A digital satellite television dedicated shielded cable fault identification system

Abstract

The application relates to the technical field of cable fault identification, in particular to a shielding type cable fault identification system special for digital satellite television, which comprises a data acquisition module, a data preprocessing module, a data analysis module and a fault identification module; the data acquisition module is used for acquiring the reflection signals of two groups of test signals and cable impedance signals of the shielding type cable in a test period; the data preprocessing module is used for carrying out denoising treatment on the acquired signals; the data analysis module is used for calculating a fault evaluation index of the shielding type cable special for digital satellite television; and the fault identification module is used for comparing the fault evaluation index with a preset fault threshold value to identify the fault of the shielding type cable special for digital satellite television. The application aims to improve the accuracy of the fault identification of the shielding type cable special for digital satellite television.

Description

Technical Field

[0001] This application relates to the field of cable fault identification technology, specifically to a fault identification system for shielded cables used in digital satellite television. Background Technology

[0002] Digital satellite television uses geostationary communication satellites as relay stations and special shielded cables to receive digital audio and video programs on the ground. Therefore, shielded cables are one of the core components that ensure the normal operation and signal stability of digital satellite television. The cable shielding layer has the function of shielding and transmitting television signals, which can effectively suppress external electromagnetic interference and internal signal radiation loss, and ensure the quality of television video picture. Therefore, accurate identification of faults in the special shielded cables for digital satellite television is of great significance to ensuring the picture quality of digital satellite television.

[0003] Time-domain reflectometry (TD-RS) is one of the commonly used methods for fault identification in shielded cables for digital satellite television. It typically involves applying a test signal to the shielding layer and capturing the reflected signal generated by impedance abrupt changes, then combining this signal with the reflection delay time to identify the fault. However, this method is only effective for severe faults such as a complete break in the shielding layer that causes a large impedance abrupt change. If there are minor faults such as individual broken braided wires in the shielding layer, the weak reflected signal characteristics corresponding to the fault are easily drowned out by noise, making it difficult to accurately capture the features related to the shielding layer fault in the reflected signal. This leads to inaccurate fault identification results for shielded cables for digital satellite television and makes it difficult to identify potential minor faults in the cable. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a fault identification system for shielded cables used in digital satellite television. The specific technical solution adopted is as follows: This application proposes a fault identification system for shielded cables used in digital satellite television, the system comprising: The data acquisition module is used to acquire the reflected signal and cable impedance signal of two sets of test signals of the shielded cable during the test period; the two sets of test signals include high-frequency test signals and low-frequency test signals. The data preprocessing module is used to denoise the reflected signals and cable impedance signals acquired by the data acquisition module; The data analysis module is used to calculate the first fault index of the two sets of test signals based on the proximity of the signal interval between the two sets of test signals and their respective received reflected signals, as well as the voltage fluctuation of the respective received reflected signals; and to calculate the second fault index of the two sets of test signals based on the similarity between the cable impedance signals of the two sets of test signals and the average peak difference between the cable impedance signals; and to obtain the fault evaluation index of the shielded cable for digital satellite television by weighted fusion of the first fault index and the second fault index. The fault identification module is used to compare the fault evaluation indicators output by the data analysis module with the preset fault thresholds in order to identify faults in the shielded cable for digital satellite television.

[0005] Preferably, the specific method for calculating the first fault indicator in the data analysis module is as follows: Calculate the signal interval proximity between the low-frequency test signal and its received reflected signal, as well as the voltage fluctuation of the received reflected signal, and calculate the low-frequency fault index of the low-frequency test signal. Calculate the signal interval proximity between the high-frequency test signal and its received reflected signal, as well as the voltage fluctuation of the received reflected signal, and calculate the high-frequency fault index of the high-frequency test signal. The first fault index is obtained by weighted fusion of low-frequency fault index and high-frequency fault index.

[0006] Preferably, the signal interval proximity is the proportion of the time interval between the test signal and the received reflected signal to the preset maximum effective reflection propagation time reference; the voltage fluctuation degree is the coefficient of variation of the voltage amplitude of all sampling points of the reflected signal within the sampling time.

