Elevator car real-time running speed calculation method and system based on inertial sensor

Abstract

The invention provides a method and system for calculating the real-time running speed of an elevator car based on an inertial sensor, comprising: installing an inertial sensor on an elevator and reading sensor data in real time; performing segmented processing on data other than the attitude angle according to the read x, y, and z-axis attitude angle data: integrating strapdown inertial navigation technology to perform attitude correction, and performing gravity compensation on z-axis acceleration data; allowing the elevator to stand for a preset time, calculating the mean value of the gravity-compensated z-axis acceleration data in the current period as an acceleration mean judgment threshold, and calculating the variance of the z-axis acceleration data as an acceleration variance judgment threshold, and as a threshold of an acceleration energy detector; performing segmented filtering processing on the x, y, and z-axis acceleration data using a Kalman filtering algorithm, and judging and correcting the acceleration data based on the threshold of the acceleration energy detector; and calculating the running speed of the elevator car using the corrected acceleration data using the integral method principle.

Description

Technical Field

[0001] The present invention relates to the technical field of elevators, and in particular to a method and system for calculating the real-time running speed of an elevator car based on an inertial sensor, and more particularly to a method and system for calculating the real-time running speed of an elevator car based on a low-cost inertial sensor. Background Art

[0002] Elevators have gradually become an indispensable infrastructure in urban life. With the development of urbanization, my country's elevator ownership, annual output, and annual growth rate are all the highest in the world. Compared with other developed countries, the use of elevators in my country is characterized by high load, large volume, and strong randomness. Elevator accidents occur from time to time, so they need to be inspected and maintained regularly by humans. The operating parameters of the elevator, especially parameters that cannot be directly measured such as speed, can intuitively reflect the operating status of the elevator. Therefore, it is of great social significance to use effective technical means to monitor the operating speed of the elevator in real time, detect elevator failures in time according to abnormal speed values, and realize the Internet of Things and information-based supervision of elevator safety.

[0003] At present, the traditional velocity calculation method is based on calculus, that is, velocity is an integral of acceleration and time. Since there is inevitable noise in the sensor during the actual measurement process, the error will accumulate over time, resulting in drift, and the data quality is difficult to guarantee in a real-time monitoring environment. However, the use of high-quality sensors requires a high cost, and it is difficult to achieve large-scale popularization.

[0004] Patent document CN107215734A (application number 201710544888.9) discloses a system for real-time acceleration, speed and position detection of elevators. The collected acceleration signal is filtered and fitted into real-time acceleration through the Kalman filter algorithm, and the real-time speed and real-time displacement are calculated respectively through primary integration and secondary integration. However, the algorithm has poor robustness and cannot solve the drift and other problems generated during the integration process in practical applications.

[0005] Patent document CN109896385B (application number 201910176669.9) discloses an offline speed processing method for an elevator with vibration during operation. The acceleration data is processed by low-pass filtering, and the counted number of accelerations greater than 0 and less than 0 is compared with a set threshold to complete subsequent speed calculation. However, this method is only applicable to the calculation of offline data and cannot meet real-time requirements.

[0006] Patent document CN108147257B (application number: 201711433434.0) discloses a posture control system for an elevator car, including: a monitoring module for collecting posture signals of the elevator car; an analysis module for receiving posture signals from the monitoring module and outputting corresponding control signals; a correction module for receiving control signals from the analysis module and correspondingly changing the matching relationship between the elevator car and the car guide rail to adjust the posture of the elevator car. Summary of the invention

[0007] In view of the defects in the prior art, the object of the present invention is to provide a method and system for calculating the real-time running speed of an elevator car based on an inertial sensor.

[0008] According to the present invention, a method for calculating the real-time running speed of an elevator car based on an inertial sensor includes:

[0009] Step S1: Install the inertial sensor calibrated in the geographic coordinate system on the elevator, and receive and read the data collected by the sensor in real time, including acceleration, angular velocity, magnetic induction intensity and attitude angle data;

[0010] Step S2: According to the read x, y, z-axis attitude angle data, other data except the attitude angle are processed in sections, and the strapdown inertial navigation technology is integrated to perform attitude correction on the section-processed data, and gravity compensation is performed on the z-axis acceleration data;

[0011] Step S3: the elevator is allowed to stand for a preset time, and the mean value of the gravity-compensated z-axis acceleration data in the current period is calculated as the acceleration mean determination threshold, and the variance of the z-axis acceleration data is calculated as the acceleration variance determination threshold, and the acceleration mean determination threshold and the acceleration variance determination threshold are used as the thresholds of the acceleration energy detector;

[0012] Step S4: using a Kalman filter algorithm to perform segmented filtering on the gravity-compensated z-axis acceleration data to obtain filtered z-axis acceleration data, and determining and correcting the filtered z-axis acceleration data based on a threshold of an acceleration energy detector;

[0013] Step S5: using the integration principle, using the corrected acceleration data to calculate the elevator car running speed;

[0014] Step S6: setting a speed determination threshold, determining the speed solution result based on the speed determination threshold, and using a zero-speed correction algorithm to determine the running state of the elevator car and correct the speed solution result;

[0015] The segmented processing is to save the data received and read by the sensor in real time, collect M pieces of data, and perform posture correction on the M pieces of data, wherein M is less than the sampling frequency of the sensor.

