Modularized collaborative layout method for gyroscopes and accelerometers

Abstract

The invention discloses a modular collaborative layout method for gyroscopes and accelerometers, relates to the technical field of inertial sensing, and solves the problem of accumulative errors of gyroscope drift and accelerometer zero offset drift in a complex geological environment under a mine, and by calculating a drift change rate Cpj and a zero offset change rate Azp in real time and executing error compensation, the gyroscope drift rate Cpj and the accelerometer zero offset change rate Azp are calculated in real time, and the accelerometer drift rate Cpj and the accelerometer zero offset change rate Azp are calculated in real time. The positioning and path tracking precision of the equipment under the condition of no GPS is improved; meanwhile, aiming at external interference factors such as vibration, noise and air flow in the mine, a data fusion strategy is adjusted in real time by calculating a dynamic interference adaptation index Xw and comparing the dynamic interference adaptation index Xw with a threshold value, and the influence of interference on attitude estimation is effectively reduced; besides, according to the scheme, sensor data are dynamically adjusted through a temperature compensation and humidity correction mechanism, the influence of temperature and humidity changes on the gyroscope and the accelerometer is compensated, the stability and precision of the system in the extreme environment are ensured, and the adaptability and reliability of the mine environment are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of inertial sensing technology, and in particular to a modular collaborative layout method of a gyroscope and an accelerometer. Background Art

[0002] Inertial sensing technology originated in the aerospace field in the mid-20th century. In the early days, high-precision mechanical gyroscopes and accelerometers were used as the core to provide navigation and attitude reference for aircraft. With the breakthrough of MEMS technology, inertial sensors achieved miniaturization, low power consumption and cost optimization at the end of the 20th century, and gradually penetrated into the field of industrial applications. In mine excavation equipment, this technology initially adopted a discrete sensor layout, using gyroscopes to measure angular velocity and accelerometers to sense linear motion. However, this discrete sensor layout method faces the following technical disadvantages in the actual application of mine excavation equipment: Accumulated error (drift problem): In the complex geological environment of the mine, the long-term operation of the tunneling equipment will cause the measurement errors of the gyroscope and accelerometer to accumulate over time, resulting in attitude estimation deviation. This error will increase over time, seriously affecting the precise positioning and path tracking of the equipment, especially when it is impossible to rely on external positioning systems (such as GPS), and the inertial navigation system (INS) may produce large positioning errors.

[0003] External interference: The complex environment in the mine, such as vibration, uneven ground, air flow, and magnetic field interference, will affect the data stability of the inertial sensor. For example, during operation, the tunneling equipment may be disturbed by the vibration of the mine equipment, surface gravel, or underground water flow, which will cause the sensor output to be unstable, further affecting the accuracy of the attitude estimation.

[0004] Poor environmental adaptability: The ambient temperature and humidity in mines vary greatly, and inertial sensors may experience performance degradation or increased drift under extreme conditions. For example, in high temperature, low temperature or high humidity environments, MEMS gyroscopes may experience zero bias drift, resulting in reduced measurement accuracy; in addition, in environments with heavy humidity, the sensor may malfunction due to water vapor intrusion, affecting its stable operation.

[0005] In recent years, the modular collaborative layout method has developed rapidly. By optimizing the sensor layout, improving data synchronization, error compensation capability and environmental adaptability, it has better performance in high-precision attitude estimation and stable control. How to apply the modular collaborative layout method of sensors to the complex geological environment of mines has become a problem that needs to be solved urgently. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a modular collaborative layout method for gyroscopes and accelerometers, which solves the technical shortcomings mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented by the following technical solutions: a modular collaborative layout method of a gyroscope and an accelerometer, comprising the following steps: S1, collecting the original measurement data of the gyroscope and the accelerometer and performing data preprocessing, obtaining the error compensation parameters, and storing the preprocessed data in association with the error compensation parameters, uploading them to the control system of the underground tunneling equipment, and constructing an initial measurement data set; S2. Based on the initial measurement data set, the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp are calculated, and compared with the preset drift threshold U1 and zero bias threshold U2 to generate an inertial error compensation instruction; S3, execute the error compensation instruction, collect the motion state of the tunneling equipment and the ground disturbance data in real time, calculate the sensor dynamic weight adjustment index Waj, adjust the measurement data fusion weight of the gyroscope and accelerometer, and record the interference correction data; S4, monitor external interference data, calculate the dynamic interference adaptation index Xw, associate the motion state of the tunneling equipment with the external interference data, calculate the correlation index Corr, and determine the influence weight of the interference signal on the posture measurement; S5. Calculate the attitude estimation stability index Xst and compare it with the preset attitude stability assessment threshold Qp, optimize the modular collaborative layout of the gyroscope and accelerometer, and improve the positioning and attitude monitoring accuracy in the absence of GPS signals during coal mine tunnel construction.

