Monorail crane track deformation monitoring method based on inertial measurement technology

Abstract

The invention provides a monorail crane rail deformation monitoring method based on an inertia measurement technology, and belongs to the technical field of coal mine underground rail monitoring. According to the technical scheme, the method comprises the following steps: S1, deploying a plurality of IMU modules at equal intervals in the extension direction of a monorail crane track; s2, collecting initial IMU data of each IMU module and a monorail crane operation speed signal; s3, resolving initial spatial position data of the track, and generating an undeformed monorail crane track reference value; s4, in the coal mine transportation process, current IMU data and speedometer speed signals of all IMU modules are collected regularly in real time; s5, calculating the current spatial position data of the track; s6, calculating a displacement difference value and an attitude angle deviation of each measuring point; and S7, determining that the region is a monorail crane track deformation point. The system has the beneficial effects that high-resolution real-time monitoring is realized through high-frequency data acquisition and multi-sensor fusion, the external interference resistance is high, and a deformation area can be accurately positioned without interrupting operation.

Description

Technical Field

[0001] The present invention relates to the technical field of underground coal mine track monitoring, in particular to a monorail crane track deformation monitoring method based on inertial measurement technology. Background Art

[0002] In underground coal mine transportation systems, monorail crane tracks serve as a critical channel for transporting materials and equipment. They are constantly subjected to heavy loads, changing geological conditions, and mechanical vibration, making them prone to deformation such as bending, sinking, and twisting. If such deformation is not promptly monitored and repaired, it can lead to monorail crane derailment, equipment damage, or even accidents, seriously threatening underground production safety.

[0003] Traditional methods for monitoring deformation of monorail crane tracks primarily include manual inspections, indirect inference of mechanical parameters, and periodic offline laser scanning. Manual inspections rely on manual measurements with tools such as total stations, resulting in low efficiency and inability to monitor in real time. Mechanical parameter methods infer deformation from stress and strain data, relying on empirical formulas and subject to significant errors. While offline laser scanning offers high accuracy, it requires suspending operations, impacting production efficiency. These methods suffer from limitations such as low accuracy and limited real-time performance. Summary of the Invention

[0004] The purpose of the present invention is to overcome the problems in the background technology and provide a monorail crane track deformation monitoring method based on inertial measurement technology.

[0005] The technical idea of the present invention is: after the monorail crane system is erected during the coal mine infrastructure construction period, the inertial measurement system is used to immediately measure the coal mine monorail crane track, calculate the initial spatial position data of the monorail crane track, and obtain the undeformed monorail crane track reference value; during the coal mine transportation process, the current spatial position of the monorail crane track is regularly measured and calculated, and compared with the monorail crane track reference value. The place where the two have differences is the deformation point of the monorail crane track.

[0006] In order to achieve the above-mentioned purpose of the invention, the technical solution adopted by the present invention is specifically: a method for monitoring deformation of a monorail crane track based on inertial measurement technology, comprising the following steps: S1: deploying multiple IMU modules at equal intervals of 8 to 15 meters along the extension direction of the monorail crane track, each IMU module is fixed to the track surface by a rigid buckle, and each IMU module includes a three-axis accelerometer, a gyroscope and a magnetometer; S2: obtaining the monorail crane running speed signal through the odometer, and synchronously collecting the initial IMU data of each IMU module, the initial IMU data including the initial three-axis acceleration, initial angular velocity and initial magnetic field data; S3: fusing the initial IMU data with the mileage based on the extended Kalman filter algorithm The speed signal of the odometer is used to calculate the initial spatial position data of the track and generate an undeformed track reference value; S4: During the transportation process, the current IMU data and odometer speed signal of each IMU module are regularly collected. The current IMU data includes the current acceleration, current angular velocity, and current magnetic field data; S5: Based on the extended Kalman filter algorithm, the current spatial position data of the track is calculated according to the current IMU data and odometer speed signal; S6: The current spatial position data of the track is compared with the track reference value point by point to calculate the displacement difference and attitude angle deviation of each measuring point; S7: When the displacement difference exceeds the set threshold or the attitude angle deviation of three consecutive measuring points exceeds 0.5°, it is determined to be a track deformation point.