[0007] Preferably, the specific method for calculating the second fault indicator in the data analysis module is as follows: Calculate the similarity between two sets of cable impedance signals under low-frequency test signals and high-frequency test signals, and denot it as the impedance similarity between the two sets of test signals; Calculate the average peak difference between the two sets of cable impedance signals under low-frequency test signals and high-frequency test signals, and denot it as the impedance deviation between the two sets of test signals; The second fault index is calculated based on the impedance similarity and the impedance deviation; wherein the second fault index is negatively correlated with the impedance similarity and positively correlated with the impedance deviation.

[0008] Preferably, the method for calculating the impedance deviation is as follows: The peak points of the two sets of cable impedance signals are obtained using a peak detection algorithm; the mean amplitude of the peak points of the two sets of cable impedance signals is calculated respectively. Calculate the absolute difference between the mean values ​​of the peak amplitudes of the two sets of cable impedance signals; The ratio of the absolute difference to the mean of the peak amplitude of the cable impedance signal of the low-frequency test signal is taken as the impedance deviation.

[0009] Preferably, before weighted fusion of the first fault index and the second fault index, the method further includes normalizing the first fault index and the second fault index respectively.

[0010] Preferably, the fault identification module is specifically used for: Determine whether the fault evaluation index is greater than or equal to the fault threshold determined in the pre-test; If yes, the shielded cable is determined to be faulty; if no, the shielded cable is determined to be fault-free.

[0011] Preferably, the fault threshold determined by the pre-test is determined by the maximum value of the fault evaluation index calculated from multiple qualified cables of the corresponding model of the shielded cable currently being tested.

[0012] Preferably, the data acquisition module is specifically used for: Two sets of test signals are transmitted at one end of the shielded cable using a signal generator, including a low-frequency test signal and a high-frequency test signal, wherein the low-frequency test signal has a frequency of 1MHz and the high-frequency test signal has a frequency of 500MHz. The reflected signals of the two sets of test signals from the shielded cable during the test period are received by the signal receiver. The cable impedance signals of the two sets of test signals were measured in real time using a time-domain reflectometer.

[0013] Preferably, the data preprocessing module is specifically used to: input the reflection signal and cable impedance signal of the two sets of test signals acquired by the data acquisition module into the Gaussian filtering denoising algorithm respectively; and obtain the denoised reflection signal and cable impedance signal.

[0014] This application has at least the following beneficial effects: 1. To address the problem that current time-domain reflection technology struggles to accurately identify minor faults in shielded cables, this application constructs a multi-dimensional fault evaluation system through a data analysis module. A first fault index is calculated by combining signal interval proximity and voltage fluctuation, characterizing the time-domain reflection features of the fault. A second fault index is calculated by combining impedance similarity and impedance deviation, characterizing the frequency-domain impedance difference features of the fault. This time-domain + frequency-domain feature fusion mechanism significantly enhances the saliency of minor fault characteristics, effectively distinguishes fault types and locations, and solves the problems of insufficient sensitivity or poor specificity of single indicators.

[0015] 2. To address the shortcomings of existing technologies that lack quantitative assessment of fault severity, which makes it impossible to accurately determine whether a cable is qualified or not, this application utilizes a fault identification module to dynamically compare the calculated fault evaluation index with a fault threshold determined in advance through testing. This step enables quantitative identification of cable fault status, which not only eliminates the subjectivity of manual experience-based judgment but also significantly improves the accuracy and efficiency of fault identification, ensuring the reliability of digital satellite television signal transmission. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a fault identification system for a shielded cable used in digital satellite television, provided as an embodiment of this application. Detailed Implementation

[0017] Please see Figure 1 The diagram illustrates a flowchart of a fault identification system for shielded cables used in digital satellite television, according to an embodiment of this application. The system includes: a data acquisition module, a data preprocessing module, a data analysis module, and a fault identification module, wherein: The data acquisition module is used to acquire the reflected signal and cable impedance signal of two sets of test signals of the shielded cable during the test period; the two sets of test signals include high-frequency test signals and low-frequency test signals. The data preprocessing module is used to denoise the reflected signals and cable impedance signals acquired by the data acquisition module; The data analysis module is used to calculate the first fault index of the two sets of test signals based on the proximity of the signal interval between the two sets of test signals and their respective received reflected signals, as well as the voltage fluctuation of the respective received reflected signals; and to calculate the second fault index of the two sets of test signals based on the similarity between the cable impedance signals of the two sets of test signals and the average peak difference between the cable impedance signals; and to obtain the fault evaluation index of the shielded cable for digital satellite television by weighted fusion of the first fault index and the second fault index. The fault identification module is used to compare the fault evaluation indicators output by the data analysis module with the preset fault thresholds in order to identify faults in the shielded cable for digital satellite television.

