CN117819397A - Swing angle feedback method and device, crane and storage medium - Google Patents
Swing angle feedback method and device, crane and storage medium Download PDFInfo
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Abstract
The invention provides a swing angle feedback method and device, a crane and a storage medium, and relates to the technical field of crane control. The method comprises the following steps: the method comprises the steps that a controller of a crane obtains a first swing angle predicted value of a lifting appliance in a target time range by utilizing a pre-constructed dynamic model aiming at the crane according to a swing angle observed value of the lifting appliance obtained at the current moment, wherein the target time range is a time range from the current moment to the moment at which the next swing angle observed value is obtained; linear fusion is carried out on the swing angle observation value of the lifting appliance obtained at the current moment, and a second swing angle prediction value of the lifting appliance in the target time range is obtained; according to the first swing angle predicted value and the second swing angle predicted value, a target swing angle predicted value of the lifting appliance is obtained, so that dangerous working condition judgment is carried out based on the target swing angle predicted value within a target time range, the limitation of camera frequency is overcome, and a controller of the bridge crane can carry out judgment of dangerous working conditions more quickly in emergency stop control.
Description
Technical Field
The invention relates to the technical field of crane control, in particular to a swing angle feedback method and device, a crane and a storage medium.
Background
Bridge cranes (or cranes) are an important general-purpose mechanical device. As a general logistics device with large load, various crane cranes, such as mining industry, steel crane, nonferrous metal crane, machining manufacturing industry and the like, can be used in most factory workshops.
The safety of bridge cranes is very important, in particular concerning personal safety. At present, when the bridge crane finds a safety problem, a mechanical band-type brake emergency stop mode is adopted. However, since the bridge crane adopts a structure of hanging the lifting appliance by using a steel wire rope, the lifting appliance can still rush forward for a very long distance after emergency stop, and damage to the previous articles or people can still occur. Meanwhile, the lifting appliance can generate a great swing angle, and hardware damage can be caused to the bridge crane. The existing swing angle feedback mode based on vision is limited by the frequency of a camera, and generally only about 30hz, so that the controller of the bridge crane cannot judge dangerous working conditions more quickly in emergency stop control.
Disclosure of Invention
In order to overcome the limitation of the frequency of a camera, the controller of the bridge crane can judge dangerous working conditions more quickly in emergency stop control, and a swing angle feedback method, a swing angle feedback device, a crane and a computer readable storage medium are provided.
The technical scheme of the invention can be realized as follows:
in a first aspect, the present invention provides a swing angle feedback method applied to a controller of a crane, the crane further including a spreader, the method including:
obtaining a first swing angle predicted value of the lifting appliance in a target time range by utilizing a pre-constructed dynamic model aiming at the lifting appliance for the swing angle observed value of the lifting appliance obtained at the current moment, wherein the target time range is a time range from the current moment to the moment of obtaining the next swing angle observed value;
linear fusion is carried out on the swing angle observation value of the lifting appliance, which is obtained at the current moment, so as to obtain a second swing angle prediction value of the lifting appliance in the target time range;
and obtaining a target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value, so as to judge dangerous working conditions based on the target swing angle predicted value within the target time range.
Optionally, before the first swing angle predicted value of the lifting appliance in the target time range is obtained by using the pre-constructed dynamic model for the crane according to the swing angle observed value of the lifting appliance acquired at the current moment, the method further includes:
determining the actual mass of the crane and the actual lifting height of the crane by utilizing the multi-group step response test data of the crane;
and constructing the dynamic model according to the actual mass of the crane and the actual lifting height of the crane.
Optionally, the step of determining the actual mass of the crane and the actual lifting height of the crane using the plurality of sets of step response test data comprises:
obtaining a deviation function aiming at the crane by utilizing the least square error of the multi-group step response test data, the nominal value of the mass of the crane and the nominal value of the lifting height of the crane;
and carrying out nonlinear optimization on the deviation function to obtain the actual mass of the crane and the actual lifting height of the crane.
Optionally, the step of linearly fusing the swing angle observation values of the lifting appliance obtained at the current moment to obtain the second swing angle predicted value of the lifting appliance in the target time range includes:
and generating a second swing angle predicted value of the lifting appliance in the target time range according to the preset sampling time and the preset predicted frequency by utilizing a linear relation of the pre-fitted swing angle observed value for the swing angle observed value of the lifting appliance acquired at the current moment.