[0016] Preferably, the step S2 adopts: after posture correction, gravity compensation is performed on the z-axis acceleration data;

[0017]

[0018] Among them, the superscripts n and b represent the navigation coordinate system and the carrier coordinate system respectively; The x, y, and z axis acceleration values ​​read by the sensor; is the projection of velocity on the navigation coordinate system; is the projection of acceleration on the navigation coordinate system; is the posture matrix; The centripetal acceleration caused by the motion of the carrier on the earth's surface; The Coriolis acceleration caused by the relative motion between the carrier and the earth; g n =[0 0-g] T is the projection of gravity acceleration on the navigation coordinate system;

[0019] The effects of centripetal acceleration and Coriolis acceleration are ignored during the solution; therefore, it can be simplified and rewritten as follows:

[0020]

[0021] The attitude angle refers to the angle between the carrier coordinate system and the geographic coordinate system, which is expressed by the roll angle φ, pitch angle θ and yaw angle ψ; R x (φ), R y (θ) and R z (ψ) represents the cosine matrix of x, y, and z axes respectively.

[0022] Preferably, the step S3 adopts:

[0023] Acceleration mean judgment threshold a ave_threshold The calculation method is as follows:

[0024]

[0025] Acceleration variance judgment threshold a var_threshold The calculation method is as follows:

[0026]

[0027] Where n represents the number of sampling times; T1 represents the elevator static time; Represents the projection of the gravity-compensated z-axis acceleration on the navigation coordinate system.

[0028] Preferably, the step S4 adopts: calculating the acceleration mean a based on the z-axis acceleration data after filtering segmented processing average With variance

[0029]

[0030] Where N represents the number of data processed in each segment; Represents the z-axis acceleration data after filtering;

[0031] If a average ave_threshold , The elevator car acceleration is determined to be zero and is forced to be zero.

[0032] Preferably, the step S5 adopts:

[0033] The principle of the integration method used is as follows:

[0034]

[0035] Among them, v k represents the velocity solution result at time k; v0 represents the initial velocity; Represents the z-axis acceleration data after filtering;

[0036]

[0037] Where Δt represents the sampling interval of the sensor; Represents the z-axis acceleration data after filtering at time k; represents the z-axis acceleration data after filtering at time k-1; v k-1 Indicates the velocity solution result at time k-1.

[0038] Preferably, the step S6 adopts:

[0039] If v k <v ave_threshold , a average <a ave_threshold , The elevator car is judged to be in a stationary state and the speed is forced to be zero; where v k represents the velocity solution result at time k; v ave_threshold Indicates the set speed judgment threshold; a average Represents the mean acceleration calculated from the z-axis acceleration data after filtering; a ave_threshold Indicates the acceleration mean judgment threshold; Represents the variance calculated from the filtered z-axis acceleration data; a var_threshold ​represents the acceleration variance determination threshold;

[0040] If v k ≥v ave_threshold , it is determined that the elevator car is in motion and the speed calculation continues.

[0041] According to the present invention, a real-time running speed calculation system of an elevator car based on an inertial sensor is provided, comprising:

[0042] Module M1: Install the inertial sensor calibrated in the geographic coordinate system on the elevator to receive and read the data collected by the sensor in real time, including acceleration, angular velocity, magnetic induction intensity and attitude angle data;

[0043] Module M2: Based on the read x, y, z-axis attitude angle data, perform segmented processing on the data except the attitude angle, integrate the strapdown inertial navigation technology to perform attitude correction on the segmented processed data, and perform gravity compensation on the z-axis acceleration data;

[0044] Module M3: Let the elevator stand for a preset time, calculate the mean value of the gravity-compensated z-axis acceleration data in the current period as the acceleration mean judgment threshold, and calculate the variance of the z-axis acceleration data as the acceleration variance judgment threshold, and use the acceleration mean judgment threshold and the acceleration variance judgment threshold as the threshold of the acceleration energy detector;

[0045] Module M4: Use the Kalman filter algorithm to perform segmented filtering on the gravity-compensated z-axis acceleration data to obtain filtered z-axis acceleration data. The filtered z-axis acceleration data is used to determine and correct the acceleration data based on the threshold of the acceleration energy detector.

[0046] Module M5: Utilizes the principle of integration method and uses the corrected acceleration data to calculate the elevator car running speed;

[0047] Module M6: Set the speed determination threshold, determine the speed solution result based on the speed determination threshold, and use the zero-speed correction algorithm to determine the running state of the elevator car and correct the speed solution result;

[0048] The segmented processing is to save the data received and read by the sensor in real time, collect M pieces of data, and perform posture correction on the M pieces of data, wherein M is less than the sampling frequency of the sensor.