[0018] Specifically, each module of the system is described in detail below: The data acquisition module is used to acquire the reflected signals and cable impedance signals of two sets of test signals of the shielded cable during the test period.

[0019] The data acquisition module mainly includes a signal generator, a signal receiver, and a time-domain reflectometer. When identifying faults in shielded cables used for digital satellite television, the signal generator transmits a low-frequency test signal with a peak value of 2V and a high-frequency test signal at one end of the shielded cable. Both test signals are pulse signals, with the low-frequency test signal having a frequency of 1MHz and the high-frequency test signal having a frequency of 500MHz. The signal receiver collects the reflected signal of the shielded cable during the test period, with each sampling duration of 500ns and a sampling frequency of 2.5GHz. The transmission time of each test signal and the time when the reflected signal is first received are recorded. The time-domain reflectometer is used to measure the cable impedance signal of the two sets of test signals in real time.

[0020] The data preprocessing module is used to denoise the reflected signals and cable impedance signals acquired by the data acquisition module.

[0021] To avoid electromagnetic interference from other electrical equipment around the digital satellite television or the testing area, the reflected signals and cable impedance signals of the two sets of test signals are input into the data preprocessing module. A Gaussian filtering denoising algorithm is used to obtain the denoised reflected signals and cable impedance signals. The denoising algorithm can be chosen by the implementer without special restrictions. Then, the denoised reflected signals and cable impedance signals are input into the data analysis module for further analysis and processing. The Gaussian denoising algorithm is a well-known technique, and its specific implementation will not be described in detail.

[0022] The data analysis module is used to calculate the first fault index of the two sets of test signals based on the proximity of the signal interval between the two sets of test signals and their respective received reflected signals, as well as the voltage fluctuation of the respective received reflected signals; and to calculate the second fault index of the two sets of test signals based on the similarity between the cable impedance signals of the two sets of test signals and the average peak difference between the cable impedance signals; and to obtain the fault evaluation index of the shielded cable for digital satellite television by weighted fusion of the first fault index and the second fault index.

[0023] S1: Calculate the first fault index of the two sets of test signals based on the proximity of the signal interval between the two sets of test signals and their respective received reflected signals, as well as the voltage fluctuation of their respective received reflected signals.

[0024] Further analysis reveals that if the shielding layer of a shielded cable fails, the impedance at the fault point will change abruptly. The transmitted test signal will generate a reflected signal before reaching the cable terminal, meaning that the time interval between receiving the reflected signal and transmitting the test signal is relatively short.

[0025] In order to analyze the proximity characteristics between the time when the received reflected signal begins and the time when the transmitted signal is transmitted, this application calculates the signal interval proximity between the high-frequency and low-frequency test signals and their respective received reflected signals.

[0026] In a preferred embodiment, the signal interval proximity is the proportion of the time interval between the test signal and the received reflected signal to a preset maximum effective reflection propagation time reference.

[0027] This embodiment takes the transmission of a low-frequency test signal as an example to obtain the time of transmission of the low-frequency test signal. and the time to start receiving reflected signals Calculate the signal interval proximity of the low-frequency test signal. ,in This represents the preset maximum effective reflection propagation time reference standard. Its value in this embodiment is equivalent to the single sampling time limit of the signal receiver, for example, set to a fixed 500ns. Signal interval proximity reflects the proximity of the time interval between the received reflected signal and the transmitted test signal. A smaller signal interval proximity indicates that the fault point in the cable is closer to the test signal transmitter; conversely, a larger signal interval proximity indicates that the fault point in the cable is farther from the test signal transmitter or that there is no fault point (the test signal only generates a reflected signal upon reaching the terminal).

