Dynamic estimation and application method, system and device for barycentric coordinates of cross-country forklift

Abstract

The invention discloses a dynamic estimation and application method, system and device for barycentric coordinates of a cross-country forklift. The method comprises the following steps: acquiring operation data of the cross-country forklift in the current time period, obtaining barycentric measurement data based on the operation data, and forming operation state data based on the barycentric measurement data and the operation data; constructing a state prediction model and a state updating model based on the operation state data; on the basis of the state prediction model, predicting the running state data of the previous time period to obtain a running state data prediction value of the current time period, updating the running state data prediction value of the current time period on the basis of the state updating model to obtain an optimal running state data prediction value of the current time period, and further obtaining optimal barycentric coordinate data; and obtaining the optimal torque of the cross-country forklift based on the optimal gravity center coordinate data and the gravity center measurement data. The torque distribution between the front wheels and the rear wheels of the cross-country forklift is adjusted through the optimal torque, so that the cross-country forklift can adapt to various cross-country working conditions.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control, and in particular to a method, system and device for dynamically estimating and applying the center of gravity coordinates of an off-road forklift. Background Art

[0002] Four-wheel drive off-road forklifts operate in complex off-road conditions, and accurately mastering their motion status and parameter changes is crucial to improving the performance, safety and reliability of the forklifts. Traditional forklift parameter monitoring and torque control methods often have problems such as low accuracy and untimely response under off-road conditions, and are difficult to meet the special needs of four-wheel drive off-road forklifts. Therefore, when facing different working scenes such as muddy and rugged terrain, flat roads, and turning in narrow spaces, how to reasonably distribute torque according to different actual working needs becomes a key link in the efficient transmission system of off-road forklifts.

[0003] At the same time, reasonable torque distribution can protect the transmission system of the forklift, because excessive torque will cause damage to the gears, transmission shafts and other components in the transmission system. Controlling the torque within a reasonable range can avoid damage to the transmission system due to overload. On the other hand, the smooth transmission of torque can also reduce the impact and wear on the transmission system and extend its service life. In the early days, the torque distribution of the forklift gearbox used a fixed gear ratio to transmit torque, which could not be adjusted according to the actual working conditions. With the application of mechanical control and electronic technology in the gearbox, torque distribution can be achieved through mechanical devices and ECU systems, but the flexibility of torque distribution is still limited. However, as a forklift equipment with strong off-road performance, the ECU system of the four-wheel drive off-road forklift is easily affected by environmental adaptability such as temperature, dust and water vapor when dealing with complex road conditions and transmission power requirements. The operation process mainly relies on the mechanical execution of specific software programs. When there are loopholes or other unstable factors, it will cause torque distribution errors, which will affect the performance and safety of the forklift. In addition, traditional forklifts cannot accurately adjust the torque according to the change of the center of gravity, resulting in insufficient stability when driving on complex terrain. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method, system and device for dynamically estimating and applying the center of gravity coordinates of an off-road forklift.

[0005] In order to solve the above technical problems, the present invention is solved by the following technical solutions: A method for dynamically estimating the center of gravity coordinates of an off-road forklift and its application, comprising the following steps: Obtaining the operating data of the off-road forklift in the current period, obtaining the center of gravity measurement data based on the operating data, and forming the operating status data based on the center of gravity measurement data and the operating data; Based on the operating status data, the state transfer matrix, control input matrix, observation matrix and Kalman gain are constructed; based on the state transfer matrix and control input matrix, the state prediction model is constructed; based on the observation matrix and Kalman gain, the state update model is constructed; Based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained; The operating state of the off-road forklift is adjusted based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

[0006] As an implementable method, the operating data at least includes the total mass of the off-road forklift and the cargo, the rolling resistance coefficient, the driving force, the braking force, the angular velocity and the turning radius, and obtaining the center of gravity measurement data based on the operating data includes the following steps: The longitudinal acceleration measurement value of the center of gravity is obtained through the driving force, braking force, rolling resistance coefficient and the total mass of the off-road forklift and the cargo. The longitudinal coordinate measurement value of the center of gravity is obtained through the longitudinal acceleration measurement value of the center of gravity and the longitudinal initial velocity of the center of gravity. Based on the angular velocity and the turning radius, the lateral acceleration measurement value of the center of gravity is obtained, and the lateral coordinate measurement value of the center of gravity is obtained through the lateral acceleration measurement value of the center of gravity and the lateral initial velocity of the center of gravity, wherein the longitudinal coordinate measurement value of the center of gravity and the lateral coordinate measurement value of the center of gravity are the center of gravity measurement data; The longitudinal acceleration measurement value of the center of gravity and the lateral acceleration measurement value of the center of gravity are respectively expressed as follows:

[0007]

[0008] The longitudinal coordinate measurement value of the center of gravity and the transverse coordinate measurement value of the center of gravity are respectively expressed as follows:

[0009]

[0010] in, represents the longitudinal acceleration measurement of the center of gravity, represents the measured value of the lateral acceleration of the center of gravity, Indicates driving force, Indicates the braking force, is the rolling resistance coefficient, Indicates the total mass of the off-road forklift and the cargo. represents the acceleration due to gravity, represents the angular velocity, Indicates the turning radius, represents the longitudinal coordinate measurement value of the center of gravity, represents the initial longitudinal coordinate of the center of gravity, represents the initial longitudinal velocity of the center of gravity, Indicates time, represents the measured value of the lateral coordinate of the center of gravity, represents the initial lateral coordinate of the center of gravity, represents the initial lateral velocity of the center of gravity.

[0011] As an implementable method, the operating data at least includes speed, torque, driving force, off-road forklift acceleration, angular velocity, step length and center of gravity data, and the state transfer matrix, control input matrix, observation matrix and Kalman gain are constructed based on the operating state data; a state prediction model is constructed based on the state transfer matrix and the control input matrix; and a state update model is constructed based on the observation matrix and the Kalman gain, including the following steps: The state transfer matrix is ​​constructed through the speed, torque, driving force and acceleration of the off-road forklift; Obtain the road surface anti-skid performance index, and construct a control input matrix through the road surface anti-skid performance index and step length; The system state vector is constructed through the center of gravity data, angular velocity, off-road forklift acceleration, torque and driving force, and then the error covariance matrix of the system state vector is obtained; The actual angular velocity, the actual acceleration of the off-road forklift, and the actual torque are obtained and combined with the center of gravity measurement data to construct an observation vector, and then the noise covariance matrix of the observation vector is obtained. Based on the observation vector, an observation matrix is ​​constructed; Construct the Kalman gain through the observation matrix, error covariance matrix and noise covariance matrix; Based on the system state vector, state transfer matrix and control input matrix, a state prediction model is constructed to obtain a priori estimation of the system state vector; Based on the prior estimate of the system state vector, Kalman gain, observation vector and observation matrix, a state update model is constructed to obtain the posterior estimate of the system state vector.

