Elevator air pressure height self-correction method and device

Abstract

The invention discloses an elevator air pressure height self-correction method and device, relates to the field of data processing, and can effectively improve the detection precision of the elevator height, and the method comprises the following steps: through a Kalman filter, utilizing an elevator state estimation parameter updated at a k-1 moment to predict an elevator state estimation parameter at a k moment; correcting the elevator state estimation parameter at the moment k through the air pressure detection value at the moment k to obtain the updated elevator state estimation parameter at the moment k; based on the relationship between the air pressure and the height, the height change gradient value of the elevator from the air pressure k-1 moment to the air pressure k moment is calculated through the air pressure estimated value updated at the k-1 moment, the air pressure change rate estimated value updated at the k-1 moment and the air pressure estimated value updated at the k moment; and the air pressure height value of the elevator at the moment k is calculated according to the height change gradient value of the elevator from the moment k-1 to the moment k, so that the height detection precision of the elevator can be effectively improved.

Description

Technical Field

[0001] The invention relates to the field of data processing, and in particular to a method and a device for self-correcting the air pressure height of an elevator. Background Art

[0002] In the current scenario of smart construction site elevators, in order to prevent the elevator from hitting the top, the elevator will move with the increase of the building floors. As the building gradually increases in height, the maximum height of the elevator is a variable state, so it is necessary to accurately measure the height of the elevator. The usual method of measuring the height of the elevator includes mechanically installing an encoder to engage the elevator rack. The sensor is limited by the structure of the elevator and can only be installed on the top of the elevator, which is very inconvenient to install. For the complex environment of the smart construction site, there is a lot of dust and strong vibration. The sensor is easily damaged and needs customized maintenance, which greatly increases the maintenance cost.

[0003] Therefore, many existing elevators use air pressure sensors to monitor the height of the elevator. However, since the detection results of the air pressure sensor depend on the environment, there is a defect that the air pressure is affected by weather and temperature, resulting in inaccurate results. In order to ensure the stability of the air pressure sensor, it will rely on Internet data, GPS, three-axis sensors and other calibration devices for data calibration.

[0004] However, Internet data maintenance is difficult and cannot guarantee complete accuracy; GPS has accuracy issues, which can also lead to inaccurate calibration results for the air pressure sensor; the reciprocating motion of the three-axis sensor has errors, which can also lead to large errors in the calibration results for the air pressure sensor. The calibration method of the above air pressure sensor does not meet the requirements for elevator height detection, and it also greatly increases the complexity of the system. Summary of the invention

[0005] In response to at least one problem existing in the prior art, the present application provides a method and device for self-correcting the air pressure height of an elevator, which can effectively improve the detection accuracy of the elevator height.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions.

[0007] In a first aspect, the present invention provides a method for self-calibration of air pressure height of an elevator, the method being as follows: Through the Kalman filter, using k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time t; k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the rate of change of air pressure updated at every moment; k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; Based on the relationship between air pressure and altitude, k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0008] Furthermore, through the Kalman filter, using k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at the moment include: , in, for k -1 The estimated parameters of the elevator state updated at each moment; for k The estimated parameters of the elevator state at time t; for k -1 The covariance matrix estimate updated at time instant; for k The estimated value of the covariance matrix at time instant; A is the state transfer matrix; T is the transpose of the matrix; Q is the process noise covariance matrix; B is the control input matrix; yes k Control input of the elevator at the moment.

[0009] Further, through k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k The elevator state estimation parameters are updated at all times, including: , in, for k Kalman gain at time t; H is the observation matrix; R is the measurement noise covariance matrix; for k The elevator state estimation parameters are updated at all times. , for k An updated estimate of air pressure, for k An updated estimate of the rate of change of air pressure; for k The air pressure detection value at the moment; for k The updated covariance matrix estimate.

[0010] Furthermore, it also includes: When the opening of the front gantry is detected, the height of the elevator is reset to 0 and the first error value is calculated. : , in, for j The lift pressure altitude value at the time, j The time is the time when the front gantry is detected to be open; Based on the first error value and k The lift pressure altitude value at the time , get the lift k Corrected altitude at the moment : , in, for k -1 elevator air pressure altitude value; for k -1 time has arrived k The gradient value of the elevator height change at the moment; for k -1 The estimated value of air pressure updated at all times; for k -1 The estimated value of the rate of change of air pressure updated at every moment; for k Process noise at time -1.