[0028] However, signal spacing proximity cannot be used as the sole indicator for fault assessment. For example, a shielded cable that is too short may result in a small signal spacing proximity, leading to a false diagnosis of a cable fault; conversely, a fault point near the terminal may have a large spacing proximity, making accurate fault identification impossible. Furthermore, the impedance at the cable fault point typically undergoes a sudden change, causing unstable voltage fluctuations in the reflected signal as it passes through the fault point.

[0029] In order to analyze the voltage amplitude fluctuation characteristics of the reflected signal, this application calculates the voltage fluctuation degree of the reflected signal received by the high-frequency and low-frequency test signals respectively.

[0030] As a preferred implementation, the coefficient of variation of the voltage amplitude at all sampling points of the reflected signal within the sampling time is calculated and denoted as the voltage fluctuation degree, which reflects the degree of fluctuation of the voltage amplitude in the reflected signal.

[0031] The smaller the proximity of the signal intervals and the greater the voltage fluctuation, the more significant the impedance abrupt change point is in the shielded cable, which is the fault point.

[0032] Based on the above analysis, the low-frequency fault index of the low-frequency test signal of the shielded cable for digital satellite television is calculated to characterize the significance of fault characteristics of the shielded cable for digital satellite television under low-frequency test signal.

[0033] In this embodiment, the calculation method for the low-frequency fault index of the low-frequency test signal is as follows: In the formula, This indicates the proximity of signal intervals in low-frequency test signals. This indicates the degree of voltage fluctuation in the low-frequency test signal. This represents a preset error parameter to avoid the denominator being 0, which would render the calculation meaningless. The value range is 0.01-0.1, and in this embodiment, the value is 0.05.

[0034] In existing data analysis methods, multiple fault characteristics of a target are typically analyzed to construct a fault index, which is then used to comprehensively assess whether a fault exists. In this scheme, to evaluate the fault characteristics of shielded cables used for digital satellite television, signal spacing proximity and voltage fluctuation are calculated separately. The smaller the signal spacing proximity and the greater the voltage fluctuation, the more significant the impedance abrupt change point in the shielded cable is. This impedance abrupt change point is the fault point. Therefore, a low-frequency fault index is constructed by linearly calculating these two factors to characterize the significant characteristics of faults in shielded cables used for digital satellite television.

[0035] Using the same calculation principles as those used to calculate low-frequency fault indicators for low-frequency test signals, high-frequency fault indicators for high-frequency test signals are calculated. This is used to characterize the severity of fault characteristics of shielded cables for digital satellite television under high-frequency test signals; the specific calculation process will not be elaborated here.

[0036] Then, the low-frequency fault index and the high-frequency fault index are weighted and fused to obtain the first fault index of the two sets of test signals.

[0037] In this embodiment, when... and The weighting coefficients for weighted fusion are 0.5, and the first fault index A is obtained by weighted summation, which is used to characterize the significance of fault characteristics of shielded cables for digital satellite television under different frequency test signals.

[0038] S2: Calculate the second fault index of the two sets of test signals based on the similarity between the cable impedance signals of the two sets of test signals and the average peak difference between the cable impedance signals.

[0039] Although the first fault indicator can identify cable faults to some extent, when the fault is a minor fault (such as the impedance change caused by the breakage of 2-5 braided wires in the shielding layer or the local cracking of the aluminum foil is not obvious) and it is far from the test signal transmitter, the voltage amplitude fluctuation is not obvious, resulting in a large signal interval proximity and a small voltage fluctuation. Consequently, the first fault indicator is small, making it difficult to accurately identify such cable faults. Therefore, it is necessary to enhance the salience of minor fault characteristics.

[0040] The shielding layer of a shielded cable can be considered as a distributed parameter network consisting of distributed resistance, inductance, and capacitance. When a minor fault occurs in the shielding layer (such as a local braid break), the conductive continuity at that point is disrupted, leading to an increase in local contact resistance. Under high-frequency test signals, the equivalent impedance change caused by this local impedance anomaly is significantly greater than that under low-frequency test signals. In other words, high-frequency signals are more sensitive to this type of impedance anomaly, resulting in a significant increase in cable impedance measured at high frequencies compared to low frequencies; while the impedance change is not significant under low-frequency test signals. If the shielded cable is fault-free, its distributed parameter network remains uniform, and the cable impedance signals measured under high and low frequency test signals are essentially the same. Distributed parameter networks and high / low frequency impedance analysis are well-known technologies in the field, and their specific implementation will not be elaborated upon. To enhance the significance of minor fault characteristics, this application calculates the similarity between two sets of cable impedance signals under low-frequency and high-frequency test signals, denoted as the impedance similarity between the two sets of test signals.