[0049] Preferably, the module M2 adopts: gravity compensation for the z-axis acceleration data after posture correction;

[0050]

[0051] Among them, the superscripts n and b represent the navigation coordinate system and the carrier coordinate system respectively; The x, y, and z axis acceleration values ​​read by the sensor; is the projection of velocity on the navigation coordinate system; is the projection of acceleration on the navigation coordinate system; is the posture matrix; The centripetal acceleration caused by the motion of the carrier on the earth's surface; The Coriolis acceleration caused by the relative motion between the carrier and the earth; g n =[0 0 - g] T is the projection of gravity acceleration on the navigation coordinate system;

[0052] The effects of centripetal acceleration and Coriolis acceleration are ignored during the solution; therefore, it can be simplified and rewritten as follows:

[0053]

[0054] The attitude angle refers to the angle between the carrier coordinate system and the geographic coordinate system, which is expressed by the roll angle φ, pitch angle θ and yaw angle ψ; R x (φ), R y Rz(θ) and Rz(ψ) represent the cosine matrices of x, y, and z axes, respectively.

[0055] The module M3 adopts:

[0056] Acceleration mean judgment threshold a ave_threshold The calculation method is as follows:

[0057]

[0058] Acceleration variance judgment threshold a var_threshold The calculation method is as follows:

[0059]

[0060] Where n represents the number of sampling times; T1 represents the elevator static time; Represents the projection of the gravity-compensated z-axis acceleration on the navigation coordinate system.

[0061] Preferably, the module M4 adopts: calculating the acceleration mean a based on the z-axis acceleration data after filtering and segmentation processing average With variance

[0062]

[0063] Where N represents the number of data processed in each segment; Represents the z-axis acceleration data after filtering;

[0064] If a average <a ave_threshold , Then the elevator car acceleration is determined to be zero and forced to be zero;

[0065] The module M5 adopts the following integration principle:

[0066]

[0067] Among them, v k represents the velocity solution result at time k; v0 represents the initial velocity; Represents the z-axis acceleration data after filtering;

[0068]

[0069] Where Δt represents the sampling interval of the sensor; Represents the z-axis acceleration data after filtering at time k; represents the z-axis acceleration data after filtering at time k-1; v k-1 Indicates the velocity solution result at time k-1.

[0070] Preferably, the module M6 adopts:

[0071] If v k <v ave_threshold , a average <a ave_threshold , The elevator car is judged to be in a stationary state and the speed is forced to be zero; where v k represents the velocity solution result at time k; v ave_threshold Indicates the set speed judgment threshold; a average Represents the mean acceleration calculated from the z-axis acceleration data after filtering; a ave_threshold Indicates the acceleration mean judgment threshold; Represents the variance calculated from the filtered z-axis acceleration data; a var_threshold represents the acceleration variance determination threshold;

[0072] If v k ≥v ave_threshold , it is determined that the elevator car is in motion and the speed calculation continues.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] 1. By adopting a segmented data processing strategy, the acceleration data is corrected according to the collected attitude angle data, and then the data segment is filtered using the Kalman filter algorithm. The cache is released after the speed of the current data segment is solved, thereby achieving the purpose of saving data storage space and real-time processing.

[0075] 2. By adopting strapdown inertial navigation technology attitude correction, z-axis acceleration data gravity compensation, Kalman filter algorithm, and zero-speed correction algorithm based on adaptive threshold, the drift in the elevator car running speed calculation process is effectively suppressed and the accumulated error is corrected. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0077] Figure 1 Flow chart of the speed calculation method for the elevator car strapdown system DETAILED DESCRIPTION

[0078] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0079] Example 1

[0080] In order to solve the problems of data drift and error accumulation caused by traditional speed solution methods and to reduce costs, the present invention proposes a method that uses a low-cost inertial sensor as an acquisition device, integrates the technology in the field of strapdown inertial navigation, and adopts a Kalman filter algorithm and a zero-speed correction algorithm to correct the speed solution data to eliminate the accumulated error, thereby improving the solution accuracy and saving costs.

[0081] The present invention provides a method for calculating the real-time running speed of an elevator car based on an inertial sensor. Figure 1 As shown, including:

[0082] Step S1: First, a low-cost inertial sensor calibrated in a geographic coordinate system is installed on the elevator to receive and read the data collected by the sensor in real time, including acceleration, angular velocity, magnetic induction intensity, and attitude angle data.

[0083] Step S2: Based on the read x, y, z-axis attitude angle data, perform segmented processing on the data except the attitude angle, integrate the strapdown inertial navigation technology to perform attitude correction on the data, and perform gravity compensation on the z-axis acceleration data.

[0084] Step S3: Let the elevator stand for a period of time, and calculate the average value of the z-axis acceleration data after gravity compensation during this period as the acceleration average judgment threshold a ave_threshold , and calculate the variance of the z-axis acceleration data as the acceleration variance judgment threshold a var_threshold , and use the above two thresholds as the thresholds of the acceleration energy detector.