[0012] As an implementable method, the state transfer matrix is ​​expressed as follows:

[0013] The road surface anti-skid performance index and the control input matrix are respectively expressed as follows:

[0014]

[0015] The observation matrix is ​​expressed as follows:

[0016] The Kalman gain is expressed as follows:

[0017] The state prediction model is expressed as follows:

[0018] The state update model is expressed as follows:

[0019] in, represents the state transfer matrix, represents the step length, Indicates the speed, Indicates torque, Indicates driving force, Indicates the acceleration of the off-road forklift, Represents a function about the running status data, Represents the road skid resistance index. Indicates the actual lateral force coefficient value, represents the minimum lateral force coefficient value, Indicates the magnitude of the center of gravity velocity in the center of gravity measurement data, , Indicates the magnitude of the longitudinal velocity of the center of gravity in the center of gravity measurement data, Indicates the magnitude of the lateral velocity of the center of gravity in the center of gravity measurement data. Indicates the direction of the center of gravity velocity in the center of gravity measurement data, , Respectively represent the coefficients, represents the control input matrix, represents the observation matrix, Both represent coefficients related to the observation vector, represents the Kalman gain of the current period, Indicates the current period. represents the error covariance matrix of the prior estimate of the system state vector in the current period, , represents the state transfer matrix of the current period, represents the error covariance matrix of the posterior estimate of the system state vector in the previous period, represents the transpose of a matrix, represents the system noise covariance matrix of the current period, represents the noise covariance matrix of the observation vector in the current period, represents the prior estimate of the system state vector in the current period, represents the posterior estimate of the system state vector in the previous period, Represents the control input matrix of the current period, represents the control input vector for the current period, represents the a posteriori estimate of the system state vector for the current period, Represents the observation vector of the current period.

[0020] As an implementable method, the state prediction model is used to predict the running state data of the previous period to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained, including the following steps: Based on the a posteriori estimate of the system state vector in the previous period, the state transfer matrix in the current period and the control input matrix in the current period, combined with the state prediction model, the a priori estimate of the system state vector in the current period is obtained; Based on the error covariance matrix of the posterior estimation of the system state vector in the previous period, the error covariance matrix of the a priori estimation of the system state vector in the current period is obtained; The Kalman gain of the current period is obtained through the error covariance matrix of the prior estimation of the system state vector of the current period, the observation matrix of the current period, and the noise covariance matrix of the observation vector of the current period, and then the error covariance matrix of the posterior estimation of the system state vector of the current period is obtained; Based on the prior estimate of the system state vector of the current period, the Kalman gain of the current period, the observation vector of the current period and the observation matrix of the current period, combined with the state update model, the posterior estimate of the system state vector of the current period is obtained; Until the iteration termination condition is met, the final a posteriori estimate of the system state vector is obtained, which is the optimal system state vector, and then the optimal center of gravity coordinate data is obtained.

[0021] As an implementable method, the torque adjustment model is constructed based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque to obtain the optimal torque of the off-road forklift, including the following steps: The actual torque of the rear wheels and the actual torque of the front wheels of the off-road forklift are obtained respectively; Based on the operating data, an adjustment coefficient is obtained; Based on the center of gravity measurement data, the optimal center of gravity coordinate data, the actual torque of the rear wheel, the actual torque of the front wheel and the adjustment coefficient, a torque adjustment model is constructed, wherein the torque adjustment model includes a rear wheel torque adjustment model and a front wheel torque adjustment model; The rear wheel torque adjustment model and the front wheel torque adjustment model are optimized to obtain the optimal adjustment coefficient, and then the optimal rear wheel torque and the optimal front wheel torque of the off-road forklift are obtained; The rear wheel torque adjustment model is expressed as follows:

[0022] The front wheel torque adjustment model is expressed as follows:

[0023] in, represents the optimal rear wheel torque, Indicates the actual rear wheel torque, represents the optimal front wheel torque, Indicates the actual front wheel torque, The longitudinal coordinate of the center of gravity representing the center of gravity measurement data, The horizontal coordinate of the center of gravity representing the center of gravity measurement data, The optimal center of gravity longitudinal coordinate representing the optimal center of gravity coordinate data, The optimal center of gravity lateral coordinate representing the optimal center of gravity coordinate data, Indicates the preset longitudinal forward displacement threshold of the center of gravity. Indicates the preset lateral forward displacement threshold of the center of gravity. Indicates the preset longitudinal rearward shift threshold of the center of gravity. Indicates the preset lateral rearward shift threshold of the center of gravity. Both represent adjustment coefficients and are positive numbers.

[0024] As an implementable method, the adjustment coefficient is obtained by the following steps: Through sensors, the lateral force, tire load, steering wheel angle and the mass of the cargo carried by the off-road forklift are obtained; Based on the lateral force and the tire load, a lateral force coefficient value is obtained, and based on the lateral force coefficient value and the center of gravity measurement data, a road surface anti-skid performance index is obtained, wherein the lateral force coefficient value = lateral force / tire load; Based on the road surface anti-skid performance index, the steering wheel angle and the weight of the cargo carried by the off-road forklift, an adjustment coefficient is obtained; Wherein, the adjustment coefficient is expressed as follows:

[0025] in, Represents the road skid resistance index. represents the preset first road surface anti-skid index threshold, represents a preset second road surface anti-skid index threshold, and , Indicates the steering wheel angle, Indicates the preset turning angle threshold. Indicates the mass of cargo carried by the off-road forklift. Indicates the preset load mass threshold, They all represent the adjustment coefficients under different conditions.

[0026] As an implementable method, the optimal adjustment coefficient is obtained by the following steps: Randomly generate an initialized particle swarm, obtain the position and velocity of each particle in the initialized particle swarm, and then obtain the initial individual optimal position of each particle and the initial global optimal position of the particle swarm, where the position of the particle is the adjustment coefficient to be solved; Based on the particle position, combined with the torque adjustment model, the torque prediction value is obtained and combined with the actual torque to build a fitness model. Based on the fitness model, the fitness of each particle's current position is obtained; If the fitness of the current position of the particle is greater than the fitness of the individual optimal position, the individual optimal position is updated. If the fitness of the current position of any particle is greater than the fitness of the global optimal position, the global optimal position is updated, and the position and speed of the particle are updated to obtain the updated particle speed and updated particle position. Until the termination condition is met, the final global optimal position is output as the optimal adjustment coefficient; Wherein, the fitness model is expressed as follows:

[0027] The updated particle velocity is expressed as follows:

[0028] The updated particle position is expressed as follows:

[0029] in, represents fitness, Respectively represent the rear wheel torque prediction value and the front wheel torque prediction value, represents the total amount of predicted rear wheel torque and front wheel torque, represents the updated particle velocity, represents the inertia weight, represents the particle velocity of the last iteration, represents the optimal position of the individual particle in the last iteration, represents the particle position of the last iteration, represents the global optimal position of the last iteration, represents the learning factor, Represents a random number, Indicates the updated particle position.

