Self-diagnosis device and method of mine microseismic monitoring system

Abstract

The application discloses a kind of self-diagnosis device and method of mine microseismic monitoring system, the device includes: sensor node, gravity angle sensor is integrated in the inside of sensor node, leveling actuating mechanism, standard signal generator and time service module;Substation controller, substation controller is connected with one or more sensor nodes, and substation controller is provided with local data buffer and power management unit;Central diagnostic server, central diagnostic server is connected with substation controller, for running diagnostic algorithm engine, to execute analysis and statistical diagnosis based on microseismic waveform data;Operation software platform, operation software platform is connected with central diagnostic server, for visual display diagnostic result and executes alarm;Wherein, self-diagnosis device is configured to automatically execute at least one of the following diagnostic items: automatic leveling diagnosis, automatic calibration diagnosis, network signal and stability diagnosis, time service accuracy diagnosis and the like.

Description

Technical Field

[0001] This invention relates to the field of mine safety technology, and in particular to a self-diagnostic device and a self-diagnostic method for a mine microseismic monitoring system. Background Technology

[0002] Microseismic monitoring systems for mines are widely used for mine safety monitoring. They collect microseismic signals through sensors deployed underground to locate and analyze the energy of rock mass fracture events. However, these systems operate in harsh environments (high temperature, high humidity, strong electromagnetic interference) for extended periods, leading to sensor aging. Existing systems largely rely on manual inspections and periodic calibrations, lacking real-time self-diagnostic capabilities, resulting in delayed fault detection.

[0003] Existing microseismic monitoring systems typically possess basic data acquisition and transmission capabilities, with some supporting manual calibration or network status monitoring. However, they lack a systematic self-diagnostic mechanism. Sensor calibration requires manual intervention, failing to achieve automatic gain adjustment and frequency consistency checks. Network anomalies are judged solely based on connectivity, lacking cached data analysis and transmission quality assessment. Sensor faults rely on manual comparison of waveforms or location results, failing to automatically identify abnormal sensors. Some system studies have attempted to diagnose sensor anomalies through waveform consistency, but a systematic diagnostic framework has not been established, nor are multi-dimensional fusion analysis and proactive alarm mechanisms. Currently, no publicly available technology integrates a complete self-diagnostic system encompassing automatic leveling, daily automatic calibration, network cache diagnosis, multi-parameter anomaly identification (frequency / residual / participation / energy), power supply health testing, timing deviation monitoring, and network optimization alerts. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the first objective of this invention is to propose a self-diagnostic device for a mine microseismic monitoring system, enabling automatic diagnosis and fault warning of key components such as sensors, networks, power supplies, and time synchronization, thereby improving system reliability and the level of intelligent operation and maintenance.

[0005] The second objective of this invention is to propose a self-diagnosis method for a mine microseismic monitoring system.

[0006] To achieve the above objectives, a first aspect of the present invention provides a self-diagnostic device for a mine microseismic monitoring system, comprising: The sensor node integrates a gravity angle sensor, a leveling actuator, a standard signal generator, and a timing module. Substation controller, which communicates with one or more sensor nodes, is equipped with a local data buffer and a power management unit; The central diagnostic server communicates with the substation controllers and is used to run the diagnostic algorithm engine to perform analysis and statistical diagnosis based on microseismic waveform data. The operating software platform is connected to the central diagnostic server and is used to visualize diagnostic results and execute alarms. The self-diagnostic device is configured to automatically perform at least one of the following diagnostic items: Automatic leveling diagnosis based on gravity angle sensors and leveling actuators; Automatic calibration and diagnosis of sensors based on standard signal generators; Network signal and stability diagnostics based on local data buffer status; Sensor-based high-frequency anomaly diagnosis, positioning residual anomaly diagnosis, positioning participation degree diagnosis, and energy calculation anomaly diagnosis based on diagnostic algorithm engine; Power health diagnosis based on the power management unit; Time synchronization accuracy diagnosis based on the time synchronization module.

[0007] In addition, the self-diagnostic device of the mine microseismic monitoring system according to the above embodiments of the present invention may also have the following additional technical features: According to one embodiment of the present invention, the self-diagnostic device is configured to perform automatic sensor leveling diagnosis, including: The gravity angle sensor monitors the installation attitude and tilt angle of the sensor node in real time; When the absolute value of the tilt angle is greater than the first preset tilt angle threshold, the leveling actuator is controlled to perform a leveling operation; After leveling, read the tilt angle again until the absolute value of the tilt angle is less than or equal to the second preset tilt angle threshold. The operating software platform records and displays the leveling status.