[0008] Preferably, S1 specifically includes: First, the angular velocity signals ωx, ωy, ωz of the gyroscope and the linear acceleration signals ax, ay, az of the accelerometer are collected respectively. The data collection is periodically sampled at a fixed time interval Δt, and the time alignment of the gyroscope and the accelerometer is ensured by a high-precision clock synchronization mechanism. For the collected raw measurement data, a bandpass filter is first performed to suppress high-frequency noise, and an adaptive mean filter method is combined to remove short-period interference signals. Subsequently, the gyroscope zero bias drift and the accelerometer zero bias are calibrated. The measurement of the zero bias drift is based on the static sampling method. When the sensor remains stationary, N groups of data are continuously collected, and the average is calculated as the reference offset value. In order to further compensate for the measurement error caused by temperature change, the temperature data T of the sensor is collected, and the temperature compensation parameters Tg and Ta are calculated based on the temperature drift model, wherein the data for constructing the temperature drift model is obtained by fitting historical data; After the error compensation parameters are calculated, the error correction calculation is performed in combination with the original measurement data of the gyroscope and accelerometer to obtain the compensated sensor data; finally, the preprocessed original measurement data and the corresponding error compensation parameters are associated and stored in time series, and the initial measurement data set is constructed based on the data index mechanism and finally uploaded to the cloud computing module.

[0009] Preferably, S2 specifically includes: Based on the initial measurement data set, the gyroscope drift change rate Cpj is calculated. The specific calculation formula is as follows: Where, t represents time, ωme represents the mean angular velocity of the gyroscope, ωva represents the variance of the angular velocity of the gyroscope, and ΔT represents the change of the current temperature relative to the calibration temperature, which is used to correct the influence of temperature on the gyroscope drift; ωme(t) represents the mean angular velocity of the gyroscope in the time window t, ωva(t) represents the variance of the angular velocity of the gyroscope in the time window t, Bgy represents the zero bias drift of the gyroscope, and Tgy represents the temperature drift.

[0010] Preferably, S2 specifically further includes: Extract the initial measurement data set and use the following formula to calculate the accelerometer's zero bias change rate Azp: Wherein, t represents time, Acm represents the mean value of accelerometer measurement, Acv represents the measurement variance of accelerometer, Acm(t) represents the mean value of accelerometer measurement within time window t, Acv(t) represents the measurement variance of accelerometer within time window t, ΔBac represents the static zero bias of accelerometer, and Dac represents the vibration interference correction coefficient of accelerometer.

[0011] Preferably, S2 specifically further includes: The drift threshold U1 of the gyroscope and the zero bias threshold U2 of the accelerometer are preset, and the drift change rate Cpj of the gyroscope is compared with the drift threshold U1, and the zero bias change rate Azp of the accelerometer is compared with the zero bias threshold U2. The specific evaluation contents are as follows: If the drift change rate Cpj of the gyroscope ≥ the drift threshold U1, it means that the gyroscope drift exceeds the normal range. At this time, correction is performed and an inertia error compensation instruction is issued; If the accelerometer zero bias change rate Azp ≥ the zero bias threshold U2, it means that the accelerometer zero bias exceeds the normal range. At this time, compensation is performed and an inertia error compensation instruction is issued; If the gyroscope drift change rate Cpj < drift threshold U1, and the accelerometer zero bias change rate Azp < zero bias threshold U2, the sensor drift is considered normal and monitoring continues.

[0012] Preferably, S3 specifically includes: First, the motion state data of the tunneling equipment are collected and analyzed, including the equipment's travel speed Vv, acceleration change rate Avar, and steering angular velocity Yawr. Then, the ground disturbance data are collected and analyzed, including the vibration disturbance amplitude Vib and the ground bump index Rd. Based on the motion state data of the tunneling equipment and the ground disturbance data, the sensor dynamic weight adjustment index Waj is calculated using the following formula: .

[0013] Preferably, S3 specifically further includes: The preset sensor dynamic weight adjustment threshold Q and sensor dynamic weight adjustment index Waj are evaluated. The specific evaluation contents are as follows: If the sensor dynamic weight adjustment index Waj ≥ sensor dynamic weight adjustment threshold Q: it means that the current motion state of the tunneling equipment is normal and the credibility of the inertial sensor data is qualified. At this time, the existing data fusion weight of the gyroscope and accelerometer is maintained; If the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, it means that the current motion state of the tunneling equipment is abnormal and the credibility of the inertial sensor data is unqualified. At this time, the data fusion weight is adjusted.

[0014] Preferably, S3 specifically further includes: When the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, the fusion weight adjustment mode is entered, the gyroscope weight W-gy and the accelerometer weight W-ac are set, and the fusion ratio of the two is adjusted according to the deviation degree of the sensor dynamic weight adjustment index Waj, wherein the sum of the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac is equal to 1; then the gyroscope and accelerometer data fusion operation is performed according to the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac, and during the operation process, the interference correction related data is recorded.