[0085] Step S4: Use the Kalman filter algorithm to perform segmented filtering on the gravity-compensated z-axis acceleration data, and use the acceleration energy detector to determine and correct the acceleration data.

[0086] Step S5: According to the principle of the integration method, the corrected acceleration data is used to calculate the elevator car running speed.

[0087] Step S6: Setting the speed determination threshold v ave_threshold , the velocity solution result is based on the threshold v ave_threshold At the same time, the zero-speed correction algorithm is used to determine the operating status of the elevator car and correct the speed calculation results to suppress data drift and cumulative errors.

[0088] Step S7: Clean up data and release the cache.

[0089] Specifically, step S2 adopts: integrating strapdown inertial navigation technology to perform attitude correction on other data except attitude angle. Since the movement form of the elevator car is relatively simple, after attitude correction, only the z-axis acceleration data needs to be compensated for gravity. The specific formula is as follows:

[0090]

[0091] Among them, the superscripts n and b represent the navigation coordinate system and the carrier coordinate system respectively. The navigation coordinate system usually uses the geographic coordinate system. The x, y, and z axis acceleration values ​​read by the sensor. is the projection of velocity on the navigation coordinate system, is the projection of acceleration on the navigation coordinate system, is the posture matrix, is the centripetal acceleration caused by the motion of the carrier on the earth's surface, The Coriolis acceleration generated by the relative motion between the carrier and the earth, g n is the projection of gravity acceleration on the navigation coordinate system, that is:

[0092]

[0093] g n =[0 0-g] T

[0094] Since ordinary low-cost inertial sensors cannot reflect the rotation of the earth, the influence of centripetal acceleration and Coriolis acceleration on the system can be ignored during the solution. Therefore, the velocity solution differential equation is simplified and rewritten as follows:

[0095]

[0096] The attitude angle refers to the angle between the carrier coordinate system and the geographic coordinate system, usually expressed by the roll angle φ, pitch angle θ and yaw angle ψ. x (φ), R y (θ) and R z (ψ) represents the cosine matrix of x, y, and z axes respectively. The three attitude angles measured by the sensor can be used for calculation. The specific steps are as follows:

[0097] (1) First, the x, y, and z axis cosine matrices are expressed by the attitude angle:

[0098]

[0099] (2) Calculate the attitude matrix using the x, y, and z axis cosine matrices in step (1)

[0100]

[0101]

[0102] The step S3 adopts: the acceleration mean judgment threshold and the acceleration variance judgment threshold are not fixed values, but are determined by the specific characteristics of each elevator and sensor. Since the accuracy of each sensor is different, the noise mean and variance contained in the collected data are also different, and the usage of each elevator is also different, so different elevators need to adapt to different judgment thresholds.

[0103] Acceleration mean judgment threshold a ave_threshold The calculation method is as follows:

[0104]

[0105] Acceleration variance judgment threshold a var_threshold The calculation method is as follows:

[0106]

[0107] Among them, n is the number of sampling times, T1 is the elevator static time, Represents the projection of the gravity-compensated z-axis acceleration on the navigation coordinate system.

[0108] The step S4 adopts: using Kalman filtering to perform segmented filtering on the z-axis acceleration after gravity compensation, which can not only reduce the influence of noise on the speed solution accuracy, but also meet the real-time requirements. The Kalman filtering process includes the following steps:

[0109] (1) Establish the state equation and observation equation of the linear dynamic system:

[0110] x k =Ax k-1 +Bu k +w k

[0111] z k =Hx k +v k

[0112] Among them, x k is the system state at time k, x k-1 is the system state at time k-1, u k is the control quantity of the system at time k, z k is the measured value at time k. A, B and H are all system parameters, A is the state transfer matrix, B is the input gain matrix, and H is the measurement matrix. k and v k They represent process noise and measurement noise with mean 0 and normal distribution, respectively, and their covariances are Q and R respectively (assuming that the covariance does not change with the change of the system state).

[0113] (2) Kalman estimation consists of two processes: prediction and correction. In the prediction stage, the filter uses the estimated value of the previous state to predict the current state:

[0114]

[0115] The above formula is the state estimation equation, which means predicting the state at the next moment. is the state prediction at time k at time k-1, is the state estimate at time k-1, u k-1 is the control amount of the system at time k-1.

[0116]

[0117] The above formula is the prediction estimation covariance equation. Among them, is the prediction error covariance matrix at time k, Pk-1 is the Kalman estimation error covariance matrix at time k-1. Q is the process noise covariance matrix. The larger the Q value, the lower the credibility of the prediction result.

[0118] (3) In the correction phase, the filter uses the observed value of the current state to correct the predicted value obtained in the prediction phase to obtain a new estimated value that is closer to the true value:

[0119]

[0120] The above formula is the Kalman gain equation, K k is the Kalman gain. R is the measurement noise covariance matrix. The larger the R value, the lower the credibility of the observation.

