A control method, device and medium of an automobile maintenance lifting platform

Abstract

This invention discloses a control method for an automotive maintenance lifting platform. The method includes: collecting pressure data from four lifting points using pressure sensors during vehicle lifting; calculating the offset angle and offset of the center of gravity projection point within the lifting plane; constructing a state vector for the vehicle lifting process and building a prediction model for the vehicle's liftoff time to calculate the predicted liftoff moment; estimating the state vector for discrete time steps of the lifting process using extended Kalman filtering and calculating the predicted power of the lifting motor for future discrete time steps; constructing an objective function for the lifting control process based on the predicted state vector and combining it with the control time domain of the lifting process; calculating the control quality target coefficient for future time steps; and correcting the control quality. This method effectively adapts to lifting control when there is uneven vehicle weight distribution or sudden load changes, avoiding instability caused by uneven load and ensuring the safety, stability, and accuracy of the lifting process.

Description

Technical Field

[0001] This invention relates to the field of control of automotive repair lifting platforms, and more specifically to a control method, equipment, and medium for automotive repair lifting platforms. Background Technology

[0002] A car lift, also known as a car maintenance platform, is a specialized piece of equipment used for lifting vehicles during automotive repair and maintenance. It achieves its lifting function through hydraulic or mechanical drive. Based on its column structure, it can be divided into five main categories: single-column, double-column, four-column, scissor, and pit-type. It uses one or more lifting motors to synchronously drive and slowly raise the car. Due to the large overall weight of a car, the lifting platform bears a heavy load. Therefore, stable control of the lifting platform is crucial for safety. With the development of intelligent technology, more and more highly intelligent car lifts are being used. However, the intelligent control of existing car lifts generally suffers from the following shortcomings:

[0003] 1. Poor load adaptability: When using fixed power output control, the platform is prone to tilting and fluctuations in lifting speed when the vehicle weight distribution is uneven or the load changes suddenly, making it impossible to achieve dynamic adjustment and matching.

[0004] 2. Low attitude control accuracy: Most rely on mechanical limits or single sensor feedback, lacking multi-dimensional attitude perception and real-time correction mechanisms, which can easily lead to safety hazards in complex maintenance scenarios (such as applying force to one side of the chassis). Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention provides a control method, equipment, and medium for an automotive maintenance lifting platform, enabling phased lifting action prediction, optimization, and dynamic control.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] A control method for an automotive repair lifting platform is provided, comprising:

[0008] Step S1: Install pressure sensors on the four lifting points of the lifting platform. When the lifting platform lifts the car, the pressure sensors collect the pressure data of the four lifting points, calculate the center of gravity projection point of the car's center of gravity vertically projected onto the lifting plane during the lifting process, and calculate the offset angle and offset amount of the center of gravity projection point in the lifting plane.

[0009] Step S2: Construct a state vector for the car lifting process using pressure data, offset angle, offset amount, and lifting height data as state elements, and construct a prediction model for the time when the car leaves the ground to calculate the predicted time when the car leaves the ground.

[0010] Step S3: Starting from the prediction time, acquire state elements to generate the observed state vector, and estimate the state vector of the discrete time step of the lifting process based on the extended Kalman filter to obtain the future state vector. M The state vector is predicted for each time step, and the predicted power of the lifting motor for the future discrete time steps is calculated.

[0011] Step S4: Based on the predicted state vector and combined with the control time domain of the lifting process, construct the objective function of the lifting control process, and calculate the future... M The control quality target coefficients are based on the predicted state vector at each time step, and the control quality for the next M time steps is evaluated to correct the control quality of the lifting process at different future time steps.

[0012] Furthermore, the specific method for calculating the offset angle and offset amount of the center of gravity projection point in the lifting plane is as follows:

[0013] Pressure data collected by pressure sensors at four lifting points The four lifting points form a rectangle within the lifting plane, utilizing pressure data. Calculate the positions of the centroid projection point on the four sides of the rectangle. The distance from the lift point;

[0014] ;

[0015] in, These are the lengths of the longer and shorter sides of the rectangle, respectively. They are the location points The distance from the lift point;

[0016] Connect the points on the two parallel sides of the rectangle with lines, and take the intersection of the lines as the centroid projection point.