[0008] According to one embodiment of the present invention, the self-diagnostic device is configured to perform automatic sensor calibration diagnosis, including: According to a preset cycle or instruction, the standard signal generator is triggered to emit an excitation signal with a known amplitude and frequency; Receive feedback signals collected by sensor nodes; Calculate the frequency gain G, where frequency gain G = feedback signal amplitude / excitation signal amplitude; When the deviation of the frequency gain G from the nominal value exceeds the first preset gain threshold, the amplifier gain of the sensor node is automatically adjusted.

[0009] According to one embodiment of the present invention, a self-diagnostic device is configured to perform network signal and stability diagnostics, including: The substation controller monitors the network connectivity status and stores the data to be uploaded in the local data cache first; Calculate the theoretical storage time corresponding to the amount of cached data in the local data cache in real time; When the theoretical storage time exceeds the first preset time threshold, it is determined to be a network anomaly and recorded.

[0010] According to one embodiment of the present invention, a self-diagnostic device is configured to perform a sensor superior frequency anomaly diagnosis, comprising: For a microseismic event, obtain the superior frequency of the waveforms acquired by multiple sensor nodes involved in the monitoring; Calculate the average superior frequency F_avg of all participating sensor nodes; For a single sensor node, if its dominant frequency Fi satisfies |Fi-F_avg| / F_avg>the first preset frequency gain threshold, it is recorded as a frequency anomaly. The system counts and displays the number of frequency anomalies for each sensor node within a preset period.

[0011] According to one embodiment of the present invention, the self-diagnostic device is configured to perform at least one of positioning disability anomaly diagnosis, positioning participation degree diagnosis, and energy calculation anomaly diagnosis, including: For the diagnosis of location residual abnormalities, in the location solution of each microseismic event, if the absolute value of the arrival time residual of a certain sensor node is greater than the first preset time threshold, it is recorded as a residual abnormality. For positioning participation diagnosis, the total number of microseismic events N and the number of events M in which a certain sensor node successfully participates in positioning are counted within a preset period, and the participation rate P = M / N is calculated. For energy calculation anomaly diagnosis, for the same microseismic event, the average value E_avg of the energy values ​​obtained by each sensor node is calculated. If the energy value Ei of a certain sensor node satisfies |Ei-E_avg| / E_avg>the first preset energy threshold, it is recorded as an energy anomaly. The operating software platform displays the sensor nodes with the most residual anomalies, the lowest participation, or the most energy anomalies within a preset period.

[0012] According to one embodiment of the present invention, a self-diagnostic device is configured to perform a power health diagnosis, including: The power management unit manages the main power supply and at least two backup batteries; During the preset test time, one of the backup batteries is controlled to discharge the simulated load, while the other backup battery or the main power supply powers the system. Estimate the remaining capacity of the backup battery under test based on the discharge curve; When the remaining capacity is lower than the first preset power threshold, it is marked as power aging; when it is lower than the second preset power threshold, it is marked as power failure and an alarm is triggered.

[0013] According to one embodiment of the present invention, a self-diagnostic device is configured to perform a timing accuracy diagnosis, including: Each time time synchronization occurs, the deviation Δt between the local clock of the sensor node and the standard time source is recorded; When the absolute value of the deviation Δt is greater than the first preset deviation threshold, it is recorded as a time synchronization anomaly. When the number of consecutive occurrences of time synchronization anomalies exceeds the first preset threshold, a high-priority alarm is triggered.

[0014] According to one embodiment of the present invention, the self-diagnostic device is configured to perform network optimization and integrated alarm, including: The operating software platform sets corresponding alarm thresholds based on the diagnostic results of one or more diagnostic items; When the diagnostic results meet the alarm threshold conditions, an alarm message is automatically triggered via at least one of the following methods: pop-up window, SMS and / or mobile application push.

