Modular collaborative layout method for gyroscope and accelerometer

Abstract

The present invention discloses a modular collaborative layout method for gyroscopes and accelerometers, which relates to the field of inertial sensing technology. This technical solution solves the cumulative error problems of gyroscope drift and accelerometer zero-bias drift in the complex geological environment underground in mines. By calculating the drift change rate Cpj and the zero-bias change rate Azp in real time and performing error compensation, the positioning and path tracking accuracy of the device under GPS-free conditions is improved. At the same time, for external interference factors such as vibration, noise, and air flow in mines, the scheme calculates the dynamic interference adaptation index Xw and compares it with the threshold value to adjust the data fusion strategy in real time, effectively reducing the impact of interference on attitude estimation. In addition, the scheme also dynamically adjusts the sensor data through a temperature compensation and humidity correction mechanism to compensate for the influence of temperature and humidity changes on gyroscopes and accelerometers, ensuring the stability and accuracy of the system in extreme environments, and enhancing the adaptability and reliability of the mine environment.

Description

Technical Field

[0001] The present invention relates to the technical field of inertial sensing, and specifically to a modular collaborative layout method for gyroscopes and accelerometers. 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 the core, providing navigation and attitude references for aircraft. With the breakthrough of MEMS technology, inertial sensors were miniaturized, low-power, and cost-optimized at the end of the 20th century, and gradually penetrated into the industrial application field. In mine tunneling equipment, this technology initially adopted a discrete sensor layout, where the gyroscope measures angular velocity and the accelerometer senses linear motion. However, this discrete sensor layout method faces the following technical drawbacks in the actual application of mine tunneling equipment:

[0003] Cumulative error (drift problem): In the complex geological environment underground in mines, the long-term operation of tunneling equipment will cause the measurement errors of gyroscopes and accelerometers to gradually 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 in the case where external positioning systems (such as GPS) cannot be relied upon, the inertial navigation system (INS) may generate large positioning errors.

[0004] Influence of external interference: The complex environment in mines, such as vibration, uneven ground, air flow, and magnetic field interference, will all affect the data stability of inertial sensors. For example, during the operation of tunneling equipment, it may be interfered by the vibration of mine equipment, surface gravel, or underground water flow, resulting in unstable sensor output and further affecting the accuracy of attitude estimation.

[0005] Poor environmental adaptability: The environmental temperature and humidity change greatly underground in mines. Under extreme conditions, inertial sensors may experience performance degradation or increased drift. For example, in high-temperature, low-temperature, or high-humidity environments, MEMS gyroscopes may exhibit zero-offset drift, resulting in reduced measurement accuracy. In addition, in a humid environment, sensors may malfunction due to water vapor intrusion, affecting their stable operation.

[0006] In recent years, the modular collaborative layout method has developed rapidly. By optimizing the sensor arrangement, it improves data synchronization, error compensation ability, and environmental adaptability, making it have 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 an urgent problem to be solved at present. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides a modular collaborative layout method for gyroscopes and accelerometers, which solves the technical drawbacks mentioned in the background art.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A modular collaborative layout method for gyroscopes and accelerometers, comprising the following steps:

[0009] S1. Collect the original measurement data of the gyroscope and the accelerometer and perform data preprocessing to obtain error compensation parameters, and associate and store the preprocessed data with the error compensation parameters, and upload them to the control system of the underground tunneling equipment to construct an initial measurement data set;

[0010] S2. Based on the initial measurement data set, calculate the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp, and compare them with the preset drift threshold U1 and zero bias threshold U2 to generate an inertial error compensation instruction;

[0011] 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 the accelerometer, and record the interference correction data;

[0012] S4. Monitor the 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 judge the influence weight of the interference signal on the attitude measurement;

[0013] S5. Calculate the attitude estimation stability index Xst, and compare it with the preset attitude stability evaluation threshold Qp to optimize the modular collaborative layout of the gyroscope and the accelerometer, and improve the positioning and attitude monitoring accuracy under the condition of no GPS signal in coal mine roadway construction.

[0014] Preferably, S1 specifically includes:

[0015] First, collect the angular velocity signals ωx, ωy, ωz of the gyroscope and the linear acceleration signals ax, ay, az of the accelerometer respectively. Among them, data collection is performed by periodic sampling at a fixed time interval Δt, and the time alignment of the gyroscope and the accelerometer is ensured through a high-precision clock synchronization mechanism; for the collected original measurement data, first perform band-pass filtering to suppress high-frequency noise, and combine the adaptive mean filtering method to remove short-period interference signals; subsequently, calibrate the gyroscope zero bias drift and the accelerometer zero bias. Among them, the measurement of the zero bias drift is based on the static sampling method. When the sensor is in a stationary state, continuously collect N groups of data and calculate the mean value as the reference offset value;

[0016] To further compensate for the measurement error caused by temperature changes, the temperature data T of the sensor is collected, a temperature drift model is constructed and based on it, the temperature compensation parameters Tg and Ta are calculated, where the data for constructing the temperature drift model is obtained by fitting historical data;

[0017] After the error compensation parameters are calculated, combined with the original measurement data of the gyroscope and accelerometer, error correction calculation is performed to obtain the compensated sensor data; finally, the preprocessed original measurement data and the corresponding error compensation parameters are stored in association according to the time series, and an initial measurement data set is constructed based on the data indexing mechanism, and finally uploaded to the cloud computing module.