[0017] Construct a two-dimensional coordinate system in the lifting plane with the center of the rectangle as the origin. Two-dimensional coordinate system of x axis, y The axes are parallel to the two adjacent sides of the rectangle; project the centroid onto the two-dimensional coordinate system. Connect the origin to obtain the projection point of the center of gravity along the lifting plane. x axis, y Axis offset angle ;

[0018] Get the endpoint coordinates of the two connecting lines and , and Using endpoint coordinates and , and Establish two lines in a two-dimensional coordinate system Find the equations of the lines within the coordinate system, and solve the two equations to obtain the projection of the centroid onto the two-dimensional coordinate system. coordinates The offset of the center of gravity projection point in the lifting plane is obtained. .

[0019] Further, step S2 includes;

[0020] Step S21: Utilize the pressure data from the four lifting points Offset angle Offset and lifting height data h The state vector for the car lifting process is constructed using state elements. ;

[0021] ;

[0022] in, i The number of the lift point. , For the first i Pressure data collected at each lifting point I For the current of the lifting motor, The acceleration due to the change in height during the lifting process. For equivalent load mass, The drag coefficient of the lifting platform. The structural stiffness coefficient of the lifting platform. The rate of change of lifting height, For the output force of the lifting motor, The equivalent tilt angle of the lift point. t For time, I This refers to the current of the lifting motor;

[0023] Step S22: From the moment the lifting platform contacts the car chassis, it collects pressure data at four lifting points in real time. Calculate the pressure data at the four lifting points and We obtained time-series pressure data and datasets from the moment the lifting platform came into contact with the car chassis. , For time t The pressure data and, and the last pressure data and The constraints are satisfied;

[0024] ;

[0025] in, g It is the acceleration due to gravity. For the quality of the car; the constraints ensured the final pressure data and The last pressure data collected by the pressure sensor before the car leaves the ground;

[0026] Step S23: Construct a prediction model for the time it takes for the car to leave the ground. , These are the variable coefficients and biases of the time-series forecasting model; and the time-series-based stress data and dataset... In the input time prediction model, the fitted... N The coefficients and biases of each variable are combined, and the following values ​​are taken: N The average of the coefficients of each variable and the bias The final fitted time prediction model is obtained. ;

[0027] Step S24: Measure the weight of the car Input the final fitted time prediction model In the calculation, the predicted moment when the car leaves the ground during the lifting process is determined. .

[0028] Further, step S3 includes;

[0029] Step S31: Predict the time As the starting moment for state element acquisition, the acquired state elements are used to construct the observation state vector, and the state vector of the discrete time step of the lifting process is estimated based on the extended Kalman filter.

[0030] ;

[0031] in, k For the discrete time steps of the lifting process, This is the state transition function. Based on the previous time step Predicted current time step k The state vector, For the previous time step The predicted state vector, For the control input of the lifting platform, Let be the covariance matrix of the state estimation error. For the previous time step The covariance matrix of the state estimation error. Current time step k Updated covariance matrix, For time step k The partial derivative matrix, The process noise covariance matrix is... Here is the Kalman gain matrix. For the observation function, For the observation function in the state vector Jacobian matrix at the location, To observe the noise covariance matrix, For the current time step k The updated state vector, For time step k The observed state vector;

[0032] Step S32: Observation state vector based on continuous time step acquisition To obtain the predicted time After the future M The predicted state vector at each time step , j To predict the time step, and from the state vector Extract the pressure data from the four predicted lift points. Lifting height data Acceleration due to changes in lifting height and the current of the lifting motor Calculate time steps Output force of the lifting motor ;

[0033] Step S33: Based on the current of the lifting motor and the output force of the lifting motor Calculate time steps Predicted power of the lifting motor ;

[0034] ;

[0035] in, For time step Predicted rate of change of lift height These are the efficiency and resistance of the lifting motor, respectively.

