Fault diagnosis and prediction method, device and medium for rail transit beacon positioning system

Abstract

The application discloses a kind of track traffic beacon positioning system fault diagnosis and prediction method, equipment and medium, the track traffic beacon positioning system includes balise transmission module BTM and trackside beacon, the method includes: whether train is through beacon, if not through beacon, trigger the periodic self-check of each functional module in BTM, obtain the first detection data indicating the state of BTM itself;If through beacon, by monitoring the real-time processing process of BTM to beacon signal, synchronously obtain the second detection data indicating the interactive state of BTM and beacon;Based on the preset fault judgment rule, the first detection data is compared with the second detection data with logic consistency, and outputs fault diagnosis result and predictive maintenance early warning.Compared with prior art, the application realizes the precise positioning and early prediction of track traffic beacon positioning system by periodic self-check and real-time interaction monitoring.

Description

Technical Field

[0001] This invention relates to the field of rail transit, and in particular to a method, equipment and medium for fault diagnosis and prediction of a rail transit beacon positioning system. Background Technology

[0002] The rapid development of a new round of technological revolution and industrial transformation has made digitalization, networking, and intelligentization the general trend in rail transit construction, and digital transformation is an inevitable path for the future development of rail transit enterprises. Fault Prediction and Health Management (PHM) is a discipline developed based on advancements in digital technology. It is a key technology for transforming equipment maintenance from reactive and scheduled maintenance to predictive maintenance. It can reduce the occurrence of emergency repair events in terms of time, the occurrence of potential emergency response events in terms of space, and financial losses in terms of economy, thereby reducing the system's cost-effectiveness ratio. The rail transit industry is also gradually conducting related research. Traditional PHM system implementations in rail transit systems typically start with input data such as on-site faults, alarms, logs, and design data, exploring aspects such as the causes of on-site faults, fault characteristic parameters, fault events, and the expected lifespan analysis of wear-and-tear components. However, this approach has also encountered some problems and challenges.

[0003] 1. For equipment such as turnout switching equipment, track circuits, and signal machines, there are obvious life models, and research on fault early warning and monitoring management systems for them is relatively mature. However, the failure of electronic systems composed of complex hardware and software systems conforms to random distribution. The random failure of individual equipment is sudden and unpredictable, and its failure mode is unrelated to the overall degradation process of the equipment.

[0004] 2. As rail transit control systems become increasingly complex, traditional hardware failure mode analysis (FMEA) methods struggle to cover errors in software logic. In particular, the dynamic behavior of complex systems is more intricate. When a system fails, especially when a software error occurs, the accuracy of the model will be significantly affected after the system is fixed, upgraded, and iterated.

[0005] A search revealed that Chinese Patent Publication No. CN106585667A discloses a system and method for detecting faults in vehicle-mounted beacon antennas. This method involves deploying detection equipment in specific track areas before the vehicle leaves the depot, using wheel sensors to trigger a ground host to read the vehicle-mounted beacon ID and detect the antenna's transmission power, thereby determining the fault and binding it to train information for reporting. This solution can achieve local fixed-point detection and energy consumption control, but it is still a trigger-based, post-hoc diagnosis, lacking the ability to continuously monitor and predict the health of the beacon positioning system around the clock and across the entire road segment. It does not cover the comprehensive fault diagnosis and data-driven prediction of each module of the BTM (Balise Transmission Module), making it difficult to meet the early warning needs of random failures and complex degradation processes in electronic systems.

[0006] Therefore, how to accurately diagnose and predict faults in the positioning system of rail transit beacons has become a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of this invention is to overcome the defects of the prior art by providing a fault diagnosis and prediction method, implementation system, equipment and medium for a railway traffic beacon positioning system.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] According to a first aspect of the present invention, a fault diagnosis and prediction method for a track traffic beacon positioning system is provided. The track traffic beacon positioning system includes a transponder transmission module (BTM) and a trackside beacon. The BTM includes an antenna loop, a receiving unit, a transmitting unit, and a main control unit. Each of the antenna loop, receiving unit, transmitting unit, and main control unit is provided with an independent detection module. Each detection module is used to collect the operating parameters, signal characteristics, and fault status of its respective unit in real time and output detection data.