[0018] Preferably, S2 specifically includes:

[0019] Based on the initial measurement data set, calculate the drift change rate Cpj of the gyroscope, and the specific calculation formula is as follows:

[0020]

[0021] In the formula, t represents time, ωme represents the average angular velocity of the gyroscope, ωva represents the variance of the angular velocity of the gyroscope, ΔT represents the change in the current temperature relative to the calibration temperature, and is used to correct the influence of temperature on the gyroscope drift;

[0022] ωme(t) represents the average angular velocity of the gyroscope within the time window t, ωva(t) represents the variance of the angular velocity of the gyroscope within the time window t, Bgy represents the zero bias drift of the gyroscope, and Tgy represents the temperature drift.

[0023] Preferably, S2 specifically further includes:

[0024] Extract the initial measurement data set, and use the following formula to calculate and obtain the zero bias change rate Azp of the accelerometer:

[0025]

[0026] In the formula, t represents time, Acm represents the average measurement of the accelerometer, Acv represents the variance of the measurement of the accelerometer, Acm(t) represents the average measurement of the accelerometer within the time window t, Acv(t) represents the variance of the measurement of the accelerometer within the time window t, ΔBac represents the static zero bias of the accelerometer, and Dac represents the vibration interference correction coefficient of the accelerometer.

[0027] Preferably, S2 specifically further includes:

[0028] Preset the drift threshold U1 of the gyroscope and the zero bias threshold U2 of the accelerometer, and compare and analyze the drift change rate Cpj of the gyroscope with the drift threshold U1 and the zero bias change rate Azp of the accelerometer with the zero bias threshold U2. The specific evaluation content is as follows:

[0029] If the drift change rate Cpj of the gyroscope ≥ the drift threshold U1, it indicates that the gyroscope drift exceeds the normal range. At this time, correction is performed and an inertial error compensation instruction is generated.

[0030] If the zero-bias change rate Azp of the accelerometer ≥ the zero-bias threshold U2, it indicates that the accelerometer zero-bias exceeds the normal range. At this time, compensation is performed and an inertial error compensation instruction is generated.

[0031] If the drift change rate Cpj of the gyroscope < the drift threshold U1 and the zero-bias change rate Azp of the accelerometer < the zero-bias threshold U2, it is considered that the sensor drift is normal. At this time, continuous monitoring is carried out.

[0032] Preferably, S3 specifically includes:

[0033] First, collect and analyze data related to the motion state of the tunneling equipment, including the traveling speed Vv, the acceleration change rate Avar, and the steering angular velocity Yawr of the equipment. Then, collect and analyze data related to ground disturbances, including the vibration interference amplitude Vib and the ground bump index Rd. Based on the data related to the motion state of the tunneling equipment and the data related to ground disturbances, calculate the sensor dynamic weight adjustment index Waj through the following formula:

[0034] 。

[0035] Preferably, S3 specifically further includes:

[0036] Evaluate the preset sensor dynamic weight adjustment threshold Q and the sensor dynamic weight adjustment index Waj. The specific evaluation content is as follows:

[0037] If the sensor dynamic weight adjustment index Waj ≥ the sensor dynamic weight adjustment threshold Q: It indicates that the current motion state of the tunneling equipment is normal and the credibility of the inertial sensor data is qualified. At this time, maintain the existing data fusion weights of the gyroscope and the accelerometer.

[0038] If the sensor dynamic weight adjustment index Waj < the sensor dynamic weight adjustment threshold Q: It indicates that the current motion state of the tunneling equipment is abnormal and the credibility of the inertial sensor data is unqualified. At this time, adjust the data fusion weights.

[0039] Preferably, S3 specifically further includes:

[0040] When the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, enter the fusion weight adjustment mode, set the gyroscope weight W-gy and the accelerometer weight W-ac, and adjust the fusion ratio of the two according to the deviation degree of the sensor dynamic weight adjustment index Waj. Among them, the sum of the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac is equal to 1; then, perform the gyroscope and accelerometer data fusion operation according to the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac, and record the interference correction related data during the operation process.