[0036] Further, step S4 includes:

[0037] Step S41: Extract the state vector Predicted offset angle Offset And combined with the control time domain of the lifting process Construct the objective function for the lifting control process;

[0038] ;

[0039] in, The weights for the impact of lifting process height accuracy, lifting motor power fluctuation, and center of gravity offset on lifting control quality are respectively, and they satisfy the following conditions: , For lifting trajectory planning in time step Ideal lifting height This refers to the rated power of the lifting motor. This is the rated offset angle during the lifting process. This is the rated offset during the lifting process. For time step The control quality target coefficient;

[0040] Step S42: Obtain the future M The predicted state vector at each time step Corresponding control quality target coefficient data And set a threshold for the control quality target coefficient. ;

[0041] Step S43: ... M Data on individual control quality target coefficients sequentially with threshold Compare;

[0042] If all conditions are met Then determine the future time step Predicted state vector The accuracy meets the requirements, satisfying the control quality requirements of the lifting process, and the output of the lifting motor in the future... M Power data at each time step The lifting motor executes future power based on predicted power. M The lifting motion is performed in one time step;

[0043] Otherwise, acquire the future M The first occurrence in each time step Time step , To ensure the required prediction accuracy across continuous time steps, output the time step. Power data from previous consecutive time steps Execute the future The lifting action is performed at each time step; afterwards, the process returns to step S31, based on the time step. Based on the previously collected observed state vector and predicted state vector, re-predict the time step. After the future M The state vector at each time step;

[0044] Step S44: Repeat steps S31-S43 until the lifting platform completes the lifting action on the car.

[0045] A terminal device is provided, comprising a processor, a transceiver, and a memory. The memory stores a computer program, and the processor retrieves and runs the computer program from the memory to control the transceiver to perform receiving or sending actions, thereby enabling the terminal device to perform a control method for an automotive repair lifting platform.

[0046] A computer storage medium is provided for storing a computer program, the computer program including instructions for executing the control method of the above-described automobile repair lifting platform.

[0047] The beneficial effects of this invention are as follows: This invention is used to achieve dynamic control of the lifting process of an automotive repair lift platform. By predicting the state elements of the discrete time step of the lifting process based on extended Kalman filtering, the motor output power of future time steps is optimized. The prediction accuracy is comprehensively evaluated based on the lifting height accuracy, the power fluctuation of the lifting motor, and the center of gravity shift during the lifting process. This enables dynamic optimization and adjustment of the future motor output power, which can effectively adapt to lifting control when the vehicle weight distribution is uneven or the load changes abruptly. It effectively avoids the instability of the lifting process caused by uneven load, ensuring the safety, stability, and accuracy of the lifting process. Attached Figure Description

[0048] Figure 1 A flowchart illustrating the control method for an automotive repair lifting platform.

[0049] Figure 2 This is a schematic diagram illustrating the calculation of the offset angle and offset amount. Detailed Implementation

[0050] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0051] like Figure 1 As shown, a control method for an automotive repair lifting platform includes:

[0052] Step S1: Install pressure sensors on the four lifting points of the lifting platform. When the lifting platform lifts the car, the pressure sensors collect the pressure data of the four lifting points, calculate the center of gravity projection point of the car's center of gravity vertically projected onto the lifting plane during the lifting process, and calculate the offset angle and offset amount of the center of gravity projection point in the lifting plane.

[0053] The specific method for calculating the offset angle and offset amount of the center of gravity projection point in the lifting plane is as follows:

[0054] like Figure 2As shown, the pressure data at the four lifting points were collected by the pressure sensors. The four lifting points form a rectangle within the lifting plane, utilizing pressure data. Calculate the positions of the centroid projection point on the four sides of the rectangle. The distance from the lift point;

[0055] ;

[0056] in, These are the lengths of the longer and shorter sides of the rectangle, respectively. They are the location points The distance from the lift point;

[0057] Connect the points on the two parallel sides of the rectangle with lines, and take the intersection of the lines as the centroid projection point.