[0015] Preferably, S4 specifically includes: Collect and analyze external interference related data, including vibration interference amplitude Vib, magnetic field intensity change rate Mvar in the mine, and rock deformation index Rd around the equipment, and calculate the dynamic interference adaptation index Xw using the following formula: The preset dynamic interference adaptation threshold W is compared with the dynamic interference adaptation index Xw for evaluation. The specific contents are as follows: If the dynamic interference adaptation index Xw ≤ the dynamic interference adaptation threshold W, it means that the current environmental interference is qualified and the inertial sensor measurement data is normal. The system directly uses the original data for attitude estimation without adjusting the data fusion weight. If the dynamic interference adaptation index Xw>dynamic interference adaptation threshold W: it means that the external environment interference is unqualified and the inertial sensor data is abnormal. At this time, the system protection mechanism is triggered, the trust in the inertial measurement data is adjusted, and the fusion weight of the visual sensor and other mine sensor data is increased.

[0016] Preferably, S4 specifically further includes: The travel speed Vv and acceleration change rate Avar in the motion state related data of the tunneling equipment are extracted respectively, and the vibration interference amplitude Vib and rock deformation index Rd in the external interference related data are correlated and calculated by the following formula to obtain the correlation index Corr: The preset correlation threshold E is compared with the correlation index Corr for evaluation. The specific evaluation contents are as follows: If the correlation index Corr is less than the correlation threshold E, it means that the external interference has no effect on the motion state of the tunneling equipment, and the credibility of the inertial measurement data is qualified; at this time, the current data fusion strategy continues to be used without adjusting the sensor weight; If the correlation index Corr ≥ correlation threshold E: it means that external interference has an impact on the motion state of the tunneling equipment, and the credibility of the inertial measurement data is unqualified; at this time, the external interference compensation mechanism is triggered, including further adjusting the credibility of the inertial measurement data, and introducing other external sensor data including lidar and vibration sensors for compensation.

[0017] Preferably, S5 specifically includes: The attitude estimation stability index Xst is calculated based on the relevant data measured by the inertial sensor in real time, including the external disturbance compensation index Ebc, the short-term attitude change rate Tvar, the gyroscope angular velocity Gyr and the accelerometer acceleration Acm; at the same time, the sensor dynamic weight adjustment index Waj is extracted and calculated in association with the relevant data measured by the inertial sensor, and the attitude estimation stability index Xst is obtained by the following formula: .

[0018] Preferably, S5 specifically further includes: The attitude estimation stability index Xst is evaluated by setting the attitude monitoring stability evaluation threshold Qp. The specific contents are as follows: If the attitude estimation stability index Xst ≤ attitude monitoring stability assessment threshold Qp: it means that the attitude estimation data is normal and the inertial measurement accuracy is qualified; maintain the current sensor arrangement and fusion strategy without adjusting the layout; record the attitude estimation stability index Xst and related data at the same time, store them in the stability database for trend analysis; If the attitude estimation stability index Xst> the attitude monitoring stability assessment threshold Qp, it means that the inertial measurement data is abnormal and the inertial measurement accuracy is unqualified; perform modular collaborative layout optimization and adjust the sensor arrangement, including: Adjust the installation position and angle of the gyroscope and accelerometer; adjust the sensor fixing stability to avoid measurement errors due to looseness or resonance; adjust the data fusion algorithm; trigger system adaptive optimization, recalculate the attitude monitoring stability assessment threshold Qp to make it more in line with the current working conditions, and adjust the data fusion parameters.