[0010] The fault diagnosis and prediction method includes the following steps:

[0011] Determine whether the train has passed the beacon. If it has not passed the beacon, trigger each detection module to perform a periodic self-check to obtain the first detection data representing the BTM's own status.

[0012] If the signal passes through a beacon, the BTM's real-time processing of the beacon signal is monitored by the various detection modules to generate second detection data;

[0013] Based on preset fault judgment rules, a logical consistency comparison is performed on the first and second detection data, and a fault diagnosis result is output; simultaneously

[0014] Trend analysis is performed on the parameters that change over time in the first and second detection data to generate predictive maintenance warnings.

[0015] As a preferred technical solution, the periodic self-test is performed by the detection module corresponding to each unit, specifically including:

[0016] The radio frequency detection circuit and the comparator circuit in the transmitting unit are used to detect the transmitted output power and the resonance state of the antenna loop, generate power detection results and loop state results, and record the flip state of the comparator circuit.

[0017] An internal test signal is injected into the AD module in the receiving unit, and the output value of the AD module is compared with a pre-stored range window to generate the AD module self-test result;

[0018] According to the EN50129 standard, the digital circuits in the main control unit are periodically self-tested to generate digital circuit self-test results.

[0019] Monitor the power supply voltage, operating current, and junction temperature parameters of each unit to generate environmental parameter data;

[0020] The power detection results, loop status results, AD module self-test results, digital circuit self-test results, and environmental parameter data obtained from periodic self-tests constitute the first detection data.

[0021] As a preferred technical solution, the generation of predictive maintenance warnings specifically involves: calculating the characteristic values ​​of the power supply voltage, operating current, and junction temperature parameters, including mean, kurtosis, and variance; generating predictive maintenance warnings by performing trend analysis on the characteristic values; and the predictive maintenance warnings including warning level information and suggested maintenance strategies.

[0022] As a preferred technical solution, the real-time processing of beacon signals by the monitoring BTM includes:

[0023] Real-time monitoring of the voltage, frequency, and phase parameters of the beacon return signal, generating corresponding parameter data;

[0024] Analyze the FSK modulation signal of the beacon and generate the corresponding FSK parsing status data;

[0025] Record the AD conversion value obtained after the AD module converts the beacon analog signal;

[0026] The parameter data, FSK parsing status data, and AD conversion value together constitute the second detection data.

[0027] As a preferred technical solution, the parameter data is compared with a preset range. If any parameter exceeds the range, a beacon signal anomaly indication is generated.

[0028] As a preferred technical solution, based on the FSK parsing status data, if the beacon information cannot be correctly parsed for a preset number of consecutive times, a beacon fault indication is generated.

[0029] As a preferred technical solution, in response to the beacon signal anomaly indication, trend analysis is performed on the historical data of the parameters that caused the anomaly;

[0030] If the historical data of the parameters shows a continuous deterioration trend, the beacon health status is predicted to deteriorate and a predictive maintenance warning is generated.

[0031] If the parameter exhibits instantaneous or random fluctuations, the anomaly is determined to originate from external environment and instantaneous interference.

[0032] As a preferred technical solution, the preset fault judgment rule includes: if the AD conversion value in the second detection data shows a linear change trend and exceeds the tolerance threshold, while the first detection data shows that the AD module self-test is normal, then it is determined that the analog circuit before the AD module in the BTM has experienced performance degradation.

[0033] As a preferred technical solution, the preset fault judgment rule includes: if the second detection data detects that the AD conversion value continuously exceeds the effective trigger threshold of the signal energy, and the first detection data does not record the comparison circuit flipping, then it is determined that the comparison circuit in the BTM has failed.

[0034] As a preferred technical solution, the preset fault judgment rule includes: if the first detection data is recorded as the comparison circuit has flipped, and the AD conversion value in the second detection data has not reached the effective trigger threshold of the signal energy, then it is determined that a physical offset has occurred between the BTM antenna and the beacon.

[0035] As a preferred technical solution, the preset fault judgment rule includes: if the fluctuation range of the AD conversion value in the second detection data exceeds the normal fluctuation threshold within one FSK signal cycle, and the first detection data shows that the BTM self-test is normal, then it is determined that there is pollution near the beacon.

[0036] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0037] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] 1. This invention achieves a shift from post-construction maintenance to pre-construction prediction by independently setting detection modules in each unit of the BTM, performing periodic self-checks when not passing beacons, and performing real-time interactive monitoring when passing beacons.