[0058] Construct a two-dimensional coordinate system in the lifting plane with the center of the rectangle as the origin. Two-dimensional coordinate system of x axis, y The axes are parallel to the two adjacent sides of the rectangle; project the centroid onto the two-dimensional coordinate system. Connect the origin to obtain the projection point of the center of gravity along the lifting plane. x axis, y Axis offset angle ;

[0059] Get the endpoint coordinates of the two connecting lines and , and Using endpoint coordinates and , and Establish two lines in a two-dimensional coordinate system Find the equations of the lines within the coordinate system, and solve the two equations to obtain the projection of the centroid onto the two-dimensional coordinate system. coordinates The offset of the center of gravity projection point in the lifting plane is obtained. ;

[0060] Offset angle This indicates the direction and amount of the center of gravity shift during the car lifting process. The offset distance is represented by the rectangle's center, which represents the ideal lifting center, and the offset angle is also represented by the rectangle's center. Offset The larger the value, the greater the car's deviation during the lifting process, and the less stable it will be. Deviation angle Offset As an important reference data for the stability of the lifting process, when the offset angle... Offset If the lifting process is too fast, it can easily cause safety problems such as falling or tilting.

[0061] Step S2: Construct a state vector for the vehicle lifting process using pressure data, offset angle, offset amount, and lifting height data as state elements, and build a prediction model for the vehicle's liftoff time to calculate the predicted moment of liftoff. Step S2 specifically includes:

[0062] Step S21: Utilize the pressure data from the four lifting points Offset angle Offset and lifting height data h The state vector for the car lifting process is constructed using state elements. ;

[0063] ;

[0064] in, i The number of the lift point. , For the first i Pressure data collected at each lifting point I For the current of the lifting motor, The acceleration due to the change in height during the lifting process. For equivalent load mass, The drag coefficient of the lifting platform. The structural stiffness coefficient of the lifting platform. The rate of change of lifting height, For the output force of the lifting motor, The equivalent tilt angle of the lift point. t For time, I This refers to the current of the lifting motor;

[0065] Step S22: From the moment the lifting platform contacts the car chassis, it collects pressure data at four lifting points in real time. Calculate the pressure data at the four lifting points and We obtained time-series pressure data and datasets from the moment the lifting platform came into contact with the car chassis. , For time t The pressure data and, and the last pressure data and The constraints are satisfied;

[0066] ;

[0067] in, g It is the acceleration due to gravity. For the quality of the car; the constraints ensured the final pressure data and The last pressure data collected by the pressure sensor before the car leaves the ground;

[0068] Step S23: Construct a prediction model for the time it takes for the car to leave the ground. , These are the variable coefficients and biases of the time-series forecasting model; and the time-series-based stress data and dataset... In the input time prediction model, the fitted... N The coefficients and biases of each variable are combined, and the following values ​​are taken: N The average of the coefficients of each variable and the bias The final fitted time prediction model is obtained. ;

[0069] Step S24: Measure the weight of the car Input the final fitted time prediction model In the calculation, the predicted moment when the car leaves the ground during the lifting process is determined. .

[0070] Step S3: Starting from the prediction time, acquire state elements to generate the observed state vector, and estimate the state vector of the discrete time step of the lifting process based on the extended Kalman filter to obtain the future state vector. M The state vector is predicted for each time step, and the predicted power of the lifting motor for the next discrete time step is calculated. Step S3 specifically includes:

[0071] Step S31: Predict the time As the starting moment for state element acquisition, the acquired state elements are used to construct the observation state vector, and the state vector of the discrete time step of the lifting process is estimated based on the extended Kalman filter.