[0019] The present invention provides a modular collaborative layout method for gyroscopes and accelerometers, which has the following beneficial effects: (1) The modular collaborative layout method of gyroscopes and accelerometers effectively solves the cumulative error problem of gyroscopes and accelerometers in the complex geological environment of mines by optimizing the modular collaborative layout of gyroscopes and accelerometers, especially the drift of gyroscopes and the zero bias drift of accelerometers. With the long-term operation of tunneling equipment, the measurement error of inertial sensors will gradually accumulate, resulting in attitude estimation deviation. However, through real-time calculation and error compensation of the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp, the error can be effectively reduced, thereby improving the equipment positioning and path tracking accuracy, especially in the absence of external positioning system (such as GPS) support, to ensure that the inertial navigation system (INS) can work stably. (2) The modular collaborative layout method of the gyroscope and accelerometer is aimed at the influence of external interference. The external interference factors inside the mine, such as ground vibration, noise of mine equipment, air flow, groundwater flow and magnetic field changes, will affect the stability of inertial sensor data. This solution collects and analyzes external disturbance related data in real time, calculates the dynamic interference adaptation index Xw and compares it with the preset interference adaptation threshold. It can identify interference signals and trigger the system protection mechanism when the interference is strong. It adjusts the data fusion strategy, thereby improving the system's adaptability and data accuracy in complex environments. (3) The modular collaborative layout method of the gyroscope and accelerometer addresses the problem of poor environmental adaptability. The ambient temperature and humidity in the mine vary greatly. Inertial sensors may experience performance degradation or increased drift under extreme temperature and humidity conditions. This solution uses a temperature compensation and humidity correction mechanism to dynamically adjust the sensor's measurement data based on real-time temperature data T to compensate for the effects of temperature and humidity on the gyroscope and accelerometer, thereby ensuring the stability and accuracy of the inertial sensor under different environmental conditions and improving the environmental adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a schematic diagram of the steps of the modular collaborative layout method of the gyroscope and the accelerometer. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1 See also Figure 1 The present invention provides a modular collaborative layout method of a gyroscope and an accelerometer, comprising the following steps: S1, collecting the original measurement data of the gyroscope and the accelerometer and performing data preprocessing, obtaining the error compensation parameters, and storing the preprocessed data in association with the error compensation parameters, uploading them to the control system of the underground tunneling equipment, and constructing an initial measurement data set; S2. Based on the initial measurement data set, the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp are calculated, and compared with the preset drift threshold U1 and zero bias threshold U2 to generate an inertial error compensation instruction; S3, execute the error compensation instruction, collect the motion state of the tunneling equipment and the ground disturbance data in real time, calculate the sensor dynamic weight adjustment index Waj, adjust the measurement data fusion weight of the gyroscope and accelerometer, and record the interference correction data; S4, monitor external interference data, calculate the dynamic interference adaptation index Xw, associate the motion state of the tunneling equipment with the external interference data, calculate the correlation index Corr, and determine the influence weight of the interference signal on the posture measurement; S5. Calculate the attitude estimation stability index Xst and compare it with the preset attitude stability assessment threshold Qp, optimize the modular collaborative layout of the gyroscope and accelerometer, and improve the positioning and attitude monitoring accuracy in the absence of GPS signals during coal mine tunnel construction.

[0023] In this embodiment, the present invention can significantly improve the positioning and attitude monitoring accuracy in the absence of GPS signals in coal mine tunnel construction through a modular collaborative layout method of gyroscopes and accelerometers; in step S1, by collecting the original measurement data of the gyroscope and the accelerometer and preprocessing them, the error compensation parameters are obtained, which can provide basic data for subsequent error correction and ensure the accuracy and stability of the data; step S2 calculates the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp, and compares them with the preset drift threshold U1 and zero bias threshold U2, and the generated error compensation instruction can effectively correct the system drift and zero bias error; step S3 adjusts the fusion weight of the measurement data in real time through the dynamic weight adjustment index Waj, which can improve the reliability of the data and reduce the influence of external interference; step S4 calculates the dynamic interference adaptation index Xw and associates it with the external interference data to optimize the interference correction strategy and reduce the influence of environmental interference on attitude estimation; step S5 calculates the attitude estimation stability index Xst and compares it with the preset stability evaluation threshold Qp to optimize the modular layout, and further improves the measurement accuracy and system stability in the GPS-free environment.

[0024] Example 2 S1 specifically includes: First, the angular velocity signals ωx, ωy, ωz of the gyroscope and the linear acceleration signals ax, ay, az of the accelerometer are collected respectively. The data collection is periodically sampled at a fixed time interval Δt, and the time alignment of the gyroscope and the accelerometer is ensured by a high-precision clock synchronization mechanism. For the collected raw measurement data, a bandpass filter is first performed to suppress high-frequency noise, and an adaptive mean filter method is combined to remove short-period interference signals. Subsequently, the gyroscope zero bias drift and the accelerometer zero bias are calibrated. The measurement of the zero bias drift is based on the static sampling method. When the sensor remains stationary, N groups of data are continuously collected, and the average is calculated as the reference offset value. In order to further compensate for the measurement error caused by temperature change, the temperature data T of the sensor is collected, and the temperature compensation parameters Tg and Ta are calculated based on the temperature drift model, wherein the data for constructing the temperature drift model is obtained by fitting historical data; After the error compensation parameters are calculated, the error correction calculation is performed in combination with the original measurement data of the gyroscope and accelerometer to obtain the compensated sensor data; finally, the preprocessed original measurement data and the corresponding error compensation parameters are associated and stored in time series, and the initial measurement data set is constructed based on the data index mechanism and finally uploaded to the cloud computing module.