[0007] Furthermore, the specific method of step S2 is as follows: after the monorail crane track is just installed or just maintained, the track is in an undeformed state, and the monorail crane running speed is calculated by the odometer according to the following formula: , where N is the number of pulses, D is the wheel diameter, and T is the sampling period.

[0008] Each IMU module is based on the same time base With fixed sampling frequency Record the initial three-axis acceleration 、 、 , initial angular velocity 、 、 and initial magnetic field strength 、 、 , and set the reference timestamp Label the data frame, ,in , is the starting time, , The measurement point number.

[0009] Furthermore, in step S3, the initial spatial position data of the track is calculated based on the initial IMU data and the odometer speed signal. The specific method is as follows: define the state vector To describe the motion state of the monorail crane, are three-dimensional coordinates, are the speeds in three directions, is the pitch angle, is the roll angle, is the heading angle.

[0010] The initial angular velocity obtained using the IMU module and initial acceleration Data, the extended Kalman filter state equation is established as a prediction model, and its equation is: , where is the state vector, is a nonlinear vector function, is the system noise allocation matrix, is the system noise.

[0011] Speed using odometer The observation value is used to construct the observation equation and calculate the Kalman gain and the corrected state estimate. The extended Kalman filter observation equation is: , where is the system measurement vector, is the measurement matrix, is the measurement noise.

[0012] The Kalman gain formula is: , the state update formula is: , the covariance update formula is: , where is the prediction covariance matrix, is the measurement matrix The transposed matrix of is the measurement noise covariance matrix, is the prior state estimate, is the predicted value of the observation equation, is the identity matrix.

[0013] By iteratively extending the Kalman filter algorithm, the state vector of each measuring point is solved , output the initial three-dimensional coordinates of each measuring point and initial attitude angle ,in is the initial pitch angle, is the initial roll angle, is the initial heading angle.

[0014] Furthermore, in step S3, the reference value of the monorail crane track without deformation is generated. The specific method is as follows: the initial IMU data is repeatedly collected for three times for all the measurement points of the track. The weighted average of multiple measurement results is used to generate an undeformed track reference value, which includes a three-dimensional coordinate reference value. and attitude angle reference value .

[0015] The orbit reference value generation rule is as follows: Measuring point The three-dimensional coordinates of the measurement are , the standard deviation is , weight is inversely proportional to the measurement variance, i.e. , then the weighted average coordinate is: , , ,in, is the measurement point number, i.e. the number of the measurement point that is evenly spaced along the track. Each IMU module corresponds to a measurement point. .

[0016] Sort the three measurements of each coordinate component separately, and then take the median as the final three-dimensional coordinate reference value, that is: , , , set up the Measuring point The attitude angle measured is , based on the attitude rotation matrix elements ,in , For the axial identification of the coordinate system, sort the attitude angles of the three measurements and take the median to obtain the final attitude angle reference value, that is: , , .

[0017] Furthermore, the specific method of step S4 is: in the coal mine operation and transportation stage, the odometer is used according to the formula Calculate the running speed of the monorail crane, where N is the number of pulses, D is the wheel diameter, and T is the sampling period.

[0018] The IMU module has a fixed sampling frequency Record the current three-axis acceleration , current angular velocity 、 、 and current magnetic field strength 、 、 ; Set the current timestamp Label the data frame to maintain time series coherence.

[0019] Furthermore, in step S5, the current spatial position data of the track is calculated based on the current IMU data and the odometer speed signal. The specific method is as follows: the state vector , through the iterative EKF algorithm, the current three-dimensional coordinates of each measuring point are obtained and current attitude angle As the current spatial position data of the track, are three-dimensional coordinates, are the speeds in three directions, is the pitch angle, is the roll angle, is the heading angle.

[0020] Furthermore, the specific method is as follows: Based on the same time base , timestamp the current data Reference timestamp with settings Align to ensure that the comparison data is at the same time point; calculate the displacement difference and attitude angle deviation of each measuring point. The specific method is as follows: For each measuring point , whose three-dimensional coordinate reference value is , the current three-dimensional coordinates are , displacement difference The calculation formula is: ; For each measuring point , and its attitude angle reference value is , the current attitude angle is , the calculation formula for attitude angle deviation is: ,in, The measurement point number.