[0011] Furthermore, it also includes: When the elevator operation time interval is greater than or equal to the set operation interval threshold, recalculate kThe change of lift pressure altitude value at any time ; The second error value is obtained by the following formula : ; The first error value is updated by the second error value to obtain a new first error value: .

[0012] In a second aspect, the present invention further provides a self-correcting device for air pressure height of an elevator, comprising: Air pressure sensor, used to collect and obtain air pressure detection value; Kalman filter module, used to use Kalman filter k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time t; k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the rate of change of air pressure updated at every moment; k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; and, for k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; processor, used to use the relationship between air pressure and altitude k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0013] Further, the Kalman filter module includes a state estimation module; State estimation module, used to utilize k The estimated parameters of the elevator state updated at time -1 are predictedk The estimated parameters of the elevator state at time: , in, for k -1 The estimated parameters of the elevator state updated at each moment; for k The estimated parameters of the elevator state at time t; for k -1 The covariance matrix estimate updated at time instant; for k The estimated value of the covariance matrix at time instant; A is the state transfer matrix; T is the transpose of the matrix; Q is the process noise covariance matrix; B is the control input matrix; yes k Control input of the elevator at the moment.

[0014] Further, the Kalman filter module also includes a state update module; Status update module, used to k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k The elevator state estimation parameters are updated at all times, including: , in, for k Kalman gain at time t; H is the observation matrix; R is the measurement noise covariance matrix; for k The elevator state estimation parameters are updated at all times. , for k An updated estimate of air pressure, for k An updated estimate of the rate of change of air pressure; for k The air pressure detection value at the moment; for k The updated covariance matrix estimate.

[0015] Furthermore, it also includes a front gantry switch state detection module The front gantry switch status detection module is used to detect the opening status signal of the front gantry and send it to the processor: The processor is also used to reset the height of the elevator to 0 when receiving the opening of the front gantry, and calculate the first error value : , in, for j The lift pressure altitude value at the time, j The time is the time when the front gantry is detected to be open; And, also for based on the first error value and k The lift pressure altitude value at the time , get the lift k Corrected altitude at the moment : , in, for k -1 elevator air pressure altitude value; for k -1 time has arrived k The gradient value of the elevator height change at the moment; for k -1 The estimated value of air pressure updated at all times; for k -1 The estimated value of the rate of change of air pressure updated at every moment; for k Process noise at time -1.

[0016] Furthermore, it also includes an operation status acquisition module: An operation status acquisition module is used to obtain the operation status of the elevator; The processor is further used to calculate a time interval between two operations of the lift; And, when the elevator operation time interval is greater than or equal to the set operation interval threshold, recalculate k The change of lift pressure altitude value at any time ; The second error value is obtained by the following formula : ; The first error value is updated by the second error value to obtain a new first error value: .

[0017] 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 the processor implements the steps of the elevator air pressure altitude self-correction method when executing the program.

[0018] 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 elevator air pressure altitude self-correction method.

[0019] 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 elevator air pressure altitude self-correction method.

[0020] The beneficial effects of the present invention are: It can be seen from the above technical solution that the present application provides a method and device for self-calibration of air pressure height of an elevator. Based on the Kalman filter, the state estimation can be updated at each moment, and the air pressure sensor data can be processed in real time and dynamically. It can also effectively smooth the random noise in the data and effectively smooth the air pressure sensor data. At the same time, the air pressure sensor is linked with the mechanical structure to achieve further self-calibration, making the height detection of the elevator more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is one of the flow charts of the method for self-calibration of the air pressure height of an elevator in an embodiment of the present application; Figure 2 The second flowchart of the method for self-calibration of air pressure height of an elevator in the embodiment of the present application; Figure 3 The third flowchart of the method for self-calibration of air pressure height of an elevator in the embodiment of the present application; Figure 4 It is a structural schematic diagram of the elevator air pressure height self-correction device in the embodiment of the present application; Figure 5 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0022] 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.

[0023] 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.

