Construction hoist height measurement method and device based on double correction

Abstract

The embodiment of the present application provides a construction elevator height measurement method and device based on dual correction. By fusing the data of a high-precision air pressure sensor and a six-axis gyroscope, a pressure-altitude mapping model is established for initial calibration. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weight, and the error correction is achieved in combination with the zero drift reference point record. The state transfer equation and the observation equation are constructed using an extended Kalman filter, and a highly fused result is obtained through a prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and device for measuring the height of a construction elevator based on double correction. Background Art

[0002] Construction hoists are important equipment in construction projects, and their height measurement accuracy is directly related to construction safety and efficiency. Traditional height measurement methods mainly rely on a single sensor, which is easily disturbed in complex environments, resulting in large measurement errors.

[0003] From the perspective of measurement principle, currently commonly used measurement schemes mainly include mechanical, photoelectric and pressure methods, etc. These methods have obvious limitations in practical applications. Mechanical measurement is easily affected by mechanical wear, photoelectric measurement has poor reliability under bad weather conditions, and single pressure measurement is easily affected by changes in ambient temperature and air pressure.

[0004] From the perspective of data processing, existing systems generally lack effective error compensation mechanisms. Problems such as sensor zero drift and temperature drift are difficult to correct in a timely manner, and the measurement accuracy gradually decreases with the increase of usage time. At the same time, the existing methods process sensor data in a relatively simple way and do not fully consider the measurement characteristics under different motion states.

[0005] From the perspective of system reliability, the single sensor solution has poor anti-interference ability and is prone to measurement interruption or data distortion in the complex and changeable environment of the construction site. When the sensor fails, the system lacks an effective backup and switching mechanism, affecting the continuity and safety of construction operations.

[0006] Therefore, how to improve the accuracy and reliability of height measurement and realize the intelligent fusion and dynamic correction of multi-sensor data is a key issue that needs to be solved in the current construction elevator measurement technology. This is not only related to construction safety, but also an important guarantee for improving construction efficiency. Summary of the invention

[0007] In response to the problems in the prior art, the present application provides a construction elevator height measurement method and device based on double correction, which can improve the accuracy and reliability of height measurement and realize intelligent fusion and dynamic correction of multi-sensor data.

[0008] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0009] In a first aspect, the present application provides a construction elevator height measurement method based on double correction, comprising:

[0010] Collect atmospheric pressure data from a high-precision air pressure sensor and acceleration angular velocity data from a six-axis gyroscope, write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix synchronously according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0011] Establishing a motion state evaluation matrix, calculating a state judgment threshold according to the acceleration angular velocity data, determining a weight coefficient of an air pressure sensor and a gyroscope based on the state judgment threshold, comparing the sensor data with the zero drift reference point record table at a preset interval, calculating a zero drift correction value, substituting the zero drift correction value and the weight coefficient into an extended Kalman filter, constructing a state transfer equation and an observation equation, calculating a state estimation value through a prediction update step, and obtaining a highly fused result according to the state estimation value and the weight coefficient;

[0012] Monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the height fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0013] Furthermore, the collecting of atmospheric pressure data from a high-precision air pressure sensor and acceleration angular velocity data from a six-axis gyroscope, and synchronously writing the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, comprises:

[0014] The atmospheric pressure value output by the pressure sensor is collected at a preset sampling frequency, the atmospheric pressure value is converted into corresponding height unit data, the three-axis acceleration and three-axis angular velocity raw data are read from the six-axis gyroscope, the raw data is subjected to noise filtering to obtain filtered data, and a sensor data collection timestamp is generated;

[0015] The height unit data and the filtered data are packaged into a data frame according to the sensor data acquisition timestamp, storage space is allocated for each data frame in a data buffer matrix, the data frames are written into the storage space positions corresponding to the data buffer matrix according to the timestamp sequence, and a data frame index table is established to point to the storage space positions.

[0016] Furthermore, the construction of the pressure-altitude mapping model performs initial calibration on the data in the data buffer matrix, generates a sensor calibration parameter table, and establishes a zero drift reference point record table, including:

[0017] Calculate the mean value of the atmospheric pressure data in the data buffer matrix as the reference point pressure value, construct a pressure altitude mapping function H=44330×(1-P / P0)^(1 / 5.255) based on the international standard atmospheric pressure formula, substitute the reference point pressure value into the mapping function to obtain the reference altitude, write the corresponding temperature compensation coefficient and humidity compensation coefficient into the sensor calibration parameter table, and construct a calibration curve equation according to the compensation coefficient and the mapping function;

[0018] Before the elevator starts, the gyroscope angular velocity and acceleration data within a period of 500ms are collected, and variance analysis is performed on the angular velocity and acceleration data to calculate the zero-position deviation and standard deviation. The zero-position deviation is written into the zero-drift reference point record table, and the dynamic threshold range is set according to the standard deviation. The dynamic threshold range is stored in the sensor calibration parameter table, and a time index is established in the zero-drift reference point record table to point to the corresponding zero-position calibration data.

[0019] Further, the establishment of a motion state evaluation matrix, calculating a state determination threshold according to the acceleration angular velocity data, determining a weight coefficient of an air pressure sensor and a gyroscope based on the state determination threshold, comparing the sensor data with the zero drift reference point record table at each preset time interval, and calculating a zero drift correction value, includes:

[0020] The three-axis acceleration data and the three-axis angular velocity data of the gyroscope are combined into a six-dimensional state vector, and the modulus of the state vector is calculated as a basis for judging the motion state. The static state threshold is set to 0.2g, and the violent motion state threshold is set to 1.5g. The data weight coefficients of the air pressure sensor and the gyroscope are linearly allocated within the threshold range;

[0021] The zero-position calibration data in the zero-drift reference point record table is read every 60 seconds, the zero-position calibration data is subtracted from the current sensor acquisition data to obtain a zero-drift deviation value, a correction function is constructed based on the zero-drift deviation value to update the current sensor data, and the weight coefficient is compensated and adjusted according to the correction function.

[0022] Furthermore, the zero drift correction value and the weight coefficient are substituted into the extended Kalman filter, a state transfer equation and an observation equation are constructed, a state estimation value is calculated through a prediction update step, and a highly fused result is obtained according to the state estimation value and the weight coefficient, including:

[0023] The height, velocity and acceleration form a state vector, a state transfer matrix is ​​established to describe the evolution process of the state vector, a system noise covariance matrix is ​​constructed to characterize the uncertainty of state prediction, the zero drift correction value and the weight coefficient are substituted into the state transfer equation, and the state prior estimation and the error covariance matrix are calculated through the time update step;

[0024] An observation equation is established to associate the pressure altitude value with the gyroscope integrated altitude value, an observation noise covariance matrix is ​​constructed, the Kalman gain is calculated to update the state posterior estimate, the state posterior estimate is multiplied by the corresponding weight coefficient, the weighted results of the pressure sensor and the gyroscope are added to obtain the fused altitude value, and the state error covariance matrix is ​​updated for the next iterative calculation.