[0121]

[0122] The above formula is the updated state estimation equation, which is the final filtering state result.

[0123]

[0124] The above formula is the updated estimated covariance equation, P k is the Kalman estimation error covariance matrix at time k. In order to keep the Kalman filter running until the end of the system process, it is necessary to update the estimated covariance at time k to prepare for the next recursion.

[0125] Next, the Kalman filter result is used for state determination through the acceleration energy detector. The acceleration energy detector includes acceleration mean detection and acceleration variance detection. The acceleration mean a is determined based on the read data. average With variance The specific calculation formula is as follows:

[0126]

[0127] Among them, N represents the number of data processed in each segment. Represents the z-axis acceleration data after filtering;

[0128] If a average <a ave_threshold , The elevator car acceleration is determined to be zero and is forced to be zero. If the above requirements are not met, no correction is performed.

[0129] The step S5 adopts: the principle of the integration method used is as follows:

[0130]

[0131] Among them, v kis the velocity solution result at time k, v0 is the initial velocity, Represents the z-axis acceleration data after filtering. Limited by the sampling frequency and sampling accuracy of the sensor, the following formula is used to calculate the velocity based on the principle of integration:

[0132]

[0133] Where Δt represents the sampling interval of the sensor, represents the z-axis acceleration data after filtering at time k, represents the z-axis acceleration data after filtering at time k-1, v k-1 Indicates the velocity solution result at time k-1.

[0134] The step S6 adopts: speed determination threshold v ave_threshold The setting needs to be based on the calculated speed of the end point of the motion after multiple integrations, that is, the deviation between the speed calculated result and the true value after the elevator car completes a motion process. k With v ave_threshold The speed calculation result is corrected based on the comparison and combined with the acceleration energy detector to determine the elevator car motion state. The judgment rules are as follows:

[0135] If v k <v ave_threshold , a average <a ave_threshold , The elevator car is judged to be in a stationary state and the speed is forced to be zero; where v k represents the velocity solution result at time k; v ave_threshold Indicates the set speed judgment threshold; a average Represents the mean acceleration calculated from the z-axis acceleration data after filtering; a ave_threshold Indicates the acceleration mean judgment threshold; Represents the variance calculated from the filtered z-axis acceleration data; a var_threshold represents the acceleration variance determination threshold;

[0136] If v k ≥v ave_threshold , it is determined that the elevator car is in motion and the speed calculation continues.

[0137] The present invention provides an elevator car real-time running speed calculation system based on an inertial sensor, comprising:

[0138] Module M1: First, install the low-cost inertial sensor calibrated in the geographic coordinate system on the elevator, and receive and read the data collected by the sensor in real time, including acceleration, angular velocity, magnetic induction intensity and attitude angle data.

[0139] Module M2: Based on the read x, y, z-axis attitude angle data, other data except the attitude angle are processed in sections, the strapdown inertial navigation technology is integrated to correct the data attitude, and the z-axis acceleration data is gravity compensated.

[0140] Module M3: Let the elevator stand for a period of time, and calculate the average value of the z-axis acceleration data after gravity compensation during this period as the acceleration average judgment threshold a ave_threshold , and calculate the variance of the z-axis acceleration data as the acceleration variance judgment threshold a var_threshold , and use the above two thresholds as the thresholds of the acceleration energy detector.

[0141] Module M4: Use the Kalman filter algorithm to perform segmented filtering on the z-axis acceleration data after gravity compensation, and use the acceleration energy detector to determine and correct the acceleration data.

[0142] Module M5: Based on the principle of integration method, the corrected acceleration data is used to calculate the elevator car running speed.

[0143] Module M6: Set speed judgment threshold v ave_threshold , the velocity solution result is based on the threshold v ave_threshold At the same time, the zero-speed correction algorithm is used to determine the operating status of the elevator car and correct the speed calculation results to suppress data drift and cumulative errors.

[0144] Module M7: Clean up data and release cache.

[0145] Specifically, the module M2 adopts: integrating strapdown inertial navigation technology to perform attitude correction on other data except attitude angle. Since the movement form of the elevator car is relatively simple, after attitude correction, only the z-axis acceleration data needs to be compensated for gravity. The specific formula is as follows:

[0146]

[0147] Among them, the superscripts n and b represent the navigation coordinate system and the carrier coordinate system respectively. The navigation coordinate system usually uses the geographic coordinate system. The x, y, and z axis acceleration values ​​read by the sensor. is the projection of velocity on the navigation coordinate system, is the projection of acceleration on the navigation coordinate system, is the posture matrix, is the centripetal acceleration caused by the motion of the carrier on the earth's surface, The Coriolis acceleration generated by the relative motion between the carrier and the earth, g n is the projection of gravity acceleration on the navigation coordinate system, that is:

[0148]