[0072] ;

[0073] in, k For the discrete time steps of the lifting process, This is the state transition function. Based on the previous time step Predicted current time step k The state vector, For the previous time step The predicted state vector, For the control input of the lifting platform, Let be the covariance matrix of the state estimation error. For the previous time step The covariance matrix of the state estimation error. Current time step k Updated covariance matrix, For time step k The partial derivative matrix, The process noise covariance matrix is... Here is the Kalman gain matrix. For the observation function, For the observation function in the state vector Jacobian matrix at the location, To observe the noise covariance matrix, For the current time step k The updated state vector, For time step k The observed state vector;

[0074] State transition function Used to indicate how from the previous time step Evolved into the current time step Utilizing the previous time step The predicted state vector output is based on the previous time step. Predicted current time step k state vector Observation function Describes the predicted state transition function This corresponds to the generation of observations, such as pressure data, offset angle, or lift height data; it maps the estimated values ​​in the state space back to the sensor space. For example, the predicted offset angle state should equal the predicted tilt angle observation, which in turn equals the actual sensor data or calculated value. Covariance matrix Covariance Matrix Representing the uncertainty in the prediction process, in the control system of a lifting platform, the partial derivative matrix... The previous time step The uncertainty is linearly propagated to the current prediction time step. k This encodes the amplification or reduction effect of system dynamics on state uncertainties. Kalman gain matrix. As the optimal fusion weight matrix, it is dynamically calculated at the current time step. k During the update, new observations (observation state vector) should be provided. What weight, and what state transition function to give it? What weight should be used when making predictions?

[0075] Step S32: Observation state vector based on continuous time step acquisition To obtain the predicted time After the future M The predicted state vector at each time step , j To predict the time step, and from the state vector Extract the pressure data from the four predicted lift points. Lifting height data Acceleration due to changes in lifting height and the current of the lifting motor Calculate time steps Output force of the lifting motor ;

[0076] Step S33: Based on the current of the lifting motor and the output force of the lifting motor Calculate time steps Predicted power of the lifting motor ;

[0077] ;

[0078] in, For time step Predicted rate of change of lift height These are the efficiency and resistance of the lifting motor, respectively.

[0079] Step S4: Based on the predicted state vector and combined with the control time domain of the lifting process, construct the objective function of the lifting control process, and calculate the future... M Step S4 specifically includes: The control quality target coefficients are calculated based on the predicted state vector at each time step, and the control quality for the next M time steps is evaluated. The control quality of the lifting process at different future time steps is then corrected.

[0080] Step S41: Extract the state vector Predicted offset angle Offset And combined with the control time domain of the lifting process Construct the objective function for the lifting control process;

[0081] ;

[0082] in, The weights for the impact of lifting process height accuracy, lifting motor power fluctuation, and center of gravity offset on lifting control quality are respectively, and they satisfy the following conditions: , For lifting trajectory planning in time step Ideal lifting height This refers to the rated power of the lifting motor. This is the rated offset angle during the lifting process. This is the rated offset during the lifting process. For time step The control quality target coefficient.

[0083] Step S42: Obtain the future M The predicted state vector at each time step Corresponding control quality target coefficient data And set a threshold for the control quality target coefficient. The magnitude of the control quality target coefficient represents the lifting quality index based on height accuracy, lifting motor power fluctuation, and center of gravity offset. The smaller the control quality target coefficient, the better the lifting quality, and vice versa.

[0084] Step S43: ... M Data on individual control quality target coefficients sequentially with threshold Compare;

[0085] If all conditions are met Then determine the future time step Predicted state vector The accuracy meets the requirements, satisfying the control quality requirements of the lifting process, and the output of the lifting motor in the future... M Power data at each time step The lifting motor executes future power based on predicted power. M The lifting motion is performed in one time step;

[0086] Otherwise, acquire the future M The first occurrence in each time step Time step , To ensure the required prediction accuracy across continuous time steps, output the time step. Power data from previous consecutive time steps Execute the future The lifting action is performed at each time step; afterwards, the process returns to step S31, based on the time step. Based on the previously collected observed state vector and predicted state vector, re-predict the time step. After the future M The state vector at each time step.

[0087] Step S11: Repeat steps S31-S43 until the lifting platform completes the lifting action on the car.