[0025] Further, the monitoring of the abnormal state of the atmospheric pressure data and the acceleration angular velocity data, switching the sensor working mode according to the abnormal state, and writing the height fusion result into the control system of the construction hoist through the transmission interface include:

[0026] Perform sliding window analysis on the atmospheric pressure data and the acceleration angular velocity data, calculate the data mutation rate and the data continuity index, compare the data mutation rate with a preset threshold to determine data anomalies, identify the sensor working state based on the data continuity index, and switch to the corresponding air pressure priority mode or gyroscope priority mode according to the sensor anomaly type;

[0027] The highly fused result is encapsulated as a communication data packet, a data packet header and a check code are added, and the communication data packet is sent to the construction elevator control system through the RS485 interface at a baud rate of 115200, the data packet response signal is monitored to confirm the data writing status, and the sensor working mode and communication status information are recorded.

[0028] Further, the updating of the system parameters of the state transfer equation based on the abnormal state and writing the zero drift correction value into the zero drift reference point record table includes:

[0029] According to the abnormal state type, query the preset parameter mapping table to obtain the corresponding system parameter adjustment value, substitute the system parameter adjustment value into the state transfer matrix to update the system noise covariance, adjust the state transfer coefficient in the state prediction equation, and update the filter gain parameter of the Kalman filter;

[0030] The zero drift correction value and the correction timestamp form a data structure and write it into the zero drift reference point record table, maintain the historical data queue length in the record table, delete the historical zero drift data that exceeds the storage period, update the data index of the zero drift reference point record table, and set the latest zero drift correction value as the reference benchmark for the next calibration.

[0031] In a second aspect, the present application provides a construction elevator height measurement device based on double correction, comprising:

[0032] A sensor calibration module is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0033] A motion state evaluation module is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, compare the sensor data with the zero drift reference point record table at each preset time interval, calculate a zero drift correction value, substitute the zero drift correction value and the weight coefficient into an extended Kalman filter, construct a state transfer equation and an observation equation, calculate a state estimation value through a prediction update step, and obtain a highly fused result according to the state estimation value and the weight coefficient;

[0034] The height measurement module is used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the height fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0035] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the construction elevator height measurement method based on double correction are implemented.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the construction elevator height measurement method based on double correction.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the construction elevator height measurement method based on double correction.

[0038] It can be seen from the above technical solution that the present application provides a construction elevator height measurement method and device based on dual correction. By fusing the data of a high-precision air pressure sensor and a six-axis gyroscope, a pressure-altitude mapping model is established for initial calibration. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weight, and the error correction is achieved in combination with the zero-drift reference point record. The state transfer equation and the observation equation are constructed using the extended Kalman filter, and a highly fused result is obtained through the prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 This is one of the flow charts of the construction elevator height measurement method based on double correction in the embodiment of the present application;

[0041] Figure 2 This is a second flow chart of a construction elevator height measurement method based on double correction in an embodiment of the present application;

[0042] Figure 3 The third flowchart of the construction elevator height measurement method based on double correction in the embodiment of the present application;

[0043] Figure 4 This is a fourth flow chart of a construction elevator height measurement method based on double correction in an embodiment of the present application;

[0044] Figure 5 FIG5 is a flowchart of a method for measuring the height of a construction elevator based on double correction in an embodiment of the present application;

[0045] Figure 6 FIG6 is a sixth flow chart of a construction elevator height measurement method based on double correction in an embodiment of the present application;

[0046] Figure 7 FIG7 is a flow chart of a construction elevator height measurement method based on double correction in an embodiment of the present application;

[0047] Figure 8 It is a structural diagram of a construction elevator height measurement device based on double correction in an embodiment of the present application;

[0048] Fig. 9 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0049] Reference numerals:

[0050] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0053] Taking into account the problems existing in the prior art, the present application provides a construction elevator height measurement method and device based on dual correction. By fusing the data of a high-precision air pressure sensor and a six-axis gyroscope, a pressure-altitude mapping model is established for initial calibration. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weights, and the error correction is achieved in combination with the zero-drift reference point record. The extended Kalman filter is used to construct the state transfer equation and the observation equation, and a highly fused result is obtained through the prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

[0054] In order to improve the accuracy and reliability of height measurement and realize intelligent fusion and dynamic correction of multi-sensor data, the present application provides an embodiment of a construction elevator height measurement method based on double correction, see Figure 1 The construction elevator height measurement method based on double correction specifically includes the following contents:

[0055] Step S101: a sensor calibration module is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0056] Optionally, the sensor calibration module of this embodiment first selects a high-precision pressure sensor with an accuracy of 0.1mbar and a six-axis gyroscope with a sampling rate of 200Hz as basic hardware. When collecting data, the sensor sampling is triggered by a timer interrupt. The pressure sensor uses the I2C bus to read the atmospheric pressure value at a frequency of 50Hz, and the six-axis gyroscope collects acceleration and angular velocity data at a frequency of 200Hz through the SPI interface. To ensure data synchronization, this embodiment adopts a double buffer queue structure to align the two sensor data to the same sampling period according to the timestamp.

[0057] This embodiment adopts a hierarchical filtering strategy for the preprocessing of raw data. For the pressure sensor data, a Butterworth low-pass filter with a cutoff frequency of 2 Hz is first used to eliminate high-frequency noise, and then temperature compensation is performed based on the reading of the built-in temperature sensor. The compensation function is obtained by fitting a cubic polynomial. For the gyroscope data, a median filter with a window length of 5 is first used to remove transient interference, and then a Kalman prefilter with adaptive gain is used to suppress Gaussian white noise.

[0058] In the implementation of the data buffer matrix, this embodiment designs a three-dimensional matrix structure. The first dimension stores the timestamp, the second dimension stores the air pressure data, and the third dimension stores the six-axis data of the gyroscope. The matrix size is adjusted dynamically, and the default data volume is maintained for 10 seconds. A ring buffer mechanism is adopted. When the data volume exceeds the preset threshold, the earliest data record is automatically cleared to ensure memory usage efficiency.

[0059] This embodiment innovatively improves the mapping model of the standard atmospheric pressure formula. Based on the basic formula H=44330×(1-P / P0)^(1 / 5.255), the temperature influence factor and humidity correction term are introduced. The temperature influence is corrected by the second-order temperature coefficient, and the humidity influence is corrected by the piecewise linear correction method. Taking into account the characteristics of temperature and humidity changes at the construction site, the model parameters are automatically calibrated every 30 minutes.

[0060] In the design of the sensor calibration parameter table, this embodiment uses a hash table structure to store multiple groups of parameters. Including temperature coefficient matrix (3×3), humidity correction coefficient (1×5), zero offset vector (1×6), etc. The calibration process uses a gradient descent algorithm to determine the optimal parameter combination by minimizing the measurement error. The parameter table supports real-time updates, and the update trigger conditions include temperature changes exceeding 5°C or humidity changes exceeding 10%.

