Method for diagnosing fault of medium wave station signal transmission based on distributed architecture

Abstract

The application discloses a medium wave station signal transmission fault rapid diagnosis method based on a distributed architecture, comprising the following steps: collecting medium wave station signal data, tracing and obtaining fault data, checking possible fault reasons of the fault data, establishing a sending and feedback model, determining a fault deduction model between the fault reasons and signal fault areas, determining a signal loss value at a monitoring time node in the fault deduction model, screening signal fault areas in a medium wave station signal emission range, determining fault reasons and fault coordinates of the fault data and outputting, through a preposed multi-dimensional signal feature screening mechanism, the application qualitatively distinguishes fault reasons in advance, greatly reduces fault omission rate and false alarm rate, first locks fault causes through data feature screening, and then carries out position checking in a targeted manner, greatly shortens fault tracing and disposal time, realizes rapid response, rapid positioning and rapid repair of faults, and the application has the characteristics of high fault diagnosis precision and strong rapid diagnosis capability.

Description

Technical Field

[0001] This invention relates to the field of medium-wave station signal fault diagnosis technology, specifically a rapid diagnosis method for medium-wave station signal transmission faults based on a distributed architecture. Background Technology

[0002] Medium-wave broadcasting is a core infrastructure for national public cultural dissemination, emergency early warning, and nationwide coverage. Relying primarily on ground waves and supplemented by sky waves at night, it achieves large-scale signal transmission. It possesses unique advantages such as strong diffraction capabilities, low reception threshold, and the ability to be received even in power outage environments. The distributed architecture of medium-wave stations sets up monitoring modules at various key nodes of the medium-wave station signal transmission network to collect signal transmission status parameters in real time. These monitoring data are distributed and stored at various nodes and processed in parallel through a distributed computing framework. Medium-wave and ground wave signals rely on the ground medium to complete long-distance transmission and are widely used for county-level broadcasting, disaster emergency broadcasting, and information coverage in remote areas, holding an irreplaceable position in the broadcasting system.

[0003] In existing technologies, when a signal transmission fault is detected, the diagnostic system analyzes the fault parameters to first confirm the fault location and then investigate the cause. However, the mutual interference between the medium-wave station environment and the transmitter makes it difficult to effectively avoid the coupling interference of the inherent environmental characteristics of the fault area on signal attenuation. Especially when there is a lot of external environmental interference, it is easy to misjudge the fault location. Relying solely on single-path monitoring data to roughly estimate the fault location and cause range is too wide, resulting in a large deviation in fault calculation. On the other hand, investigating the cause of the fault first will lead to too many fault causes, and traversing all causes is a huge workload, significantly reducing the efficiency of fault diagnosis. Therefore, it is necessary to design a rapid fault diagnosis method for medium-wave station signal transmission based on a distributed architecture with high fault diagnosis accuracy and strong rapid diagnosis capability. Summary of the Invention

[0004] The purpose of this invention is to provide a rapid diagnostic method for signal transmission faults in medium-wave radio stations based on a distributed architecture, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a rapid diagnosis method for signal transmission faults in a medium-wave radio station based on a distributed architecture, comprising:

[0006] Establish a signal monitoring range, collect medium wave station signal data within the signal monitoring range, trace the source of the medium wave station signal data, and obtain fault data therein;

[0007] The fault data is analyzed and filtered to investigate possible causes of the fault.

[0008] Establish a transmission and feedback model for medium wave station signal data between medium wave stations within the signal monitoring range, determine a fault inference model between the fault cause and the signal fault area, determine the signal loss value at the monitoring time node within the fault inference model, and screen the signal fault area within the medium wave station signal transmission range based on possible fault causes.

[0009] The faulty signal area is investigated to determine the cause and coordinates of the fault data, and the diagnostic results are output.

[0010] According to the above technical solution, at least two medium-wave stations and one medium-wave signal receiving end capable of receiving signal data transmitted by all medium-wave stations within the signal monitoring range are provided.