[0015] To achieve the above objectives, a second aspect of the present invention provides a self-diagnosis method for a mine microseismic monitoring system, applied to the self-diagnosis device of the mine microseismic monitoring system as described in the above embodiments. The method includes: Automatically perform at least two of the following diagnostics: automatic sensor leveling diagnosis, automatic sensor calibration diagnosis, network signal and stability diagnosis, abnormal sensor frequency, abnormal positioning deficit, abnormal positioning participation, abnormal energy calculation, abnormal power supply health, and accurate timing. Summarize and display all diagnostic results; According to preset alarm rules, a tiered alarm is triggered based on the diagnostic results.

[0016] This invention significantly improves the reliability, availability, and maintainability of mine microseismic monitoring systems by constructing a multi-layered, automated self-diagnostic system. 1. Automatic leveling and calibration ensure the accuracy of sensor data and the precision of the data source; 2. By using network caching and anomaly detection, data loss can be prevented and communication failures can be warned in advance, thereby improving the reliability of data transmission; 3. Through multi-dimensional analysis of waveforms, positioning, energy, etc., potential sensor faults can be identified early, and multi-parameter anomaly identification can detect degraded sensors early, avoiding "operation with defects"; 4. Power supply and timing monitoring ensure long-term stable system operation; 5. A strong reminder mechanism promotes the shift of operation and maintenance from "passive response" to "proactive intervention", reducing security risks.

[0017] This invention significantly improves the automation level, data quality, and operation and maintenance efficiency of mine microseismic monitoring systems, while reducing manual maintenance costs and system failure risks.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] Figure 1 This is a block diagram of a self-diagnostic device for a mine microseismic monitoring system according to an embodiment of the present invention. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] The self-diagnostic device and self-diagnostic method for a mine microseismic monitoring system proposed in the embodiments of the present invention are described below with reference to the accompanying drawings.

[0022] like Figure 1 As shown, the self-diagnostic device 100 of the mine microseismic monitoring system of this embodiment may include: sensor node 10, substation controller 20, central diagnostic server 30 and operating software platform 40.

[0023] The sensor node 10 integrates a gravity angle sensor, a leveling actuator, a standard signal generator, and a timing module. The sensor node 10 can be deployed underground in mines to collect micro-seismic signals. The gravity angle sensor measures the tilt angle of the sensor node 10 relative to the direction of gravity in real time; the leveling actuator, such as a micro stepper motor and adjustment mechanism, automatically adjusts the horizontal attitude of the sensor body according to control commands; the standard signal generator produces electrical or mechanical excitation signals with precisely known amplitude and frequency for self-calibration; and the timing module receives standard time signals such as GPS, BeiDou, or NTP to provide accurate timestamps for data acquisition.

[0024] Substation controller 20 is communicatively connected to one or more sensor nodes 10. Substation controller 20 is equipped with a local data buffer and a power management unit. Central diagnostic server 30 is communicatively connected to substation controller 20 and is used to run diagnostic algorithm engine to perform analysis and statistical diagnosis based on microseismic waveform data. Operating software platform 40 is connected to central diagnostic server 30 and is used to visualize diagnostic results and execute alarms.

[0025] The self-diagnostic device 100 is configured to automatically perform at least one of the following diagnostic items: automatic sensor leveling diagnosis based on the gravity angle sensor and leveling actuator; automatic sensor calibration diagnosis based on the standard signal generator; network signal and stability diagnosis based on the local data buffer status; sensor superior frequency anomaly diagnosis, positioning residual anomaly diagnosis, positioning participation degree diagnosis, and energy calculation anomaly diagnosis based on the diagnostic algorithm engine; power health diagnosis based on the power management unit; and time synchronization accuracy diagnosis based on the time synchronization module.

[0026] According to one embodiment of the present invention, the self-diagnostic device 100 is configured to perform automatic sensor leveling diagnosis, including: real-time monitoring of the installation attitude tilt angle of the sensor node 10 by a gravity angle sensor; when the absolute value of the tilt angle is greater than a first preset tilt angle threshold, controlling the leveling actuator to perform a leveling operation; reading the tilt angle again after leveling until the absolute value of the tilt angle is less than or equal to a second preset tilt angle threshold; and the operation software platform 40 recording and displaying the leveling status. The first preset tilt angle threshold and the second preset tilt angle threshold can be calibrated according to actual conditions; for example, the first preset tilt angle threshold can be 2°, and the second preset tilt angle threshold can be 1°.