[0024] In view of the problems existing in the prior art, the present application provides a method and device for self-calibration of the air pressure height of an elevator. Based on the Kalman filter, the state estimation can be updated at every moment, and the air pressure sensor data can be processed in real time and dynamically. It can also effectively smooth the random noise in the data and effectively smooth the air pressure sensor data. At the same time, the air pressure sensor is linked with the mechanical structure to achieve further self-calibration, making the height detection of the elevator more accurate.

[0025] The detection accuracy of the elevator height can be effectively improved. The present application provides an embodiment of a self-calibration method for the air pressure height of an elevator. Figure 1 , a self-correction method for the air pressure height of an elevator is as follows: Step S101: Using the Kalman filter, k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time t; k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the rate of change of air pressure updated at every moment; k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; Optionally, in this embodiment, this step describes a process of state prediction using the Kalman filter principle, which can update the estimate of the system state in real time by recursively processing input data in the presence of noise. In this step, the recursive action of the Kalman filter is used to estimate the current air pressure state of the elevator from the known air pressure state of the elevator at the previous moment.

[0026] in, k -1 moment and k moment represent two adjacent moments, and k At the moment k -1 moment later. k The value can be set to k is a positive integer greater than or equal to 1; when k When -1=0, time 0 is taken as the initial time. At the initial time, the initial air pressure state can be reasonably set according to prior knowledge, such as experimental data, elevator working parameters, ambient environment parameters, etc., that is, the air pressure setting value at the initial time (as the estimated air pressure updated at time 0) and the air pressure change rate setting value at the initial time (as the estimated air pressure change rate updated at time 0).

[0027] The working principle of the Kalman filter is based on two key steps: prediction and update. In the prediction step, the filter predicts the state at the next moment through the known state transfer matrix. This step belongs to the prediction step, that is, through the above-mentioned initial moment air pressure setting value and the Kalman filter, as the elevator runs, the current moment air pressure state of the elevator is continuously predicted through the previous moment air pressure state.

[0028] Step S102: k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; Optionally, in this embodiment, this step describes a step of using the Kalman filter principle to correct the predicted state. k The air pressure detection value at the moment is used to correct the predicted state estimation to obtain a more accurate state estimation.

[0029] The more accurate state estimate mentioned above is specifically k The elevator state estimation parameters that are updated at all times include k Always updated lift pressure estimate and k A constantly updated estimate of the rate of change of air pressure.

[0030] The above steps construct a Kalman filter model to dynamically predict and correct the measurement value of the air pressure sensor, effectively eliminate the noise interference caused by ambient temperature, weather changes, etc., and improve the stability and accuracy of the data.

[0031] Step S103: Based on the relationship between air pressure and altitude, k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0032] Optionally, in this embodiment, this step describes obtaining a more accurate k After the elevator state estimation parameters are updated at all times, the k The elevator state estimation parameters and k-1 The elevator state estimation parameters updated at time 1 are used to calculate the air pressure k -1 time has arrived k The process of the gradient value of the elevator height changing at each moment.

[0033] Since the atmospheric pressure decreases with the increase of altitude, showing a certain linear relationship, the altitude change at two moments can be calculated by the pressure values ​​at two moments (in a short period of time, the external environment can be regarded as stable), that is, k -1 time has arrived k The value of the elevator height change gradient at the moment. k Under the premise that the elevator height at time -1 is known, the current k The elevator air pressure altitude value at the moment.

[0034] Since this altitude value is predicted and updated (corrected) by the Kalman filter and indirectly obtained through the air pressure, it can be used as the corrected altitude of the elevator.

[0035] In this step, the Kalman filter is used to predict and correct the air pressure state of the elevator in real time. By fusing the predicted air pressure state estimate and the actual measured air pressure value, the prediction error can be reduced, the prediction accuracy of the elevator air pressure state can be improved, and the measurement accuracy of the elevator height can be improved. And because the Kalman filter can quickly respond to new actual measured air pressure values, even if the air pressure changes before and after, the real-time update and prediction of the elevator height can be achieved.

[0036] In addition, since the air pressure state may be affected by environmental noise, air pressure fluctuations and temperature changes, the Kalman filter can effectively filter out high-frequency noise, retain useful low-frequency components, and provide a smooth output.