[0149] g n =[0 0 - g] T

[0150] Since ordinary low-cost inertial sensors cannot reflect the rotation of the earth, the influence of centripetal acceleration and Coriolis acceleration on the system can be ignored during the solution. Therefore, the velocity solution differential equation is simplified and rewritten as follows:

[0151]

[0152] The attitude angle refers to the angle between the carrier coordinate system and the geographic coordinate system, usually expressed by the roll angle φ, pitch angle θ and yaw angle ψ. x (φ), R y (θ) and R z (ψ) represents the cosine matrix of x, y, and z axes respectively. The three attitude angles measured by the sensor can be used for calculation. The specific steps are as follows:

[0153] (1) First, the x, y, and z axis cosine matrices are expressed by the attitude angle:

[0154]

[0155] (2) Calculate the attitude matrix using the x, y, and z axis cosine matrices in step (1)

[0156]

[0157] The module M3 adopts: the acceleration mean judgment threshold and the acceleration variance judgment threshold are not fixed values, but are determined by the specific characteristics of each elevator and sensor. Since the accuracy of each sensor is different, the noise mean and variance contained in the collected data are also different, and the usage of each elevator is also different, so different elevators need to adapt to different judgment thresholds.

[0158] Acceleration mean judgment threshold a ave_threshold The calculation method is as follows:

[0159]

[0160] Acceleration variance judgment threshold a var_thresholdThe calculation method is as follows:

[0161]

[0162] Among them, n is the number of sampling times, T1 is the elevator static time, Represents the projection of the gravity-compensated z-axis acceleration on the navigation coordinate system.

[0163] The module M4 adopts: using Kalman filtering to perform segmented filtering on the z-axis acceleration after gravity compensation, which can not only reduce the impact of noise on the speed solution accuracy, but also meet the real-time requirements. The Kalman filtering process includes the following steps:

[0164] (1) Establish the state equation and observation equation of the linear dynamic system:

[0165] x k =Ax k-1 +Bu k +w k

[0166] z k =Hx k +v k

[0167] Among them, x k is the system state at time k, x k-1 is the system state at time k-1, u k is the control quantity of the system at time k, z k is the measured value at time k. A, B and H are all system parameters, A is the state transfer matrix, B is the input gain matrix, and H is the measurement matrix. k and v k They represent process noise and measurement noise with mean 0 and normal distribution, respectively, and their covariances are Q and R respectively (assuming that the covariance does not change with the change of the system state).

[0168] (2) Kalman estimation consists of two processes: prediction and correction. In the prediction stage, the filter uses the estimated value of the previous state to predict the current state:

[0169]

[0170] The above formula is the state estimation equation, which means predicting the state at the next moment. is the state prediction at time k at time k-1, is the state estimate at time k-1, u k-1 is the control amount of the system at time k-1.

[0171]

[0172] The above formula is the prediction estimation covariance equation. Among them, is the prediction error covariance matrix at time k, P k-1 is the Kalman estimation error covariance matrix at time k-1. Q is the process noise covariance matrix. The larger the Q value, the lower the credibility of the prediction result.

[0173] (3) In the correction phase, the filter uses the observed value of the current state to correct the predicted value obtained in the prediction phase to obtain a new estimated value that is closer to the true value:

[0174]

[0175] The above formula is the Kalman gain equation, K k is the Kalman gain. R is the measurement noise covariance matrix. The larger the R value, the lower the credibility of the observation.

[0176]

[0177] The above formula is the updated state estimation equation, which is the final filtering state result.

[0178]

[0179] The above formula is the updated estimated covariance equation, P k is the Kalman estimation error covariance matrix at time k. In order to keep the Kalman filter running until the end of the system process, it is necessary to update the estimated covariance at time k to prepare for the next recursion.

[0180] Next, the Kalman filter result is used for state determination through the acceleration energy detector. The acceleration energy detector includes acceleration mean detection and acceleration variance detection. The acceleration mean a is determined based on the read data. average With variance The specific calculation formula is as follows:

[0181]

[0182] Among them, N represents the number of data processed in each segment. Represents the z-axis acceleration data after filtering;

[0183] If a average <a ave_threshold , The elevator car acceleration is determined to be zero and is forced to be zero. If the above requirements are not met, no correction is performed.

[0184] The module M5 adopts the following integration principle:

[0185]

[0186] Among them, v k is the velocity solution result at time k, v0 is the initial velocity, Represents the z-axis acceleration data after filtering. Limited by the sampling frequency and sampling accuracy of the sensor, the following formula is used to calculate the velocity based on the principle of integration:

[0187]

[0188] Where Δt represents the sampling interval of the sensor, represents the z-axis acceleration data after filtering at time k, represents the z-axis acceleration data after filtering at time k-1, v k-1 Indicates the velocity solution result at time k-1.