[0088] This invention enables dynamic control of the lifting process of an automotive repair lift platform. It predicts the state elements of the discrete time steps of the lifting process using an extended Kalman filter, optimizes the motor output power for future time steps, and comprehensively evaluates the prediction accuracy based on lifting height accuracy, motor power fluctuations, and center of gravity shift during the lifting process. This allows for dynamic optimization and adjustment of the future motor output power, effectively adapting to lifting control when there is uneven vehicle weight distribution or sudden load changes. It effectively avoids instability in the lifting process caused by uneven load, ensuring the safety, stability, and accuracy of the lifting process.

Claims

1. A control method for an automobile repair lifting platform, characterized in that, include: Step S1: Install pressure sensors on the four lifting points of the lifting platform. When the lifting platform lifts the car, the pressure sensors collect the pressure data of the four lifting points, calculate the center of gravity projection point of the car's center of gravity vertically projected onto the lifting plane during the lifting process, and calculate the offset angle and offset amount of the center of gravity projection point in the lifting plane. Step S2: Construct a state vector for the vehicle lifting process using pressure data, offset angle, offset amount, and lifting height data as state elements, and build a prediction model for the time when the vehicle leaves the ground to calculate the predicted time of vehicle departure. ; Step S3: From the predicted time The process begins by acquiring state elements to generate an observation state vector. Then, based on an extended Kalman filter, the state vector for each discrete time step of the lifting process is estimated to obtain the future state vector. M The state vector is predicted for each time step, and the predicted power of the lifting motor for the future discrete time steps is calculated. Step S4: Based on the predicted state vector and combined with the control time domain of the lifting process, construct the objective function of the lifting control process, and calculate the future... M Each time step is based on the predicted state vector to determine the control quality target coefficients and to evaluate the future. M The control quality at each time step is used to correct the control quality of the lifting process at different future time steps; Step S3 includes: Step S31: Predict the time As the starting moment for state element acquisition, the acquired state elements are used to construct the observation state vector, and the state vector of the discrete time step of the lifting process is estimated based on the extended Kalman filter. ； in, k For the discrete time steps of the lifting process, This is the state transition function. Based on the previous time step Predicted current time step k The state vector, For the previous time step The predicted state vector, For the control input of the lifting platform, Let be the covariance matrix of the state estimation error. For the previous time step The covariance matrix of the state estimation error. For the current time step k Updated covariance matrix, For time step k The partial derivative matrix, The process noise covariance matrix is... Here is the Kalman gain matrix. For the observation function, For the observation function in the state vector Jacobian matrix at the location, To observe the noise covariance matrix, For the current time step k The updated state vector, For time step k The observed state vector; Step S32: Observation state vector based on continuous time step acquisition To obtain the predicted time After the future M The predicted state vector at each time step , j To predict the time step, and from the state vector Extract the pressure data from the four predicted lift points. Lifting height data Acceleration due to changes in lifting height and the current of the lifting motor Calculate time steps Output force of the lifting motor ; Step S33: Based on the current of the lifting motor and the output force of the lifting motor Calculate time steps Predicted power of the lifting motor ; ； in, For time step Predicted rate of change of lift height These are the efficiency and resistance of the lifting motor, respectively. Step S4 includes: Step S41: Extract the state vector Predicted offset angle Offset And combined with the control time domain of the lifting process Construct the objective function for the lifting control process; ； in, The weights for the impact of lifting process height accuracy, lifting motor power fluctuation, and center of gravity offset on lifting control quality are respectively, and they satisfy the following conditions: , For lifting trajectory planning in time step Ideal lifting height This refers to the rated power of the lifting motor. This is the rated offset angle during the lifting process. This is the rated offset during the lifting process. For time step The control quality target coefficient; Step S42: Obtain the future M The predicted state vector at each time step Corresponding control quality target coefficient data And set a threshold for the control quality target coefficient. ; Step S43: ... M Data on individual control quality target coefficients sequentially with threshold Compare; If all conditions are met Then determine the future time step Predicted state vector The accuracy meets the requirements, satisfying the control quality requirements of the lifting process, and the output of the lifting motor in the future... M Power data at each time step The lifting motor executes future power based on predicted power. M The lifting motion is performed in one time step; Otherwise, acquire the future M The first occurrence in each time step Time step , To ensure the required prediction accuracy across continuous time steps, output the time step. Power data from previous consecutive time steps Execute the future The lifting action is performed at each time step; afterwards, the process returns to step S31, based on the time step. Based on the previously collected observed state vector and predicted state vector, re-predict the time step. After the future M The state vector at each time step; Step S44: Repeat steps S31-S43 until the lifting platform completes the lifting action on the car.