[0041] Preferably, S4 specifically includes:

[0042] Collect and analyze the external interference related data, including the vibration interference amplitude Vib, the change rate of the magnetic field strength Mvar in the mine, and the rock deformation index Rd around the equipment, and calculate the dynamic interference adaptation index Xw by combining the following formula:

[0043]

[0044] Compare and evaluate the preset dynamic interference adaptation threshold W with the dynamic interference adaptation index Xw. The specific content is as follows:

[0045] If the dynamic interference adaptation index Xw ≤ the dynamic interference adaptation threshold W: it indicates that the current environmental interference is qualified, the inertial sensor measurement data is normal, and the system directly uses the original data for attitude estimation at this time without adjusting the data fusion weight;

[0046] If the dynamic interference adaptation index Xw > the dynamic interference adaptation threshold W: it indicates that the external environmental interference is unqualified and the inertial sensor data is abnormal. At this time, trigger the system protection mechanism, adjust the trust degree of the inertial measurement data, and increase the fusion weight of the visual sensor and other mine sensor data.

[0047] Preferably, S4 specifically further includes:

[0048] Extract the traveling speed Vv and the acceleration change rate Avar in the tunneling equipment motion state related data respectively, and correlate and calculate them with the vibration interference amplitude Vib and the rock deformation index Rd in the external interference related data through the following formula to obtain the correlation index Corr:

[0049]

[0050] Compare and evaluate the preset correlation threshold E with the correlation index Corr. The specific evaluation content is as follows:

[0051] If the correlation index Corr < the correlation threshold E: it indicates that 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, continue to adopt the current data fusion strategy without adjusting the sensor weights;

[0052] If the correlation index Corr ≥ the correlation threshold E: it indicates 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, trigger the external interference compensation mechanism, including further adjusting the trust level of the inertial measurement data, and at the same time introducing data of other external sensors such as lidar and vibration sensors for compensation.

[0053] Preferably, S5 specifically includes:

[0054] Collect relevant data in real time based on inertial sensors and calculate the attitude estimation stability index Xst, 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, extract the sensor dynamic weight adjustment index Waj and calculate it in association with the relevant data measured by the inertial sensors. Obtain the attitude estimation stability index Xst through the following formula:

[0055] 。

[0056] Preferably, S5 specifically further includes:

[0057] Evaluate the attitude estimation stability index Xst by setting the attitude monitoring stability evaluation threshold Qp. The specific content is as follows:

[0058] If the attitude estimation stability index Xst ≤ the attitude monitoring stability evaluation threshold Qp: it indicates 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; at the same time, record the attitude estimation stability index Xst and relevant data and store them in the stability database for trend analysis;

[0059] If the attitude estimation stability index Xst > the attitude monitoring stability evaluation threshold Qp: it indicates 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:

[0060] Adjust the installation position and angle of the gyroscope and accelerometer; adjust the fixing stability of the sensor to avoid measurement errors caused by loosening or resonance; adjust the data fusion algorithm; trigger system adaptive optimization, recalculate the attitude monitoring stability evaluation threshold Qp to make it more in line with the current working conditions, and adjust the data fusion parameters.

[0061] The present invention provides a modular collaborative layout method for a gyroscope and an accelerometer. It has the following beneficial effects:

[0062] (1) The modular collaborative layout method of the gyroscope and accelerometer effectively solves the cumulative error problems of the gyroscope and accelerometer in the complex geological environment underground mines, especially the drift of the gyroscope and the zero-bias drift of the accelerometer, by optimizing the modular collaborative layout of the gyroscope and accelerometer. As the tunneling equipment operates for a long time, the measurement errors of the inertial sensors will gradually accumulate, leading to attitude estimation deviation. However, by calculating the real-time change rate Cpj of the gyroscope drift and the change rate Azp of the accelerometer zero-bias and compensating for the errors, this error can be effectively reduced, thereby improving the equipment positioning and path tracking accuracy. Especially in the absence of external positioning systems (such as GPS), it ensures the stable operation of the inertial navigation system (INS).

[0063] (2) Regarding the influence of external interference, external interference factors inside the mine, such as ground vibration, noise of mine equipment, air flow, groundwater flow, and magnetic field changes, will all affect the stability of the inertial sensor data. This solution can identify interference signals by collecting and analyzing data related to external disturbances in real time, calculating the dynamic interference adaptation index Xw and comparing it with the preset interference adaptation threshold, and triggering the system protection mechanism when the interference is strong, adjusting the data fusion strategy, thus improving the adaptability and data accuracy of the system in complex environments.