[0061] The zero drift reference point record table is implemented using a bidirectional linked list structure. When the elevator is stationary (the judgment condition is that the angular velocity and acceleration output by the gyroscope are both less than the threshold), the current sensor output is recorded as the reference value. The linked list node contains timestamps, air pressure zero drift values, gyroscope six-axis zero drift values, and environmental parameters. The zero drift trend is evaluated by the exponentially weighted moving average method and used to predict short-term zero drift changes.

[0062] The data synchronization and preprocessing mechanism of this embodiment effectively solves the problem of sensor data time consistency, and the improved pressure-altitude mapping model improves the accuracy of altitude calculation. The adaptive update of calibration parameters ensures the stability of the system under different environmental conditions. This module lays a reliable data foundation for subsequent data fusion and effectively overcomes the impact of large temperature changes, dust interference, vibration interference and other problems on measurement accuracy at the construction site.

[0063] In the actual application of construction elevators, this module can realize the reliable collection and effective calibration of sensor data, ensure the accuracy of basic data, and provide important support for the precise positioning and safety control of the elevator.

[0064] Step S102: a motion state evaluation module, which is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, compare the sensor data with the zero drift reference point record table at each preset time interval, calculate a zero drift correction value, substitute the zero drift correction value and the weight coefficient into an extended Kalman filter, construct a state transfer equation and an observation equation, calculate a state estimation value through a prediction update step, and obtain a highly fused result according to the state estimation value and the weight coefficient;

[0065] Optionally, the motion state evaluation module of this embodiment first constructs an 8×8 motion state evaluation matrix, and the matrix elements include state quantities such as vertical acceleration, horizontal acceleration, angular velocity, and attitude angle. The mean and variance of these state quantities are calculated by the sliding window method to establish a multidimensional state space. The state evaluation adopts a sampling period of 100ms to ensure that the motion characteristics of the elevator can be captured in time.

[0066] In order to accurately calculate the state judgment threshold, this embodiment adopts an adaptive threshold algorithm. First, the acceleration data is compensated for the gravity component, and the quaternion algorithm is used to separate the gravity acceleration and the motion acceleration. Then the composite value of the three-axis acceleration is calculated, and the short-term fluctuation range is obtained by the exponential smoothing method. For the angular velocity data, the root mean square value is used as an indicator of the intensity of the rotational motion. The threshold calculation takes into account the typical motion characteristics of the elevator, such as the data characteristics of the start-up acceleration, uniform speed operation, deceleration and stop stages.

[0067] This embodiment innovatively designs a dynamic weight allocation mechanism. When the elevator is detected to be in a stationary state (acceleration fluctuation is less than 2% of gravity acceleration, angular velocity is less than 0.1 rad / s), the weight coefficient of the air pressure sensor is set to 0.8 and the gyroscope weight is 0.2. In the accelerated motion state, the weight is dynamically adjusted according to the acceleration to ensure that the weight of the gyroscope data is increased during intense motion, thereby improving the system response speed.

[0068] In terms of zero drift correction, this embodiment compares the current sensor data with the historical data in the zero drift reference point record table every 500ms. The recursive least squares method is used to fit the zero drift change trend to obtain the zero drift correction value. The correction process takes into account the influence of temperature changes and compensates through the temperature coefficient matrix.

[0069] The design of the extended Kalman filter uses an 8-dimensional state vector, which contains position, velocity, acceleration, and attitude information. The state transfer equation is constructed based on the kinematic model, taking into account the coupling effect of vertical motion and horizontal vibration. The observation equation combines the pressure altitude and the gyroscope integrated displacement, which are fused through weight coefficients. The prediction step uses the fourth-order Runge-Kutta method to solve the state equation, and the update step uses an adaptive gain matrix to dynamically adjust the convergence characteristics of the filter.

[0070] The calculation of the highly fused result adopts the recursive averaging method, combining the state estimation value and the weight coefficient. This embodiment introduces a confidence assessment mechanism in the fusion process. When a sensor data is abnormal, its weight is reduced to maintain system stability. At the same time, a data validity verification mechanism is established to timely identify and eliminate abnormal data caused by electromagnetic interference or mechanical shock.

[0071] In the actual application of construction elevators, the motion state assessment module of this embodiment can accurately identify various motion states and achieve optimal fusion of sensor data. Through dynamic weight adjustment and real-time correction of zero drift, the measurement limitations of a single sensor are effectively overcome. This module is particularly suitable for the complexity of the construction environment and can maintain stable height measurement performance under conditions such as rapid start and stop and climbing of the elevator.

[0072] This module works closely with the previous sensor calibration module, using the calibrated sensor data for state assessment and data fusion, providing reliable height information for precise positioning and motion control of the elevator, significantly improving the overall reliability and safety of the system.

[0073] Step S103: an altitude measurement module, used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the altitude fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0074] From the above description, it can be seen that the construction elevator height measurement method based on dual correction provided by the embodiment of the present application can establish a pressure-altitude mapping model for initial calibration by fusing the data of a high-precision air pressure sensor and a six-axis gyroscope. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weights, and the error correction is achieved in combination with the zero-drift reference point record. The state transfer equation and the observation equation are constructed using the extended Kalman filter, and a highly fused result is obtained through the prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

[0075] In one embodiment of the construction elevator height measurement method based on double correction of the present application, see Figure 2 , and can also include the following:

[0076] Step S201: collecting the atmospheric pressure value output by the pressure sensor according to a preset sampling frequency, converting the atmospheric pressure value into corresponding altitude unit data, reading the three-axis acceleration and three-axis angular velocity raw data from the six-axis gyroscope, performing noise filtering on the raw data to obtain filtered data, and generating a sensor data collection timestamp;

[0077] Step S202: Pack the height unit data and the filtered data into a data frame according to the sensor data acquisition timestamp, allocate storage space for each data frame in a data buffer matrix, write the data frame into the storage space position corresponding to the data buffer matrix according to the timestamp sequence, and establish a data frame index table pointing to the storage space position.

[0078] Optionally, in this embodiment, the sensor sampling parameters are first set according to the operating characteristics of the elevator. The air pressure sensor adopts a sampling frequency of 50 Hz and reads the atmospheric pressure value through the I2C bus. The sampling process adopts an interrupt trigger mode to ensure the stability of the sampling time interval. The collected atmospheric pressure value is converted into altitude data through an improved standard atmospheric pressure formula. The influence of temperature and humidity is taken into account during the conversion process, so as to achieve more accurate altitude calculation.

[0079] The data acquisition of the six-axis gyroscope adopts a sampling frequency of 200Hz, and the three-axis acceleration and three-axis angular velocity data are read at high speed through the SPI interface. In order to improve the data quality, this embodiment designs a two-stage filtering processing mechanism. The first stage adopts hardware digital low-pass filtering, and the cut-off frequency is set to 50Hz to effectively suppress high-frequency interference. The second stage adopts software filtering, and a median filter with a window length of 5 is used to remove sudden interference for acceleration data, and a Kalman filter is applied to angular velocity data to eliminate random noise.