[0030] A dynamic estimation and application system of the center of gravity coordinates of an off-road forklift, comprising a data acquisition module, a model building module, a center of gravity estimation module and a state adjustment module; The data acquisition module acquires the operating data of the off-road forklift in the current period, obtains the center of gravity measurement data based on the operating data, and forms the operating status data based on the center of gravity measurement data and the operating data; The model building module builds a state transfer matrix, a control input matrix, an observation matrix and a Kalman gain based on the operating state data; builds a state prediction model based on the state transfer matrix and the control input matrix; and builds a state update model based on the observation matrix and the Kalman gain; The center of gravity estimation module predicts the running state data of the previous period based on the state prediction model to obtain the predicted value of the running state data of the current period, and updates the predicted value of the running state data of the current period based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then obtains the optimal center of gravity coordinate data; The state adjustment module adjusts the operating state of the off-road forklift based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

[0031] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following method is implemented: Obtaining the operating data of the off-road forklift in the current period, obtaining the center of gravity measurement data based on the operating data, and forming the operating status data based on the center of gravity measurement data and the operating data; Based on the operating status data, the state transfer matrix, control input matrix, observation matrix and Kalman gain are constructed; based on the state transfer matrix and control input matrix, the state prediction model is constructed; based on the observation matrix and Kalman gain, the state update model is constructed; Based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained; The operating state of the off-road forklift is adjusted based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

[0032] A device for dynamic estimation and application of the center of gravity coordinates of an off-road forklift, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the following method when executing the computer program: Obtaining the operating data of the off-road forklift in the current period, obtaining the center of gravity measurement data based on the operating data, and forming the operating status data based on the center of gravity measurement data and the operating data; Based on the operating status data, the state transfer matrix, control input matrix, observation matrix and Kalman gain are constructed; based on the state transfer matrix and control input matrix, the state prediction model is constructed; based on the observation matrix and Kalman gain, the state update model is constructed; Based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained; The operating state of the off-road forklift is adjusted based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

[0033] The present invention has significant technical effects due to the adoption of the above technical solutions: the present invention provides a method, system and device for dynamically estimating and applying the center of gravity coordinates of an off-road forklift. The present invention obtains center of gravity measurement data through the operation data of the off-road forklift, constructs a state prediction model and a state update model based on the operation data and the center of gravity measurement data, and predicts and updates the operation data and the center of gravity measurement data of the previous period to obtain the optimal center of gravity coordinate data of the current period. Based on the optimal center of gravity coordinate data and the center of gravity measurement data, the optimal rear wheel torque and the optimal front wheel of the off-road forklift are obtained, thereby adjusting the torque distribution between the front and rear wheels of the off-road forklift, so that the off-road forklift can maintain good power performance and passability under various off-road conditions, greatly improving the stability and safety of the off-road forklift. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 It is an overall schematic diagram of the method of the present invention; Figure 2It is an overall schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with the embodiments, which are explanations of the present invention but are not limited to the following embodiments. In the absence of conflict, the features in the following embodiments can be combined with each other.

[0037] Embodiment 1: A method for dynamic estimation of the center of gravity coordinates of an off-road forklift and its application, such as Figure 1 As shown, the following steps are included: S100: Acquire the operating data of the off-road forklift in the current period, obtain the center of gravity measurement data based on the operating data, and form the operating status data based on the center of gravity measurement data and the operating data; S200: constructing a state transfer matrix, a control input matrix, an observation matrix and a Kalman gain based on the operation state data; constructing a state prediction model based on the state transfer matrix and the control input matrix; constructing a state update model based on the observation matrix and the Kalman gain; S300: Based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained; S400: adjusting the operating state of the off-road forklift based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: constructing a torque adjustment model based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque to obtain the optimal torque of the off-road forklift.

[0038] In S100, the operation data of the off-road forklift in the current period is obtained, and the operation data at least includes the total mass of the off-road forklift and the cargo, the rolling resistance coefficient, the driving force, the braking force, the angular velocity and the turning radius. Based on the operation data, the center of gravity measurement data is obtained, and the operation status data is formed based on the center of gravity measurement data and the operation data, including the following steps: S110: Obtaining operation data of the off-road forklift in the current period through a sensor, wherein the operation data at least includes the total mass of the off-road forklift and the cargo , rolling resistance coefficient , Driving force Braking force , angular velocity and turning radius . The sensors installed on the off-road forklift can obtain the operating data and vehicle parameters of the gearbox of the off-road forklift under different off-road conditions at any time. The sensors include but are not limited to: position sensor (used to obtain the position of the forklift), speed sensor, angular velocity sensor, acceleration sensor, driving force sensor and center of gravity coordinate sensor (used to verify and calibrate the calculation results of the dynamic center of gravity coordinates), inertial sensor (acceleration, speed, direction and angular velocity, etc.).

[0039] S120: Obtain a measurement value of the longitudinal acceleration of the center of gravity through the driving force, the braking force, the rolling resistance coefficient, and the total mass of the off-road forklift and the cargo, and obtain a measurement value of the longitudinal coordinate of the center of gravity through the measurement value of the longitudinal acceleration of the center of gravity and the longitudinal initial velocity of the center of gravity; According to Newton's second law, the relationship between the operating data and the measured value of the longitudinal acceleration of the center of gravity is expressed as follows:

[0040] Therefore, the longitudinal acceleration measurement of the center of gravity is expressed as follows:

[0041] in, Indicates the total mass of the off-road forklift and the cargo. represents the longitudinal acceleration measurement of the center of gravity, Indicates driving force, Indicates the braking force, is the rolling resistance coefficient, It is a dimensionless parameter, which is affected by many factors such as tire structure, material, air pressure, road conditions and vehicle speed. It is usually obtained through experimental measurement or by consulting relevant manuals. Represents the acceleration due to gravity.

[0042] The longitudinal coordinate measurement value of the center of gravity of the off-road forklift (the center of gravity is The position in the axis direction (longitudinal direction) is expressed as follows:

[0043] in, represents the longitudinal coordinate measurement value of the center of gravity, represents the initial longitudinal coordinate of the center of gravity, represents the initial longitudinal velocity of the center of gravity, represents time, the initial longitudinal coordinate of the center of gravity and the initial longitudinal velocity of the center of gravity are all known quantities and are obtained through sensors.