[0011] According to the above technical solution, the step of tracing the source of the medium-wave station signal data and obtaining fault data therein includes:

[0012] A radio frequency (RF) signal transmitter is configured at the medium-wave signal receiver. The RF signal transmitter also contains an Internet of Things (IoT) communication device. After receiving the medium-wave signal, the medium-wave signal receiver locates the first medium-wave station that emitted the medium-wave signal. The RF signal transmitter then transmits the received signal data from the medium-wave signal to the backend server via the IoT communication device. The backend server detects the signal parameters of the signal data and marks the signal data corresponding to abnormal signal parameters as fault data. The fault data is signal data collected at the transmitting end of the first medium-wave station before the transmitted signal enters the external propagation environment and before the signal propagation process is superimposed with external environmental interference. It is used to characterize the original signal state when the medium-wave station emits the corresponding signal.

[0013] According to the above technical solution, the step of analyzing and filtering the fault data to investigate possible causes of the fault data includes:

[0014] Retrieve fixed environmental features within the propagation range of medium-wave station signals from the database, classify the fault causes corresponding to the signal parameters in each fixed environmental feature threshold range, input the corresponding signal parameters from the fault data, and output a set of fault causes for each fixed environmental feature.

[0015] Based on the corresponding signal parameters in the fault data, the causes of the fault data are weighted and classified, and possible causes of the fault are screened within the set of causes of faults for each fixed environmental feature.

[0016] According to the above technical solution, establishing a transmission and feedback model for medium-wave station signal data between medium-wave stations within the signal monitoring range includes:

[0017] A second intermediate wave station is set up. The setting criteria for the second intermediate wave station are as follows: the intermediate wave station closest to the first intermediate wave station within the receiving range of the signal data receiving end of the radio frequency signal transmitter is set up as the second intermediate wave station. A test data with the same original signal waveform, carrier frequency and modulation parameters as the fault data is sent to the second intermediate wave station at a first time point when other signal data sent by the second intermediate wave station will not be subject to superposition interference. The second intermediate wave station receives the test data and sends the test data to the first intermediate wave station at a second time point when other signal data sent by the first intermediate wave station will not be subject to superposition interference.

[0018] The third medium wave station is set up according to the setting standard of the second medium wave station, and the test data is transmitted synchronously. The test data received by all medium wave stations is input into the background server. The test data transmitted by the first medium wave station, the second medium wave station and the third medium wave station have different transmitter inherent parameters, spatial propagation parameters and receiver observation parameters.

[0019] According to the above technical solution, the fault deduction model for determining the relationship between the fault cause and the signal fault area includes:

[0020] The fault inference model is set with a first inference factor and a second inference factor. The first inference factor marks the cause of the fault by detecting the generation mechanism of signal loss, and the second inference factor marks the fault area by detecting the spatial distribution pattern of signal loss. Different spatial distribution patterns of signal loss are classified according to different generation mechanisms of signal loss.

[0021] According to the above technical solution, the step of determining the signal loss value at the monitoring time node within the fault simulation model and screening the signal fault area within the medium-wave station signal transmission range based on possible fault causes includes:

[0022] The test data received by the first and second medium-wave stations are input into the fault simulation model to locate the area where the signal loss reaches the threshold each time during the data transmission process at the transmitting and feedback ends. The model is then used to simulate the fixed environmental characteristics of the area to obtain the estimated transmission time when the signal loss reaches the threshold each time. Each estimated transmission time is input into the direction from the first medium-wave station to the signal data receiving end to obtain the possible fault area. The monitoring time nodes include daytime and nighttime, and the signal loss thresholds are different during the day and night.

[0023] According to the above technical solution, the step of investigating the signal fault area, determining the fault cause and fault coordinates of the fault data, and outputting diagnostic results includes:

[0024] Based on the possible fault regions, the ratio of the actual field strength to the theoretical field strength of each fault region is calculated to obtain the abnormal loss value.

[0025] Substitute the abnormal loss value into the ground wave attenuation model stored in the database, and compare it with the ground wave attenuation amount of each distance interval in the ground wave attenuation model.

[0026] By solving multiple sets of distance and orientation information simultaneously, the fixed environmental features corresponding to the fault area between the first medium wave station, the second medium wave station, and the third medium wave station are determined. The corresponding fixed environmental features located between the first medium wave station and the medium wave signal receiving end are used to infer the actual distance from the first medium wave station to the fault coordinates, and finally the geographical coordinates of the signal data fault are output.

[0027] According to the above technical solution, the rapid fault diagnosis system for medium-wave station signal transmission based on a distributed architecture includes:

[0028] The data acquisition module is used to establish a signal monitoring range, acquire medium wave station signal data within the signal monitoring range, trace the source of the medium wave station signal data, and obtain fault data therein.