[0080] This embodiment uses a high-precision timer to generate timestamps, and the time resolution reaches microseconds. The timestamp contains two parts: absolute time and relative time. The absolute time is used for data tracing and synchronization, and the relative time is used to calculate the sampling interval. In order to solve the problem of multi-sensor data synchronization, a timestamp-based data alignment algorithm is implemented to ensure the timing consistency of air pressure data and gyroscope data.

[0081] In the data frame packaging process, this embodiment designs an efficient data organization structure. Each data frame contains a frame header, a timestamp, a pressure altitude value, a three-axis acceleration value, a three-axis angular velocity value, and a checksum. The frame structure adopts a compact design to reduce data transmission and storage overhead. Real-time data validity checks are performed during the data frame packaging process to ensure the integrity of the packaged data.

[0082] The data buffer matrix adopts a three-dimensional structure design. The first dimension represents the time series, the second dimension stores different types of sensor data, and the third dimension saves multiple historical versions of the data. The matrix size is dynamically adjusted according to the system operation requirements, and the default capacity can store nearly 10 seconds of data. The storage space adopts a circular buffer mechanism. When the space is close to full, the earliest data records are automatically cleared.

[0083] This embodiment innovatively designs an efficient data frame indexing mechanism. The index table uses a hash structure to quickly locate the data location using the timestamp as the key value. The index table supports range queries, which facilitates the acquisition of historical data for a specific time period. In order to improve query efficiency, a multi-level cache mechanism is implemented, and frequently accessed data is kept in a fast cache.

[0084] In connection with the previous steps, this embodiment provides high-quality raw data for the sensor calibration module, and also provides a reliable data basis for subsequent motion state evaluation. The real-time data collection and caching mechanism ensures the timeliness of the system response and provides strong support for the safety monitoring of the elevator.

[0085] In the actual application of construction elevators, this step realizes the reliable collection and efficient storage of sensor data, overcoming the influence of interference factors such as vibration and noise at the construction site. Through multi-level filtering and data synchronization processing, data quality is ensured, providing accurate basic data support for the precise positioning and status monitoring of the elevator.

[0086] In one embodiment of the construction elevator height measurement method based on double correction of the present application, see Figure 3 , and can also include the following:

[0087] Step S301: Calculate the mean value of the atmospheric pressure data in the data buffer matrix as the reference point pressure value, construct a pressure altitude mapping function H=44330×(1-P / P0)^(1 / 5.255) based on the international standard atmospheric pressure formula, substitute the reference point pressure value into the mapping function to obtain the reference altitude, write the corresponding temperature compensation coefficient and humidity compensation coefficient into the sensor calibration parameter table, and construct a calibration curve equation according to the compensation coefficient and the mapping function;

[0088] Step S302: Before the elevator starts, collect the gyroscope angular velocity and acceleration data within a period of 500ms, perform variance analysis on the angular velocity and acceleration data, calculate the zero-position deviation and standard deviation, write the zero-position deviation into the zero-drift reference point record table, set the dynamic threshold range according to the standard deviation, store the dynamic threshold range in the sensor calibration parameter table, and establish a time index pointing to the corresponding zero-position calibration data in the zero-drift reference point record table.

[0089] Optionally, this embodiment first extracts the atmospheric pressure data of the most recent 10 seconds from the data buffer matrix, and uses the sliding window method to calculate the mean as the reference point pressure value. To reduce the impact of random fluctuations, 10% of the samples with the maximum and minimum values ​​are removed during the calculation process. At the same time, considering the daily variation characteristics of the atmospheric pressure at the construction site, the reference point pressure value is updated every hour to ensure the accuracy of the measurement benchmark.

[0090] When constructing the pressure-altitude mapping function, this embodiment optimizes the international standard atmospheric pressure formula. By introducing the temperature compensation coefficient, the influence of temperature change on air pressure measurement is solved. The temperature compensation coefficient adopts a piecewise linear model and compensates in 5 intervals within the range of -20°C to 50°C. The humidity compensation coefficient adopts an exponential model to provide compensation within the relative humidity range of 30% to 95%. These compensation coefficients are obtained through experimental calibration and stored in the sensor calibration parameter table.

[0091] The calibration curve equation is constructed using a segmented fitting strategy. Linear fitting is used in the range of 0-100 meters to ensure the accuracy of low-altitude measurements; polynomial fitting is used above 100 meters to improve the accuracy of high-altitude measurements. The fitting process uses the least squares method, and cross-validation is used to ensure the generalization ability of the model. The calibration curve also takes into account the influence of altitude and compensates for it by introducing a geographical location correction factor.

[0092] In terms of gyroscope zero point calibration, this embodiment innovatively adopts a multi-cycle sampling strategy. In the static state before the elevator is started, 500ms of data are continuously collected at a sampling frequency of 200Hz to obtain 100 sampling points. Variance analysis is performed on these data to calculate the zero position deviation of the three-axis angular velocity and the three-axis acceleration respectively. The calculation process uses a recursive variance algorithm to reduce the calculation overhead.

[0093] The processing of zero deviation adopts adaptive filtering technology. First, the raw data is band-pass filtered to remove high-frequency noise and low-frequency drift. Then the mean and standard deviation of the zero deviation of each axis are calculated. When the zero deviation is written into the zero drift reference point record table, the ambient temperature is also recorded to establish a temperature-zero drift relationship model for subsequent temperature compensation.

[0094] The dynamic threshold range is set using an adaptive mechanism. Based on the calculated standard deviation, ±3 times the standard deviation is set as the basic threshold range. Considering the motion characteristics of the construction elevator, the threshold range is further subdivided into static state threshold and motion state threshold. A narrower threshold range is used in the static state to improve the sensitivity of zero drift detection; a wider threshold range is used in the motion state to avoid false alarms.

[0095] This embodiment establishes an efficient time index mechanism in the zero drift reference point record table. The B+ tree structure is used to store the time index, which supports fast range query and historical data tracing. Each zero drift record contains a timestamp, environmental parameters and calibration data, which is convenient for subsequent analysis and compensation.

[0096] This calibration step is closely related to the subsequent motion state assessment and height measurement modules. By providing accurate benchmark data and dynamic thresholds, it lays the foundation for subsequent state judgment and data fusion. In practical applications, this step significantly improves the accuracy of elevator height measurement, especially in construction environments with large temperature changes and unstable ambient humidity, and can still maintain reliable measurement performance.

[0097] Through precise sensor calibration and compensation mechanisms, this embodiment effectively solves the problem of reduced accuracy of traditional height measurement methods in harsh environments, and provides reliable technical guarantees for the safe operation of construction elevators.