[0044] S130: Based on the angular velocity and the turning radius, a measurement value of the lateral acceleration of the center of gravity is obtained, and a measurement value of the lateral coordinate of the center of gravity is obtained through the measurement value of the lateral acceleration of the center of gravity and the lateral initial velocity of the center of gravity.

[0045] In the horizontal direction, the centripetal force on the off-road forklift when turning is mainly considered. , the centripetal force will affect the lateral position of the center of gravity of the off-road forklift, as shown below:

[0046] Therefore, the measured value of the lateral acceleration of the center of gravity is expressed as follows:

[0047] in, Represents the centripetal force of the off-road forklift. represents the angular velocity of the off-road forklift, Indicates the turning radius of the off-road forklift. Represents the lateral acceleration measurement of the center of gravity.

[0048] The lateral coordinate measurement value of the center of gravity of the off-road forklift (the center of gravity is The position in the axial direction (lateral direction) is expressed as follows:

[0049] in, Indicates the lateral coordinate measurement value of the center of gravity of the off-road forklift. represents the initial lateral coordinate of the center of gravity, Represents the initial lateral velocity of the center of gravity. The initial lateral coordinate of the center of gravity and the initial lateral velocity of the center of gravity are both known quantities and are obtained through sensors.

[0050] The longitudinal coordinate measurement value of the center of gravity and the transverse coordinate measurement value of the center of gravity are the center of gravity measurement data. The center of gravity measurement data is the center of gravity coordinate of the off-road forklift before adjustment. It is a value obtained based on theoretical calculations and there are still many noise interferences. There is still a large error with the actual center of gravity coordinates. Therefore, denoising intervention is required to obtain the forklift center of gravity coordinates that are closer to the actual value after iterative calculation.

[0051] In S200, the operation data at least includes speed, torque, driving force, off-road forklift acceleration, angular velocity, step length and center of gravity data, and the operation data is obtained through sensors on the off-road forklift. Based on the operation state data, a state transfer matrix, a control input matrix, an observation matrix and a Kalman gain are constructed; based on the state transfer matrix and the control input matrix, a state prediction model is constructed; based on the observation matrix and the Kalman gain, a state update model is constructed, and the following steps are performed: S210: State transfer matrix of off-road forklift It is necessary to consider the motion characteristics of the off-road forklift and the influence of the gearbox parameters. , Torque and driving force Linear correlation, vertical motion and forklift acceleration , Torque and driving force Linearly related, therefore, the state transfer matrix , which is expressed as follows:

[0052] in, represents the state transfer matrix, represents the step length, Indicates the speed, It indicates torque. It should be noted that, unless otherwise specified, the torque in the present invention refers to the total output torque of the gearbox. It can be approximately considered that the total output torque of the off-road forklift is the sum of the torques distributed to the front and rear wheels through the transmission system (before considering the loss). Indicates driving force, Indicates the acceleration of the off-road forklift, A function representing data about the running status.

[0053] S220: Obtain a road surface anti-skid performance index through the center of gravity measurement data, and construct a control input matrix through the road surface anti-skid performance index and the step length. The road surface anti-skid performance index and the control input matrix are respectively expressed as follows:

[0054]

[0055] in, It indicates the road surface anti-skid performance index. During the operation of the off-road forklift, it is necessary to consider the influence of the size and direction of the center of gravity speed on the road surface anti-skid performance. When the center of gravity speed is large, the stability of the off-road forklift is more affected. Indicates the actual lateral force coefficient value, = lateral force / tire load, represents the minimum lateral force coefficient value, Generally determined according to road grade and design requirements. Indicates the magnitude of the center of gravity velocity in the center of gravity measurement data, , Indicates the magnitude of the longitudinal velocity of the center of gravity in the center of gravity measurement data, Indicates the magnitude of the lateral velocity of the center of gravity in the center of gravity measurement data. Indicates the direction of the center of gravity velocity in the center of gravity measurement data, , Respectively represent the coefficients, Represents model parameters, usually based on experience, and can be defined under off-road conditions The value of is 2, The coefficients representing the magnitude and direction of the center of gravity speed of the off-road forklift can be defined under off-road conditions. The value is 1. It comprehensively reflects the friction and anti-skid ability of the road surface to the vehicle tires when it is wet, wet or in other adverse conditions. SRI A high value indicates that the road surface has good anti-skid performance, which can reduce the possibility of dangerous situations such as skidding and side sliding during driving, thereby improving driving safety and stability. represents the control input matrix, which includes the external input factors of off-road conditions. Represents the transpose of a matrix.

[0056] S230: Construct a system state vector through the center of gravity data, angular velocity, off-road forklift acceleration, torque and driving force, and then obtain the error covariance matrix of the system state vector. The system state vector is expressed as follows:

[0057] in, represents the system state vector, Indicates the longitudinal center of gravity coordinates in the center of gravity data. Represents the lateral center of gravity coordinates in the center of gravity data, represents the longitudinal velocity of the center of gravity in the center of gravity data, represents the lateral velocity of the center of gravity in the center of gravity data, represents the angular velocity, Indicates the acceleration of the off-road forklift, Indicates torque data, Represents the driving force. It should be noted that these variables of the system state vector are all obtained through estimation.

[0058] S240: Acquire the actual angular velocity, the actual off-road forklift acceleration and the actual torque and construct an observation vector in combination with the center of gravity measurement data, and then obtain the noise covariance matrix of the observation vector. Based on the observation vector, construct an observation matrix, wherein the observation vector is expressed as follows:

[0059] The observation matrix is ​​expressed as follows:

[0060] in, Indicates the current period ( time period), They represent the longitudinal coordinate measurement value of the center of gravity in the current period and the transverse coordinate measurement value of the center of gravity in the current period, respectively. They represent the longitudinal velocity of the center of gravity of the current period and the lateral velocity of the center of gravity of the current period, respectively. Indicates the actual angular velocity of the current period. Indicates the actual acceleration of the off-road forklift in the current period. It represents the actual torque in the current period. In the present invention, the center of gravity measurement data is used as the actual center of gravity data of the off-road forklift, and the actual angular velocity, actual off-road forklift acceleration and actual torque are obtained through the sensors of the off-road forklift. represents the observation matrix, Both represent coefficients related to the observation vector.

[0061] S250: Constructing a Kalman gain through the observation matrix, the error covariance matrix and the noise covariance matrix. The Kalman gain is expressed as follows:

[0062] in, Represents the Kalman gain of the current period. The Kalman gain can automatically adjust the weights of the prior estimation and observation vectors, and generate anti-interference capabilities for measuring target variables and system uncertainties. Indicates the current period. represents the error covariance matrix of the prior estimate of the system state vector in the current period, , represents the state transfer matrix of the current period, represents the error covariance matrix of the posterior estimate of the system state vector in the previous period, represents the transpose of a matrix, represents the system noise covariance matrix of the current period, Represents the noise covariance matrix of the observation vector in the current period.