[0025] In this embodiment, step S1 ensures the time alignment of the gyroscope angular velocity signals ωx, ωy, ωz and the accelerometer linear acceleration signals ax, ay, az through a high-precision clock synchronization mechanism and periodic sampling at a fixed time interval Δt, thereby ensuring the consistency of data acquisition; bandpass filtering and adaptive mean filtering methods are used to effectively suppress high-frequency noise and short-period interference, thereby improving the accuracy and stability of the data; the gyroscope zero bias drift and accelerometer zero bias are calibrated through a static sampling method, thereby further eliminating the offset error of the sensor; the temperature drift model is combined to calculate the sensor temperature compensation parameters Tg and Ta, which can It can effectively correct the errors caused by temperature changes and improve the environmental adaptability of the measurement; by performing error correction calculations, the compensated sensor data is obtained, which improves the reliability of the data; finally, the preprocessed data and error compensation parameters are stored in a time series relationship and uploaded to the cloud computing module to ensure the real-time and traceability of the data; this module can provide accurate and reliable initial data support for subsequent data fusion and error compensation in the system, greatly improving the accuracy and stability of the coordinated measurement of the gyroscope and accelerometer, which is of great significance for the construction positioning and attitude monitoring of coal mine tunnels without GPS signals.

[0026] Example 3 S2 specifically includes: Based on the initial measurement data set, the gyroscope drift change rate Cpj is calculated. The specific calculation formula is as follows: Where, t represents time, ωme represents the mean angular velocity of the gyroscope, ωva represents the variance of the angular velocity of the gyroscope, and ΔT represents the change of the current temperature relative to the calibration temperature, which is used to correct the influence of temperature on the gyroscope drift; ωme(t) represents the mean angular velocity of the gyroscope in the time window t, ωva(t) represents the variance of the angular velocity of the gyroscope in the time window t, Bgy represents the zero bias drift of the gyroscope, and Tgy represents the temperature drift.

[0027] S2 specifically includes: Extract the initial measurement data set and use the following formula to calculate the accelerometer's zero bias change rate Azp: Wherein, t represents time, Acm represents the mean value of accelerometer measurement, Acv represents the measurement variance of accelerometer, Acm(t) represents the mean value of accelerometer measurement within time window t, Acv(t) represents the measurement variance of accelerometer within time window t, ΔBac represents the static zero bias of accelerometer, and Dac represents the vibration interference correction coefficient of accelerometer.

[0028] S2 specifically includes: The drift threshold U1 of the gyroscope and the zero bias threshold U2 of the accelerometer are preset, and the drift change rate Cpj of the gyroscope is compared with the drift threshold U1, and the zero bias change rate Azp of the accelerometer is compared with the zero bias threshold U2. The specific evaluation contents are as follows: If the drift change rate Cpj of the gyroscope ≥ the drift threshold U1, it means that the gyroscope drift exceeds the normal range. At this time, correction is performed and an inertia error compensation instruction is issued; If the accelerometer zero bias change rate Azp ≥ the zero bias threshold U2, it means that the accelerometer zero bias exceeds the normal range. At this time, compensation is performed and an inertia error compensation instruction is issued; If the gyroscope drift change rate Cpj < drift threshold U1, and the accelerometer zero bias change rate Azp < zero bias threshold U2, the sensor drift is considered normal and monitoring continues.

[0029] S3 specifically includes: First, the motion state data of the tunneling equipment are collected and analyzed, including the equipment's travel speed Vv, acceleration change rate Avar, and steering angular velocity Yawr. Then, the ground disturbance data are collected and analyzed, including the vibration disturbance amplitude Vib and the ground bump index Rd. Based on the motion state data of the tunneling equipment and the ground disturbance data, the sensor dynamic weight adjustment index Waj is calculated using the following formula: .

[0030] S3 specifically includes: The preset sensor dynamic weight adjustment threshold Q and sensor dynamic weight adjustment index Waj are evaluated. The specific evaluation contents are as follows: If the sensor dynamic weight adjustment index Waj ≥ sensor dynamic weight adjustment threshold Q: it means that the current motion state of the tunneling equipment is normal and the credibility of the inertial sensor data is qualified. At this time, the existing data fusion weight of the gyroscope and accelerometer is maintained; If the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, it means that the current motion state of the tunneling equipment is abnormal and the credibility of the inertial sensor data is unqualified. At this time, the data fusion weight is adjusted.

[0031] S3 specifically includes: When the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, the fusion weight adjustment mode is entered, the gyroscope weight W-gy and the accelerometer weight W-ac are set, and the fusion ratio of the two is adjusted according to the deviation degree of the sensor dynamic weight adjustment index Waj, wherein the sum of the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac is equal to 1; then the gyroscope and accelerometer data fusion operation is performed according to the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac, and during the operation process, the interference correction related data is recorded.

[0032] S4 specifically includes: Collect and analyze external interference related data, including vibration interference amplitude Vib, magnetic field intensity change rate Mvar in the mine, and rock deformation index Rd around the equipment, and calculate the dynamic interference adaptation index Xw using the following formula: The preset dynamic interference adaptation threshold W is compared with the dynamic interference adaptation index Xw for evaluation. The specific contents are as follows: If the dynamic interference adaptation index Xw ≤ the dynamic interference adaptation threshold W, it means that the current environmental interference is qualified and the inertial sensor measurement data is normal. The system directly uses the original data for attitude estimation without adjusting the data fusion weight. If the dynamic interference adaptation index Xw>dynamic interference adaptation threshold W: it means that the external environment interference is unqualified and the inertial sensor data is abnormal. At this time, the system protection mechanism is triggered, the trust in the inertial measurement data is adjusted, and the fusion weight of the visual sensor and other mine sensor data is increased.