[0040] 2. This invention achieves precise isolation and intelligent diagnosis of fault root causes by logically comparing the first detection data representing its own state with the second detection data representing the interaction state, thereby improving the accuracy of diagnosis and the efficiency of operation and maintenance.

[0041] 3. This invention performs trend analysis based on historical data to generate predictive maintenance warnings, providing data support for the decision-making of operation and maintenance personnel and improving the reliability of rail transit systems. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the composition architecture of the rail transit beacon positioning system of the present invention;

[0043] Figure 2 This is a flowchart of the method of the present invention;

[0044] Figure 3 This is the periodic loop detection step of the beacon positioning system of the present invention;

[0045] Figure 4 This refers to the detection steps on the beacon reading loop of the beacon positioning system of the present invention; Detailed Implementation

[0046] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0047] Example 1:

[0048] The main technical implementation methods of this invention include:

[0049] 1) For analog circuits, state monitoring and fault diagnosis can be achieved by filtering characteristic parameters that affect faults. By selecting appropriate characteristic parameters as trend monitoring targets, such as mean, peak value, kurtosis, and variance, the state monitoring of analog circuits can be effectively realized. Statistical characteristic parameters are a commonly used method in analog circuit state monitoring.

[0050] 2) For digital circuits, periodic self-testing designs can be considered for electronic components or circuit modules that are severely affected by faults. This allows for remote monitoring of fault events, providing the possibility for fault early warning and proactive maintenance. EN50129 provides self-testing methods for key electronic components such as CPUs, memory, and FPGA devices; online circuit testing should be performed according to the methods recommended in EN50129.

[0051] 3) For circuit diagnosis affected by the environment, electronic circuits are usually designed with circuits for temperature detection, voltage detection, current detection and clock detection. These real-time detection parameters have a linear relationship and can reflect changes in the environment or external interfaces, internal working status, etc.

[0052] 4) In terms of reducing system failures at the design stage, the MBSE method is used to enhance the ability to deliver high-quality products on the first attempt by leveraging early model verification and the accuracy of simulation.

[0053] Taking the track traffic beacon positioning system as an example, this invention provides an intelligent fault diagnosis and prediction method for the track traffic beacon positioning system. Based on a dual-mode triggering mechanism, the detection strategy is dynamically switched. By combining BTM self-test data and dynamic monitoring data for cross-validation and fusion analysis, the method can accurately locate the fault source and predict its health status, providing a basis for the formulation of proactive fault maintenance strategies.

[0054] like Figure 2 As shown, the specific process of this invention includes the following steps:

[0055] Step S0: Determine whether the train has passed the beacon;

[0056] Step S1: If the beacon is not passed, trigger each detection module to perform a periodic self-test to obtain the first detection data representing the state of the BTM itself;

[0057] Step S2: If the signal passes the beacon, the BTM monitors the real-time processing of the beacon signal through each detection module and generates second detection data;

[0058] Step S3: Based on the preset fault judgment rules, perform a logical consistency comparison on the first detection data and the second detection data, and output the fault diagnosis result; at the same time, perform trend analysis on the parameters in the first detection data and the second detection data that change over time, and generate a predictive maintenance warning.

[0059] This invention determines whether a train has passed a beacon, automatically switches between two detection modes, collects equipment self-test data and beacon interaction data respectively, and achieves accurate fault location and early warning of the track traffic beacon system through cross-validation and parameter trend analysis.

[0060] like Figure 3 and Figure 4 As shown, the steps of the present invention are described in detail below:

[0061] When the train travels to a section without beacons, the system triggers a periodic self-check, which includes:

[0062] Step S101: The transmitting output terminal of the receiving unit starts the radio frequency detection circuit to detect the 27.095MHz radio frequency signal transmitted by the BTM system itself. The DC level signal obtained after detection is used as the basis for transmitting power self-test. At the same time, the comparison circuit controls the alternating output of the 70KHz and 140KHz self-test signals. The antenna loop transmits and receives the reflected signal, and records the frequency deviation of the two signals.

[0063] Step S102: Preset different reference voltages at different amplification stage output points, compare the level signal with the reference voltage, and diagnose the health status of the amplification circuit and the comparator itself step by step by comparing with the expected flip situation.