[0029] The fault cause analysis module is used to analyze and filter the fault data and investigate possible fault causes.

[0030] The fault location investigation module is used to establish a transmission and feedback model of medium wave station signal data between medium wave stations within the signal monitoring range, determine the fault inference model between the fault cause and the signal fault area, determine the signal loss value at the monitoring time node within the fault inference model, and screen the signal fault area within the medium wave station signal transmission range based on possible fault causes.

[0031] The fault diagnosis mode is used to investigate the fault area of ​​the signal, determine the cause and coordinates of the fault data, and output the diagnosis results.

[0032] According to the above technical solution, a storage medium stores a computer program thereon, which, when executed by a processor, implements the method for rapid diagnosis of signal transmission faults of a medium-wave station based on a distributed architecture as described in the above technical solution.

[0033] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0034] 1. By using a pre-emptive multi-dimensional signal feature screening mechanism, combined with core data such as carrier power, harmonic ratio, signal-to-noise ratio, and multi-point field strength difference, the cause of the fault can be qualitatively distinguished in advance. This can effectively identify hidden and periodic faults such as multipath crosstalk at night and weak regional signal coverage, significantly reducing the fault detection rate and false alarm rate, and providing a reliable basis for subsequent accurate investigation.

[0035] 2. Employing a progressive diagnostic logic of "first determine the cause, then locate the source," the system first identifies the cause of the fault through data feature filtering, and then conducts targeted location investigations. This eliminates the redundant process of indiscriminate full-link inspections. Relying on spatial difference data from distributed monitoring nodes, it conducts targeted investigations of the ground network, coverage area, and interference source location, significantly narrowing the scope of fault investigation. This greatly shortens the time for fault tracing and handling, enabling rapid fault response, rapid location, and rapid repair.

[0036] 3. Based on a distributed multi-node network monitoring architecture, the system cross-validates field strength and signal quality data collected synchronously from multiple points. Combined with reverse deduction using a medium-wave ground wave propagation attenuation model, it can accurately locate the specific geographical location of the fault, the fault radiation range, and the degree of signal attenuation. Parallel processing is performed through a distributed computing framework, and fault diagnosis algorithms are used to conduct in-depth analysis of the collected data. The algorithm first compares the parameter thresholds under normal conditions to preliminarily determine whether a fault exists. If an anomaly is found, a correlation analysis algorithm is further used to establish a correlation model between multiple medium-wave stations to find changes in parameters of other nodes related to the fault, thereby determining the specific location and cause of the fault. This greatly shortens the fault diagnosis time and improves the reliability and stability of medium-wave station signal transmission. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0038] Figure 1 This is a flowchart of a method for rapid diagnosis of signal transmission faults in a medium-wave station based on a distributed architecture, provided by an embodiment of the present invention.

[0039] Figure 2 This is a reference diagram illustrating the fixed environment characteristics and weighted classification of a rapid diagnosis method for medium-wave station signal transmission faults based on a distributed architecture, provided by an embodiment of the present invention.

[0040] Figure 3 This is a reference diagram illustrating the solution of the fault geographic coordinate map by combining the abnormal loss value and the ground wave attenuation model in the rapid fault diagnosis method for medium-wave station signal transmission based on a distributed architecture provided by an embodiment of the present invention.

[0041] Figure 4This is a block diagram of a rapid diagnostic system for signal transmission faults in a medium-wave radio station based on a distributed architecture, provided in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 The flowchart illustrates a rapid fault diagnosis method for medium-wave station signal transmission based on a distributed architecture, as provided in an embodiment of the present invention. Figure 1 It can be seen that the rapid diagnosis method for signal transmission faults of medium-wave radio stations based on a distributed architecture includes:

[0044] Step S1: Establish a signal monitoring range, collect medium wave station signal data within the signal monitoring range, trace the source of the medium wave station signal data, and obtain fault data therein;

[0045] Step S2: Analyze and filter the fault data to investigate possible causes of the fault data;

[0046] Step S3: Establish a transmission and feedback model for medium wave station signal data between medium wave stations within the signal monitoring range, determine a fault inference model between the fault cause and the signal fault area, determine the signal loss value at the monitoring time node within the fault inference model, and screen the signal fault area within the medium wave station signal transmission range based on possible fault causes.