[0063] S260: Based on the system state vector, the state transfer matrix and the control input matrix, a state prediction model is constructed to obtain a priori estimation of the system state vector. The state prediction model is expressed as follows:

[0064] in, Represents the prior estimate of the system state vector in the current period. The prior estimate is the estimate of the system state vector without considering the observation vector in the current period. Indicates the time period, represents the state transfer matrix of the current period, represents the posterior estimate of the system state vector in the previous period, Represents the control input matrix of the current period, represents the control input vector of the current period, which contains external information that affects the system state change, including the road surface anti-skid performance. The a posteriori estimate of the system state vector can be directly set by the physical characteristics or prior knowledge of the system.

[0065] S270: Based on the prior estimate of the system state vector, the Kalman gain, the observation vector and the observation matrix, a state update model is constructed to obtain a posteriori estimate of the system state vector. The state update model is expressed as follows:

[0066] in, represents the a posteriori estimate of the system state vector for the current period, Represents the observation vector of the current period.

[0067] In S300, based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained, including the following steps: S310: Based on the a posteriori estimate of the system state vector in the previous period, the state transfer matrix in the current period, and the control input matrix in the current period, a priori estimate of the system state vector in the current period is obtained in combination with the state prediction model; S320: obtaining an error covariance matrix of a priori estimation of the system state vector in the current period based on the error covariance matrix of the posterior estimation of the system state vector in the previous period; S330: Obtain the Kalman gain of the current period through the error covariance matrix of the prior estimation of the system state vector of the current period, the observation matrix of the current period, and the noise covariance matrix of the observation vector of the current period, and then obtain the error covariance matrix of the a posteriori estimation of the system state vector of the current period; S340: Based on the prior estimation of the system state vector of the current period, the Kalman gain of the current period, the observation vector of the current period and the observation matrix of the current period, in combination with the state update model, obtain a posterior estimation of the system state vector of the current period; S350: until the iteration termination condition is met, the final a posteriori estimate of the system state vector is obtained as the optimal system state vector, and then the optimal center of gravity coordinate data is obtained. The iteration termination condition includes satisfying the convergence condition or reaching a predetermined number of iterations, and the convergence condition includes that the error of the a posteriori estimate of the system state vector is less than a preset state threshold and / or the diagonal elements of the error covariance matrix are less than a preset matrix threshold.

[0068] In S400, the operating state of the off-road forklift is adjusted based on the optimal center of gravity coordinate data and the center of gravity measurement data, wherein at least the optimal torque of the off-road forklift is adjusted, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift, including the following steps: S410: Obtaining the actual torque of the rear wheels and the actual torque of the front wheels of the off-road forklift through sensors.

[0069] S420: Based on the operation data, an adjustment coefficient is obtained, wherein the adjustment coefficient is obtained by the following steps: (1) The sensors are used to obtain the lateral force, tire load, steering wheel angle, and mass of the cargo carried by the off-road forklift.

[0070] (2) Based on the lateral force and tire load, the lateral force coefficient value is obtained. The lateral force coefficient value = lateral force / tire load. Based on the lateral force coefficient value and the center of gravity measurement data, the road surface anti-skid performance index is obtained. The method for obtaining the road surface anti-skid performance index is the same as the previous article and will not be repeated here.

[0071] (3) Based on the road surface anti-skid performance index, the steering wheel angle and the weight of the cargo carried by the off-road forklift, an adjustment coefficient is obtained. The adjustment coefficient is expressed as follows:

[0072] in, represents the adjustment coefficient, Represents the road skid resistance index. represents the preset first road surface anti-skid index threshold, represents a preset second road surface anti-skid index threshold, and , Indicates the steering wheel angle, Indicates the preset turning angle threshold. Indicates the mass of cargo carried by the off-road forklift. Indicates the preset load mass threshold, All represent adjustment coefficients under different conditions. Among them, the road skid resistance index The smaller the adjustment coefficient, the larger the , , and , the relationship between the adjustment coefficients are: , and Regardless of the previous situation, when the mass of the cargo carried by the off-road forklift exceeds the preset load mass threshold, the adjustment coefficient should be increased as a whole. The relationship between the adjustment coefficients is: .

[0073] When the steering wheel angle exceeds the preset angle threshold, the adjustment coefficient needs to be adjusted comprehensively according to the specific situation. The relationship between the adjustment coefficients is: .

[0074] According to experience, The value can be 0.8. It can be set to 0.5. The corner threshold can be set near the maximum value of the conventional corner range, such as setting the corner threshold to the maximum value of the conventional corner. The preset load mass threshold can be set to the maximum load of the off-road forklift. Based on this, an example of the adjustment coefficient is as follows: .

[0075] S430: Based on the center of gravity measurement data, the optimal center of gravity coordinate data, the actual torque of the rear wheels, the actual torque of the front wheels and the adjustment coefficient, a torque adjustment model is constructed. The concept of the torque adjustment model is as follows: when the center of gravity state is the center of gravity moving forward, the rear wheel torque is increased to improve stability; when the center of gravity state is the center of gravity moving backward, the front wheel torque is increased to maintain balance; when the center of gravity state is the center of gravity stable, the front and rear wheel torque balance is maintained, and the dynamic distribution of torque is achieved by controlling the shifting strategy of the first to fifth clutches. Among them, the torque adjustment model includes a rear wheel torque adjustment model and a front wheel torque adjustment model, and the rear wheel torque adjustment model and the front wheel torque adjustment model are respectively expressed as follows:

[0076]

[0077] in, represents the optimal rear wheel torque, Indicates the actual rear wheel torque, represents the optimal front wheel torque, Indicates the actual front wheel torque, The longitudinal coordinate of the center of gravity representing the center of gravity measurement data, The horizontal coordinate of the center of gravity representing the center of gravity measurement data, The optimal center of gravity longitudinal coordinate representing the optimal center of gravity coordinate data, The optimal center of gravity lateral coordinate representing the optimal center of gravity coordinate data, Indicates the preset longitudinal forward displacement threshold of the center of gravity. Indicates the preset lateral forward displacement threshold of the center of gravity. Indicates the preset longitudinal rearward shift threshold of the center of gravity. Indicates the preset lateral rearward shift threshold of the center of gravity. Both represent adjustment coefficients and are positive numbers.