[0027] Specifically, it's important to understand that micro-vibration sensors are sensitive to installation angles; a tilt angle >5° can lead to a vertical component error >10%. A gravity angle sensor monitors the sensor's attitude in real time. If the absolute tilt angle >2°, a leveling motor is activated; after leveling, the angle is read again until it is ≤1°. The leveling status (success / failure / number of attempts) is recorded and displayed on the software interface. Thus, through automatic leveling and calibration, the accuracy of sensor data and the precision of the data source are ensured. It should be noted that in addition to motor leveling, hydraulic or pneumatic leveling mechanisms can also be used.

[0028] According to one embodiment of the present invention, the self-diagnostic device 100 is configured to perform automatic sensor calibration and diagnosis, including: triggering a standard signal generator to emit an excitation signal with known amplitude and frequency according to a preset period or instruction; receiving a feedback signal collected by the sensor node 10; calculating a frequency gain G, wherein the frequency gain G = feedback signal amplitude / excitation signal amplitude; and automatically adjusting the amplifier gain of the sensor node 10 when the deviation of the frequency gain G from the nominal value exceeds a first preset gain threshold. The first preset gain threshold can be calibrated according to actual conditions; for example, the first preset gain threshold can be 10%.

[0029] Specifically, it's important to understand that energy calculation relies on waveform integration, and gain drift directly leads to a proportional deviation in energy values. Every day at 00:00, the self-diagnostic device 100 automatically triggers the calibration process; the sensor's internal standard signal generator sends an excitation signal with a known amplitude / frequency; the receiving end collects the feedback signal and calculates the gain G at different frequencies as feedback signal amplitude / excitation signal amplitude; if |G-1|>10%, the amplifier gain is automatically adjusted, and recalibration is performed; manual real-time calibration is also supported, with the same process. It should be noted that the standard signal can be a multi-band combination signal instead of a single standard signal; it can be an electrical signal, mechanical vibration, or acoustic pulse. Therefore, through multi-dimensional analysis of waveform, location, and energy, potential sensor faults can be identified early, and multi-parameter anomaly identification can detect degraded sensors early, avoiding "operation with defects."

[0030] According to one embodiment of the present invention, the self-diagnostic device 100 is configured to perform network signal and stability diagnosis, including: the substation controller 20 monitors the network connectivity status and stores the data to be uploaded in a local data buffer; calculates in real time the theoretical storage time corresponding to the amount of cached data in the local data buffer; when the theoretical storage time exceeds a first preset time threshold, it is determined as a network anomaly and recorded. The first preset time threshold can be calibrated according to actual conditions; for example, the first preset time threshold can be 2 minutes.

[0031] Specifically, during network signal and stability diagnostics, the substation controller 20 continuously monitors network connectivity; data is first written to a local cache before being uploaded; the cached data is cleared after each successful transmission; the time corresponding to the amount of cached data is calculated in real time; if the theoretical storage time corresponding to the amount of cached data is ≥ 2 minutes, it is determined as a "network anomaly," and this anomaly is recorded and displayed in the software. Thus, by using network caching and anomaly detection, data loss is prevented and communication failures are given early warning, improving data transmission reliability. Additionally, traditional indicators such as packet loss rate and latency can be introduced for network signal and stability diagnostics.

[0032] According to one embodiment of the present invention, the self-diagnostic device 100 is configured to perform sensor dominant frequency anomaly diagnosis, including: for a micro-vibration event, acquiring the dominant frequencies of waveforms collected by multiple sensor nodes 10 participating in the monitoring; calculating the average dominant frequency F_avg of all participating sensor nodes 10; for a single sensor node 10, if its dominant frequency Fi satisfies |Fi-F_avg| / F_avg>a first preset frequency gain threshold, then recording it as a frequency anomaly; counting and displaying the number of frequency anomalies of each sensor node 10 within a preset period. The first preset frequency gain threshold can be calibrated according to actual conditions; for example, the first preset frequency gain threshold can be 30%.

[0033] Specifically, microseismic events refer to weak seismic signals generated during mining operations due to rock fracturing or stress release, used for coal mine disaster monitoring and early warning. Microseismic monitoring involves using sensors deployed within the rock mass to monitor the weak vibration signals generated by rock fracturing, used to assess rock mass stability. The dominant frequency refers to the frequency component with the most concentrated energy in the microseismic waveform, and is related to the characteristics of the seismic source and sensors. In diagnosing dominant frequency anomalies, each microseismic event is monitored simultaneously by multiple sensors, the dominant frequency of each sensor waveform is calculated, and then the average value (Favg) of all sensor dominant frequencies is calculated; if the first preset frequency gain threshold of a certain sensor is |Fi A frequency anomaly is defined as Favg∣ / Favg > 30%. The sensor with the most anomalies is identified and displayed weekly.