[0064] (3) For the problem of poor environmental adaptability, the temperature and humidity changes greatly underground mines, and the inertial sensors may experience performance degradation or increased drift under extreme temperature and humidity conditions. This solution ensures the stability and accuracy of the inertial sensors under different environmental conditions and improves the environmental adaptability of the system by means of a temperature compensation and humidity correction mechanism, dynamically adjusting the measurement data of the sensors based on the real-time temperature data T to compensate for the effects of temperature and humidity on the gyroscope and accelerometer. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the step flow of the modular collaborative layout method of the gyroscope and accelerometer of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] Embodiment 1

[0068] Please refer toFigure 1 , the present invention provides a modular collaborative layout method for gyroscopes and accelerometers, including the following steps:

[0069] S1. Collect the original measurement data of the gyroscope and accelerometer, perform data preprocessing, obtain error compensation parameters, and store the preprocessed data in association with the error compensation parameters, then upload them to the control system of the underground tunneling equipment to construct an initial measurement data set;

[0070] S2. Based on the initial measurement data set, calculate the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp, compare them with the preset drift threshold U1 and zero bias threshold U2, and generate an inertial error compensation instruction;

[0071] S3. Execute the error compensation instruction, collect the motion state of the tunneling equipment and ground disturbance data in real time, calculate the sensor dynamic weight adjustment index Waj, adjust the measurement data fusion weights of the gyroscope and accelerometer, and record the interference correction data;

[0072] S4. Monitor the 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 judge the influence weight of the interference signal on the attitude measurement;

[0073] S5. Calculate the attitude estimation stability index Xst, compare it with the preset attitude stability evaluation threshold Qp, optimize the modular collaborative layout of the gyroscope and accelerometer, and improve the positioning and attitude monitoring accuracy under the condition of no GPS signal during coal mine roadway construction.

[0074] In this embodiment, through the modular collaborative layout method of the gyroscope and accelerometer, the present invention can significantly improve the positioning and attitude monitoring accuracy under the condition of no GPS signal during coal mine roadway construction; in step S1, by collecting the original measurement data of the gyroscope and accelerometer and performing preprocessing to obtain error compensation parameters, it can provide basic data for subsequent error correction and ensure the accuracy and stability of the data; in step S2, by calculating the gyroscope drift change rate Cpj and the accelerometer zero bias change rate Azp and comparing them with the preset drift threshold U1 and zero bias threshold U2, the generated error compensation instruction can effectively correct the system drift and zero bias error; in step S3, by using the dynamic weight adjustment index Waj to adjust the fusion weight of the measurement data in real time, it can improve the reliability of the data and reduce the influence of external interference; in step S4, by calculating the dynamic interference adaptation index Xw and associating it with the external interference data, optimizing the interference correction strategy, and reducing the influence of environmental interference on the attitude estimation; in step S5, by calculating the attitude estimation stability index Xst and comparing it with the preset stability evaluation threshold Qp, optimizing the modular layout, and further improving the measurement accuracy and system stability in the GPS-free environment.

[0075] Example 2

[0076] S1 specifically includes:

[0077] First, collect the angular velocity signals ωx, ωy, ωz of the gyroscope and the linear acceleration signals ax, ay, az of the accelerometer respectively. Among them, data collection is performed by periodic sampling at a fixed time interval Δt, and the time alignment between the gyroscope and the accelerometer is ensured through a high-precision clock synchronization mechanism; for the collected original measurement data, first perform band-pass filtering to suppress high-frequency noise, and combine the adaptive mean filtering method to remove short-period interference signals; subsequently, calibrate the zero-bias drift of the gyroscope and the zero-bias of the accelerometer. Among them, the measurement of the zero-bias drift is based on the static sampling method. When the sensor is in a stationary state, continuously collect N groups of data and calculate the mean value as the reference offset value;

[0078] In order to further compensate for the measurement error caused by temperature changes, collect the temperature data T of the sensor, construct and based on the temperature drift model, calculate the temperature compensation parameters Tg and Ta, where the data for constructing the temperature drift model is obtained by fitting historical data;

[0079] After the error compensation parameters are calculated, combine the original measurement data of the gyroscope and the accelerometer, perform error correction calculation, and obtain the compensated sensor data; finally, associate and store the preprocessed original measurement data and the corresponding error compensation parameters according to the time series, and construct an initial measurement data set based on the data indexing mechanism, and finally upload it to the cloud computing module.