[0078] S440: Optimize the rear wheel torque adjustment model and the front wheel torque adjustment model to obtain the optimal adjustment coefficient, and then obtain the optimal rear wheel torque and the optimal front wheel torque of the off-road forklift. The optimal adjustment coefficient can be obtained by an artificial intelligence algorithm. The artificial intelligence that can be used to obtain the optimal adjustment coefficient includes: machine learning algorithms such as nonlinear regression, support vector machine (SVM), decision tree and random forest; deep learning algorithms such as feedforward neural network, convolutional neural network (CNN), recurrent neural network (RNN) and deep learning framework; integrated learning algorithms such as Bagging, Boosting and reinforcement learning algorithms. In this embodiment, a particle swarm algorithm is used to obtain the adjustment coefficient, including the following steps: (1) Randomly generate an initialized particle swarm, obtain the position and velocity of each particle in the initialized particle swarm, and then obtain the initial individual optimal position of each particle and the initial global optimal position of the particle swarm, where the position of the particle is the adjustment coefficient to be solved.

[0079] (2) Based on the particle position, combined with the torque adjustment model, the torque prediction value is obtained and combined with the actual torque to construct a fitness model. Based on the fitness model, the fitness of each particle's current position is obtained. The fitness model is expressed as follows:

[0080] in, represents fitness, Respectively represent the rear wheel torque prediction value and the front wheel torque prediction value, Represents the total amount of predicted rear wheel torque and front wheel torque.

[0081] (3) If the fitness of the current position of the particle is greater than the fitness of the individual optimal position, the individual optimal position is updated. If the fitness of the current position of any particle is greater than the fitness of the global optimal position, the global optimal position is updated, and the position and speed of the particle are updated to obtain an updated particle speed and an updated particle position. The updated particle speed and the updated particle position are expressed as follows:

[0082]

[0083] in, represents the updated particle velocity, represents the inertia weight, represents the particle velocity of the last iteration, represents the optimal position of the individual particle in the last iteration, represents the particle position of the last iteration, represents the global optimal position of the last iteration, represents the learning factor, Represents a random number, Indicates the updated particle position.

[0084] (4) Until the termination condition is met, the final global optimal position is output as the optimal adjustment coefficient. The termination condition can be reaching a preset number of iterations, or the fitness change is less than a certain threshold, or other forms.

[0085] Embodiment 2: In this embodiment, specific data is used to further illustrate the dynamic estimation and application method of the center of gravity coordinates of an off-road forklift provided by the present invention: S1: Obtain the current period operation data of the off-road forklift, obtain the center of gravity measurement data based on the operation data, and form the operation status data based on the center of gravity measurement data and the operation data. The main calculation process is as follows: The operating parameters are calculated by sensors installed on the off-road forklift. The initial data (t=0s) is: the total mass of the off-road forklift and the cargo. for , rolling resistance coefficient of off-road forklift is 0.05, normal load , driving force for , braking force for , angular velocity for , turning radius for .

[0086] The longitudinal force of the off-road forklift is:

[0087]

[0088]

[0089] It is known that when t=1s, , , then the longitudinal coordinate measurement value of the center of gravity of the off-road forklift at this time is:

[0090] The lateral force and acceleration of the off-road forklift are:

[0091]

[0092] It is known that when t=1s, , , then the measured value of the lateral coordinate of the center of gravity of the off-road forklift at this time is:

[0093] Finally, the coordinate value of the center of gravity of the off-road forklift at t=1s is (1.07 m , 1.409 m ), but the center of gravity coordinates obtained at this time still have a lot of noise interference, so denoising intervention is needed.

[0094] S2: Based on the operating status data, the state transfer matrix, control input matrix, observation matrix and Kalman gain are constructed; based on the state transfer matrix and control input matrix, the state prediction model is constructed; based on the observation matrix and Kalman gain, the state update model is constructed.

[0095] The functional relationship in the state transfer matrix is ​​defined as follows:

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] The coefficients in the observation matrix are defined as follows: .

[0112] Therefore, the noise covariance matrix of the observation vector is:

[0113] The error covariance matrix of the initial estimated system state vector is:

[0114] S3: Based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained. It is known that the center of gravity coordinates of the off-road forklift in the initial period (0s) are , the time is 1s and the coordinates of the forklift's center of gravity are , the initial velocity is , , initial angular velocity , initial acceleration of the off-road forklift , initial torque , initial driving force .

[0115] The torque and related parameter data output by the gearbox at different time periods are obtained as follows: When t=1s,

[0116] When t=2s,

[0117] When t=3s,

[0118] The observation vectors of other parameters of the off-road forklift obtained by the sensor are as follows: t =1 s hour, ; t =2 s hour, ; t =3 s hour, ; The iterative calculation steps are as follows: exist t =1 s When the prior estimate ,in , calculate the state transfer matrix The values ​​of each element are as follows for demonstration only, and the calculation is not shown one by one:

[0119]

[0120] Then, in t =1 s The prior estimate of the system state vector is:

[0121] exist t =1 s When , the Kalman gain is: .

[0122] in, , represents the known value of the system noise covariance matrix.

[0123] exist t =1 s The posterior estimate of the system state vector is:

[0124] Among them, the calculation is t =1 s The observation vector at .

[0125] Similarly, after t =2 s and t =3 s After iterative calculation, the estimated coordinates of the center of gravity of the off-road forklift at different times can be obtained. For example, assuming t =1 s When , the calculated system state vector is expressed as:

[0126] The first two elements and This is the optimal center of gravity coordinate data of the off-road forklift at this time.

[0127] S4: Adjust the running state of the off-road forklift based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: construct a torque adjustment model based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque to obtain the optimal torque of the off-road forklift. Now, the road skid resistance index ( SRI ) is 0.3, that is, the anti-skid performance index is less than 0.5, which means that the road surface anti-skid performance is low and the center of gravity is unstable. When the center of gravity moves forward or backward, in order to maintain the stability of the off-road forklift, the torque output of the front and rear wheels needs to be adjusted.

[0128] Embodiment 3: A dynamic estimation and application system of the center of gravity coordinates of an off-road forklift Figure 2 As shown, it includes a data acquisition module, a model building module, a center of gravity estimation module and a state adjustment module; The data acquisition module acquires the operating data of the off-road forklift in the current period, obtains the center of gravity measurement data based on the operating data, and forms the operating status data based on the center of gravity measurement data and the operating data; The model building module builds a state transfer matrix, a control input matrix, an observation matrix and a Kalman gain based on the operating state data; builds a state prediction model based on the state transfer matrix and the control input matrix; and builds a state update model based on the observation matrix and the Kalman gain; The center of gravity estimation module predicts the running state data of the previous period based on the state prediction model to obtain the predicted value of the running state data of the current period, and updates the predicted value of the running state data of the current period based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then obtains the optimal center of gravity coordinate data; The state adjustment module adjusts the operating state of the off-road forklift based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

[0129] Various changes and modifications can be made without departing from the spirit and scope of the present invention, and all equivalent technical solutions also belong to the scope of the present invention.

[0130] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0131] It will be appreciated 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. 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.