Optionally, the step of obtaining the target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value includes:
constructing a dynamic error model of the first swing angle predicted value and a dynamic error model of the second swing angle predicted value;
obtaining a first normal distribution according to a dynamic error model of the first swing angle predicted value, wherein the first normal distribution is a normal distribution obeyed by errors of the first swing angle predicted value;
obtaining a second normal distribution according to the dynamic error model of the second swing angle predicted value, wherein the second normal distribution is a normal distribution obeyed by errors of the second swing angle predicted value;
and according to the first normal distribution and the second normal distribution, fusing the first swing angle predicted value and the second swing angle predicted value to obtain the target swing angle predicted value.
Optionally, the step of fusing the first tilt angle predicted value and the second tilt angle predicted value according to the first normal distribution and the second normal distribution to obtain the target tilt angle predicted value includes:
obtaining fusion parameters according to the variance of the first normal distribution and the variance of the second normal distribution;
and fusing the first swing angle predicted value and the second swing angle predicted value by using the fusion parameter to obtain the target swing angle predicted value.
Optionally, the step of fusing the first tilt angle predicted value and the second tilt angle predicted value by using the fusion parameter to obtain the target tilt angle predicted value includes:
obtaining a swing angle prediction gain by using the second swing angle prediction value, the first swing angle prediction value and the fusion parameter;
and taking the sum value of the first swing angle predicted value and the swing angle predicted gain as the target swing angle predicted value.
In a second aspect, the present invention provides a swing angle feedback device for use in a controller of a crane, the crane further comprising a spreader, the device comprising:
the first prediction module is used for obtaining a first swing angle predicted value of the lifting appliance in a target time range from the current moment to the moment of obtaining the next swing angle observed value by utilizing a pre-constructed dynamic model aiming at the lifting appliance for the swing angle observed value of the lifting appliance at the current moment;
the second prediction module is used for carrying out linear fusion on the swing angle observation value of the lifting appliance, which is acquired at the current moment, so as to obtain a second swing angle prediction value of the lifting appliance in the target time range;
and the processing module is used for obtaining a target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value so as to judge dangerous working conditions based on the target swing angle predicted value within the target time range.
In a third aspect, the invention provides a crane comprising a spreader and a controller programmed with a computer program which when executed implements the method of yaw angle feedback as described in the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the yaw angle feedback method of the first aspect described above.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a swing angle feedback method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a dynamic model construction according to an embodiment of the present invention;
fig. 3 is a flow chart illustrating a process of implementing step S202 according to an embodiment of the present invention;
fig. 4 is a flow chart illustrating a process of implementing step S103 according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an example of tilt angle feedback data provided by an embodiment of the present invention;
fig. 6 is a functional unit block diagram of a swing angle feedback device according to an embodiment of the present invention.
Icon: 100-swing angle feedback device; 101-a first prediction module; 102-a second prediction module; 103-a processing module; 104-building a module.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
In order to overcome the limitation of the frequency of a camera, the invention provides a swing angle feedback method for enabling a controller of a bridge crane to judge dangerous working conditions more quickly in emergency stop control, and the swing angle feedback method is described in detail below.
Referring to fig. 1, the swing angle feedback method includes steps S101 to S103.
S101, obtaining a first swing angle predicted value of the lifting appliance in a target time range by utilizing a pre-constructed dynamic model aiming at the crane according to a swing angle observed value of the lifting appliance obtained at the current moment.
The first swing angle predicted value comprises a first swing angle position predicted value and a first swing angle speed predicted value, and the target time range is a time range from the current time to the acquisition time of the next swing angle observed value.
The swing angle observation value is a swing angle of the lifting appliance in a visual image frame acquired by a camera, and in the embodiment of the invention, the frequency of the camera is generally about 30hz, and the time interval between the acquisition moments of two adjacent swing angle observation values can be obtained by converting the frequency of the camera.
By way of example, assuming a frequency of the camera of 25hz, i.e. one frame of images is acquired every 40ms and the angle of oscillation of the spreader is obtained based on the acquired images, it is understood that the interval between the current moment and the moment of acquisition of the next angle of oscillation observation is 40ms.
The change conditions of the swing angle position and the swing angle speed of the lifting appliance in 40ms can be predicted by utilizing a pre-constructed dynamic model for the crane, namely, a first swing angle position predicted value and a first swing angle speed predicted value are obtained.