[0041] Specifically, two sets of cable impedance signals measured under low-frequency and high-frequency test signals are acquired, and the similarity between the two sets of cable impedance signals is calculated. The similarity can be measured by cosine similarity or Pearson correlation coefficient, without special restrictions. This embodiment selects Pearson correlation coefficient. Since Pearson correlation coefficient may be negative, it is used... The method linearly maps the Pearson correlation coefficient to the 0-1 range, where p represents the Pearson correlation coefficient. The linearly mapped Pearson correlation coefficient is then denoted as the impedance similarity between the two sets of test signals. This reflects the similarity of cable impedance signals under different frequency test signals. The greater the impedance similarity, the higher the similarity of cable impedance signals under different frequency test signals, and the less likely the shielded cable is to have a fault.

[0042] To quantify the degree of cable impedance deviation caused by the fault point, this application calculates the average peak difference between two sets of cable impedance signals under low-frequency test signals and high-frequency test signals, which is denoted as the impedance deviation between the two sets of test signals.

[0043] Specifically, peak points of the two sets of cable impedance signals are obtained using a peak detection algorithm, denoted as impedance abrupt change points. The peak detection algorithm can be AMPD, MK, etc., without special restrictions; this embodiment selects the MK peak detection algorithm. Next, the average amplitude of the impedance abrupt change points of the two sets of cable impedance signals is calculated and denoted as... , Next, the impedance deviation between the two sets of test signals is calculated. ,in, , These represent the mean amplitude of the impedance abrupt change point and the impedance deviation, respectively, of the cable impedance signals measured under low-frequency and high-frequency test signals. This can reflect the degree of difference between impedance abrupt change points in cable impedance signals under different frequency test signals. A larger impedance deviation indicates a greater difference in impedance abrupt change points under different frequency test signals, meaning that minor fault characteristics become more significant under different frequency test signals. The peak detection algorithm is a well-known technique, and its specific implementation will not be elaborated upon.

[0044] It is worth noting that during the peak detection algorithm analysis, if there is a case where the peak set is empty when obtaining the peaks of the two sets of cable impedance signals, then directly set... .

[0045] It should be noted that the smaller the impedance similarity and the greater the impedance deviation, the greater the difference in cable impedance signal under different frequency test signals. By enhancing the expression of slight impedance characteristics, the significance of slight fault characteristics can be enhanced.

[0046] Based on the above analysis, this application calculates the second fault index according to the impedance similarity and the impedance deviation; wherein the second fault index is negatively correlated with the impedance similarity and positively correlated with the impedance deviation.

[0047] It is understandable that a positive correlation means that the dependent variable increases as the independent variable increases, and decreases as the independent variable decreases; a negative correlation means that the dependent variable decreases as the independent variable increases, and increases as the independent variable decreases. This is determined by the actual application, and this application does not impose any special restrictions.

[0048] Specifically, in this embodiment, the second fault index of the two sets of test signals The specific calculation method is as follows: .

[0049] In existing data analysis methods, multiple fault characteristics of a target are typically analyzed to construct a fault index, which is then used to comprehensively assess whether a fault exists in the target. In this approach, to evaluate the fault characteristics of shielded cables used for digital satellite television, impedance similarity and impedance deviation are calculated separately. The smaller the impedance similarity and the larger the impedance deviation, the greater the difference in the cable impedance signal under different frequency test signals. By enhancing the expression of minor impedance characteristics, the significance of minor fault characteristics is enhanced. Therefore, a second fault index is constructed using these two calculations to characterize the significant characteristics of faults occurring in shielded cables used for digital satellite television.

[0050] S3: Weighted fusion of the first and second fault indicators to obtain fault evaluation indicators for shielded cables used in digital satellite television.