2. The control method for the automobile repair lifting platform according to claim 1, characterized in that, The specific method for calculating the offset angle and offset amount of the center of gravity projection point in the lifting plane is as follows: Pressure data collected by pressure sensors at four lifting points The four lifting points form a rectangle within the lifting plane, utilizing pressure data. Calculate the positions of the centroid projection point on the four sides of the rectangle. The distance from the lift point; ； in, These are the lengths of the longer and shorter sides of the rectangle, respectively. They are the location points The distance from the lift point; Connect the points on the two parallel sides of the rectangle with lines, and take the intersection of the lines as the centroid projection point. Construct a two-dimensional coordinate system in the lifting plane with the center of the rectangle as the origin. Two-dimensional coordinate system of x axis, y The axes are parallel to the two adjacent sides of the rectangle; project the centroid onto the two-dimensional coordinate system. Connect the origin to obtain the projection point of the center of gravity along the lifting plane. x axis, y Axis offset angle ; Get the endpoint coordinates of the two connecting lines and , and Using endpoint coordinates and , and Establish two lines in a two-dimensional coordinate system Find the equations of the lines within the coordinate system, and solve the two equations to obtain the projection of the centroid onto the two-dimensional coordinate system. coordinates The offset of the center of gravity projection point in the lifting plane is obtained. .

3. The control method for the automobile repair lifting platform according to claim 2, characterized in that, Step S2 includes: Step S21: Utilize the pressure data from the four lifting points Offset angle Offset and lifting height data h The state vector for the car lifting process is constructed using state elements. ; ； in, i The number of the lift point. , For the first i Pressure data collected at each lifting point I For the current of the lifting motor, The acceleration due to the change in height during the lifting process. For equivalent load mass, The drag coefficient of the lifting platform. The structural stiffness coefficient of the lifting platform. The rate of change of lifting height, For the output force of the lifting motor, The equivalent tilt angle of the lift point. t For time; Step S22: From the moment the lifting platform contacts the car chassis, it collects pressure data at four lifting points in real time. Calculate the pressure data at the four lifting points and We obtained time-series pressure data and datasets from the moment the lifting platform came into contact with the car chassis. , For time t The pressure data and, and the last pressure data and The constraints are satisfied; ； in, g It is the acceleration due to gravity. For the quality of the car; the constraints ensured the final pressure data and The last pressure data collected by the pressure sensor before the car leaves the ground; Step S23: Construct a prediction model for the time it takes for the car to leave the ground. , These are the variable coefficients and biases of the time-series forecasting model; and the time-series-based stress data and dataset... In the input time prediction model, the fitted... N The coefficients and biases of each variable are combined, and the following values ​​are taken: N The average of the coefficients of each variable and the bias The final fitted time prediction model is obtained. ; Step S24: Measure the weight of the car Input the final fitted time prediction model In the calculation, the predicted moment when the car leaves the ground during the lifting process is determined. .

4. A terminal device, characterized in that, The device includes a processor, a transceiver, and a memory. The memory stores a computer program, and the processor retrieves and runs the computer program from the memory. The transceiver is used to control the transceiver to perform receiving or sending actions, thereby enabling the terminal device to execute the control method for the automotive repair lifting platform as described in any one of claims 1-3.

5. A computer storage medium, characterized in that, The computer program is used to store a computer program, the computer program including instructions for performing the control method of the vehicle maintenance lift platform as described in any one of claims 1-3.

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