[0037] In one embodiment of the elevator air pressure altitude self-calibration method of the present application, the following contents may also be specifically included: Through the Kalman filter, using k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at the moment include: , in, for k -1 The estimated parameters of the elevator state updated at each moment; for k The estimated parameters of the elevator state at time t; for k -1 The covariance matrix estimate updated at time instant; for kThe estimated value of the covariance matrix at time instant; A is the state transfer matrix; T is the transpose of the matrix; Q is the process noise covariance matrix; B is the control input matrix; yes k Control input of the elevator at the moment.

[0038] Optionally, in this embodiment, a process of how to perform state estimation and prediction through a specific Kalman filter is described.

[0039] in, A is the state transfer matrix. In the Kalman filter, the state transfer matrix A It is usually known or can be calculated from known system parameters, such as based on physical models, system dynamic equations, discretization processes, experimental data or theoretical derivation.

[0040] B is the control input matrix, yes k The control input of the elevator at the moment, these two parameters are related to the actual elevator control system and can be obtained through the elevator control system. In the elevator control system, the control input matrix B Usually associated with actuators (such as motors, hydraulic systems). Control inputs are physical quantities that can be directly manipulated, and they control the movement of the lift through the actuator. For example, by adjusting the current and torque of the motor, the lifting, lowering and stopping of the lift platform can be controlled.

[0041] The estimated value of the covariance matrix is ​​mainly used for the subsequent calculation of the Kalman gain, and the size of the covariance directly affects the size of the Kalman gain.

[0042] Process noise covariance matrix Q : Set the estimated uncertainty based on the rate of change of air pressure. The specific value can be determined through field tests and empirical data.

[0043] The initial elevator state estimation parameters and the initial covariance matrix estimation values ​​can be reasonably set according to the initial conditions of the elevator to ensure the convergence of the Kalman filter.

[0044] In one embodiment of the elevator air pressure altitude self-calibration method of the present application, the following contents may also be specifically included: pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k The elevator state estimation parameters are updated at all times, including: , in, for k Kalman gain at time t; H is the observation matrix; R is the measurement noise covariance matrix; for k The elevator state estimation parameters are updated at all times. , for k An updated estimate of air pressure, for k An updated estimate of the rate of change of air pressure; for k The air pressure detection value at the moment; for k The updated covariance matrix estimate.

[0045] Optionally, in this embodiment, a specific process of correcting and updating the Kalman filter after performing state estimation and prediction is described.

[0046] for k The Kalman gain at the moment determines the weight of the measured value and the predicted value in the prediction, allowing the Kalman filter to find an optimal balance between the predicted elevator state estimation parameters and the measured actual air pressure value to obtain the most accurate state estimation. It also enables the Kalman filter to work recursively, that is, to update the state estimation with new measurement data at each moment without storing all past measurement data.

[0047] The measurement noise covariance R can be set based on the noise characteristics of the air pressure sensor through experiments or data provided by the manufacturer.

[0048] and The air pressure value is directly measured by sensors such as air pressure sensors. The observation model can be expressed as: , in, for k The actual air pressure value at the moment, and from the detection of the actual air pressure value, it is inevitable that it will be affected by the detection instrument, so there should be measurement noise between the actual air pressure value and the air pressure detection value. , assuming White noise.

[0049] In one embodiment of the present application, a method for self-calibration of the air pressure height of an elevator is described. Figure 2 , and may also include the following: S201, when the opening of the front gantry is detected, the height of the elevator is reset to 0, and the first error value is calculated : , in, for j The lift pressure altitude value at the time, j The time is the time when the front gantry is detected to be open; S202, based on the first error value and k The lift pressure altitude value at the time , get the lift k Corrected altitude at the moment : , in, for k -1 elevator air pressure altitude value; for k -1 time has arrived k The gradient value of the elevator height change at the moment; for k -1 The estimated value of air pressure updated at all times; for k -1 The estimated value of the rate of change of air pressure updated at every moment; for k Process noise at time -1.

[0050] Optionally, in this embodiment, a process of self-correcting the pressure altitude and the actual altitude is described, that is, correcting the altitude by the front gantry opening state, thereby further correcting the altitude of the elevator.