[0064] Step S103: Inject an internal test signal into the AD module and monitor the AD conversion value of the AD module;

[0065] Step S104: After the main control unit sends the self-test signal, it receives the return signal and compares it bit by bit with the standard test signal sequence stored locally. If they match, it is determined to be normal.

[0066] Step S105: Use the mean to monitor steady-state performance, the peak value to monitor instantaneous impact, and kurtosis and variance to monitor signal distribution characteristics; the system operates under known normal conditions for a long period of time, collects data of each parameter, calculates its mean μ and standard deviation σ, initially sets the threshold to μ±3σ, and dynamically updates the threshold range based on the equipment's own historical normal operation data; the system periodically calculates characteristic parameters, and triggers an early warning if the parameters deviate from the threshold for a preset number of consecutive times.

[0067] When a train reaches the trackside beacon section, real-time monitoring is initiated, including:

[0068] Step S201: The receiving unit collects the voltage, frequency and phase parameters of the beacon return signal in real time and compares them with the expected range. If any parameter exceeds the range, a beacon signal abnormality indication is generated.

[0069] If the historical data of the parameters shows a continuous deterioration trend, the beacon health status is predicted to deteriorate and a predictive maintenance warning is generated.

[0070] If the parameter exhibits instantaneous or random fluctuations, the anomaly is determined to originate from external environmental factors and instantaneous disturbances.

[0071] Step S202: The main control unit parses the FSK modulation signal of the beacon and extracts the line number and location coordinates stored in the beacon. If the beacon information cannot be extracted correctly after 3 consecutive parses, the FSK parsing is marked as abnormal, and the beacon's own storage unit is preliminarily determined to be faulty.

[0072] Step S301: The AD module performs real-time conversion on the beacon analog signal. The main control unit continuously monitors the changing trend of the AD conversion value and performs a logical consistency comparison based on the BTM self-test results.

[0073] (1) If the AD conversion value changes linearly, and the periodic self-test shows that the AD module self-test is normal, it is determined that the analog circuit before the AD module in the BTM has degraded in performance.

[0074] (2) If the AD conversion value is continuously detected to exceed the effective trigger threshold of the signal energy, and the flipping of the comparator circuit is not detected at this time, it is determined that the comparator circuit in the BTM has failed.

[0075] (3) If the comparison circuit is detected to have flipped, but the AD conversion value is never detected to reach the effective trigger threshold of the signal energy, it is determined that there is a physical offset between the BTM antenna and the beacon.

[0076] (4) If the AD conversion value is detected to fluctuate within an FSK signal cycle beyond the normal fluctuation threshold, and the first detection data shows that the BTM self-test is normal, then it is determined that there is contamination near the beacon.

[0077] Example 2:

[0078] like Figure 1 As shown, the system for implementing the fault diagnosis and prediction method of the track traffic beacon positioning system according to the present invention includes: an antenna loop, a receiving unit, a transmitting unit, and a main control unit;

[0079] The antenna loop is used to receive signals emitted by the beacon;

[0080] The receiving unit is used to filter, amplify, and detect the signal received by the antenna loop;

[0081] The transmitting unit is used to output self-test signals and signals for interaction with the beacon;

[0082] The main control unit is used to control the coordinated operation of the antenna loop, receiving unit, and transmitting unit, and to perform logical judgments for fault diagnosis and prediction.

[0083] Each of the antenna loop, receiving unit, transmitting unit, and main control unit is equipped with an independent detection module. Each detection module is used to collect and preliminarily determine the operating parameters, signal characteristics, and fault status of its respective unit in real time.

[0084] This system serves as the implementation vehicle for the fault diagnosis and prediction method of the rail transit beacon positioning system of this invention. Through the core architecture of antenna loop, receiving unit, transmitting unit, and main control unit, and the independent detection modules of each unit, it realizes the reception, processing, and interaction of beacon signals. Through the collaborative judgment of the main control unit, it achieves full coverage monitoring of the core unit, providing support for accurate diagnosis, prediction, and proactive maintenance.

[0085] Example 3

[0086] This invention also provides an electronic device including a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0087] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] The processing unit performs the various methods and processes described above, such as the methods of the present invention. For example, in some embodiments, the methods of the present invention may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the methods of the present invention by any other suitable means (e.g., by means of firmware).