[0047] Step S4: Investigate the signal fault area, determine the fault cause and fault coordinates of the fault data, and output the diagnostic results.

[0048] The embodiments of this invention, through a pre-emptive multi-dimensional signal feature screening mechanism, can qualitatively distinguish the causes of faults in advance, accurately identify the types of equipment faults, significantly reduce the rate of missed fault detection and false alarms, and provide a reliable basis for subsequent accurate troubleshooting. By first identifying the cause of the fault through data feature screening, and then conducting targeted location investigations, the time for fault tracing and handling is greatly shortened, enabling rapid fault response, rapid location, and rapid repair. Based on a distributed multi-node network monitoring architecture, by cross-validating field strength data and signal quality data collected synchronously from multiple points, and combining this with reverse deduction using a medium-wave ground wave propagation attenuation model, the specific geographical location of the fault, the fault radiation range, and the degree of signal attenuation can be accurately located, greatly shortening the fault diagnosis time and improving the reliability and stability of medium-wave station signal transmission.

[0049] In some preferred embodiments, at least two medium-wave stations and a medium-wave signal receiving end capable of receiving signal data transmitted by all medium-wave stations within the signal monitoring range are provided.

[0050] In some preferred embodiments, tracing the medium-wave station signal data to obtain fault data includes:

[0051] Step S11: Configure an RF signal transmitter at the medium-wave signal receiver. The RF signal transmitter also contains an IoT communication device. After receiving the medium-wave signal, the medium-wave signal receiver locates the first medium-wave station that emitted the medium-wave signal. The RF signal transmitter transmits the received signal data from the medium-wave signal to the backend server via the IoT communication device. The backend server detects the signal parameters of the signal data and marks the signal data corresponding to abnormal signal parameters as fault data. The fault data is the signal data collected at the transmitting end of the first medium-wave station before the signal emitted by the first medium-wave station enters the external propagation environment and before the signal propagation process is superimposed with external environmental interference. It is used to characterize the original signal state when the medium-wave station emits the corresponding signal.

[0052] After the signal data is transmitted to the medium wave signal receiving end, the average received power of the carrier, the instantaneous peak power, the baseband DC offset, and the original waveform data are transmitted to the medium wave station server.

[0053] The backend server performs FFT operations on the waveform data to solve for the second harmonic proportion, the third harmonic proportion, the signal-to-noise ratio, and the ambient noise floor.

[0054] Calculate the power variance within a 15-second sliding window;

[0055] The backend server sequentially compares each parameter with its corresponding normal value range, and includes parameters that are not in the corresponding normal value range in the fault candidate conditions. When the proportion of fault candidate conditions exceeds Q%, and at least two parameters have values ​​that exceed 50% of the normal range threshold, the medium wave signal data is marked as fault data, wherein Q is preferably 0.8.

[0056] The backend server calculates the received power variance and the duration of abnormal parameters based on a preset duration window; the average received power of the carrier is used to assess the overall spatial loss of electromagnetic waves; the instantaneous peak power is used to identify external pulse interference; the baseband DC offset is used to characterize the interference of external electromagnetic fields on the demodulation process of the receiver; the harmonic amplitude ratio is used to monitor the mixing of industrial stray signals; the signal-to-noise ratio and ambient noise floor are used to evaluate the quality of the spatial electromagnetic environment; the received power variance is used to identify signal fluctuations caused by multipath propagation and ionospheric changes; and the duration of abnormalities is used to eliminate instantaneous environmental disturbances.

[0057] Please see Figure 2 In some preferred embodiments, the analysis and screening of the fault data to investigate possible causes of the fault data includes:

[0058] Step S21: Retrieve fixed environmental features within the propagation range of the medium wave station signal in the database, classify the fault causes corresponding to the signal parameters in each fixed environmental feature threshold range, input the corresponding signal parameters in the fault data, and output the set of fault causes for each fixed environmental feature.

[0059] Step S22: Based on the corresponding signal parameters in the fault data, the fault causes of the fault data are weighted and classified. Possible fault causes are screened within the fault cause set of each fixed environmental feature. The main scenario targeted by this invention is fault factors caused by multiple external interference environments. Therefore, the fault identification weight of the internal equipment of the medium wave station is relatively small in this invention, and can be avoided by preprocessing the internal equipment, thus avoiding the problem of excessive workload caused by traversing all causes.