[0051] Generally, if a shielded cable has a serious fault, the calculated first fault index and second fault index will usually increase significantly. If the shielded cable has a minor fault, the first fault index will be too low, making it difficult to accurately identify the faulty cable. However, the second fault index, by enhancing the representation of fault characteristics, can be combined with the first fault index to accurately identify faults in shielded cables used for digital satellite television.

[0052] Based on the above analysis, this application uses a weighted fusion of the first and second fault indices to obtain a fault evaluation index for shielded cables used in digital satellite television, which is used to assess whether the shielded cable has failed.

[0053] It is worth noting that, before weighted fusion of the first and second fault indices, in order to eliminate the dimensional influence between the first and second fault indices, the first and second fault indices are first normalized separately, and then weighted fusion is performed.

[0054] Specifically, in this embodiment, the fault evaluation index of the shielded cable for digital satellite television is... The specific calculation method is as follows: In the formula, This represents a preset weighting coefficient, with a value range of 0-1, without any special restrictions. In this embodiment, 0.5 is preferred. The normalization method is indicated here, and no specific restriction is placed on the specific normalization method. This embodiment selects the maximum value normalization method. Based on the maximum values ​​of the first and second fault indices calculated from historical faulty cables, the first and second fault indices calculated for the current cable are normalized to their maximum values, respectively. It should be noted that if the first or second fault indices calculated for the current cable are greater than the maximum values ​​calculated from historical faulty cables, then... Win The result is set to 1.

[0055] Based on the prior knowledge of weighted fusion, if two feature indicators analyze the same feature of a target from two different perspectives, a weighted fusion method is usually used to jointly represent the target feature. In this scheme, the first fault indicator mainly analyzes the fault features in the cable's reflected signal, and the second fault indicator mainly analyzes the fault features in the cable's impedance signal. Fault features are extracted from the two types of data respectively, and the two fault indicators are weighted and fused to obtain a fault evaluation index, which is then used to jointly assess whether the shielded cable has a fault.

[0056] The fault identification module is used to compare the fault evaluation indicators output by the data analysis module with the fault thresholds determined by pre-testing in order to identify faults in shielded cables used for digital satellite television.

[0057] The fault evaluation indicators output by the data analysis module The data is input into the fault identification module, which compares the detected shielded cable model with its fault threshold. If... If the fault value is greater than or equal to the fault threshold, the shielded cable is considered to be faulty; otherwise, if... If the value is less than the fault threshold, the shielded cable is considered to be fault-free.

[0058] The fault threshold is determined through pre-testing, specifically by the maximum value of the fault evaluation index calculated from multiple qualified cables of the corresponding model of the shielded cable being tested. The calculation method for the fault evaluation index from multiple qualified cables is the same as that used in the data analysis module described above.

[0059] Specifically, assuming there are N types of shielded cables for digital satellite television, taking the nth type of shielded cable as an example, select M qualified nth type shielded cables for testing, calculate M sets of fault evaluation indicators according to the above method, and record the maximum value of the M sets of fault evaluation indicators as the fault threshold. The value of M can be set by the implementer without special restrictions. In this embodiment, it is set to 20.

[0060] The fault thresholds for all types of shielded cables are then stored in the fault identification module. Before identifying cable faults, the fault identification system selects the corresponding fault threshold based on the model of the shielded cable being detected. Ultimately, this achieves accurate fault identification for shielded cables used in digital satellite television.

[0061] It is worth noting that, for all the calculations mentioned in this application, if the denominator is 0, a small positive number needs to be added to the denominator. (like (The implementer can also choose a value according to the dimensions) to prevent the denominator from being zero.

Claims

1. A fault identification system for shielded cables used in digital satellite television, characterized in that, The system includes: The data acquisition module is used to acquire the reflected signal and cable impedance signal of two sets of test signals of the shielded cable during the test period; the two sets of test signals include high-frequency test signals and low-frequency test signals. The data preprocessing module is used to denoise the reflected signals and cable impedance signals acquired by the data acquisition module; The data analysis module is used to calculate the first fault index of the two sets of test signals based on the proximity of the signal interval between the two sets of test signals and their respective received reflected signals, as well as the voltage fluctuation of the respective received reflected signals; and to calculate the second fault index of the two sets of test signals based on the similarity between the cable impedance signals of the two sets of test signals and the average peak difference between the cable impedance signals; and to obtain the fault evaluation index of the shielded cable for digital satellite television by weighted fusion of the first fault index and the second fault index. The fault identification module is used to compare the fault evaluation indicators output by the data analysis module with the preset fault thresholds in order to identify faults in the shielded cable for digital satellite television.