[0051] The elevator includes a front gantry and a rear gantry. The front gantry faces the outside of the building and will only open on the first floor, while the rear gantry faces the inside of the building and can be opened on all floors. Therefore, by obtaining the opening status of the front gantry, it can be determined whether the elevator is on the first floor, and the actual height of the first floor is set to 0. Therefore, after the last time the front gantry was opened, the elevator has run for at least one moment, and there will be accumulated errors. The switch status of the front and rear gantry can be obtained to reset the height data and mark it as 0 meters.

[0052] Assume that the actual height of the elevator in this embodiment, i.e., the calibration height, is , the pressure altitude data is , the error coefficient is , as shown in the following formula: , Since the air pressure sensor converts the air pressure difference into relative height, βThe value is the constant 1.

[0053] , Based on this, the error value is automatically reset every time the current gantry opening state changes. , so that when the elevator reaches the first floor, the height will automatically return to zero.

[0054] in, To detect the elevator pressure height value when the front gantry is opened, since the time when the elevator moves to the front gantry is random, Can be used for elevator operation time At any time in the process, if the elevator reaches the front gantry (front gantry is open), then j The time is the current time, that is, this time .

[0055] Set the current state of the elevator to the air pressure value and the rate of change of air pressure, the state vector In this embodiment, since the elevator state estimation parameters are obtained by Kalman filter prediction, the table is .

[0056] Based on the physical properties of the elevator, the relationship between air pressure and altitude can be described by the following equation: , The above formula indicates that the atmospheric pressure decreases with the increase of altitude, showing a certain linear relationship. Therefore, the altitude change at two moments can be calculated by the atmospheric pressure values ​​at two moments (in a short period of time, the external environment can be regarded as stable), that is, k -1 time has arrived k The gradient value of the elevator height change at the moment .

[0057] for k The process noise at time -1 is assumed to be Gaussian white noise, which is used to simulate the air pressure fluctuations caused by environmental factors (such as temperature and weather changes).

[0058] This step further corrects the height of the elevator by the on / off state of the front gantry, and can obtain a higher accuracy height value while taking into account the existence of the first error value.

[0059] In one embodiment of the present application, a method for self-calibration of the air pressure height of an elevator is described. Figure 3 , and may also include the following: S301, when the elevator operation time interval is greater than or equal to the set operation interval threshold, recalculate and obtain k The change of lift pressure altitude value at any time ; S302, obtain the second error value by the following formula : ; S303, updating the first error value by the second error value to obtain a new first error value: .

[0060] Optionally, in this embodiment, a method for correcting barometric altitude changes with temperature and weather is described, and the process of further correcting the altitude of the elevator can reduce the altitude error of the elevator when the temperature and weather change, thereby obtaining a more accurate altitude.

[0061] The barometric pressure of the pressure sensor will change with the environment. If the elevator runs continuously in a short period of time, it can be regarded as running in a stable temperature and weather condition. However, if the elevator stops at a certain floor for too long, the time interval between two operations of the elevator will be greater than or equal to the set operation interval threshold, and the barometric pressure will generate a new error value. The error value generated within the time interval between two operations (the time interval between elevator operations) can be calculated by obtaining the operation status of the elevator. .

[0062] The last moment of the previous operation can be regarded as the old k moment, and the initial moment of the current operation can be regarded as the new k moment. The air pressure detection value can be introduced again to predict and update through the Kalman filter. Since it is considered that the air pressure has changed between the previous operation and the current operation, the new k-time air pressure state can be obtained, thereby obtaining the air pressure height error between the two operations. , then based on the first error Based on the first error To update: , Finally, the corrected height after further correction is obtained .

[0063] This step further corrects the height of the elevator according to the elevator operating state, and can obtain a more accurate height value by taking into account the first error value and the error caused by the drift of the air pressure sensor (air pressure altitude error).

[0064] For the purpose of technical effect, the present application provides an embodiment of an elevator air pressure height self-correction device for realizing all or part of the contents of the elevator air pressure height self-correction method, see Figure 4 , a self-correcting device for air pressure height of an elevator, comprising: Air pressure sensor, used to collect and obtain air pressure detection value; Kalman filter module, used to use Kalman filter k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time t; k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the rate of change of air pressure updated at every moment; k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; and, for k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; processor, used to use the relationship between air pressure and altitude k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0065] Optionally, this embodiment provides a dynamic elevator air pressure altitude self-correction device, mainly comprising: 1. The air pressure sensor installed on the elevator can collect the air pressure of the current environment in real time, at a set detection cycle, or after receiving the collection instruction of the processor and obtain the air pressure detection value and send it to the processor for subsequent processing.