[0060] The signal parameters are weighted as follows: average received carrier power is weighted at 0.95, second harmonic distortion is weighted at 0.90, signal-to-noise ratio is weighted at 0.95, ambient noise floor is weighted at 0.90, and the total weight is [not specified]. .

[0061] Using the threshold deviation algorithm, the value range is [0, 1], and the core parameter is anomaly. , where X i X0 represents the real-time sampled value, and X0 represents the standard threshold or critical value of the threshold range for the signal parameter. The parameter allows a maximum fluctuation threshold when Time to take The maximum value for severe anomalies is 1.

[0062] Core Parameter Comprehensive Anomaly Score .

[0063] Final Fault Confidence Score ,in, The comprehensive fit degree is [0, 1] for each fixed environmental feature, and the value of S is [0, 100].

[0064] Standard thresholds for retrieving signal parameters under composite environments: average carrier received power [42, 48] dBm, second harmonic ratio ≤3%, signal-to-noise ratio ≥40 dB, ambient noise floor ≤15 dB;

[0065] The measured average received power of the carrier is 45dBm, which is within the standard threshold range of [42, 48]dBm, so the parameters are normal, D1=0; the measured second harmonic ratio is 4.2%, which is higher than the standard threshold of 3%, so it is considered abnormal, D2=0.45; the measured signal-to-noise ratio is 24dB, which is lower than the standard threshold of 40dB, so the signal quality is degraded, so it is considered abnormal, D3=1; the measured ambient noise floor is 26dB, which exceeds the standard threshold of 15dB, so the environmental interference exceeds the standard, so it is considered abnormal, D4=1;

[0066] Set the overall environment adaptability S e Substituting 0.88 into the simplified weighted formula, the core parameter comprehensive score is calculated as: S p =(0.95×0+0.90×0.45+0.95×1+0.90×1)÷3.7=(0+0.405+0.95+0.9)÷3.7=2.255÷3.7≈0.61, the overall signal parameters are abnormal.

[0067] Set the adaptability S for each external environment. e The specific values ​​are used to calculate the final score for each external environmental fault: hilly terrain blocking signal attenuation (53.68 points), long-distance atmospheric propagation link loss (45.75 points), suburban industrial electromagnetic interference (42.09 points), urban power grid harmonic crosstalk (38.43 points), high-density vegetation blocking attenuation (35.38 points), and road traffic dynamic clutter interference (25.62 points).

[0068] The preset effective fault threshold is 40 points. The system filters out dynamic noise interference from road traffic, attenuation due to high-density vegetation, and harmonic crosstalk from urban power grids. The possible causes of faults are identified as signal attenuation due to hilly terrain, loss of long-distance atmospheric propagation links, and electromagnetic interference from suburban industrial areas.

[0069] In some preferred embodiments, establishing a transmission and feedback model for medium-wave station signal data between medium-wave stations within the signal monitoring range includes:

[0070] Step S31: Set up a second intermediate wave station. The setting criteria for the second intermediate wave station are as follows: set up another intermediate wave station closest to the first intermediate wave station within the receiving range of the signal data receiving end of the radio frequency signal transmitter as the second intermediate wave station. Send a test data with the same original signal waveform, carrier frequency and modulation parameters as the fault data to the second intermediate wave station at a first time point when other signal data sent by the second intermediate wave station will not be subject to superposition interference. The second intermediate wave station receives the test data and sends the test data to the first intermediate wave station at a second time point when other signal data sent by the first intermediate wave station will not be subject to superposition interference.

[0071] Step S32: Set up the third medium wave station according to the setting standard of the second medium wave station, synchronously transmit the test data, and input the test data received by all medium wave stations into the background server. The test data transmitted by the first medium wave station, the second medium wave station and the third medium wave station have different transmitter inherent parameters, spatial propagation parameters and receiver observation parameters.

[0072] The process by which medium-wave station B sends the test data to medium-wave station A is as follows:

[0073] The two medium wave stations synchronize the second pulse, dividing one second into several time slots, including the broadcast time slot of medium wave station A, the transmission time slot of medium wave station B, and the guard interval time slot;

[0074] Set a period T cycle =1s;

[0075] Medium wave station A transmits normally and continuously from 0 to 0.7 seconds, while medium wave station B transmits continuously from 0.7 to 0.7 seconds. g The test data can be sent within 1 second; otherwise, the medium wave station B remains silent.