[0189] The module M6 adopts: speed determination threshold v ave_threshold The setting needs to be based on the calculated speed of the end point of the motion after multiple integrations, that is, the deviation between the speed calculated result and the true value after the elevator car completes a motion process. k With v ave_threshold The speed calculation result is corrected based on the comparison and combined with the acceleration energy detector to determine the elevator car motion state. The judgment rules are as follows:

[0190] If v k <v ave_threshold , a average <a ave_threshold , The elevator car is judged to be in a stationary state and the speed is forced to be zero; where v k represents the velocity solution result at time k; v ave_threshold Indicates the set speed judgment threshold; a average Represents the mean acceleration calculated from the z-axis acceleration data after filtering; a ave_threshold Indicates the acceleration mean judgment threshold; Represents the variance calculated from the filtered z-axis acceleration data; a var_threshold represents the acceleration variance determination threshold;

[0191] If v k ≥v ave_threshold , it is determined that the elevator car is in motion and the speed calculation continues.

[0192] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0193] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for calculating the real-time running speed of an elevator car based on an inertial sensor, characterized in that: include: Step S1: Install the inertial sensor calibrated in the geographic coordinate system on the elevator, and receive and read the data collected by the sensor in real time, including acceleration, angular velocity, magnetic induction intensity and attitude angle data; Step S2: According to the read x, y, z-axis attitude angle data, other data except the attitude angle are processed in sections, and the strapdown inertial navigation technology is integrated to perform attitude correction on the section-processed data, and gravity compensation is performed on the z-axis acceleration data; Step S3: the elevator is allowed to stand for a preset time, and the mean value of the gravity-compensated z-axis acceleration data in the current period is calculated as the acceleration mean determination threshold, and the variance of the z-axis acceleration data is calculated as the acceleration variance determination threshold, and the acceleration mean determination threshold and the acceleration variance determination threshold are used as the thresholds of the acceleration energy detector; Step S4: using a Kalman filter algorithm to perform segmented filtering on the gravity-compensated z-axis acceleration data to obtain filtered z-axis acceleration data, and determining and correcting the filtered z-axis acceleration data based on a threshold of an acceleration energy detector; Step S5: using the integration principle, using the corrected acceleration data to calculate the elevator car running speed; Step S6: setting a speed determination threshold, determining the speed solution result based on the speed determination threshold, and using a zero-speed correction algorithm to determine the running state of the elevator car and correct the speed solution result; The segmented processing is to save the data received and read by the sensor in real time, collect M pieces of data, and perform posture correction on the M pieces of data, wherein M is less than the sampling frequency of the sensor.

2. The method for calculating the real-time running speed of an elevator car based on an inertial sensor according to claim 1, characterized in that: The step S2 adopts: after the posture correction, gravity compensation is performed on the z-axis acceleration data; Among them, the superscripts n and b represent the navigation coordinate system and the carrier coordinate system respectively; The x, y, and z axis acceleration values ​​read by the sensor; is the projection of velocity on the navigation coordinate system; is the projection of acceleration on the navigation coordinate system; is the posture matrix; The centripetal acceleration caused by the motion of the carrier on the earth's surface; The Coriolis acceleration caused by the relative motion between the carrier and the earth; g n =[0 0-g] T is the projection of gravity acceleration on the navigation coordinate system; The effects of centripetal acceleration and Coriolis acceleration are ignored during the solution; therefore, it can be simplified and rewritten as follows: The attitude angle refers to the angle between the carrier coordinate system and the geographic coordinate system, which is expressed by the roll angle φ, pitch angle θ and yaw angle ψ; R x (φ), R y (θ) and R z (ψ) represents the cosine matrix of x, y, and z axes respectively.

3. The method for calculating the real-time running speed of an elevator car based on an inertial sensor according to claim 1, characterized in that: The step S3 adopts: Acceleration mean judgment threshold a ave_threshold The calculation method is as follows: Acceleration variance judgment threshold a var_threshold The calculation method is as follows: Where n represents the number of sampling times; T1 represents the elevator static time; Represents the projection of the gravity-compensated z-axis acceleration on the navigation coordinate system.

4. The method for calculating the real-time running speed of an elevator car based on an inertial sensor according to claim 1, characterized in that: The step S4 adopts: calculating the acceleration mean a based on the z-axis acceleration data after filtering and segmentation processing average With variance Where N represents the number of data processed in each segment; Represents the z-axis acceleration data after filtering; If a average ave_threshold , The elevator car acceleration is determined to be zero and is forced to be zero.​ 5. The method for calculating the real-time running speed of an elevator car based on an inertial sensor according to claim 1, characterized in that: The step S5 adopts: The principle of the integration method used is as follows: Among them, v k represents the velocity solution result at time k; v0 represents the initial velocity; Represents the z-axis acceleration data after filtering; Where Δt represents the sampling interval of the sensor; Represents the z-axis acceleration data after filtering at time k; represents the z-axis acceleration data after filtering at time k-1; v k-1 Indicates the velocity solution result at time k-1.