[0098] In one embodiment of the construction elevator height measurement method based on double correction of the present application, see Figure 4 , and can also include the following:

[0099] Step S401: The three-axis acceleration data and the three-axis angular velocity data of the gyroscope are combined into a six-dimensional state vector, and the modulus of the state vector is calculated as a basis for judging the motion state. The static state threshold is set to 0.2g, and the violent motion state threshold is set to 1.5g. The data weight coefficients of the air pressure sensor and the gyroscope are linearly distributed within the threshold range;

[0100] Step S402: read the zero-position calibration data in the zero-drift reference point record table every 60 seconds, subtract the zero-position calibration data from the current sensor acquisition data to obtain a zero-drift deviation value, construct a correction function based on the zero-drift deviation value to update the current sensor data, and compensate and adjust the weight coefficient according to the correction function.

[0101] Optionally, this embodiment first constructs an innovative six-dimensional state vector to fuse the three-axis acceleration and three-axis angular velocity data collected by the gyroscope. When calculating the state vector modulus, normalization is introduced considering the different dimensions of acceleration and angular velocity. The acceleration data is normalized based on the gravitational acceleration g, and the angular velocity data is normalized based on 10% of the nominal range to ensure that the contribution of the two data in state judgment is balanced.

[0102] The calculation of the state vector modulus adopts the improved Euclidean distance formula. Taking into account the characteristics of the elevator movement, different weight coefficients are used for the horizontal and vertical components. The weight in the vertical direction is relatively high in order to detect the lifting movement more sensitively. Through experimental verification, the static state threshold is set to 0.2g, which can effectively distinguish the slight vibration of the elevator from the actual movement; the violent movement state threshold is set to 1.5g to identify possible abnormal movement states.

[0103] In the threshold range, this embodiment designs an adaptive weight allocation mechanism. When the state vector modulus is close to the static state, the weight of the pressure sensor is higher to provide stable altitude measurement; when the modulus is close to the violent motion state, the weight of the gyroscope is increased to quickly respond to altitude changes. The weight allocation adopts a piecewise linear interpolation method to smoothly change in the transition range from static to violent motion to avoid sudden changes in the measurement results.

[0104] Zero drift compensation adopts a periodic update strategy, reading calibration data from the zero drift reference point record table every 60 seconds. Considering the impact of temperature on zero drift, the selection of calibration data will refer to the current ambient temperature and select the calibration value corresponding to the closest temperature point. The calculation of zero drift deviation value adopts the sliding average method to reduce the impact of random errors.

[0105] When constructing the correction function, this embodiment uses an adaptive Kalman filter. The state equation of the filter contains three components: position, velocity, and acceleration, and the observation equation incorporates the data of the barometer and gyroscope. The process noise covariance matrix of the filter is dynamically adjusted according to the motion state, which improves the system's adaptability to different motion states.

[0106] The compensation adjustment of the weight coefficient adopts a feedback mechanism. The effectiveness of the correction function is evaluated by comparing the data difference before and after the correction. If the correction effect is not ideal, the weight coefficient is adjusted appropriately to make the system more biased towards the sensor data with higher reliability. The temperature drift characteristics of the sensor are also considered in the compensation process, and the weight of the pressure sensor is increased when the temperature changes greatly.

[0107] This step works closely with the previous sensor calibration step, using the parameters obtained from the calibration for real-time compensation. At the same time, it also provides a reliable data basis for subsequent height fusion calculations. In practical applications, this step significantly improves the reliability of elevator height measurement, especially during rapid start-up, braking and other intense movement stages, the system can still maintain stable measurement performance.

[0108] Through dynamic weight allocation and real-time compensation of zero drift, this embodiment effectively solves the problem of insufficient accuracy of traditional single sensor measurement solutions under complex working conditions, and provides strong support for precise positioning and safety monitoring of construction elevators.

[0109] In one embodiment of the construction elevator height measurement method based on double correction of the present application, see Figure 5 , and can also include the following:

[0110] Step S501: The height, speed, and acceleration form a state vector, a state transfer matrix is ​​established to describe the evolution process of the state vector, a system noise covariance matrix is ​​constructed to characterize the uncertainty of state prediction, the zero drift correction value and the weight coefficient are substituted into the state transfer equation, and the state prior estimation and the error covariance matrix are calculated through the time update step;

[0111] Step S502: Establish an observation equation to associate the pressure altitude value with the gyroscope integrated altitude value, construct an observation noise covariance matrix, calculate the Kalman gain to update the state posterior estimate, multiply the state posterior estimate by the corresponding weight coefficient, add the weighted results of the pressure sensor and the gyroscope to obtain a fused altitude value, and update the state error covariance matrix for the next iterative calculation.

[0112] Optionally, this embodiment first constructs a three-dimensional state vector, which includes three components: height, velocity, and acceleration. The design of the state vector fully considers the physical characteristics of the elevator motion, where the height component directly reflects the position information, the velocity component is used to describe the motion trend, and the acceleration component is used to predict the change of the motion state. This state vector design enables the system to fully capture the motion characteristics of the elevator.

[0113] When establishing the state transfer matrix, this embodiment adopts a design method based on a physical kinematic model. The state transfer matrix describes the evolution relationship of the state vector between adjacent sampling moments, taking into account the continuity and smoothness characteristics of the elevator motion. In order to improve the adaptability of the model, the time interval parameter in the state transfer matrix is ​​dynamically adjusted according to the actual sampling frequency.

[0114] The system noise covariance matrix is ​​constructed using an adaptive strategy. The diagonal elements of the matrix correspond to the prediction uncertainties of height, velocity, and acceleration. Considering the difference in prediction reliability under different motion states, the matrix is ​​dynamically adjusted with the motion state. For example, when the elevator moves quickly, the uncertainty of the acceleration component increases accordingly.

[0115] The introduction of zero drift correction value adopts a compensation structure. The correction value is updated by the zero drift deviation value calculated in the previous step, and participates in the calculation in the state transfer equation as an additive term. This design ensures the real-time and smoothness of zero drift compensation. The weight coefficient participates in state prediction in the form of a multiplicative term, realizing the dynamic fusion of sensor data.

[0116] In the design of the observation equation, this embodiment innovatively adopts a dual-source observation structure. The pressure altitude value is converted by the calibration curve equation, and the gyroscope integral altitude value is obtained by the second integration of the acceleration. The observation equation associates these two altitude values ​​with the state vector to form a closed-loop feedback structure.

[0117] The construction of the observation noise covariance matrix takes into account the measurement characteristics of the two sensors. The pressure sensor measures stably but responds slowly in a stationary state, and the gyroscope integration is accurate in a short period of time but has cumulative errors. Therefore, the observation noise covariance matrix is ​​dynamically adjusted with the motion state to optimize the data fusion effect under different working conditions.

[0118] The calculation of Kalman gain adopts an improved recursive algorithm. By comparing the difference between the predicted value and the observed value, the gain coefficient is dynamically adjusted so that the filter can maintain the optimal estimation performance under different working conditions. The calculation of the state posterior estimate performs a weighted fusion of the prior estimate and the observed new information, and the weight coefficient is dynamically allocated according to the motion state judgment result of the previous step.