[0076] Inter-station propagation delay Where L is the distance between the two medium-wave stations A and B, and c is the speed of light. When L is 300 km, ,

[0077] The protection time interval is greater than the inter-station propagation delay, τ g >τ AB +τ0, where τ0 is the reserved additional time delay for ground wave multipath scattering, preferably in the range of [0.5, 2] ms;

[0078] After the medium wave station A turns off its signal at the end of the time slot, and all the electromagnetic waves emitted by medium wave station A have left the coverage area of ​​medium wave station B, medium wave station B is allowed to start transmitting again to prevent the residual radio waves of medium wave station A and the signal of medium wave station B from superimposing in the service area.

[0079] In some preferred embodiments, determining the fault deduction model between the fault cause and the signal fault region includes:

[0080] Step S33: The fault inference model is set with a first inference factor and a second inference factor. The first inference factor marks the fault cause by detecting the generation mechanism of signal loss, and the second inference factor marks the fault area by detecting the spatial distribution pattern of signal loss. Different spatial distribution patterns of signal loss are classified according to different generation mechanisms of signal loss. This is used to clarify the specific causes of the fault after the test data has problems in multiple different areas, so that the correspondence between each fault cause and each fault area is simplified.

[0081] In some preferred embodiments, determining the signal loss value at monitoring time points within the fault simulation model and screening the signal fault area within the medium-wave station's signal transmission range based on possible fault causes includes:

[0082] Step S34: Input the test data received by the first and second medium wave stations into the fault simulation model. The test data is used to simulate the areas where the signal loss reaches the threshold each time during data transmission between the transmitting and feedback ends. Combined with the fixed environmental characteristics of the areas, the model is used to simulate the fault and obtain the estimated transmission time when the signal loss reaches the threshold each time. Each estimated transmission time is input into the direction from the first medium wave station to the signal data receiving end to obtain the possible fault area. The monitoring time nodes include daytime and nighttime. The signal loss thresholds are different during the day and night. The different fixed environmental characteristics correspond to different signal loss thresholds. Transmitting the test data between the first, second, and third medium wave stations may not necessarily cause the test data to malfunction, but the signal will also be lost to a certain extent. The signal between the first and second medium wave stations is relatively visible.

[0083] Please see Figure 3 In some preferred embodiments, the step of investigating the signal fault area, determining the cause and coordinates of the fault data, and outputting diagnostic results includes:

[0084] Step S41: Calculate the ratio of the actual field strength to the theoretical field strength for each possible fault region to obtain the abnormal loss value;

[0085] Step S42: Substitute the abnormal loss value into the ground wave attenuation model stored in the database, and compare it with the ground wave attenuation amount of each distance interval in the ground wave attenuation model.

[0086] Step S43: By solving multiple sets of distance and orientation information simultaneously, determine the fixed environmental features corresponding to the fault area between the first medium wave station, the second medium wave station, and the third medium wave station. Based on the fixed environmental features located between the first medium wave station and the medium wave signal receiving end, deduce the actual distance from the first medium wave station to the fault coordinates, and finally output the geographical coordinates of the fault in the signal data.

[0087] The first medium wave station is set to a frequency f of 0.9 (MHz) and a power P of 10 (kW). The database includes a built-in medium wave ground wave attenuation model for transmission attenuation. Where d is the transmission distance (km), the omnidirectional antenna gain G=0dBi, and the model standard attenuation correspondence is: the average standard attenuation in the 10~15km range. .

[0088] The process for obtaining the actual distance from the first intermediate wave station to the fault coordinates includes:

[0089] Step A1: Calculate the field strength difference to obtain the abnormal loss value;

[0090] The theoretical field strength is calculated using the medium wave ground wave field strength formula, and the fault anomaly loss is calculated by combining the measured field strength at the receiver.

[0091] Theoretical field strength Abnormal losses ;

[0092] Fixed correction factor =0.92, initial monitoring distance d=12km, measured field strength E0=18.6μV / m.

[0093] The calculated value is E≈41.9μV / m, indicating abnormal fault loss. .

[0094] Step A2: Match the ground wave attenuation model and correct the total attenuation;

[0095] The total attenuation is the sum of the standard attenuation and the fault-related loss. Substitute the parameters to calculate: L A =63.45+7.04=70.49dB. The reverse matching model confirms that the distance range corresponding to the fault is 15~20km, and the initial ranging error is corrected.