[0034] According to an embodiment of the present invention, the self-diagnostic device 100 is configured to perform at least one of positioning residual anomaly diagnosis, positioning participation diagnosis, and energy calculation anomaly diagnosis, including: for positioning residual anomaly diagnosis, in the positioning solution of each microseismic event, if the absolute value of the arrival time residual of a certain sensor node 10 is greater than a first preset time threshold, it is recorded as a residual anomaly; for positioning participation diagnosis, the total number of microseismic events N and the number of events in which a certain sensor node 10 successfully participates in positioning are counted within a preset period, and the participation rate P=M / N is calculated; for energy calculation anomaly diagnosis, for the same microseismic event, the average value E_avg of the energy values ​​obtained by each sensor node 10 is calculated, and if the energy value Ei of a certain sensor node 10 satisfies |Ei-E_avg| / E_avg>a first preset energy threshold, it is recorded as an energy anomaly; the operation software platform 40 displays the sensor node 10 with the most residual anomalies, the lowest participation rate, or the most energy anomalies within the preset period. The first preset time threshold and the first preset energy threshold can be calibrated according to the actual situation. For example, the first preset time threshold can be 100ms and the first preset energy threshold can be 50%.

[0035] Specifically, the residual value is the deviation between the actual arrival time of a sensor and the theoretically calculated arrival time in the microseismic event location calculation, expressed in milliseconds (ms). Location participation is the proportion of microseismic events in which a sensor successfully participated in location calculations out of the total number of microseismic events. When diagnosing location residual anomalies, the time residual Ri (in ms) for each sensor is recorded in the location calculation for each microseismic event; if |Ri| > 100ms, it is considered an anomaly; the sensors with the most anomalies are statistically analyzed and displayed weekly. When diagnosing location participation, the total number of microseismic events N within the statistical period is calculated, and the number of location events in which a sensor participated is M; participation = M / N; the 10 sensors with the lowest participation are displayed weekly after sorting. When diagnosing energy calculation anomalies, for the same event, each sensor independently calculates the microseismic energy Ei; the average energy Eavg is calculated; if |Ei| > 100ms, the participation rate is calculated. An energy anomaly is defined as Eavg∣ / Eavg > 50%. The 10 sensors with the most anomalies are statistically analyzed and displayed weekly. Thus, through multi-dimensional analysis of waveforms, positioning, and energy, potential sensor faults can be identified early. Multi-parameter anomaly identification can detect degraded sensors early, preventing "operation with defects." It's important to understand that low participation may stem from sensor damage, loose installation, or poor placement, requiring focused investigation. Excellent frequency analysis can reflect the sensor's installation or performance status. Positioning residuals are affected by the wave velocity model and positioning algorithm; theoretically, all residuals should be zero.

[0036] According to one embodiment of the present invention, a self-diagnostic device 100 is configured to perform power health diagnosis, including: a power management unit managing a main power supply and at least two backup batteries; controlling one of the backup batteries to discharge a simulated load during a preset test time, while the other backup battery or the main power supply powers the system simultaneously; estimating the remaining capacity of the tested backup battery based on the discharge curve; identifying power aging when the remaining capacity is lower than a first preset power threshold; and identifying power failure and issuing an alarm when the remaining capacity is lower than a second preset power threshold. The first and second preset power thresholds can be calibrated according to actual conditions; for example, the first preset power threshold can be a design value of 75%, and the second preset power threshold can be a design value of 40%.

[0037] Specifically, when diagnosing power health, discharge tests (load simulation) can be performed on the two backup batteries sequentially at 01:00 on the 1st of each month; the voltage and current of the main and backup power supplies are monitored in real time; the other battery supplies power during the test to avoid system power outages; the remaining capacity is estimated based on the discharge curve, and if it is below 75% of the design value, it is marked as "power aging", and if it is below 40% of the design value, it is marked as "power failure". Real-time alarms are triggered when the power level is abnormal.