[0021] Furthermore, the specific method of step S7 is as follows: setting the displacement difference threshold , single point attitude angle deviation threshold ( , , )for ; For a single measuring point, if the measuring point The displacement difference , it will be marked as a suspected deformation point; if the displacement difference of the three adjacent measuring points exceeds , and its spatial position is continuous and there is no gap between measuring points, it is determined to be a linear deformation area; for a single measuring point, if the measuring point Any attitude angle deviation 、 ,or Exceed , it will be marked as a suspected deformation point; if the same attitude angle deviation of the three adjacent measuring points exceeds , it is determined to be a local distortion area; if a single measuring point satisfies If any attitude angle deviation exceeds the limit, it will be determined as a confirmed deformation point.

[0022] The beneficial effects of the present invention are: 1. By deploying multiple sets of inertial measurement units (IMUs), combining odometer speed signals, and using the extended Kalman filter (EKF) algorithm to perform real-time fusion of three-axis acceleration, angular velocity and magnetic field data, this method can effectively suppress sensor noise and dynamic environmental interference, significantly improve the accuracy of orbital spatial position solution, and significantly reduce the error rate compared to the error accumulation problem caused by the traditional mechanical parameter method relying on empirical formulas.

[0023] 2. Traditional manual inspections rely on offline total station measurements, which are inefficient and unable to capture instantaneous deformations. Offline laser scanning, while highly accurate, requires suspending operations, severely impacting production efficiency. The IMU's high-frequency data acquisition (sampling frequency exceeding 100 Hz) and real-time calculations allow for continuous monitoring of the track status during the operation of the monorail crane without interrupting transportation operations. At the same time, the IMU module is fixed with a rigid snap-fit structure to ensure a close fit between the sensor and the track surface, avoiding measurement deviations caused by vibration. This design addresses the core pain points of traditional methods, such as poor environmental adaptability and insufficient real-time performance, and provides all-weather protection for safe underground transportation.

[0024] 3. The inertial measurement system does not rely on external signals (such as GPS) and can still work stably in the complex electromagnetic environment of coal mines. The reference value is generated by weighted average and median method, which further eliminates the influence of accidental errors. Compared with high-end technologies such as laser scanning, the hardware cost of this solution is greatly reduced and maintenance is simple. In actual applications, the system can accurately locate linear deformation areas and local distortion points, helping operation and maintenance personnel to quickly formulate repair strategies and reduce the risk of safety accidents. These advantages make this invention significantly competitive in the field of track health monitoring and provide reliable technical support for the construction of intelligent mines.

[0025] 4. The inertial measurement system in the present invention can accurately monitor the subtle deformation of the monorail crane track through high data acquisition frequency and high spatial resolution, capture the deformation point in real time and accurately locate it; at the same time, the system does not rely on external signals and can work stably under various environmental conditions, avoiding external interference that affects the monitoring accuracy. These advantages make the inertial measurement system efficient, reliable and flexible in track safety monitoring, and provide accurate real-time data support for track maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 1 is a flow chart of the monitoring method of the present invention.

[0027] Figure 2 is a flow chart of inertial measurement data processing of the present invention.

[0028] Figure 3 This is a schematic diagram of IMU deployment.

[0029] Figure 4 It is a schematic diagram of the transformation between coordinate systems for calculating displacement difference and attitude angle deviation. DETAILED DESCRIPTION

[0030] Next, the technical solution 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 embodiments shown only represent a part of the present invention, not all embodiments. Based on the embodiments of the present invention, any other embodiments proposed by any ordinary technician without the need for creative work should be considered as the scope of protection of the present invention.

[0031] like Figure 1 As shown, this embodiment provides a monorail crane track deformation monitoring method based on inertial measurement technology, and the specific steps are as follows.

[0032] Step 1: Deploy multiple inertial measurement units (IMUs) equidistantly along the monorail track, with a spacing of 8 to 15 meters. Each IMU module is fixed to the track surface through a rigid snap-on structure. The IMU module includes a three-axis accelerometer, gyroscope, and magnetometer.

[0033] Step 2: After the monorail track is installed or maintained, the track is still in an undeformed state. The speed signal of the monorail crane is obtained in real time through the odometer, and the initial three-axis acceleration, initial angular velocity and initial magnetic field data of each IMU module are simultaneously collected.