[0089] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0090] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A fault diagnosis and prediction method for a track traffic beacon positioning system, wherein the track traffic beacon positioning system includes a transponder transmission module (BTM) and a trackside beacon, the BTM including an antenna loop, a receiving unit, a transmitting unit, and a main control unit, characterized in that, The antenna loop, receiving unit, transmitting unit, and main control unit are each equipped with an independent detection module, wherein... Each detection module is used to collect the operating parameters, signal characteristics and fault status of its unit in real time, and output the detection data; The fault diagnosis and prediction method includes the following steps: Determine whether the train has passed the beacon. If it has not passed the beacon, trigger each detection module to perform a periodic self-check to obtain the first detection data representing the BTM's own status. If the signal passes through a beacon, the BTM's real-time processing of the beacon signal is monitored by the various detection modules to generate second detection data; Based on preset fault judgment rules, a logical consistency comparison is performed on the first and second detection data, and a fault diagnosis result is output; simultaneously Trend analysis is performed on the parameters that change over time in the first and second detection data to generate predictive maintenance warnings; The periodic self-test is performed by the detection module corresponding to each unit, specifically including: The radio frequency detection circuit and the comparator circuit in the transmitting unit are used to detect the transmitted output power and the resonance state of the antenna loop, generate power detection results and loop state results, and record the flip state of the comparator circuit. An internal test signal is injected into the AD module in the receiving unit, and the output value of the AD module is compared with a pre-stored range window to generate the AD module self-test result; According to the EN50129 standard, the digital circuits in the main control unit are periodically self-tested to generate digital circuit self-test results. Monitor the power supply voltage, operating current, and junction temperature parameters of each unit to generate environmental parameter data; The first detection data consists of the power detection results, loop status results, AD module self-test results, digital circuit self-test results, and environmental parameter data obtained from periodic self-tests. The real-time processing of beacon signals by the monitoring BTM includes: Real-time monitoring of the voltage, frequency, and phase parameters of the beacon return signal, generating corresponding parameter data; Analyze the FSK modulation signal of the beacon and generate the corresponding FSK parsing status data; Record the AD conversion value obtained after the AD module converts the beacon analog signal; The parameter data, FSK parsing status data, and AD conversion value together constitute the second detection data.

2. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, The specific steps for generating predictive maintenance warnings are as follows: calculating the characteristic values ​​of the power supply voltage, operating current, and junction temperature parameters, including mean, kurtosis, and variance; performing trend analysis on the characteristic values ​​to generate predictive maintenance warnings; and generating predictive maintenance warnings that include warning level information and recommended maintenance strategies.

3. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, The parameter data is compared with a preset range. If any parameter exceeds the range, a beacon signal anomaly indication is generated.

4. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, Based on the FSK parsing status data, if the beacon information cannot be correctly parsed for a preset number of consecutive times, a beacon fault indication is generated.

5. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 3, characterized in that, In response to the beacon signal anomaly indication, perform trend analysis on the historical data of the parameters that caused the anomaly; If the historical data of the parameters shows a continuous deterioration trend, the beacon health status is predicted to deteriorate and a predictive maintenance warning is generated. If the parameter exhibits instantaneous or random fluctuations, the anomaly is determined to originate from external environment and instantaneous interference.

6. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, The preset fault judgment rules include: if the AD conversion value in the second detection data shows a linear change trend and exceeds the tolerance threshold, while the first detection data shows that the AD module self-test is normal, then it is determined that the analog circuit before the AD module in the BTM has experienced performance degradation.

7. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, The preset fault judgment rules include: if the second detection data detects that the AD conversion value continuously exceeds the effective trigger threshold of the signal energy, and the first detection data does not record the comparison circuit flipping, then it is determined that the comparison circuit in the BTM has failed.

8. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, The preset fault judgment rules include: if the first detection data is recorded as the comparison circuit has flipped, and the AD conversion value in the second detection data has not reached the effective trigger threshold of the signal energy, then it is determined that a physical offset has occurred between the BTM antenna and the beacon.

9. The fault diagnosis and prediction method for a track traffic beacon positioning system according to claim 1, characterized in that, The preset fault judgment rules include: if the fluctuation range of the AD conversion value in the second detection data exceeds the normal fluctuation threshold within one FSK signal cycle, while the first detection data shows that the BTM self-test is normal, then it is determined that there is pollution near the beacon.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 9.

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