[0066] 2. A Kalman filter module configured in the elevator control part or remotely, which can be integrated with the processor, is used to estimate the state of the elevator through a responsive Kalman filter algorithm.

[0067] 3. The processor is capable of transmitting corresponding data and performing corresponding calculations through pre-configured algorithms, such as calculating the elevator height change gradient value and the elevator pressure altitude value.

[0068] At the same time, a memory should also be configured to store and read the intermediate data and result data in the above process, and a corresponding data transmission module, such as a wireless / wired transmission module, can send the intermediate data and result data to the host computer.

[0069] In one embodiment of the elevator air pressure altitude self-correction device of the present application, the Kalman filter module includes a state estimation module; State estimation module, used to utilize k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time: , in, for k -1 The estimated parameters of the elevator state updated at each moment; for k The estimated parameters of the elevator state at time t; for k -1 The covariance matrix estimate updated at time instant; for k The estimated value of the covariance matrix at time instant; A is the state transfer matrix; T is the transpose of the matrix; Q is the process noise covariance matrix; B is the control input matrix; yes k Control input of the elevator at the moment.

[0070] In one embodiment of the elevator air pressure altitude self-correction device of the present application, the Kalman filter module further includes a state update module; Status update module, used to k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k The elevator state estimation parameters are updated at all times, including: , in, for k Kalman gain at time t; H is the observation matrix; R is the measurement noise covariance matrix; for k The elevator state estimation parameters are updated at all times. , for k An updated estimate of air pressure, for k An updated estimate of the rate of change of air pressure; for k The air pressure detection value at the moment; for k The updated covariance matrix estimate.

[0071] In one embodiment of the elevator air pressure height self-correction device of the present application, the front gantry switch state detection module is also included. The front gantry switch status detection module is used to detect the opening status signal of the front gantry and send it to the processor: The processor is also used to reset the height of the elevator to 0 when receiving the opening of the front gantry, and calculate the first error value : , in, for j The lift pressure altitude value at the time, j The time is the time when the front gantry is detected to be open; And, also for based on the first error value and k The lift pressure altitude value at the time , get the lift k Corrected altitude at the moment : , in, for k -1 elevator air pressure altitude value; for k -1 time has arrived k The gradient value of the elevator height change at the moment; for k -1 The estimated value of air pressure updated at all times; for k -1 The estimated value of the rate of change of air pressure updated at every moment; for k Process noise at time -1.

[0072] Optionally, in the present embodiment, a front gantry switch status detection module is provided for obtaining the switch status of the front gantry of the elevator. The front gantry switch status detection module may use a travel switch, a micro switch, a proximity switch, a door magnetic switch or a photoelectric switch, etc., and send a corresponding switch signal to the processor after detecting that the front gantry is opened or closed.

[0073] After the processor receives the switch signal corresponding to the opening of the front gantry, it resets the actual height of the elevator to 0 through the pre-configured algorithm (including the above formula) to calibrate the elevator. If the air pressure height of the elevator is not 0 at this time, the corresponding first error value will be generated after the reset , in the subsequent actual height calculation, by introducing the first error value , can get more accurate correction height .

[0074] In one embodiment of the elevator air pressure height self-correction device of the present application, an operation status acquisition module is also included: An operation status acquisition module is used to obtain the operation status of the elevator; The processor is further used to calculate a time interval between two operations of the lift; And, when the elevator operation time interval is greater than or equal to the set operation interval threshold, recalculate k The change of lift pressure altitude value at any time ; The second error value is obtained by the following formula : ; The first error value is updated by the second error value to obtain a new first error value: .

[0075] Optionally, in this embodiment, an operating status acquisition module for obtaining the control status is provided, which obtains the control status of the elevator (the corresponding signal can be collected from the power mechanism such as the starter of the elevator to determine whether the elevator is started and running, or the acceleration status of the elevator is obtained through the acceleration sensor), calculates the drift of the air pressure with the change of the environment, and performs data correction.