2. The fault identification system for shielded cables dedicated to digital satellite television as described in claim 1, characterized in that, The specific method for calculating the first fault indicator in the data analysis module is as follows: Calculate the signal interval proximity between the low-frequency test signal and its received reflected signal, as well as the voltage fluctuation of the received reflected signal, and calculate the low-frequency fault index of the low-frequency test signal. Calculate the signal interval proximity between the high-frequency test signal and its received reflected signal, as well as the voltage fluctuation of the received reflected signal, and calculate the high-frequency fault index of the high-frequency test signal. The first fault index is obtained by weighted fusion of low-frequency fault index and high-frequency fault index.

3. The fault identification system for shielded cables dedicated to digital satellite television as described in claim 2, characterized in that, The signal interval proximity is the proportion of the time interval between the test signal and the received reflected signal to the preset maximum effective reflection propagation time reference; the voltage fluctuation degree is the coefficient of variation of the voltage amplitude of all sampling points of the reflected signal within the sampling time.

4. The fault identification system for shielded cables dedicated to digital satellite television as described in claim 1, characterized in that, The specific method for calculating the second fault indicator in the data analysis module is as follows: Calculate the similarity between two sets of cable impedance signals under low-frequency test signals and high-frequency test signals, and denot it as the impedance similarity between the two sets of test signals; Calculate the average peak difference between the two sets of cable impedance signals under low-frequency test signals and high-frequency test signals, and denot it as the impedance deviation between the two sets of test signals; The second fault index is calculated based on the impedance similarity and the impedance deviation; wherein the second fault index is negatively correlated with the impedance similarity and positively correlated with the impedance deviation.

5. A fault identification system for shielded cables dedicated to digital satellite television as described in claim 4, characterized in that, The method for calculating the impedance deviation is as follows: The peak points of the two sets of cable impedance signals are obtained using a peak detection algorithm; the mean amplitude of the peak points of the two sets of cable impedance signals is calculated respectively. Calculate the absolute difference between the mean values ​​of the peak amplitudes of the two sets of cable impedance signals; The ratio of the absolute difference to the mean of the peak amplitude of the cable impedance signal of the low-frequency test signal is taken as the impedance deviation.

6. A fault identification system for shielded cables dedicated to digital satellite television as described in claim 1, characterized in that, Before weighted fusion of the first and second fault indicators, normalization processing is performed on the first and second fault indicators respectively.

7. A fault identification system for shielded cables dedicated to digital satellite television as described in claim 1, characterized in that, The fault identification module is specifically used for: Determine whether the fault evaluation index is greater than or equal to the fault threshold determined in the pre-test; If yes, the shielded cable is determined to be faulty; if no, the shielded cable is determined to be fault-free.

8. A fault identification system for shielded cables used in digital satellite television as described in claim 7, characterized in that, The fault threshold determined by the pre-test is determined by the maximum value of the fault evaluation index calculated from multiple qualified cables of the corresponding model of the shielded cable currently being tested.

9. A fault identification system for shielded cables used in digital satellite television as described in any one of claims 1-8, characterized in that, The data acquisition module is specifically used for: Two sets of test signals are transmitted at one end of the shielded cable using a signal generator, including a low-frequency test signal and a high-frequency test signal, wherein the low-frequency test signal has a frequency of 1MHz and the high-frequency test signal has a frequency of 500MHz. The reflected signals of the two sets of test signals from the shielded cable during the test period are received by the signal receiver. The cable impedance signals of the two sets of test signals were measured in real time using a time-domain reflectometer.

10. A fault identification system for shielded cables dedicated to digital satellite television as described in claim 9, characterized in that, The data preprocessing module is specifically used to: input the reflection signal and cable impedance signal of the two sets of test signals acquired by the data acquisition module into the Gaussian filtering denoising algorithm respectively; and obtain the denoised reflection signal and cable impedance signal.

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