[0080] In this embodiment, the S1 step ensures the time alignment of the gyroscope angular velocity signals ωx, ωy, ωz and the accelerometer linear acceleration signals ax, ay, az through the high-precision clock synchronization mechanism and the periodic sampling at a fixed time interval Δt, ensuring the consistency of data collection; the use of band-pass filtering and adaptive mean filtering methods effectively suppresses high-frequency noise and short-period interference, improving the accuracy and stability of the data; calibrating the gyroscope zero-bias drift and accelerometer zero-bias through the static sampling method further eliminates the offset error of the sensor; the calculation of the sensor temperature compensation parameters Tg and Ta by combining the temperature drift model can effectively correct the error caused by temperature changes and improve the environmental adaptability of the measurement; by performing error correction calculation, the compensated sensor data is obtained, improving the reliability of the data; finally, the preprocessed data and error compensation parameters are associated and stored according to the time series and uploaded to the cloud computing module, ensuring 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 collaborative measurement of the gyroscope and the accelerometer, and is of great significance for the construction positioning and attitude monitoring of coal mine roadways under the condition of no GPS signal.

[0081] Example 3

[0082] S2 specifically includes:

[0083] Based on the initial measurement data set, calculate the drift change rate Cpj of the gyroscope. The specific calculation formula is as follows:

[0084]

[0085] In the formula, t represents time, ωme represents the average angular velocity of the gyroscope, ωva represents the variance of the angular velocity of the gyroscope, and ΔT represents the change in the current temperature relative to the calibration temperature, which is used to correct the influence of temperature on the gyroscope drift;

[0086] ωme(t) represents the average angular velocity of the gyroscope within the time window t, ωva(t) represents the variance of the angular velocity of the gyroscope within the time window t, Bgy represents the zero bias drift of the gyroscope, and Tgy represents the temperature drift.

[0087] S2 specifically further includes:

[0088] Extract the initial measurement data set and use the following formula to calculate and obtain the zero bias change rate Azp of the accelerometer:

[0089]

[0090] In the formula, t represents time, Acm represents the average measurement of the accelerometer, Acv represents the variance of the accelerometer measurement, Acm(t) represents the average measurement of the accelerometer within the time window t, Acv(t) represents the variance of the accelerometer measurement within the time window t, ΔBac represents the static zero bias of the accelerometer, and Dac represents the vibration interference correction coefficient of the accelerometer.

[0091] S2 specifically further includes:

[0092] Preset the drift threshold U1 of the gyroscope and the zero bias threshold U2 of the accelerometer, and compare and analyze the drift change rate Cpj of the gyroscope with the drift threshold U1 and the zero bias change rate Azp of the accelerometer with the zero bias threshold U2. The specific evaluation content is as follows:

[0093] If the drift change rate Cpj of the gyroscope ≥ the drift threshold U1, it indicates that the gyroscope drift exceeds the normal range. At this time, correction is performed and an inertial error compensation instruction is generated and sent;

[0094] If the zero bias change rate Azp of the accelerometer ≥ the zero bias threshold U2, it indicates that the zero bias of the accelerometer exceeds the normal range. At this time, compensation is performed and an inertial error compensation instruction is generated and sent;

[0095] If the drift change rate Cpj of the gyroscope < the drift threshold U1 and the zero - bias change rate Azp of the accelerometer < the zero - bias threshold U2, it is considered that the sensor drift is normal, and monitoring continues at this time.

[0096] S3 specifically includes:

[0097] First, collect and analyze data related to the motion state of the tunneling equipment, including the traveling speed Vv, the acceleration change rate Avar, and the steering angular velocity Yawr of the equipment. Then, collect and analyze data related to ground disturbances, including the vibration interference amplitude Vib and the ground bump index Rd. Based on the data related to the motion state of the tunneling equipment and the data related to ground disturbances, calculate the sensor dynamic weight adjustment index Waj through the following formula:

[0098] 。

[0099] S3 also specifically includes:

[0100] Evaluate the preset sensor dynamic weight adjustment threshold Q and the sensor dynamic weight adjustment index Waj. The specific evaluation content is as follows:

[0101] If the sensor dynamic weight adjustment index Waj ≥ the sensor dynamic weight adjustment threshold Q: It indicates that the current motion state of the tunneling equipment is normal, and the credibility of the inertial sensor data is qualified. At this time, maintain the existing data fusion weights of the gyroscope and the accelerometer;

[0102] If the sensor dynamic weight adjustment index Waj < the sensor dynamic weight adjustment threshold Q: It indicates that the current motion state of the tunneling equipment is abnormal, and the credibility of the inertial sensor data is unqualified. At this time, adjust the data fusion weights.

[0103] S3 also specifically includes:

[0104] When the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, enter the fusion weight adjustment mode. Set the gyroscope weight W - gy and the accelerometer weight W - ac, and adjust the fusion ratio of the two according to the deviation degree of the sensor dynamic weight adjustment index Waj. Among them, the sum of the adjusted gyroscope weight W - gy and the adjusted accelerometer weight W - ac is equal to 1. Then, perform the data fusion operation of the gyroscope and the accelerometer according to the adjusted gyroscope weight W - gy and the adjusted accelerometer weight W - ac, and record the data related to interference correction during the operation process.