[0132] The present invention is described with reference to the flowcharts and / or block diagrams of the method, terminal device (system), and computer program product according to 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 the 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 terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal 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.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including 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.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement 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.

[0135] It should be noted that: The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0136] In addition, it should be noted that the shapes and names of the parts and components of the specific embodiments described in this specification may be different. Any equivalent or simple changes made based on the structure, features and principles described in the patent concept of the present invention are included in the protection scope of the patent of the present invention. The technicians in the technical field of the present invention can make various modifications or supplements to the specific embodiments described or replace them in a similar manner, as long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for dynamic estimation of center of gravity coordinates of an off-road forklift and its application, characterized in that: The following steps are involved: Obtaining the operating data of the off-road forklift in the current period, obtaining the center of gravity measurement data based on the operating data, and forming the operating status data based on the center of gravity measurement data and the operating data; Based on the operating status data, the state transfer matrix, control input matrix, observation matrix and Kalman gain are constructed; based on the state transfer matrix and control input matrix, the state prediction model is constructed; Based on the observation matrix and Kalman gain, a state update model is constructed; Based on the state prediction model, the running state data of the previous period is predicted to obtain the predicted value of the running state data of the current period, and the predicted value of the running state data of the current period is updated based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then the optimal center of gravity coordinate data is obtained; The operating state of the off-road forklift is adjusted based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

2. The method for dynamic estimation and application of center of gravity coordinates of an off-road forklift according to claim 1, characterized in that: The operation data at least includes the total mass of the off-road forklift and the cargo, the rolling resistance coefficient, the driving force, the braking force, the angular velocity and the turning radius. The obtaining of the center of gravity measurement data based on the operation data includes the following steps: The longitudinal acceleration measurement value of the center of gravity is obtained through the driving force, braking force, rolling resistance coefficient and the total mass of the off-road forklift and the cargo. The longitudinal coordinate measurement value of the center of gravity is obtained through the longitudinal acceleration measurement value of the center of gravity and the longitudinal initial velocity of the center of gravity. Based on the angular velocity and the turning radius, the lateral acceleration measurement value of the center of gravity is obtained, and the lateral coordinate measurement value of the center of gravity is obtained through the lateral acceleration measurement value of the center of gravity and the lateral initial velocity of the center of gravity, wherein the longitudinal coordinate measurement value of the center of gravity and the lateral coordinate measurement value of the center of gravity are the center of gravity measurement data; The longitudinal acceleration measurement value of the center of gravity and the lateral acceleration measurement value of the center of gravity are respectively expressed as follows: The longitudinal coordinate measurement value of the center of gravity and the transverse coordinate measurement value of the center of gravity are respectively expressed as follows: in, represents the longitudinal acceleration measurement of the center of gravity, represents the measured value of the lateral acceleration of the center of gravity, Indicates driving force, Indicates the braking force, is the rolling resistance coefficient, Indicates the total mass of the off-road forklift and the cargo. represents the acceleration due to gravity, represents the angular velocity, Indicates the turning radius, represents the longitudinal coordinate measurement value of the center of gravity, represents the initial longitudinal coordinate of the center of gravity, represents the initial longitudinal velocity of the center of gravity, Indicates time, represents the measured value of the lateral coordinate of the center of gravity, represents the initial lateral coordinate of the center of gravity, represents the initial lateral velocity of the center of gravity.

3. The method for dynamic estimation and application of center of gravity coordinates of an off-road forklift according to claim 1, characterized in that: The operation data at least includes speed, torque, driving force, off-road forklift acceleration, angular velocity, step length and center of gravity data; based on the operation state data, a state transfer matrix, a control input matrix, an observation matrix and a Kalman gain are constructed; based on the state transfer matrix and the control input matrix, a state prediction model is constructed; Based on the observation matrix and Kalman gain, a state update model is constructed, which includes the following steps: The state transfer matrix is ​​constructed through the speed, torque, driving force and acceleration of the off-road forklift; Obtain the road surface anti-skid performance index, and construct a control input matrix through the road surface anti-skid performance index and step length; The system state vector is constructed through the center of gravity data, angular velocity, off-road forklift acceleration, torque and driving force, and then the error covariance matrix of the system state vector is obtained; The actual angular velocity, the actual acceleration of the off-road forklift, and the actual torque are obtained and combined with the center of gravity measurement data to construct an observation vector, and then the noise covariance matrix of the observation vector is obtained. Based on the observation vector, an observation matrix is ​​constructed; Construct the Kalman gain through the observation matrix, error covariance matrix and noise covariance matrix; Based on the system state vector, state transfer matrix and control input matrix, a state prediction model is constructed to obtain a priori estimation of the system state vector; Based on the prior estimate of the system state vector, Kalman gain, observation vector and observation matrix, a state update model is constructed to obtain the posterior estimate of the system state vector.

4. The method for dynamic estimation and application of center of gravity coordinates of an off-road forklift according to claim 3, characterized in that: The state transfer matrix is ​​expressed as follows: The road surface anti-skid performance index and the control input matrix are respectively expressed as follows: The observation matrix is ​​expressed as follows: The Kalman gain is expressed as follows: The state prediction model is expressed as follows: The state update model is expressed as follows: in, represents the state transfer matrix, represents the step length, Indicates the speed, Indicates torque, Indicates driving force, Indicates the acceleration of the off-road forklift, Represents a function about the running status data, Represents the road skid resistance index. Indicates the actual lateral force coefficient value, represents the minimum lateral force coefficient value, Indicates the magnitude of the center of gravity velocity in the center of gravity measurement data, , Indicates the magnitude of the longitudinal velocity of the center of gravity in the center of gravity measurement data, Indicates the magnitude of the lateral velocity of the center of gravity in the center of gravity measurement data. Indicates the direction of the center of gravity velocity in the center of gravity measurement data, , Respectively represent the coefficients, represents the control input matrix, represents the observation matrix, Both represent coefficients related to the observation vector, represents the Kalman gain of the current period, Indicates the current period. represents the error covariance matrix of the prior estimate of the system state vector in the current period, , represents the state transfer matrix of the current period, represents the error covariance matrix of the posterior estimate of the system state vector in the previous period, represents the transpose of a matrix, represents the system noise covariance matrix of the current period, represents the noise covariance matrix of the observation vector in the current period, represents the prior estimate of the system state vector in the current period, represents the posterior estimate of the system state vector in the previous period, represents the control input matrix of the current period, represents the control input vector for the current period, represents the a posteriori estimate of the system state vector for the current period, Represents the observation vector of the current period.

