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent the situation where A exists alone, A and B exist simultaneously, or B exists alone. Where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c may be single or multiple.

[0035] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0036] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0037] In several embodiments provided by the present application, if any function is implemented in the form of 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 the present application, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0038] The above are only the embodiments of the present invention, and thus do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for detecting faults in dual-mode HPLC and HRF communication, characterized in that: The following steps are involved: Collecting the original receiving signals of the HPLC channel and the HRF channel of the HPLC and HRF dual-mode communication module and preprocessing the original receiving signals; A composite coherence analysis model was constructed to calculate the composite coherence of the HPLC channel and the HRF channel using the preprocessed original received signals; A composite channel difference model is constructed to calculate the channel differences of the HPLC channel and the HRF channel through the preprocessed original received signals; Construct an instantaneous phase difference measurement model, and calculate the instantaneous phase difference of the HPLC channel and the HRF channel through the preprocessed original received signal; The composite coherence, channel difference and instantaneous phase difference of the HPLC channel and the HRF channel are combined to obtain the fault index of the HPLC and HRF dual-mode communication modules, and a fault index threshold is preset. When the fault index reaches the preset fault index threshold, it is determined that the HPLC and HRF dual-mode communication modules are faulty.

2. A HPLC and HRF dual-mode communication fault detection method according to claim 1, characterized in that, The specific steps of preprocessing the original received signal are: Filter and reduce noise on the original received signals of the collected HPLC channel and HRF channel; The signal energy of the HPLC channel in a fixed period of time is calculated as shown below: ； in: Indicates that the HPLC channel is The signal energy at the moment; It represents the filtered noise reduction signal within a fixed period of time for the HPLC channel; Indicates the starting time; represents the fractional differential operator; Calculate the signal energy of the HRF channel within a fixed period of time, as shown in the following formula: ； in: Indicates that the HRF channel is The signal energy at the moment; Represents the filtered noise reduction signal within a fixed period of time of the HRF channel; represents the integration variable.

3. A HPLC and HRF dual-mode communication fault detection method according to claim 1, characterized in that, The composite coherence analysis model is specifically shown in the following formula: ； in: express The composite coherence of moments; Indicates the frequency interval size of the original received signal; Indicates the lowest frequency point in the frequency interval; Indicates the highest frequency point in the frequency range; Represents the exponential weighting coefficient within the time window; Indicates the scaling factor for adjusting the frequency axis; It represents the deviation sensitivity coefficient; express Moment The channel coherence coefficient of each frequency point is as follows: ； in: represents the normalized time-frequency representation of the corresponding channel, ; represents the conjugate operation; The normalized time-frequency representation calculation formula for the corresponding channel is: ； in: represents a natural constant; represents the imaginary unit, ; Represents the filtered noise reduction signal within a fixed period of the corresponding channel, .

4. A HPLC and HRF dual-mode communication fault detection method according to claim 2, characterized in that, The composite channel difference model is optimized by a machine learning model, as shown in the following formula: ； in: express Channel variability at each moment; , Represent the weight factors of different items respectively; represents the adjustment coefficient; Indicates the original received signal acquisition period; represents the signal attenuation factor; Indicates the total number of signal modulation types; Indicates Modulation components of the signal modulation type; Indicates the frequency adjustment coefficient.

5. A HPLC and HRF dual-mode communication fault detection method according to claim 2, characterized in that, The instantaneous phase difference measurement model is specifically shown in the following formula: ； in: express The instantaneous phase difference at a moment; represents the signal energy proportionality coefficient of the HPLC channel; Represents the signal energy proportional coefficient of the HRF channel; Indicates that the HPLC channel is The instantaneous phase information at the moment; Indicates that the HRF channel is The instantaneous phase information at the moment; represents the channel phase difference control coefficient; Represents the scaling factor.

6. A HPLC and HRF dual-mode communication fault detection method according to claim 1, characterized in that, The calculation formula of the fault index of the HPLC and HRF dual-mode communication module is: ； in: Indicates that the HPLC and HRF dual-mode communication modules are Fault indicators at the moment; , , They represent the weight indices of composite coherence, channel diversity, and instantaneous phase difference respectively.

7. A HPLC and HRF dual-mode communication fault detection system, characterized in that: It includes a data acquisition module, a composite coherence analysis module, a channel difference analysis module, an instantaneous phase difference measurement module and a fault detection module; The data acquisition module is used to collect the original received signals of the HPLC channel and the HRF channel of the HPLC and HRF dual-mode communication module and to pre-process the original received signals; The composite coherence analysis module is used to construct a composite coherence analysis model, and calculate the composite coherence of the HPLC channel and the HRF channel through the preprocessed original received signal; The channel difference analysis module is used to construct a composite channel difference model, and calculate the channel differences of the HPLC channel and the HRF channel through the pre-processed original received signal; The instantaneous phase difference measurement module is used to construct an instantaneous phase difference measurement model, and calculate the instantaneous phase difference of the HPLC channel and the HRF channel through the preprocessed original received signal; The fault detection module is used to obtain the fault index of the HPLC and HRF dual-mode communication modules by comprehensively analyzing the composite coherence, channel difference and instantaneous phase difference of the HPLC channel and the HRF channel, and to preset a fault index threshold. When the fault index reaches the preset fault index threshold, it is determined that the HPLC and HRF dual-mode communication modules are faulty.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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