[0105] S4 specifically includes:

[0106] Collect and analyze data related to external disturbances, including the vibration interference amplitude Vib, the change rate of the magnetic field intensity Mvar in the mine, and the rock deformation index Rd around the equipment. Calculate and obtain the dynamic interference adaptation index Xw by combining the following formula:

[0107]

[0108] The preset dynamic interference adaptation threshold W is compared and evaluated with the dynamic interference adaptation index Xw, and the specific content is as follows:

[0109] If the dynamic interference adaptation index Xw ≤ the dynamic interference adaptation threshold W: It indicates that the current environmental interference is qualified, the inertial sensor measurement data is normal, and the system directly uses the original data for attitude estimation at this time without adjusting the data fusion weight;

[0110] If the dynamic interference adaptation index Xw > the dynamic interference adaptation threshold W: It indicates that the external environmental interference is unqualified and the inertial sensor data is abnormal. At this time, the system protection mechanism is triggered to adjust the trust in the inertial measurement data and increase the fusion weight of the visual sensor and other mine sensor data.

[0111] S4 specifically further includes:

[0112] The traveling speed Vv and the acceleration change rate Avar in the data related to the motion state of the tunneling equipment are respectively extracted, and the vibration interference amplitude Vib and the rock formation deformation index Rd in the data related to the external interference are correlated and calculated through the following formula to obtain the correlation index Corr:

[0113]

[0114] The preset correlation threshold E is compared and evaluated with the correlation index Corr, and the specific evaluation content is as follows:

[0115] If the correlation index Corr < the correlation threshold E: It means that the external interference has no influence 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 is continued without adjusting the sensor weight;

[0116] If the correlation index Corr ≥ the correlation threshold E: It means that the external interference has an influence 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 trust in the inertial measurement data and introducing the data of other external sensors such as lidar and vibration sensors for compensation.

[0117] S5 specifically includes:

[0118] Collect relevant data in real time and measure it based on inertial sensors, and calculate the attitude estimation stability index Xst, including the external disturbance compensation index Ebc, the short-time attitude change rate Tvar, the gyroscope angular velocity Gyr, and the accelerometer acceleration Acm; at the same time, extract the sensor dynamic weight adjustment index Waj and calculate it in association with the relevant data measured by the inertial sensors. The attitude estimation stability index Xst is obtained through the following formula:

[0119] .

[0120] S5 specifically further includes:

[0121] Evaluate the attitude estimation stability index Xst by setting the attitude monitoring stability evaluation threshold Qp. The specific content is as follows:

[0122] If the attitude estimation stability index Xst ≤ the attitude monitoring stability evaluation threshold Qp: It indicates 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; at the same time, record the attitude estimation stability index Xst and relevant data and store them in the stability database for trend analysis;

[0123] If the attitude estimation stability index Xst > the attitude monitoring stability evaluation threshold Qp: It indicates 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:

[0124] Adjust the installation position and angle of the gyroscope and accelerometer; adjust the sensor fixing stability to avoid measurement errors caused by loosening or resonance; adjust the data fusion algorithm; trigger system adaptive optimization, recalculate the attitude monitoring stability evaluation threshold Qp to make it more suitable for the current working condition, and adjust the data fusion parameters.

[0125] In this embodiment, in step S2, by extracting the initial measurement data set and calculating the zero-bias change rate Azp of the accelerometer, the zero-bias change of the accelerometer can be detected in a timely manner to ensure the accuracy of the accelerometer data. Through the comparison and analysis of the preset drift threshold U1 and zero-bias threshold U2 of the gyroscope with the gyroscope drift change rate Cpj and the accelerometer zero-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 inertial error compensation instruction is issued to ensure the high-precision output of the sensor. In step S3, by collecting data related to the motion state of the tunneling equipment (such as the traveling speed Vv, the acceleration change rate Avar, and the steering angular velocity Yawr) and the ground disturbance data (such as the vibration interference amplitude Vib and the ground bump index Rd), the sensor dynamic weight adjustment index Waj is calculated to realize the dynamic monitoring of the equipment motion state. Combining the preset weight adjustment threshold Q, the data fusion weight can be dynamically adjusted, thereby improving the credibility of the sensor data. In step S4, by collecting data related to external interference (such as the vibration interference amplitude Vib, the change rate Mvar of the magnetic field intensity in the mine, and the 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 judge the influence of external interference on the inertial sensor. When necessary, the data fusion strategy is adjusted to ensure the stability of the attitude estimation. The interference compensation is performed by calculating the correlation index Corr and comparing it with the preset correlation threshold E to ensure the data quality. In step S5, by calculating the attitude estimation stability index Xst, combining data such as the external disturbance compensation index Ebc, the short-time attitude change rate Tvar, the gyroscope angular velocity Gyr, and the accelerometer acceleration Acm, the stability of the attitude estimation is evaluated in real time. When the stability index exceeds the threshold Qp, by optimizing the modular collaborative layout, the sensor configuration and the data fusion strategy are adjusted to further improve the measurement accuracy and stability of the system, and finally provide more accurate and reliable support for the attitude monitoring and positioning in the coal mine roadway construction.