5. The method for dynamic estimation of center of gravity coordinates of an off-road forklift according to claim 1, characterized in that: The method predicts the running state data of the previous period based on the state prediction model to obtain the predicted value of the running state data of the current period, and updates the predicted value of the running state data of the current period based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then obtains the optimal center of gravity coordinate data, including the following steps: Based on the a posteriori estimate of the system state vector in the previous period, the state transfer matrix in the current period and the control input matrix in the current period, combined with the state prediction model, the a priori estimate of the system state vector in the current period is obtained; Based on the error covariance matrix of the posterior estimation of the system state vector in the previous period, the error covariance matrix of the a priori estimation of the system state vector in the current period is obtained; The Kalman gain of the current period is obtained through the error covariance matrix of the prior estimation of the system state vector of the current period, the observation matrix of the current period, and the noise covariance matrix of the observation vector of the current period, and then the error covariance matrix of the posterior estimation of the system state vector of the current period is obtained; Based on the prior estimate of the system state vector of the current period, the Kalman gain of the current period, the observation vector of the current period and the observation matrix of the current period, combined with the state update model, the posterior estimate of the system state vector of the current period is obtained; Until the iteration termination condition is met, the final a posteriori estimate of the system state vector is obtained, which is the optimal system state vector, and then the optimal center of gravity coordinate data is obtained.

6. The method for dynamic estimation of center of gravity coordinates of an off-road forklift according to claim 1, characterized in that: The method of constructing a torque adjustment model based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque to obtain the optimal torque of the off-road forklift includes the following steps: The actual torque of the rear wheels and the actual torque of the front wheels of the off-road forklift are obtained respectively; Based on the operating data, an adjustment coefficient is obtained; Based on the center of gravity measurement data, the optimal center of gravity coordinate data, the actual torque of the rear wheel, the actual torque of the front wheel and the adjustment coefficient, a torque adjustment model is constructed, wherein the torque adjustment model includes a rear wheel torque adjustment model and a front wheel torque adjustment model; The rear wheel torque adjustment model and the front wheel torque adjustment model are optimized to obtain the optimal adjustment coefficient, and then the optimal rear wheel torque and the optimal front wheel torque of the off-road forklift are obtained; The rear wheel torque adjustment model is expressed as follows: The front wheel torque adjustment model is expressed as follows: in, represents the optimal rear wheel torque, Indicates the actual rear wheel torque, represents the optimal front wheel torque, Indicates the actual front wheel torque, The longitudinal coordinate of the center of gravity representing the center of gravity measurement data, The horizontal coordinate of the center of gravity representing the center of gravity measurement data, The optimal center of gravity longitudinal coordinate representing the optimal center of gravity coordinate data, The optimal center of gravity lateral coordinate representing the optimal center of gravity coordinate data, Indicates the preset longitudinal forward displacement threshold of the center of gravity. Indicates the preset lateral forward displacement threshold of the center of gravity. Indicates the preset longitudinal rearward shift threshold of the center of gravity. Indicates the preset lateral rearward shift threshold of the center of gravity. Both represent adjustment coefficients and are positive numbers.

7. The method for dynamic estimation and application of center of gravity coordinates of an off-road forklift according to claim 6, characterized in that: The adjustment coefficient is obtained by the following steps: Through sensors, the lateral force, tire load, steering wheel angle and the mass of the cargo carried by the off-road forklift are obtained; Based on the lateral force and the tire load, a lateral force coefficient value is obtained, and based on the lateral force coefficient value and the center of gravity measurement data, a road surface anti-skid performance index is obtained, wherein the lateral force coefficient value = lateral force / tire load; Based on the road surface anti-skid performance index, the steering wheel angle and the weight of the cargo carried by the off-road forklift, an adjustment coefficient is obtained; Wherein, the adjustment coefficient is expressed as follows: in, Represents the road skid resistance index. represents the preset first road surface anti-skid index threshold, represents a preset second road surface anti-skid index threshold, and , Indicates the steering wheel angle, Indicates the preset turning angle threshold. Indicates the mass of cargo carried by the off-road forklift. Indicates the preset load mass threshold, They all represent the adjustment coefficients under different conditions.

8. The method for dynamic estimation and application of center of gravity coordinates of an off-road forklift according to claim 6, characterized in that: The optimal adjustment coefficient is obtained by the following steps: Randomly generate an initialized particle swarm, obtain the position and velocity of each particle in the initialized particle swarm, and then obtain the initial individual optimal position of each particle and the initial global optimal position of the particle swarm, where the position of the particle is the adjustment coefficient to be solved; Based on the particle position, combined with the torque adjustment model, the torque prediction value is obtained and combined with the actual torque to build a fitness model. Based on the fitness model, the fitness of each particle's current position is obtained; If the fitness of the current position of the particle is greater than the fitness of the individual optimal position, the individual optimal position is updated. If the fitness of the current position of any particle is greater than the fitness of the global optimal position, the global optimal position is updated, and the position and speed of the particle are updated to obtain the updated particle speed and updated particle position. Until the termination condition is met, the final global optimal position is output as the optimal adjustment coefficient; Wherein, the fitness model is expressed as follows: The updated particle velocity is expressed as follows: The updated particle position is expressed as follows: in, represents fitness, Respectively represent the rear wheel torque prediction value and the front wheel torque prediction value, represents the total amount of predicted rear wheel torque and front wheel torque, represents the updated particle velocity, represents the inertia weight, represents the particle velocity of the last iteration, represents the optimal position of the individual particle in the last iteration, represents the particle position of the last iteration, represents the global optimal position of the last iteration, represents the learning factor, Represents a random number, Indicates the updated particle position.

9. A dynamic estimation and application system of the center of gravity coordinates of an off-road forklift, characterized in that: It includes data acquisition module, model building module, center of gravity estimation module and state adjustment module; The data acquisition module acquires the operating data of the off-road forklift in the current period, obtains the center of gravity measurement data based on the operating data, and forms the operating status data based on the center of gravity measurement data and the operating data; The model building module builds a state transfer matrix, a control input matrix, an observation matrix and a Kalman gain based on the operating state data; and builds a state prediction model based on the state transfer matrix and the control input matrix; Based on the observation matrix and Kalman gain, a state update model is constructed; The center of gravity estimation module predicts the running state data of the previous period based on the state prediction model to obtain the predicted value of the running state data of the current period, and updates the predicted value of the running state data of the current period based on the state update model to obtain the optimal predicted value of the running state data of the current period, and then obtains the optimal center of gravity coordinate data; The state adjustment module adjusts the operating state of the off-road forklift based on the optimal center of gravity coordinate data and the center of gravity measurement data, which at least includes adjusting the optimal torque of the off-road forklift, specifically: based on the optimal center of gravity coordinate data, the center of gravity measurement data and the actual torque, a torque adjustment model is constructed to obtain the optimal torque of the off-road forklift.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

11. A device for dynamically estimating the center of gravity coordinates of an off-road forklift and its application, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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