[0034] Among them, Figure 3 As shown, multiple inertial measurement units (IMUs) are deployed equidistantly along the extension direction of the monorail crane track, and each IMU module is based on the same time reference. With fixed sampling frequency ( )Record the initial three-axis acceleration 、 、 , initial angular velocity 、 、 and initial magnetic field strength 、 、 , and set the reference timestamp Labeled data frame where , is the starting time, is the measurement point number, i.e. the number of the measurement point that is evenly spaced along the track. Each IMU module corresponds to a measurement point.

[0035] The odometer calculates the monorail crane's operating speed according to the following formula: , where N is the number of pulses, D is the wheel diameter, and T is the sampling period.

[0036] Step 3: Based on the extended Kalman filter algorithm, the initial IMU data and the odometry signal are fused to calculate the initial spatial position data of the track and generate the undeformed monorail crane track reference value.

[0037] Among them, Figure 2 As shown, define the state vector To describe the motion state of the monorail crane, are three-dimensional coordinates, are the speeds in three directions, is the pitch angle, is the roll angle, is the heading angle.

[0038] The initial angular velocity obtained using the IMU module and initial acceleration Data, the extended Kalman filter state equation is established as a prediction model, and its equation is: , where is the state vector, is a nonlinear vector function, Assign the matrix to the system noise, is the system noise.

[0039] Speed using odometer The observation value is used to construct the observation equation and calculate the Kalman gain and the corrected state estimate. The extended Kalman filter observation equation is: , where is the system measurement vector, is the measurement matrix, is the measurement noise.

[0040] The Kalman gain formula is: , the state update formula is: , the covariance update formula is: , where is the prediction covariance matrix, is the measurement matrix The transposed matrix of is the measurement noise covariance matrix, is the prior state estimate, is the predicted value of the observation equation, is the identity matrix.

[0041] By iteratively extending the Kalman filter algorithm, the state vector of each measuring point is solved , and finally output the initial three-dimensional coordinates of each measuring point and initial attitude angle ,in is the initial pitch angle, is the initial roll angle, is the initial heading angle.

[0042] The initial IMU data of all track measurement points are collected 3 times repeatedly. The weighted average of multiple measurement results is used to generate an undeformed track reference value, which includes a three-dimensional coordinate reference value. and attitude angle reference value , the generation rules are as follows: Let Measuring point The three-dimensional coordinates of the measurement are , the standard deviation is , weight is inversely proportional to the measurement variance, i.e. , then the weighted average coordinate is: , , ,in, is the measurement point number, i.e. the number of the measurement point that is evenly spaced along the track. Each IMU module corresponds to a measurement point. .

[0043] Three measurements for each coordinate component , , Sort them separately, , Similarly, the median is taken as the final three-dimensional coordinate reference value: , , , set up the Measuring point The attitude angle measured is , based on the attitude rotation matrix elements ,in , For the axial identification of the coordinate system, sort the attitude angles of the three measurements and take the median to obtain the final attitude angle reference value, that is: , , .

[0044] Step 4: During the coal mine transportation process, the current three-axis acceleration, current angular velocity, and current magnetic field data of each IMU module are regularly collected in real time, and the speed signal of the odometer is obtained synchronously.

[0045] Odometer keeps real-time speed Calculation, that is, the odometer according to the formula (N is the number of pulses, D is the wheel diameter, and T is the sampling period) to calculate the speed.

[0046] Use the fixed sampling frequency described in step 2 Record the current three-axis acceleration , current angular velocity 、 、 and current magnetic field strength 、 、 And set the current timestamp Label the data frame to maintain time series coherence.

[0047] Step 5: Based on the extended Kalman filter algorithm, the current IMU data collected regularly and the odometer speed signal are integrated to calculate the current spatial position data of the track.

[0048] Using the state vector ,in are three-dimensional coordinates, are the speeds in three directions, is the pitch angle, is the roll angle, is the heading angle; through the iterative EKF algorithm, the current three-dimensional coordinates of each measuring point are finally calculated and current attitude angle , as the current spatial position data of the track.

[0049] Step 6: Compare the current spatial position data of the track with the reference value of the monorail crane track point by point, and calculate the displacement difference and attitude angle deviation of each measuring point.