[0126] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A modular collaborative layout method for a gyroscope and an accelerometer, characterized in that: It includes the following steps: S1. Collect the original measurement data of the gyroscope and accelerometer, perform data preprocessing to obtain error compensation parameters, associate and store the preprocessed data with the error compensation parameters, upload them to the control system of the underground tunneling equipment, and construct an initial measurement dataset; Collect the angular velocity signals ωx, ωy, ωz of the gyroscope and the linear acceleration signals ax, ay, az of the accelerometer respectively. Among them, data collection is performed by periodic sampling at a fixed time interval Δt, and the time alignment of the gyroscope and accelerometer is ensured through a high-precision clock synchronization mechanism. For the collected original measurement data, first perform band-pass filtering to suppress high-frequency noise, and combine the adaptive mean filtering method to remove short-period interference signals. Subsequently, calibrate the zero-bias drift of the gyroscope and the zero-bias of the accelerometer. Among them, the measurement of the zero-bias drift is based on the static sampling method. When the sensor is in a stationary state, N groups of data are continuously collected, and the mean value is calculated as the reference offset value; To further compensate for the measurement error caused by temperature changes, collect the temperature data T of the sensor, construct and based on the temperature drift model, calculate the temperature compensation parameters Tg and Ta, where the data for constructing the temperature drift model is obtained by fitting historical data; After the error compensation parameters are calculated, combine the original measurement data of the gyroscope and accelerometer, perform error correction calculation to obtain the compensated sensor data. Finally, associate and store the preprocessed original measurement data and the corresponding error compensation parameters according to the time series, construct an initial measurement dataset based on the data indexing mechanism, and finally upload it to the cloud computing module; S2. Based on the initial measurement dataset, calculate the gyroscope drift change rate Cpj and the accelerometer zero-bias change rate Azp, compare them with the preset drift threshold U1 and zero-bias threshold U2, and generate an inertial error compensation instruction; Preset the drift threshold U1 of the gyroscope and the zero-bias threshold U2 of the accelerometer, and compare and analyze the drift change rate Cpj of the gyroscope with the drift threshold U1 and the zero-bias change rate Azp of the accelerometer with the zero-bias threshold U2. The specific evaluation content is as follows: If the drift change rate Cpj of the gyroscope ≥ drift threshold U1, it means that the gyroscope drift exceeds the normal range. At this time, perform correction and generate an inertial error compensation instruction; If the zero-bias change rate Azp of the accelerometer ≥ zero-bias threshold U2, it means that the accelerometer zero-bias exceeds the normal range. At this time, perform compensation and generate an inertial error compensation instruction; If the drift change rate Cpj of the gyroscope < drift threshold U1 and the zero-bias change rate Azp of the accelerometer < zero-bias threshold U2, it is considered that the sensor drift is normal, and continue to monitor at this time; 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 weights of the gyroscope and accelerometer, and record the interference correction data; First, collect and analyze data related to the motion state of the tunneling equipment, including the traveling speed Vv, the acceleration change rate Avar, and the steering angular velocity Yawr of the equipment. Then, collect and analyze data related to ground disturbances, including the vibration interference amplitude Vib and the ground bumpiness index Rd. Based on the data related to the motion state of the tunneling equipment and the data related to ground disturbances, calculate the sensor dynamic weight adjustment index Waj through the following formula: Evaluate the preset sensor dynamic weight adjustment threshold Q and the sensor dynamic weight adjustment index Waj. The specific evaluation content is as follows: If the sensor dynamic weight adjustment index Waj ≥ the sensor dynamic weight adjustment threshold Q: It indicates that the current motion state of the tunneling equipment is normal, and the credibility of the inertial sensor data is qualified. At this time, maintain the existing data fusion weights of the gyroscope and the accelerometer; If the sensor dynamic weight adjustment index Waj < the sensor dynamic weight adjustment threshold Q: It indicates that the current motion state of the tunneling equipment is abnormal, and the credibility of the inertial sensor data is unqualified. At this time, adjust the data fusion weights; When the sensor dynamic weight adjustment index Waj is less than the sensor dynamic weight adjustment threshold Q, enter the fusion weight adjustment mode. Set the gyroscope weight W-gy and the accelerometer weight W-ac, and adjust the fusion ratio of the two according to the deviation degree of the sensor dynamic weight adjustment index Waj. Among them, the sum of the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac is equal to 1. Then, perform the data fusion operation of the gyroscope and the accelerometer according to the adjusted gyroscope weight W-gy and the adjusted accelerometer weight W-ac, and record the data related to interference correction during the operation process; S4. Monitor the 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 judge the influence weight of the interference signal on the attitude measurement; If the dynamic interference adaptation index Xw ≤ the dynamic interference adaptation threshold W: It indicates 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 at this time without adjusting the data fusion weights; If the dynamic interference adaptation index Xw > the dynamic interference adaptation threshold W: It indicates that the external environmental interference is unqualified and the inertial sensor data is abnormal. At this time, trigger the system protection mechanism, adjust the trust in the inertial measurement data, and increase the fusion weight of the visual sensor and other mine sensor data; If the correlation index Corr < the correlation threshold E: It indicates 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, continue to adopt the current data fusion strategy without adjusting the sensor weights; If the correlation index Corr ≥ the correlation threshold E: It indicates that the 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, trigger the external interference compensation mechanism, including further adjusting the trust in the inertial measurement data, and at the same time introducing the data of other external sensors such as lidar and vibration sensors for compensation; S5. Calculate the attitude estimation stability index Xst, compare it with the preset attitude stability evaluation threshold Qp, optimize the modular collaborative layout of the gyroscope and accelerometer, and improve the positioning and attitude monitoring accuracy under the condition of no GPS signal during the construction of coal mine roadways; Evaluate the attitude estimation stability index Xst by setting the attitude monitoring stability evaluation threshold Qp. The specific content is as follows: If the attitude estimation stability index Xst ≤ the attitude monitoring stability evaluation threshold Qp: It indicates 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; at the same time, record the attitude estimation stability index Xst and related data and store them in the stability database for trend analysis; If the attitude estimation stability index Xst > the attitude monitoring stability evaluation threshold Qp: It indicates 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 fixation stability to avoid measurement errors caused by loosening or resonance; adjust the data fusion algorithm; trigger system adaptive optimization, recalculate the attitude monitoring stability evaluation threshold Qp to make it more suitable for the current working condition, and adjust the data fusion parameters.