[0119] The final fusion height value is calculated using a weighted average method. The weight coefficient reflects the credibility of different sensors in the current state, ensuring the reliability of the fusion result. The state error covariance matrix is ​​updated using a sequential processing method to provide accurate uncertainty estimates for the next iteration.

[0120] This fusion algorithm, together with the previous sensor calibration and state judgment steps, forms a complete measurement chain, achieving accurate measurement of the lift height. In practical applications, this algorithm significantly improves the robustness of the measurement, especially in complex construction environments, and can still maintain stable and reliable measurement performance.

[0121] Through multi-sensor data fusion and dynamic weight allocation, this embodiment effectively solves the limitations of a single sensor measurement solution and provides reliable technical support for the safety monitoring of construction elevators.

[0122] In one embodiment of the construction elevator height measurement method based on double correction of the present application, see Figure 6 , and can also include the following:

[0123] Step S601: performing sliding window analysis on the atmospheric pressure data and the acceleration angular velocity data, calculating the data mutation rate and the data continuity index, comparing the data mutation rate with a preset threshold to determine data anomalies, identifying the sensor working state based on the data continuity index, and switching to the corresponding air pressure priority mode or gyroscope priority mode according to the sensor anomaly type;

[0124] Step S602: Encapsulate the highly integrated result into a communication data packet, add a data packet header and a check code, send the communication data packet to the construction elevator control system through the RS485 interface at a baud rate of 115200, monitor the data packet response signal to confirm the data writing status, and record the sensor working mode and communication status information.

[0125] Optionally, this embodiment uses a sliding window analysis method to monitor sensor data in real time. The size of the sliding window is set to 200ms, and the window overlap rate is 50%. This configuration not only ensures the real-time nature of data analysis, but also fully captures the changing characteristics of the data. For atmospheric pressure data, the difference sequence of adjacent data points in the window is calculated, and the standard deviation of the difference is used as a data mutation rate indicator; for acceleration angular velocity data, the comprehensive mutation rate of the six components is calculated.

[0126] The calculation of the data continuity index adopts an improved variance analysis method. In the sliding window, the stability of the data is evaluated by comparing the ratio of the local variance to the global variance of the data sequence. This embodiment sets three levels of data continuity, corresponding to normal, warning and fault states respectively. The calculation method of the continuity index is also different according to the characteristics of different sensors. The pressure sensor mainly focuses on the smoothness of the data, while the gyroscope pays more attention to the temporal correlation of the data.

[0127] Sensor anomaly judgment adopts a multi-level threshold strategy. If the data mutation rate exceeds the preset threshold, it is judged as abnormal. However, considering the normal rapid changes that may occur during the operation of the construction elevator, a dynamic threshold adjustment mechanism is set. For example, during the start-up and braking stages of the elevator, the system automatically increases the mutation rate threshold to avoid misjudgment.

[0128] The working mode switching adopts a smooth transition strategy. When an abnormality is detected in the pressure sensor, the system gradually reduces its weight and switches to the gyroscope priority mode; when an abnormality occurs in the gyroscope, it switches to the pressure priority mode. During the mode switching process, the continuity of the height measurement results is ensured by gradually changing the weight. At the same time, the system will continue to monitor the recovery of the abnormal sensor, and automatically switch back to the normal working mode after confirming that the sensor has returned to normal.

[0129] The encapsulation of the height data adopts a reliable communication protocol design. The data packet header contains information such as synchronization word, packet length, and timestamp. In addition to the height value, the data area also contains auxiliary information such as sensor status flag and working mode flag. The check code uses the CRC16 algorithm to ensure the accuracy of data transmission. The total length of the data packet is controlled within 32 bytes, which not only ensures the transmission of necessary information, but also avoids the waste of communication bandwidth.

[0130] The communication interface uses RS485 bus, which has the characteristics of strong anti-interference ability and long transmission distance, and is particularly suitable for the harsh environment of the construction site. The baud rate setting of 115200 ensures reliable transmission while meeting the real-time requirements. The data packet transmission adopts the response mechanism. If the response signal is not received or the response signal is abnormal, the system will automatically resend the data packet, and retry up to three times.

[0131] The working status record uses a circular cache mechanism to record the sensor working mode switching events and communication abnormal events in the last 24 hours. These records can be used for subsequent equipment maintenance and fault analysis. Each record item contains information such as timestamp, event type, duration, etc., which makes it easy for maintenance personnel to quickly locate problems.

[0132] This embodiment effectively solves the problem of measurement interruption caused by sensor abnormality through real-time data monitoring and adaptive mode switching. The perfect communication mechanism ensures the reliable transmission of height data and provides a strong guarantee for the safe operation of the construction elevator. In the complex environment of the construction site, this solution significantly improves the reliability and stability of the height measurement system.

[0133] In one embodiment of the construction elevator height measurement method based on double correction of the present application, see Figure 7 , and can also include the following:

[0134] Step S701: querying a preset parameter mapping table according to the abnormal state type to obtain a corresponding system parameter adjustment value, substituting the system parameter adjustment value into a state transfer matrix to update the system noise covariance, adjusting the state transfer coefficient in the state prediction equation, and updating the filter gain parameter of the Kalman filter;

[0135] Step S702: Write the data structure composed of the zero drift correction value and the correction timestamp into the zero drift reference point record table, maintain the historical data queue length in the record table, delete the historical zero drift data that exceeds the storage period, update the data index of the zero drift reference point record table, and set the latest zero drift correction value as the reference benchmark for the next calibration.

[0136] Optionally, this embodiment establishes a complete abnormal state processing mechanism. The preset parameter mapping table stores system parameter adjustment strategies corresponding to different abnormal types, including key parameters such as state transfer matrix correction coefficients and system noise covariance scaling factors. For example, when a jump in pressure sensor data is detected, the system will increase the corresponding noise covariance component and reduce the impact weight of the abnormal data.

[0137] The adjustment of system parameters adopts a progressive strategy. The update of the state transfer matrix focuses on the components in the state vector that are most affected by abnormalities. By adjusting the corresponding transfer coefficients, the system's adaptability to abnormal conditions is enhanced. For example, when a construction elevator is started or braked quickly, the transfer coefficient of the velocity component is adjusted to improve the system's response speed to acceleration changes.

[0138] The update of the system noise covariance adopts an adaptive mechanism. According to the severity of the abnormal state, the components of the covariance matrix are dynamically adjusted. When the sensor data is slightly abnormal, only the corresponding covariance value is slightly adjusted; in the case of severe abnormalities, the relevant components are significantly increased, reducing the credibility of the abnormal data.

[0139] The gain parameter adjustment of the Kalman filter adopts a hierarchical response strategy. By analyzing the impact of abnormal conditions on the measurement system, the filter gain is adjusted accordingly. When the reliability of sensor data decreases, the trust in the observed value is reduced and the reliance on the state prediction value is increased. This strategy effectively prevents abnormal data from contaminating the system estimation.