[0076] Because the elevator stops at a certain floor for a period of time and then operates again, the air pressure height of the air pressure sensor will change with the environment, and the air pressure height will produce a new error value.

[0077] Therefore, after receiving the elevator control state signal, the processor can re-introduce the air pressure detection value and predict and update the first error value through the Kalman filter: , Finally, the corrected height after further correction is obtained .

[0078] From the hardware level, in order to achieve full product traceability, have intelligent account period management capabilities, and be able to make accurate consumption predictions based on historical data, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the elevator air pressure height self-correction method, and the electronic device specifically includes the following contents: 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 elevator air pressure height self-correction device and related equipment such as the core business system, user terminal and related database; 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 elevator air pressure height self-correction method and the embodiment of the elevator air pressure height self-correction device in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0079] 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.

[0080] In practical applications, part of the elevator air pressure height self-correction method can be executed 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.

[0081] 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.

[0082] Figure 5 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 5As 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 Figure 5 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0083] In one embodiment, the elevator air pressure altitude self-calibration method function can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control: Step S101: Using the Kalman filter, k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the air pressure change rate updated at each moment, k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; Step S102: Pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; Step S103: Based on the relationship between air pressure and altitude, use k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0084] From the above description, it can be seen that the electronic device provided in the embodiment of the present application can update the state estimation at each moment based on the Kalman filter, and can process the air pressure sensor data in real time and dynamically. It can also effectively smooth the random noise in the data and effectively smooth the air pressure sensor data. At the same time, the air pressure sensor is linked with the mechanical structure to achieve further self-correction, making the height detection of the elevator more accurate.

[0085] In another embodiment, the elevator air pressure altitude self-correction device can be configured separately from the central processing unit 9100. For example, the elevator air pressure altitude self-correction device can be configured as a chip connected to the central processing unit 9100, and the elevator air pressure altitude self-correction method function can be implemented through the control of the central processing unit.

[0086] like Figure 5 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 Figure 5 In addition, the electronic device 9600 may also include Figure 5 For components not shown, reference may be made to the prior art.

[0087] like Figure 5 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.).

[0092] 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.

[0093] 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.

[0094] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the elevator air pressure height self-calibration method 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 elevator air pressure height self-calibration method in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented: Step S101: Using the Kalman filter, k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the air pressure change rate updated at each moment, k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; Step S102: Pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; Step S103: Based on the relationship between air pressure and altitude, use k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0095] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application can update the state estimation at each moment based on the Kalman filter, and can process the air pressure sensor data in real time and dynamically. It can also effectively smooth the random noise in the data and effectively smooth the air pressure sensor data. At the same time, the air pressure sensor is linked with the mechanical structure to achieve further self-correction, making the height detection of the elevator more accurate.

[0096] The embodiments of the present application also provide a computer program product capable of implementing all the steps of the elevator air pressure height self-calibration method in the above-mentioned embodiment, the execution subject of which is a server or a client. When the computer program / instruction is executed by a processor, the steps of the elevator air pressure height self-calibration method are implemented. For example, the computer program / instruction implements the following steps: Step S101: Using the Kalman filter, k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the air pressure change rate updated at each moment, k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; Step S102: Pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; Step S103: Based on the relationship between air pressure and altitude, use k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

[0097] From the above description, it can be seen that the computer program product provided in the embodiment of the present application can update the state estimation at each moment based on the Kalman filter, and can process the air pressure sensor data in real time and dynamically. It can also effectively smooth the random noise in the data and effectively smooth the air pressure sensor data. At the same time, the air pressure sensor is linked with the mechanical structure to achieve further self-correction, making the height detection of the elevator more accurate.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 method for self-calibration of air pressure height of an elevator, characterized in that: The method comprises: Through the Kalman filter, using k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the air pressure change rate updated at each moment, k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; Based on the relationship between air pressure and altitude, k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

2. The method for self-calibration of air pressure height of an elevator according to claim 1, characterized in that: Through the Kalman filter, using k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at the moment include: ， in, for k -1 The estimated parameters of the elevator state updated at each moment; for k The estimated parameters of the elevator state at time t; for k -1 The covariance matrix estimate updated at time instant; for k The estimated value of the covariance matrix at time instant; A is the state transfer matrix; T is the transpose of the matrix; Q is the process noise covariance matrix; B is the control input matrix; yes k Control input of the elevator at the moment.