2. The modular collaborative layout method of the gyroscope and the accelerometer according to claim 1, wherein: S2 specifically includes: Based on the initial measurement data set, calculate the drift change rate Cpj of the gyroscope. The specific calculation formula is as follows: In the formula, t represents time, ωme represents the average angular velocity of the gyroscope, ωva represents the variance of the angular velocity of the gyroscope, and ΔT represents the change in 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 average angular velocity of the gyroscope within the time window t, ωva(t) represents the variance of the angular velocity of the gyroscope within the time window t, Bgy represents the zero bias drift of the gyroscope, and Tgy represents the temperature drift.

3. The modular collaborative layout method of the gyroscope and the accelerometer according to claim 1, wherein: S2 also specifically includes: Extract the initial measurement data set and calculate the zero bias change rate Azp of the accelerometer using the following formula: In the formula, t represents time, Acm represents the average measurement of the accelerometer, Acv represents the variance of the accelerometer measurement, Acm(t) represents the average measurement of the accelerometer within the time window t, Acv(t) represents the variance of the accelerometer measurement within the time window t, ΔBac represents the static zero bias of the accelerometer, and Dac represents the vibration interference correction coefficient of the accelerometer.

4. The modular collaborative layout method of the gyroscope and the accelerometer according to claim 1, characterized in that: S4 specifically includes: Collect and analyze the external interference related data, including the vibration interference amplitude Vib, the change rate of the magnetic field strength Mvar in the mine, and the rock formation deformation index Rd around the equipment, and calculate the dynamic interference adaptation index Xw by combining the following formula:

5. The modular collaborative layout method of the gyroscope and the accelerometer according to claim 1, characterized in that: S4 also specifically includes: Extract the traveling speed Vv and the acceleration change rate Avar in the tunneling equipment motion state related data respectively, and correlate and calculate them with the vibration interference amplitude Vib and the rock formation deformation index Rd in the external interference related data through the following formula to obtain the correlation index Corr:

6. The modular collaborative layout method of the gyroscope and the accelerometer according to claim 1, characterized in that: S5 specifically includes: Collect relevant data in real time and measure it based on inertial sensors, and calculate the attitude estimation stability index Xst, including the external disturbance compensation index Ebc, the short-time attitude change rate Tvar, the gyroscope angular velocity Gyr, and the accelerometer acceleration Acm; at the same time, extract the sensor dynamic weight adjustment index Waj and calculate it in association with the relevant data measured by the inertial sensors, and obtain the attitude estimation stability index Xst through the following formula:

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