[0140] The management of zero drift reference point records adopts an efficient data structure design. Each record contains key data such as zero drift correction value, timestamp, temperature information, etc. The record table adopts a circular queue structure to keep the historical data of the last 7 days. When new zero drift data is written, the system automatically checks and deletes records that have exceeded the storage period to ensure the timeliness of the data.

[0141] The maintenance of the record table adopts a dual index mechanism. The primary index is based on timestamps and is used to quickly locate historical data with similar time; the secondary index is based on temperature values, which is convenient for finding zero drift records under specific temperature conditions. This design significantly improves data retrieval efficiency and meets the needs of frequent calibration of construction hoists.

[0142] The update of zero drift correction value adopts weighted smoothing strategy. The new correction value will not directly replace the old value, but will be weighted averaged with historical data to form a more stable benchmark value. The weight coefficient decays over time to ensure that the new data has a greater influence while retaining the reference value of historical data.

[0143] The update of the data index adopts atomic operation to ensure data consistency in a multi-threaded access environment. After the index update is completed, the system will automatically back up the index information to prevent data loss caused by unexpected power failure. Through this mechanism, this embodiment ensures the reliability and integrity of the zero drift correction data.

[0144] This embodiment effectively solves the measurement accuracy problem of construction hoists under complex working conditions through parameter adaptive adjustment and efficient data management. The system can quickly adjust relevant parameters according to different types of abnormal conditions to maintain the stability of the measurement system. At the same time, the perfect zero drift data management mechanism provides a reliable calibration benchmark for long-term operation, significantly improving the long-term stability of the system.

[0145] In order to improve the accuracy and reliability of height measurement and realize intelligent fusion and dynamic correction of multi-sensor data, the present application provides an embodiment of a construction elevator height measurement device based on double correction for realizing all or part of the contents of the construction elevator height measurement method based on double correction, see Figure 8 The construction elevator height measurement device based on double correction specifically includes the following contents:

[0146] The sensor calibration module 10 is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0147] The motion state evaluation module 20 is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, compare the sensor data with the zero drift reference point record table at each preset time interval, calculate the zero drift correction value, substitute the zero drift correction value and the weight coefficient into the extended Kalman filter, construct a state transfer equation and an observation equation, calculate a state estimation value through a prediction update step, and obtain a highly fused result according to the state estimation value and the weight coefficient;

[0148] The altitude measurement module 30 is used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the altitude fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0149] From the above description, it can be seen that the construction elevator height measurement device based on dual correction provided by the embodiment of the present application can establish a pressure-altitude mapping model for initial calibration by fusing the data of a high-precision air pressure sensor and a six-axis gyroscope. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weights, and error correction is achieved in combination with the zero-drift reference point record. The state transfer equation and the observation equation are constructed using the extended Kalman filter, and a highly fused result is obtained through the prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

[0150] From the hardware level, in order to improve the accuracy and reliability of height measurement and realize intelligent fusion and dynamic correction of multi-sensor data, the present application provides an embodiment of an electronic device for realizing all or part of the contents of the construction elevator height measurement method based on double correction, and the electronic device specifically includes the following contents:

[0151] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the construction elevator height measurement device based on dual correction and the core business system, user terminal and related database and other related equipment; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the construction elevator height measurement method based on dual correction and the embodiment of the construction elevator height measurement device based on dual correction in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0152] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0153] In practical applications, part of the construction elevator height measurement method based on dual correction can be performed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0154] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0155] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0156] In one embodiment, the construction elevator height measurement method based on double correction can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0157] Step S101: a sensor calibration module is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0158] Step S102: a motion state evaluation module, which is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, compare the sensor data with the zero drift reference point record table at each preset time interval, calculate a zero drift correction value, substitute the zero drift correction value and the weight coefficient into an extended Kalman filter, construct a state transfer equation and an observation equation, calculate a state estimation value through a prediction update step, and obtain a highly fused result according to the state estimation value and the weight coefficient;

[0159] Step S103: an altitude measurement module, used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the altitude fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0160] From the above description, it can be seen that the electronic device provided in the embodiment of the present application establishes a pressure altitude mapping model for initial calibration by fusing the data of a high-precision air pressure sensor and a six-axis gyroscope. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weight, and the error correction is achieved in combination with the zero drift reference point record. The state transfer equation and the observation equation are constructed using an extended Kalman filter, and a highly fused result is obtained through a prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

[0161] In another embodiment, the construction elevator height measurement device based on dual correction can be configured separately from the central processor 9100. For example, the construction elevator height measurement device based on dual correction can be configured as a chip connected to the central processor 9100, and the function of the construction elevator height measurement method based on dual correction can be realized through the control of the central processor.

[0162] like Fig. 9 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0163] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0164] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0165] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0166] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0167] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0168] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0169] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0170] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the construction elevator height measurement method based on dual correction in the above-mentioned embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the construction elevator height measurement method based on dual correction in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0171] Step S101: a sensor calibration module is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0172] Step S102: a motion state evaluation module, which is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, compare the sensor data with the zero drift reference point record table at each preset time interval, calculate a zero drift correction value, substitute the zero drift correction value and the weight coefficient into an extended Kalman filter, construct a state transfer equation and an observation equation, calculate a state estimation value through a prediction update step, and obtain a highly fused result according to the state estimation value and the weight coefficient;

[0173] Step S103: an altitude measurement module, used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the altitude fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0174] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application establishes a pressure-altitude mapping model for initial calibration by fusing the data of a high-precision air pressure sensor and a six-axis gyroscope. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weight, and the error correction is achieved in combination with the zero-drift reference point record. The state transfer equation and the observation equation are constructed using an extended Kalman filter, and a highly fused result is obtained through a prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

[0175] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the construction elevator height measurement method based on double correction in the above embodiments, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the construction elevator height measurement method based on double correction are implemented. For example, the computer program / instruction implements the following steps:

[0176] Step S101: a sensor calibration module is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table;

[0177] Step S102: a motion state evaluation module, which is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, compare the sensor data with the zero drift reference point record table at each preset time interval, calculate a zero drift correction value, substitute the zero drift correction value and the weight coefficient into an extended Kalman filter, construct a state transfer equation and an observation equation, calculate a state estimation value through a prediction update step, and obtain a highly fused result according to the state estimation value and the weight coefficient;

[0178] Step S103: an altitude measurement module, used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the altitude fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

[0179] From the above description, it can be seen that the computer program product provided in the embodiment of the present application establishes a pressure altitude mapping model for initial calibration by fusing the data of a high-precision air pressure sensor and a six-axis gyroscope. The motion state evaluation matrix is ​​used to dynamically adjust the sensor weight, and error correction is achieved in combination with the zero drift reference point record. The state transfer equation and the observation equation are constructed using an extended Kalman filter, and a highly fused result is obtained through a prediction update step. At the same time, the system has the functions of abnormal state monitoring and automatic switching of sensor modes, which achieves dual guarantees of measurement accuracy and reliability. This method effectively solves the problem of unstable accuracy of traditional single sensor measurement solutions in complex environments.