[0050] Among them, Figure 4 As shown, based on the same time base , the timestamp of the current data Reference timestamp with settings Alignment, ensure that the comparison data is at the same time point, the specific steps to determine the displacement difference and attitude angle deviation are as follows: For each measuring point , and its three-dimensional coordinate reference value is , the current three-dimensional coordinates are , displacement difference The calculation formula is .

[0051] For each measuring point , and its attitude angle reference value is , , , the current attitude angle is , , .

[0052] The calculation formula for attitude angle deviation is: ,in, is the measurement point number, i.e. the number of the measurement point that is evenly spaced along the track. Each IMU module corresponds to a measurement point.

[0053] Step 7: When the displacement difference exceeds the set threshold or the attitude angle deviation of three consecutive measuring points exceeds 0.5°, the area is determined to be a deformation point of the monorail crane track.

[0054] The displacement difference threshold is set based on the allowable deformation specified in the industry standard "Technical Specifications for Safety of Coal Mine Monorail Crane Tracks" (MT / T1182-2020). According to the attitude angle measurement accuracy requirements in the "Inertial Navigation System Performance Test Method" (GB / T38705-2020), the attitude angle deviation threshold of a single measuring point is set. for .

[0055] For a single measuring point, if the measuring point The displacement difference , it will be marked as a suspected deformation point; if the displacement difference of the three adjacent measuring points exceeds , and its spatial position is continuous and there are no gaps in the measuring points, it is determined to be a linear deformation area.

[0056] For a single measuring point, if the measuring point Any attitude angle deviation 、 ,or Exceed , it will be marked as a suspected deformation point; if the same attitude angle deviation of the three adjacent measuring points exceeds , it is determined to be a local distortion area.

[0057] In addition, if a single measuring point satisfies If any attitude angle deviation exceeds the limit, it will be determined as a confirmed deformation point.

[0058] Therefore, in this monitoring method, the present invention can accurately monitor the subtle deformation of the monorail crane track through high data acquisition frequency and high spatial resolution, capture the deformation point in real time and accurately locate it; at the same time, the system does not rely on external signals, can work stably under various environmental conditions, and avoid external interference affecting the monitoring accuracy. These advantages make the inertial measurement system efficient, reliable and flexible in track safety monitoring, and provide accurate real-time data support for track maintenance.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring deformation of a monorail crane track based on inertial measurement technology, characterized in that: The steps include: S1: Multiple IMU modules are deployed at equal intervals of 8 to 15 meters along the monorail track. Each IMU module is fixed to the track surface with rigid clips. Each IMU module contains a three-axis accelerometer, gyroscope, and magnetometer. S2: Obtain the monorail crane's running speed signal through the odometer and synchronously collect the initial IMU data of each IMU module. The initial IMU data includes initial three-axis acceleration, initial angular velocity, and initial magnetic field data; S3: Based on the extended Kalman filter algorithm, the initial IMU data and the odometer velocity signal are fused to calculate the initial spatial position data of the track and generate the undeformed track reference value; S4: Regularly collect the current IMU data and odometer speed signal of each IMU module during transportation. The current IMU data includes current acceleration, current angular velocity, and current magnetic field data. S5: Based on the extended Kalman filter algorithm, the current spatial position data of the track is calculated according to the current IMU data and odometer speed signal; S6: Compare the current spatial position data of the track with the track reference value point by point, and calculate the displacement difference and attitude angle deviation of each measuring point; S7: When the displacement difference exceeds the set threshold or the attitude angle deviation of three consecutive measuring points exceeds 0.5°, it is determined to be a track deformation point.

2. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 1, characterized in that: The specific method of step S2 is: after the monorail crane track is just installed or just maintained, the track is in an undeformed state, and the speed of the monorail crane is calculated by the odometer according to the following formula: ， Where N is the number of pulses, D is the wheel diameter, and T is the sampling period; Each IMU module is based on the same time base With fixed sampling frequency Record the initial three-axis acceleration 、 、 , initial angular velocity 、 、 and initial magnetic field strength 、 、 , and set the reference timestamp Label the data frame, ,in , is the starting time, , The measurement point number.

3. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 2 is characterized in that: In step S3, the initial spatial position data of the track is calculated based on the initial IMU data and the odometer speed signal. The specific method is as follows: Define the state vector To describe the motion state of the monorail crane, are three-dimensional coordinates, are the speeds in three directions, is the pitch angle, is the roll angle, is the heading angle; The initial angular velocity obtained using the IMU module and initial acceleration Data, the extended Kalman filter state equation is established as a prediction model, and its equation is: ， Where, is the state vector, is a nonlinear vector function, Assign the matrix to the system noise, is the system noise; Speed using odometer The observation value is used to construct the observation equation and calculate the Kalman gain and the corrected state estimate. The extended Kalman filter observation equation is: ， Where, is the system measurement vector, is the measurement matrix, is the measurement noise; The Kalman gain formula is: , The state update formula is: , The covariance update formula is: , Where, is the prediction covariance matrix, is the measurement matrix The transposed matrix of is the measurement noise covariance matrix, is the prior state estimate, is the predicted value of the observation equation, is the identity matrix; By iteratively extending the Kalman filter algorithm, the state vector of each measuring point is solved , output the initial three-dimensional coordinates of each measuring point and initial attitude angle ,in is the initial pitch angle, is the initial roll angle, is the initial heading angle.

4. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 3 is characterized in that: In step S3, the undeformed monorail track reference value is generated, and the specific method is as follows: Repeat the collection of initial IMU data for 3 times for all measurement points on the entire track. The weighted average of multiple measurement results is used to generate an undeformed track reference value, which includes a three-dimensional coordinate reference value. and attitude angle reference value , the generation rules are as follows: Set up the first Measuring point The three-dimensional coordinates of the measurement are , the standard deviation is , weight is inversely proportional to the measurement variance, i.e. , then the weighted average coordinate is: ， ， ， in, is the measurement point number, i.e. the number of the measurement point that is evenly spaced along the track. Each IMU module corresponds to a measurement point. ; Sort the three measurements of each coordinate component separately, and then take the median as the final three-dimensional coordinate reference value, that is: ， ， ， Set up the first Measuring point The attitude angle measured is , based on the attitude rotation matrix elements ,in , For the axial identification of the coordinate system, sort the attitude angles of the three measurements and take the median to obtain the final attitude angle reference value, that is: ， ， 。 5. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 4 is characterized in that: The specific method of step S4 is: During the coal mine operation and transportation phase, the mileage is measured according to the formula Calculate the speed of the monorail crane, where N is the number of pulses, D is the wheel diameter, and T is the sampling period; The IMU module has a fixed sampling frequency Record the current three-axis acceleration , current angular velocity 、 、 and current magnetic field strength 、 、 ; Set the current timestamp Label the data frame to maintain time series coherence.

6. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 5 is characterized in that: In step S5, the current spatial position data of the track is calculated based on the current IMU data and the odometer speed signal. The specific method is as follows: Using the state vector , through the iterative EKF algorithm, the current three-dimensional coordinates of each measuring point are obtained and current attitude angle As the current spatial position data of the track, are three-dimensional coordinates, are the speeds in three directions, is the pitch angle, is the roll angle, is the heading angle.

7. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 6 is characterized in that: In step S6, the current spatial position data of the track is compared with the track reference value point by point. The specific method is as follows: Based on the same time base , timestamp the current data Reference timestamp with settings Alignment to ensure that the comparison data is at the same time point; Calculate the displacement difference and attitude angle deviation of each measuring point. The specific method is as follows: For each measuring point , whose three-dimensional coordinate reference value is , the current three-dimensional coordinates are , displacement difference The calculation formula is: ； For each measuring point , and its attitude angle reference value is , the current attitude angle is , the calculation formula for attitude angle deviation is: ， in, The measurement point number.

8. The method for monitoring deformation of a monorail crane track based on inertial measurement technology according to claim 7 is characterized in that: The specific method of step S7 is as follows: Set the displacement difference threshold , single point attitude angle deviation threshold ( , , )for ; For a single measuring point, if the measuring point The displacement difference , it will be marked as a suspected deformation point; if the displacement differences of the three adjacent measuring points are all greater than , and its spatial position is continuous and there are no gaps in the measuring points, then it is determined to be a linear deformation area; For a single measuring point, if the measuring point Any attitude angle deviation 、 ,or Exceed , it will be marked as a suspected deformation point; if the same attitude angle deviation of the three adjacent measuring points exceeds , it is determined to be a local distortion area; If a single measuring point satisfies If any attitude angle deviation exceeds the limit, it will be determined as a confirmed deformation point.

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