3. The method for self-calibration of air pressure height of an elevator according to claim 2, characterized in that: pass k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k The elevator state estimation parameters are updated at all times, including: ， in, for k Kalman gain at time t; H is the observation matrix; R is the measurement noise covariance matrix; for k The elevator state estimation parameters are updated at all times. , for k An updated estimate of air pressure, for k An updated estimate of the rate of change of air pressure; for k The air pressure detection value at the moment; for k The updated covariance matrix estimate.

4. The method for self-calibration of air pressure height of an elevator according to claim 1, 2 or 3, characterized in that: Also includes: When the opening of the front gantry is detected, the height of the elevator is reset to 0 and the first error value is calculated. : ， in, for j The lift pressure altitude value at the moment, j The time is the time when the front gantry is detected to be open; Based on the first error value and k The lift pressure altitude value at the time , get the lift k Corrected altitude at the moment : ， in, for k -1 elevator air pressure altitude value; for k -1 time has arrived k The gradient value of the elevator height change at the moment; for k -1 The estimated value of air pressure updated at all times; for k -1 The estimated value of the rate of change of air pressure updated at every moment; for k Process noise at time -1.

5. The method for self-calibration of air pressure height of an elevator according to claim 4, characterized in that: Also includes: When the elevator operation time interval is greater than or equal to the set operation interval threshold, recalculate k The change of lift pressure altitude value at any time ; The second error value is obtained by the following formula : ， The first error value is updated by the second error value to obtain a new first error value: 。 6. A self-calibration device for air pressure height of an elevator, characterized in that: include: Air pressure sensor, used to collect and obtain air pressure detection value; Kalman filter module, used to use Kalman filter k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time t; Said k The estimated parameters of the elevator state updated at time -1 include k -1 The estimated pressure value updated at any time and k -1 The estimated value of the rate of change of air pressure updated at every moment; Said k The estimated parameters of the elevator state at time include k The estimated air pressure at the time and k The estimated rate of change of air pressure at the moment; and, for k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k Elevator state estimation parameters updated at all times; processor, used to use the relationship between air pressure and altitude k -1 The estimated air pressure value updated at every moment, k -1 The estimated pressure change rate updated at each moment and k The pressure is calculated by the estimated pressure value updated at all times k -1 time has arrived k The value of the elevator height change gradient at the moment; and through k -1 elevator pressure altitude value and k -1 time has arrived k The gradient value of the elevator height change at the moment is calculated k The lift pressure altitude value at the moment.

7. The self-calibration device for air pressure height of an elevator according to claim 1, characterized in that: The Kalman filter module includes a state estimation module; The state estimation module is used to utilize k The estimated parameters of the elevator state updated at time -1 are predicted k The estimated parameters of the elevator state at time: ， in, for k -1 The estimated parameters of the elevator state updated at each moment; for k The estimated parameters of the elevator state at time t; for k -1 The covariance matrix estimate updated at time instant; for k The estimated value of the covariance matrix at time instant; A is the state transfer matrix; T is the transpose of the matrix; Q is the process noise covariance matrix; B is the control input matrix; yes k Control input of the elevator at the moment.

8. The self-calibration device for air pressure height of an elevator according to claim 7, characterized in that: The Kalman filter module also includes a state update module; The status update module is used to k The air pressure detection value at the moment k The estimated parameters of the elevator state at time t are corrected to obtain k The elevator state estimation parameters are updated at all times, including: ， in, for k Kalman gain at time t; H is the observation matrix; R is the measurement noise covariance matrix; for k The elevator state estimation parameters are updated at all times. , for k An updated estimate of air pressure, for k An updated estimate of the rate of change of air pressure; for k The air pressure detection value at the moment; for k The updated covariance matrix estimate.

9. 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 elevator air pressure altitude self-correction method described in any one of claims 1 to 5 are implemented.

10. 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 elevator air pressure altitude self-correction method described in any one of claims 1 to 5 are implemented.

Images