[0033] S4 specifically includes: The travel speed Vv and acceleration change rate Avar in the motion state related data of the tunneling equipment are extracted respectively, and the vibration interference amplitude Vib and rock deformation index Rd in the external interference related data are correlated and calculated by the following formula to obtain the correlation index Corr: The preset correlation threshold E is compared with the correlation index Corr for evaluation. The specific evaluation contents are as follows: If the correlation index Corr is less than the correlation threshold E, it means that the external interference has no effect on the motion state of the tunneling equipment, and the credibility of the inertial measurement data is qualified; at this time, the current data fusion strategy continues to be used without adjusting the sensor weight; If the correlation index Corr ≥ correlation threshold E: it means that external interference has an impact on the motion state of the tunneling equipment, and the credibility of the inertial measurement data is unqualified; at this time, the external interference compensation mechanism is triggered, including further adjusting the credibility of the inertial measurement data, and introducing other external sensor data including lidar and vibration sensors for compensation.

[0034] S5 specifically includes: The attitude estimation stability index Xst is calculated based on the relevant data measured by the inertial sensor in real time, including the external disturbance compensation index Ebc, the short-term attitude change rate Tvar, the gyroscope angular velocity Gyr and the accelerometer acceleration Acm; at the same time, the sensor dynamic weight adjustment index Waj is extracted and calculated in association with the relevant data measured by the inertial sensor, and the attitude estimation stability index Xst is obtained by the following formula: .

[0035] S5 specifically includes: The attitude estimation stability index Xst is evaluated by setting the attitude monitoring stability evaluation threshold Qp. The specific contents are as follows: If the attitude estimation stability index Xst ≤ attitude monitoring stability assessment threshold Qp: it means that the attitude estimation data is normal and the inertial measurement accuracy is qualified; maintain the current sensor arrangement and fusion strategy without adjusting the layout; record the attitude estimation stability index Xst and related data at the same time, store them in the stability database for trend analysis; If the attitude estimation stability index Xst> the attitude monitoring stability assessment threshold Qp, it means that the inertial measurement data is abnormal and the inertial measurement accuracy is unqualified; perform modular collaborative layout optimization and adjust the sensor arrangement, including: Adjust the installation position and angle of the gyroscope and accelerometer; adjust the sensor fixing stability to avoid measurement errors due to looseness or resonance; adjust the data fusion algorithm; trigger system adaptive optimization, recalculate the attitude monitoring stability assessment threshold Qp to make it more in line with the current working conditions, and adjust the data fusion parameters.

[0036] In this embodiment, step S2 extracts the initial measurement data set and calculates the accelerometer bias change rate Azp, so as to timely detect the accelerometer bias change and ensure the accuracy of the accelerometer data. By comparing and analyzing the preset gyroscope drift threshold U1 and bias threshold U2 with the gyroscope drift change rate Cpj and the accelerometer bias change rate Azp, the drift state of the sensor can be detected in real time. When the deviation exceeds the normal range, correction is performed and an inertia error compensation instruction is issued, thereby ensuring the high-precision output of the sensor. Step S3 collects the motion state-related data of the tunneling equipment (such as the travel speed Vv, the acceleration change rate Avar, the steering angular velocity Yawr) and the ground disturbance data (such as the vibration disturbance amplitude Vib, the ground bump index Rd), calculates the sensor dynamic weight adjustment index Waj, realizes the dynamic monitoring of the equipment motion state, and can dynamically adjust the data fusion weight in combination with the preset weight adjustment threshold Q, thereby improving the credibility of the sensor data. Step S4 By collecting external interference related data (such as vibration interference amplitude Vib, magnetic field intensity change rate Mvar in the mine, and rock deformation index Rd around the equipment), the dynamic interference adaptation index Xw is calculated and compared with the preset interference adaptation threshold W to determine the impact of external interference on the inertial sensor. If necessary, the data fusion strategy is adjusted to ensure the stability of attitude estimation; interference compensation is performed by calculating the correlation index Corr and the preset correlation threshold E to ensure data quality; step S5 calculates the attitude estimation stability index Xst, combined with external disturbance compensation index Ebc, short-term attitude change rate Tvar, gyroscope angular velocity Gyr and accelerometer acceleration Acm and other data, to evaluate the stability of attitude estimation in real time. When the stability index exceeds the threshold Qp, the modular collaborative layout is optimized, the sensor configuration and data fusion strategy are adjusted to further improve the measurement accuracy and stability of the system, ultimately providing more accurate and reliable support for attitude monitoring and positioning in coal mine tunnel construction.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A modular collaborative layout method of a gyroscope and an accelerometer, characterized in that: The following steps are involved: S1, collecting the original measurement data of the gyroscope and the accelerometer and performing data preprocessing, obtaining the error compensation parameters, and storing the preprocessed data in association with the error compensation parameters, uploading them to the control system of the underground tunneling equipment, and constructing an initial measurement data set; S2. Based on the initial measurement data set, the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp are calculated, and compared with the preset drift threshold U1 and zero bias threshold U2 to generate an inertial error compensation instruction; S3, execute the error compensation instruction, collect the motion state of the tunneling equipment and the ground disturbance data in real time, calculate the sensor dynamic weight adjustment index Waj, adjust the measurement data fusion weight of the gyroscope and accelerometer, and record the interference correction data; S4, monitor external interference data, calculate the dynamic interference adaptation index Xw, associate the motion state of the tunneling equipment with the external interference data, calculate the correlation index Corr, and determine the influence weight of the interference signal on the posture measurement; S5. Calculate the attitude estimation stability index Xst and compare it with the preset attitude stability assessment threshold Qp, optimize the modular collaborative layout of the gyroscope and accelerometer, and improve the positioning and attitude monitoring accuracy in the absence of GPS signals during coal mine tunnel construction.

2. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S1 specifically includes: First, the angular velocity signals ωx, ωy, ωz of the gyroscope and the linear acceleration signals ax, ay, az of the accelerometer are collected respectively. The data collection is periodically sampled at a fixed time interval Δt, and the time alignment of the gyroscope and the accelerometer is ensured by a high-precision clock synchronization mechanism. For the collected raw measurement data, a bandpass filter is first performed to suppress high-frequency noise, and an adaptive mean filter method is combined to remove short-period interference signals. Subsequently, the gyroscope zero bias drift and the accelerometer zero bias are calibrated. The measurement of the zero bias drift is based on the static sampling method. When the sensor remains stationary, N groups of data are continuously collected, and the average is calculated as the reference offset value. In order to further compensate for the measurement error caused by temperature change, the temperature data T of the sensor is collected, and the temperature compensation parameters Tg and Ta are calculated based on the temperature drift model, wherein the data for constructing the temperature drift model is obtained by fitting historical data; After the error compensation parameters are calculated, the error correction calculation is performed in combination with the original measurement data of the gyroscope and accelerometer to obtain the compensated sensor data; finally, the preprocessed original measurement data and the corresponding error compensation parameters are associated and stored in time series, and the initial measurement data set is constructed based on the data index mechanism and finally uploaded to the cloud computing module.

3. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S2 specifically includes: Based on the initial measurement data set, the gyroscope drift change rate Cpj is calculated. The specific calculation formula is as follows: Where, t represents time, ωme represents the mean angular velocity of the gyroscope, ωva represents the variance of the angular velocity of the gyroscope, and ΔT represents the change of the current temperature relative to the calibration temperature, which is used to correct the influence of temperature on the gyroscope drift; ωme(t) represents the mean angular velocity of the gyroscope in the time window t, ωva(t) represents the variance of the angular velocity of the gyroscope in the time window t, Bgy represents the zero bias drift of the gyroscope, and Tgy represents the temperature drift.

4. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S2 specifically includes: Extract the initial measurement data set and use the following formula to calculate the accelerometer's zero bias change rate Azp: Wherein, t represents time, Acm represents the mean value of accelerometer measurement, Acv represents the measurement variance of accelerometer, Acm(t) represents the mean value of accelerometer measurement within time window t, Acv(t) represents the measurement variance of accelerometer within time window t, ΔBac represents the static zero bias of accelerometer, and Dac represents the vibration interference correction coefficient of accelerometer.

5. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S2 specifically includes: The drift threshold U1 of the gyroscope and the zero bias threshold U2 of the accelerometer are preset, and the drift change rate Cpj of the gyroscope is compared with the drift threshold U1, and the zero bias change rate Azp of the accelerometer is compared with the zero bias threshold U2. The specific evaluation contents are as follows: If the drift change rate Cpj of the gyroscope ≥ the drift threshold U1, it means that the gyroscope drift exceeds the normal range. At this time, correction is performed and an inertia error compensation instruction is issued; If the accelerometer zero bias change rate Azp ≥ zero bias threshold U2, it means that the accelerometer zero bias exceeds the normal range, and compensation is performed and an inertia error compensation instruction is issued; If the gyroscope drift change rate Cpj < drift threshold U1, and the accelerometer zero bias change rate Azp < zero bias threshold U2, the sensor drift is considered normal and monitoring continues.

6. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S3 specifically includes: First, the motion state data of the tunneling equipment are collected and analyzed, including the equipment's travel speed Vv, acceleration change rate Avar, and steering angular velocity Yawr. Then, the ground disturbance data are collected and analyzed, including the vibration disturbance amplitude Vib and the ground bump index Rd. Based on the motion state data of the tunneling equipment and the ground disturbance data, the sensor dynamic weight adjustment index Waj is calculated using the following formula: 。 7. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S3 specifically includes: The preset sensor dynamic weight adjustment threshold Q and sensor dynamic weight adjustment index Waj are evaluated. The specific evaluation contents are as follows: If the sensor dynamic weight adjustment index Waj ≥ sensor dynamic weight adjustment threshold Q: it means that the current motion state of the tunneling equipment is normal and the credibility of the inertial sensor data is qualified. At this time, the existing data fusion weight of the gyroscope and accelerometer is maintained; If the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, it means that the current motion state of the tunneling equipment is abnormal and the credibility of the inertial sensor data is unqualified. At this time, the data fusion weight is adjusted.

8. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S3 specifically includes: When the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, the fusion weight adjustment mode is entered, the gyroscope weight W-gy and the accelerometer weight W-ac are set, and the fusion ratio of the two is adjusted according to the deviation degree of the sensor dynamic weight adjustment index Waj, wherein the sum of the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac is equal to 1; then the gyroscope and accelerometer data fusion operation is performed according to the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac, and during the operation process, the interference correction related data is recorded.

9. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S4 specifically includes: Collect and analyze external interference related data, including vibration interference amplitude Vib, magnetic field intensity change rate Mvar in the mine, and rock deformation index Rd around the equipment, and calculate the dynamic interference adaptation index Xw using the following formula: The preset dynamic interference adaptation threshold W is compared with the dynamic interference adaptation index Xw for evaluation. The specific contents are as follows: If the dynamic interference adaptation index Xw ≤ the dynamic interference adaptation threshold W, it means that the current environmental interference is qualified and the inertial sensor measurement data is normal. The system directly uses the original data for attitude estimation without adjusting the data fusion weight. If the dynamic interference adaptation index Xw>dynamic interference adaptation threshold W: it means that the external environment interference is unqualified and the inertial sensor data is abnormal. At this time, the system protection mechanism is triggered, the trust in the inertial measurement data is adjusted, and the fusion weight of the visual sensor and other mine sensor data is increased.

10. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S4 specifically includes: The travel speed Vv and acceleration change rate Avar in the motion state related data of the tunneling equipment are extracted respectively, and the vibration interference amplitude Vib and rock deformation index Rd in the external interference related data are correlated and calculated by the following formula to obtain the correlation index Corr: The preset correlation threshold E is compared with the correlation index Corr for evaluation. The specific evaluation contents are as follows: If the correlation index Corr is less than the correlation threshold E, it means that the external interference has no effect on the motion state of the tunneling equipment, and the credibility of the inertial measurement data is qualified; at this time, the current data fusion strategy continues to be used without adjusting the sensor weight; If the correlation index Corr ≥ correlation threshold E: it means that external interference has an impact on the motion state of the tunneling equipment, and the credibility of the inertial measurement data is unqualified; at this time, the external interference compensation mechanism is triggered, including further adjusting the credibility of the inertial measurement data, and introducing other external sensor data including lidar and vibration sensors for compensation.

11. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S5 specifically includes: The attitude estimation stability index Xst is calculated based on the relevant data measured by the inertial sensor in real time, including the external disturbance compensation index Ebc, the short-term attitude change rate Tvar, the gyroscope angular velocity Gyr and the accelerometer acceleration Acm; at the same time, the sensor dynamic weight adjustment index Waj is extracted and calculated in association with the relevant data measured by the inertial sensor, and the attitude estimation stability index Xst is obtained by the following formula: 。 12. The modular collaborative layout method of gyroscopes and accelerometers according to claim 1, characterized in that: S5 specifically includes: The attitude estimation stability index Xst is evaluated by setting the attitude monitoring stability evaluation threshold Qp. The specific contents are as follows: If the attitude estimation stability index Xst ≤ the attitude monitoring stability assessment threshold Qp: it means that the attitude estimation data is normal and the inertial measurement accuracy is qualified; maintain the current sensor arrangement and fusion strategy without adjusting the layout; record the attitude estimation stability index Xst and related data at the same time, store them in the stability database for trend analysis; If the attitude estimation stability index Xst> the attitude monitoring stability assessment threshold Qp, it means that the inertial measurement data is abnormal and the inertial measurement accuracy is unqualified; perform modular collaborative layout optimization and adjust the sensor arrangement, including: Adjust the installation position and angle of the gyroscope and accelerometer; adjust the sensor fixing stability to avoid measurement errors due to looseness or resonance; adjust the data fusion algorithm; trigger system adaptive optimization, recalculate the attitude monitoring stability assessment threshold Qp to make it more in line with the current working conditions, and adjust the data fusion parameters.

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