[0096] Step A3: Solve the fixed environmental characteristics by simultaneously solving the data from the three stations;

[0097] Using soil electrical conductivity σ and topographic shading coefficient η as fixed environmental characteristics, a system of simultaneous equations for attenuation at three stations was constructed and solved using multiple sets of distance and orientation information. , , In the formula, L0, L1, and L2 are the measured total attenuation of the three medium-wave stations, respectively; d0, d1, and d2 are the monitoring distances from the three stations to the fault area. This is a correction term for environmental characteristic gain.

[0098] The specific environmental parameters of the fault area were solved by elimination, and the values ​​of the first medium wave station were obtained as d0=12km and L0=70.49dB, the second medium wave station as d1=13.2km and L1=72.13dB, and the third medium wave station as d2=11.8km and L2=69.87dB.

[0099] The fixed environmental characteristics of the fault area are σ=0.85S / m and η=0.78.

[0100] Step A4: Actual distance from the thrust reverser station to the fault point;

[0101] Based on the modified ground wave propagation formula and combined with the solved environmental characteristics, the precise transmission distance is calculated in reverse. Where Z is a preset base for the transmission distance, preferably 4, and the gain is adjusted accordingly. =2.3dBi parameter, the calculated D=17.3km is the actual fault distance.

[0102] Please see Figure 4 Based on the same concept as the above embodiments, embodiments of the present invention also provide a rapid diagnosis system for signal transmission faults of a medium-wave radio station based on a distributed architecture, including:

[0103] The data acquisition module is used to establish a signal monitoring range, acquire medium wave station signal data within the signal monitoring range, trace the source of the medium wave station signal data, and obtain fault data therein.

[0104] The fault cause analysis module is used to analyze and filter the fault data and investigate possible fault causes.

[0105] The fault location investigation module is used to establish a transmission and feedback model of medium wave station signal data between medium wave stations within the signal monitoring range, determine the fault inference model between the fault cause and the signal fault area, determine the signal loss value at the monitoring time node within the fault inference model, and screen the signal fault area within the medium wave station signal transmission range based on possible fault causes.

[0106] The fault diagnosis module is used to investigate the fault area of ​​the signal, determine the cause and coordinates of the fault data, and output the diagnosis results.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0108] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rapid fault diagnosis method for medium-wave station signal transmission based on a distributed architecture, characterized by: include: Establish a signal monitoring range, collect medium wave station signal data within the signal monitoring range, trace the source of the medium wave station signal data, and obtain fault data therein; The fault data is analyzed and filtered to investigate possible causes of the fault. Establish a transmission and feedback model for medium wave station signal data between medium wave stations within the signal monitoring range, determine a fault inference model between the fault cause and the signal fault area, determine the signal loss value at the monitoring time node within the fault inference model, and screen the signal fault area within the medium wave station signal transmission range based on possible fault causes. The faulty signal area is investigated to determine the cause and coordinates of the fault data, and the diagnostic results are output.

2. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 1, characterized in that: At least two medium-wave stations and one medium-wave signal receiving terminal capable of receiving signal data transmitted by all medium-wave stations within the signal monitoring range are provided within the signal monitoring range.

3. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 1, characterized in that: The step of tracing the source of the medium-wave station signal data to obtain fault data includes: A radio frequency (RF) signal transmitter is configured at the medium-wave signal receiver. The RF signal transmitter also contains an Internet of Things (IoT) communication device. After receiving the medium-wave signal, the medium-wave signal receiver locates the first medium-wave station that emitted the medium-wave signal. The RF signal transmitter then transmits the received signal data from the medium-wave signal to the backend server via the IoT communication device. The backend server detects the signal parameters of the signal data and marks the signal data corresponding to abnormal signal parameters as fault data. The fault data is signal data collected at the transmitting end of the first medium-wave station before the transmitted signal enters the external propagation environment and before the signal propagation process is superimposed with external environmental interference. It is used to characterize the original signal state when the medium-wave station emits the corresponding signal.

4. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 3, characterized in that: The process of analyzing and filtering the fault data to investigate possible causes of the faults includes: Retrieve fixed environmental features within the propagation range of medium-wave station signals from the database, classify the fault causes corresponding to the signal parameters in each fixed environmental feature threshold range, input the corresponding signal parameters from the fault data, and output a set of fault causes for each fixed environmental feature. Based on the corresponding signal parameters in the fault data, the causes of the fault data are weighted and classified, and possible causes of the fault are screened within the set of causes of faults for each fixed environmental feature.

5. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 4, characterized in that: The establishment of a transmission and feedback model for medium-wave station signal data between medium-wave stations within the signal monitoring range includes: A second intermediate wave station is set up. The setting criteria for the second intermediate wave station are as follows: the intermediate wave station closest to the first intermediate wave station within the receiving range of the signal data receiving end of the radio frequency signal transmitter is set up as the second intermediate wave station. A test data with the same original signal waveform, carrier frequency and modulation parameters as the fault data is sent to the second intermediate wave station at a first time point when other signal data sent by the second intermediate wave station will not be subject to superposition interference. The second intermediate wave station receives the test data and sends the test data to the first intermediate wave station at a second time point when other signal data sent by the first intermediate wave station will not be subject to superposition interference. The third medium wave station is set up according to the setting standard of the second medium wave station, and the test data is transmitted synchronously. The test data received by all medium wave stations is input into the background server. The test data transmitted by the first medium wave station, the second medium wave station and the third medium wave station have different transmitter inherent parameters, spatial propagation parameters and receiver observation parameters.

6. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 5, characterized in that: The fault deduction model for determining the relationship between the fault cause and the signal fault area includes: The fault inference model is set with a first inference factor and a second inference factor. The first inference factor marks the cause of the fault by detecting the generation mechanism of signal loss, and the second inference factor marks the fault area by detecting the spatial distribution pattern of signal loss. Different spatial distribution patterns of signal loss are classified according to different generation mechanisms of signal loss.

7. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 6, characterized in that: The process of determining signal loss values ​​at monitoring time points within the fault simulation model and filtering signal fault areas within the medium-wave station's signal transmission range based on possible fault causes includes: The test data received by the first and second medium-wave stations are input into the fault simulation model to locate the area where the signal loss reaches the threshold each time during the data transmission process at the transmitting and feedback ends. The model is then used to simulate the fixed environmental characteristics of the area to obtain the estimated transmission time when the signal loss reaches the threshold each time. Each estimated transmission time is input into the direction from the first medium-wave station to the signal data receiving end to obtain the possible fault area. The monitoring time nodes include daytime and nighttime, and the signal loss thresholds are different during the day and night.

8. The method for rapid diagnosis of signal transmission faults in a medium-wave radio station based on a distributed architecture as described in claim 7, characterized in that: The process of investigating the signal fault area, determining the cause and coordinates of the fault data, and outputting diagnostic results includes: Based on the possible fault regions, the ratio of the actual field strength to the theoretical field strength of each fault region is calculated to obtain the abnormal loss value. Substitute the abnormal loss value into the ground wave attenuation model stored in the database, and compare it with the ground wave attenuation amount of each distance interval in the ground wave attenuation model. By solving multiple sets of distance and orientation information simultaneously, the fixed environmental features corresponding to the fault area between the first medium wave station, the second medium wave station, and the third medium wave station are determined. The corresponding fixed environmental features located between the first medium wave station and the medium wave signal receiving end are used to infer the actual distance from the first medium wave station to the fault coordinates, and finally the geographical coordinates of the signal data fault are output.

9. A rapid fault diagnosis system for medium-wave station signal transmission based on a distributed architecture, characterized in that, include: The data acquisition module is used to establish a signal monitoring range, acquire medium wave station signal data within the signal monitoring range, trace the source of the medium wave station signal data, and obtain fault data therein. The fault cause analysis module is used to analyze and filter the fault data and investigate possible fault causes. The fault location investigation module is used to establish a transmission and feedback model of medium wave station signal data between medium wave stations within the signal monitoring range, determine the fault inference model between the fault cause and the signal fault area, determine the signal loss value at the monitoring time node within the fault inference model, and screen the signal fault area within the medium wave station signal transmission range based on possible fault causes. The fault diagnosis module is used to investigate the fault area of ​​the signal, determine the cause and coordinates of the fault data, and output the diagnosis results.

10. A storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for rapid diagnosis of signal transmission faults in a medium-wave station based on a distributed architecture as described in any one of claims 1-8.

Images