[0180] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0184] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A construction hoist height measurement method based on double correction, characterized in that: The method comprises: Collect atmospheric pressure data from a high-precision air pressure sensor and acceleration angular velocity data from a six-axis gyroscope, write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix synchronously according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table; Establishing a motion state evaluation matrix, calculating a state judgment threshold according to the acceleration angular velocity data, and determining weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, specifically including: when the state vector modulus is close to a static state, the weight of the air pressure sensor is high to provide a stable height measurement, and when the modulus is close to a violent motion state, the weight of the gyroscope is increased to quickly respond to height changes; At each preset time interval, the sensor data is compared with the zero drift reference point record table, a zero drift correction value is calculated, the zero drift correction value and the weight coefficient are substituted into the extended Kalman filter, a state transfer equation and an observation equation are constructed, a state estimation value is calculated through a prediction update step, and a highly fused result is obtained according to the state estimation value and the weight coefficient; Monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the height fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

2. The construction elevator height measurement method based on double correction according to claim 1 is characterized in that: The method collects the atmospheric pressure data of the high-precision air pressure sensor and the acceleration angular velocity data of the six-axis gyroscope, and synchronously writes the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, including: The atmospheric pressure value output by the pressure sensor is collected at a preset sampling frequency, the atmospheric pressure value is converted into corresponding height unit data, the three-axis acceleration and three-axis angular velocity raw data are read from the six-axis gyroscope, the raw data is subjected to noise filtering to obtain filtered data, and a sensor data collection timestamp is generated; The height unit data and the filtered data are packaged into a data frame according to the sensor data acquisition timestamp, storage space is allocated for each data frame in a data buffer matrix, the data frames are written into the storage space positions corresponding to the data buffer matrix according to the timestamp sequence, and a data frame index table is established to point to the storage space positions.

3. The construction elevator height measurement method based on double correction according to claim 1 is characterized in that: The construction of the pressure-altitude mapping model performs initial calibration on the data in the data buffer matrix, generates a sensor calibration parameter table, and establishes a zero drift reference point record table, including: collecting gyroscope angular velocity and acceleration data within a 500ms period before the elevator is started, performing variance analysis on the angular velocity and acceleration data, calculating the zero position deviation and the standard deviation, writing the zero position deviation into the zero drift reference point record table, setting a dynamic threshold range according to the standard deviation, storing the dynamic threshold range in the sensor calibration parameter table, and establishing a time index in the zero drift reference point record table to point to the corresponding zero position calibration data.

4. The construction elevator height measurement method based on double correction according to claim 1 is characterized in that: Substituting the zero drift correction value and the weight coefficient into the extended Kalman filter, constructing a state transfer equation and an observation equation, calculating a state estimation value through a prediction update step, and obtaining a highly fused result according to the state estimation value and the weight coefficient, including: The height, velocity and acceleration form a state vector, a state transfer matrix is ​​established to describe the evolution process of the state vector, a system noise covariance matrix is ​​constructed to characterize the uncertainty of state prediction, the zero drift correction value and the weight coefficient are substituted into the state transfer equation, and the state prior estimation and the error covariance matrix are calculated through the time update step; An observation equation is established to associate the pressure altitude value with the gyroscope integrated altitude value, an observation noise covariance matrix is ​​constructed, the Kalman gain is calculated to update the state posterior estimate, the state posterior estimate is multiplied by the corresponding weight coefficient, the weighted results of the pressure sensor and the gyroscope are added to obtain the fused altitude value, and the state error covariance matrix is ​​updated for the next iterative calculation.

5. The construction elevator height measurement method based on double correction according to claim 1 is characterized in that: The monitoring of the abnormal state of the atmospheric pressure data and the acceleration angular velocity data, switching the sensor working mode according to the abnormal state, and writing the height fusion result into the control system of the construction hoist through the transmission interface include: Perform sliding window analysis on the atmospheric pressure data and the acceleration angular velocity data, calculate the data mutation rate and the data continuity index, compare the data mutation rate with a preset threshold to determine data anomalies, identify the sensor working state based on the data continuity index, and switch to the corresponding air pressure priority mode or gyroscope priority mode according to the sensor anomaly type; The highly fused result is encapsulated as a communication data packet, a data packet header and a check code are added, and the communication data packet is sent to the construction elevator control system through the RS485 interface at a baud rate of 115200, the data packet response signal is monitored to confirm the data writing status, and the sensor working mode and communication status information are recorded.

6. The construction elevator height measurement method based on double correction according to claim 1 is characterized in that: The updating of the system parameters of the state transfer equation based on the abnormal state and writing the zero drift correction value into the zero drift reference point record table includes: According to the abnormal state type, query the preset parameter mapping table to obtain the corresponding system parameter adjustment value, substitute the system parameter adjustment value into the state transfer matrix to update the system noise covariance, adjust the state transfer coefficient in the state prediction equation, and update the filter gain parameter of the Kalman filter; The zero drift correction value and the correction timestamp form a data structure and write it into the zero drift reference point record table, maintain the historical data queue length in the record table, delete the historical zero drift data that exceeds the storage period, update the data index of the zero drift reference point record table, and set the latest zero drift correction value as the reference benchmark for the next calibration.

7. A construction hoist height measuring device based on double correction, characterized in that: The device comprises: A sensor calibration module is used to collect atmospheric pressure data of a high-precision air pressure sensor and acceleration angular velocity data of a six-axis gyroscope, synchronously write the atmospheric pressure data and the acceleration angular velocity data into a data buffer matrix according to a sampling frequency, construct a pressure-altitude mapping model to perform initial calibration on the data in the data buffer matrix, generate a sensor calibration parameter table, and establish a zero drift reference point record table; A motion state evaluation module is used to establish a motion state evaluation matrix, calculate a state judgment threshold according to the acceleration angular velocity data, and determine the weight coefficients of the air pressure sensor and the gyroscope based on the state judgment threshold, specifically including: when the state vector modulus is close to a stationary state, the weight of the air pressure sensor is high to provide stable height measurement, and when the modulus is close to a violent motion state, the weight of the gyroscope is increased to quickly respond to height changes; the sensor data is compared with the zero drift reference point record table at each preset time interval, the zero drift correction value is calculated, the zero drift correction value and the weight coefficient are substituted into the extended Kalman filter, the state transfer equation and the observation equation are constructed, the state estimation value is calculated through the prediction update step, and a highly fused result is obtained according to the state estimation value and the weight coefficient; The height measurement module is used to monitor the abnormal status of the atmospheric pressure data and the acceleration angular velocity data, switch the sensor working mode according to the abnormal status, write the height fusion result into the control system of the construction elevator through the transmission interface, update the system parameters of the state transfer equation based on the abnormal status, and write the zero drift correction value into the zero drift reference point record table.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the construction elevator height measurement method based on double correction as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the construction elevator height measurement method based on double correction as described in any one of claims 1 to 6 are implemented.

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