6. The method for calculating the real-time running speed of an elevator car based on an inertial sensor according to claim 1, characterized in that: The step S6 adopts: If v k <v ave_threshold , a average ave_threshold , The elevator car is judged to be stationary and the speed is forced to zero;​ Among them, v k represents the velocity solution result at time k; v ave_threshold Indicates the set speed judgment threshold; a average Represents the mean acceleration calculated from the z-axis acceleration data after filtering; a ave_threshold Indicates the acceleration mean judgment threshold; Represents the variance calculated from the filtered z-axis acceleration data; a var_threshold represents the acceleration variance determination threshold; If v k ≥v ave_threshold , it is determined that the elevator car is in motion and the speed calculation continues.

7. An elevator car real-time running speed calculation system based on inertial sensors, characterized in that: include: Module M1: Install the inertial sensor calibrated in the geographic coordinate system on the elevator to receive and read the data collected by the sensor in real time, including acceleration, angular velocity, magnetic induction intensity and attitude angle data; Module M2: Based on the read x, y, z-axis attitude angle data, perform segmented processing on the data except the attitude angle, integrate the strapdown inertial navigation technology to perform attitude correction on the segmented processed data, and perform gravity compensation on the z-axis acceleration data; Module M3: Let the elevator stand for a preset time, calculate the mean value of the gravity-compensated z-axis acceleration data in the current period as the acceleration mean judgment threshold, and calculate the variance of the z-axis acceleration data as the acceleration variance judgment threshold, and use the acceleration mean judgment threshold and the acceleration variance judgment threshold as the threshold of the acceleration energy detector; Module M4: Use the Kalman filter algorithm to perform segmented filtering on the gravity-compensated z-axis acceleration data to obtain filtered z-axis acceleration data. The filtered z-axis acceleration data is used to determine and correct the acceleration data based on the threshold of the acceleration energy detector. Module M5: Utilizes the principle of integration method and uses the corrected acceleration data to calculate the elevator car running speed; Module M6: Set the speed determination threshold, determine the speed solution result based on the speed determination threshold, and use the zero-speed correction algorithm to determine the running state of the elevator car and correct the speed solution result; The segmented processing is to save the data received and read by the sensor in real time, collect M pieces of data, and perform posture correction on the M pieces of data, wherein M is less than the sampling frequency of the sensor.

8. The elevator car real-time running speed calculation system based on inertial sensor according to claim 7 is characterized in that: The module M2 adopts: gravity compensation for the z-axis acceleration data after posture correction; Among them, the superscripts n and b represent the navigation coordinate system and the carrier coordinate system respectively; The x, y, and z axis acceleration values ​​read by the sensor; is the projection of velocity on the navigation coordinate system; is the projection of acceleration on the navigation coordinate system; is the posture matrix; The centripetal acceleration caused by the motion of the carrier on the earth's surface; The Coriolis acceleration caused by the relative motion between the carrier and the earth; g n =[0 0-g] T is the projection of gravity acceleration on the navigation coordinate system; The effects of centripetal acceleration and Coriolis acceleration are ignored during the solution; therefore, it can be simplified and rewritten as follows: The attitude angle refers to the angle between the carrier coordinate system and the geographic coordinate system, which is expressed by the roll angle φ, pitch angle θ and yaw angle ψ; R x (φ), R y (θ) and R z (ψ) represents the cosine matrix of x, y, and z axes respectively; The module M3 adopts: Acceleration mean judgment threshold a ave_threshold The calculation method is as follows: Acceleration variance judgment threshold a var_threshold The calculation method is as follows: Where n represents the number of sampling times; T1 represents the elevator static time; Represents the projection of the gravity-compensated z-axis acceleration on the navigation coordinate system.

9. The elevator car real-time running speed calculation system based on inertial sensor according to claim 7 is characterized in that: The module M4 uses: based on the z-axis acceleration data after filtering and segmentation processing to calculate the acceleration mean a average With variance Where N represents the number of data processed in each segment; Represents the z-axis acceleration data after filtering; If a average ave_threshold , Then the elevator car acceleration is determined to be zero and forced to be zero;​ The module M5 adopts the following integration principle: Among them, v k represents the velocity solution result at time k; v0 represents the initial velocity; Represents the z-axis acceleration data after filtering; Where Δt represents the sampling interval of the sensor; Represents the z-axis acceleration data after filtering at time k; represents the z-axis acceleration data after filtering at time k-1; v k-1 Indicates the velocity solution result at time k-1.

10. The elevator car real-time running speed calculation system based on inertial sensor according to claim 7, characterized in that: The module M6 adopts: If v k <v ave_threshold , a average ave_threshold , The elevator car is judged to be stationary and the speed is forced to zero;​ Among them, v k represents the velocity solution result at time k; v ave_threshold Indicates the set speed judgment threshold; a average Represents the mean acceleration calculated from the z-axis acceleration data after filtering; a ave_threshold Indicates the acceleration mean judgment threshold; Represents the variance calculated from the filtered z-axis acceleration data; a var_threshold represents the acceleration variance determination threshold; If v k ≥v ave_threshold , it is determined that the elevator car is in motion and the speed calculation continues.

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