[0038] According to one embodiment of the present invention, the self-diagnostic device 100 is configured to perform time synchronization accuracy diagnosis, including: recording the deviation Δt between the local clock of the sensor node 10 and the standard time source each time time synchronization occurs; recording a time synchronization anomaly when the absolute value of the deviation Δt is greater than a first preset deviation threshold; and triggering a high-priority alarm when the number of consecutive occurrences of time synchronization anomalies exceeds a first preset number threshold. The first preset deviation threshold and the first preset number threshold can be calibrated according to actual conditions; for example, the first preset deviation threshold can be 100ms, and the first preset number threshold can be 10 times.

[0039] Specifically, it's important to understand that system time synchronization deviations can severely impact event location accuracy. During time synchronization accuracy diagnosis, the deviation Δt between the local clock and the standard time source is recorded for each synchronization. If |Δt| > 100ms, it's recorded as a time synchronization anomaly and displayed in the operating software platform 40. Ten consecutive anomalies trigger a high-priority alarm. Power supply and time synchronization monitoring ensure long-term stable system operation.

[0040] According to one embodiment of the present invention, the self-diagnostic device 100 is configured to perform network optimization and comprehensive alarm, including: the operating software platform 40 sets corresponding alarm thresholds based on the diagnostic results of one or more diagnostic items; when the diagnostic results meet the alarm threshold conditions, it automatically triggers alarm information issued through at least one of pop-up windows, SMS and / or mobile application push.

[0041] Specifically, when optimizing the monitoring network and implementing comprehensive alarms, thresholds are set based on the above diagnostic data (e.g., abnormal frequency ≥ 100 times / week, participation rate < 20%). When the threshold is exceeded, the software automatically pops up a window, sends an SMS to a designated mobile phone number, and simultaneously pushes an APP notification to remind managers to promptly verify and handle the situation. Adaptive thresholds can be used instead of fixed thresholds, or a fixed threshold range can be set and adjusted according to the geological characteristics of the mining area. Furthermore, alarm methods can be expanded to include email, voice calls, and integration with the work order system.

[0042] Corresponding to the above embodiments, the present invention also proposes a self-diagnosis method for a mine microseismic monitoring system.

[0043] The self-diagnosis method for a mine microseismic monitoring system according to embodiments of the present invention, applied to the self-diagnosis device of the mine microseismic monitoring system as described above, may include the following steps: Automatically perform at least two of the following diagnostics: automatic sensor leveling diagnosis, automatic sensor calibration diagnosis, network signal and stability diagnosis, abnormal sensor frequency, abnormal positioning deficit, abnormal positioning participation, abnormal energy calculation, abnormal power supply health, and accurate timing. Summarize and display all diagnostic results; According to preset alarm rules, a tiered alarm is triggered based on the diagnostic results.

[0044] It should be noted that for details not disclosed in the self-diagnosis method of the mine microseismic monitoring system in this embodiment of the invention, please refer to the details disclosed in the self-diagnosis device of the mine microseismic monitoring system in this embodiment of the invention, which will not be repeated here.

[0045] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0046] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0047] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0048] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A self-diagnostic device for a mine microseismic monitoring system, characterized in that, include: The sensor node integrates a gravity angle sensor, a leveling actuator, a standard signal generator, and a timing module. Substation controller, which is communicatively connected to one or more of the sensor nodes, is equipped with a local data buffer and a power management unit; A central diagnostic server, which is communicatively connected to the substation controller, is used to run a diagnostic algorithm engine to perform analysis and statistical diagnosis based on microseismic waveform data. An operating software platform, which is connected to the central diagnostic server, is used to visualize diagnostic results and execute alarms; The self-diagnostic device is configured to automatically perform at least one of the following diagnostic items: Automatic leveling diagnosis based on the gravity angle sensor and the leveling actuator; Automatic sensor calibration and diagnosis based on the standard signal generator; Network signal and stability diagnosis based on the local data buffer status; The diagnostic algorithm engine is used for sensor frequency anomaly diagnosis, positioning residual anomaly diagnosis, positioning participation degree diagnosis, and energy calculation anomaly diagnosis. Power health diagnosis based on the power management unit; Time synchronization accuracy diagnosis based on the time synchronization module.

2. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform automatic sensor leveling diagnosis, including: The gravity angle sensor monitors the installation attitude and tilt angle of the sensor node in real time; When the absolute value of the tilt angle is greater than the first preset tilt angle threshold, the leveling actuator is controlled to perform a leveling operation; After leveling, read the tilt angle again until the absolute value of the tilt angle is less than or equal to the second preset tilt angle threshold. The operating software platform records and displays the leveling status.

3. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform automatic sensor calibration and diagnosis, including: According to a preset cycle or instruction, the standard signal generator is triggered to emit an excitation signal with a known amplitude and frequency; Receive feedback signals collected by the sensor nodes; Calculate the frequency gain G, where frequency gain G = feedback signal amplitude / excitation signal amplitude; When the deviation of the frequency gain G from the nominal value exceeds a first preset gain threshold, the amplifier gain of the sensor node is automatically adjusted.

4. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform network signal and stability diagnostics, including: The substation controller monitors the network connectivity status and stores the data to be uploaded in the local data cache. Calculate in real time the theoretical storage time corresponding to the amount of cached data in the local data cache; When the theoretical storage duration exceeds the first preset duration threshold, it is determined to be a network anomaly and recorded.

5. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform sensor overclocking frequency anomaly diagnosis, including: For a microseismic event, obtain the superior frequency of the waveforms acquired by multiple sensor nodes involved in the monitoring; Calculate the average superior frequency F_avg of all participating sensor nodes; For a single sensor node, if its dominant frequency Fi satisfies |Fi-F_avg| / F_avg>the first preset frequency gain threshold, it is recorded as a frequency anomaly. The system counts and displays the number of frequency anomalies for each sensor node within a preset period.

6. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform at least one of the following: positioning disability anomaly diagnosis, positioning participation degree diagnosis, and energy calculation anomaly diagnosis, including: For the diagnosis of location residual abnormalities, in the location solution of each microseismic event, if the absolute value of the arrival time residual of a certain sensor node is greater than the first preset time threshold, it is recorded as a residual abnormality. For positioning participation diagnosis, the total number of microseismic events N and the number of events M in which a certain sensor node successfully participates in positioning are counted within a preset period, and the participation rate P = M / N is calculated. For energy calculation anomaly diagnosis, for the same microseismic event, the average value E_avg of the energy values ​​obtained by each sensor node is calculated. If the energy value Ei of a certain sensor node satisfies |Ei-E_avg| / E_avg>the first preset energy threshold, it is recorded as an energy anomaly. The operating software platform displays the sensor nodes with the most residual anomalies, the lowest participation rate, or the most energy anomalies within a preset period.

7. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform power health diagnostics, including: The power management unit manages the main power supply and at least two backup batteries; During the preset test time, one of the backup batteries is controlled to discharge the simulated load, while the other backup battery or the main power supply powers the system. Estimate the remaining capacity of the backup battery under test based on the discharge curve; When the remaining capacity is lower than the first preset power threshold, it is marked as power aging; when it is lower than the second preset power threshold, it is marked as power failure and an alarm is triggered.

8. The self-diagnostic device for the mine microseismic monitoring system according to claim 1, characterized in that, The self-diagnostic device is configured to perform timing accuracy diagnostics, including: Each time time synchronization occurs, the deviation Δt between the local clock of the sensor node and the standard time source is recorded; When the absolute value of the deviation Δt is greater than the first preset deviation threshold, it is recorded as a time synchronization anomaly; When the number of consecutive occurrences of time synchronization anomalies exceeds the first preset threshold, a high-priority alarm is triggered.

9. The self-diagnostic device for a mine microseismic monitoring system according to any one of claims 1 to 8, characterized in that, The self-diagnostic device is configured to perform network optimization and integrated alarms, including: The operating software platform sets corresponding alarm thresholds based on the diagnostic results of one or more of the diagnostic items; When the diagnostic results meet the alarm threshold conditions, an alarm message is automatically triggered via at least one of the following methods: pop-up window, SMS and / or mobile application push.

10. A self-diagnosis method for a mine microseismic monitoring system, applied to the self-diagnosis device of the mine microseismic monitoring system as described in any one of claims 1 to 9, characterized in that, The method includes: Automatically perform at least two of the following diagnostics: automatic sensor leveling diagnosis, automatic sensor calibration diagnosis, network signal and stability diagnosis, abnormal sensor frequency, abnormal positioning deficit, abnormal positioning participation, abnormal energy calculation, abnormal power supply health, and accurate timing. Summarize and display all diagnostic results; According to preset alarm rules, a tiered alarm is triggered based on the diagnostic results.

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