The pre-built dynamics model for the crane can be expressed by the following formula (1):
wherein x is 1 Is the position of the crane, x 2 For the speed of the crane x 3 For the swing angle position of the lifting appliance, x 4 The swing angle speed of the lifting appliance is M, the mass of the lifting appliance is L, the lifting height of the lifting appliance is L, M is the load mass (which can be directly obtained by a weight sensor on the lifting appliance), F is the force of a direction motor of the lifting appliance, and g is the gravity acceleration.
Due to manufacturing errors and installation errors, the nominal mass value (the value determined in design) and the nominal lifting height value (the value given by the encoder) of the crane cannot be directly used, so that the actual mass and the actual lifting height of the crane need to be acquired through a system identification method when a dynamic model is constructed.
Referring to fig. 2, the process of constructing the dynamic model includes steps S201 to S202.
S201, determining the actual mass of the crane and the actual lifting height of the crane by utilizing the multi-group step response test data of the crane.
In the embodiment of the invention, the controller of the crane automatically controls at a frequency of 25hz, and the control quantity is constant in one control period (40 ms).
And starting from a state that lifting of the crane is completed to be stationary through a moment control mode, giving different steps with moment, and recording the position and speed of the crane, and the swing angle position and swing angle speed of the lifting appliance.
Each step was recorded for 25 seconds in response to the test, and a total of 625 sets of data were collected for each step, each set of data includingFour data, where x obj For crane position->For crane speed, theta obj For the swing angle position of the lifting appliance, < > for>Is the swing angle speed of the lifting appliance.
In a possible implementation, referring to FIG. 3, step S201 includes sub-steps S201-1 to S201-2.
S201-1, obtaining a deviation function aiming at the crane by utilizing the least square error of the multi-group step response test data, the nominal value of the mass of the crane and the nominal value of the lifting height of the crane.
In the embodiment of the invention, the least square error of a plurality of groups of swing angle test data is calculated by adopting a least square method through the following formula (2).
Wherein n is the index of the test and is tested 10 times in total, i is the time step index of each test, T is the total time step number of each test, and x obj To test the obtained crane position x m For crane positions obtained using the kinetic model,for testing the obtained crane speed +.>For crane speed, θ, using kinetic models obj To test the obtained swing angle position of the lifting appliance, theta m For the pendulum angle position of the spreader obtained with the kinetic model, < >>For testing the obtained pendulum angular velocity of the spreader, < +.>The pendulum angular velocity of the spreader is obtained by using a dynamic model.
The deviation function for the crane satisfies the following formula (3):
wherein M is the mass of the crane, l is the lifting height of the crane, M and l are variables to be optimized,as a deviation function +.>Is the swing angle test data.
S201-2, nonlinear optimization is carried out on the deviation function, and the actual mass of the crane and the actual lifting height of the crane are obtained.
For the deviation function, the initial value of the given variable M is the nominal value of the crane mass, and the initial value of the given variable l is the nominal value of the crane lifting height.
Based on the C++ Ceres library, nonlinear optimization is carried out on the deviation function, wherein the optimization problem is constructed in the following standard Ceres form:
the optimization step length can be designed to be 0.01, the optimization iteration number can be designed to be 10000, and the deviation of the optimized M relative to the nominal value of the mass of the crane and the deviation of the optimized I relative to the nominal value of the lifting height of the crane are obtained.
It will be appreciated that the optimal M is taken as the actual mass of the crane and the optimal l is taken as the actual elevation of the crane.
S202, constructing a dynamic model according to the actual mass of the crane and the actual lifting height of the crane.
Substituting the actual mass and the actual lifting height of the crane obtained by a system identification method into the formula (1) to obtain a dynamic model, and adopting a 4-order Dragon-Gregory tower method to obtain the following prediction formula (4).
Wherein y is i+1 For the state prediction value of the next moment, y i Is the state value of the last moment. h is the calculation step size, set to 0.1, f is the deviation function for the crane, and the predicted value is updated at a frequency of 100 hz.
And predicting a first swing angle predicted value of the lifting appliance in the target time range by taking the swing angle observed value of the lifting appliance obtained at the current moment as an input state value.
S102, linearly fusing the swing angle observation values of the lifting appliance obtained at the current moment to obtain a second swing angle prediction value of the lifting appliance in the target time range.
In a possible implementation, the implementation process of step S102 is to generate, according to the preset sampling time and the preset prediction frequency, a second swing angle predicted value of the lifting appliance within the target time range by using a linear relationship of the previously fitted swing angle observed values, which is obtained at the current moment.
The second swing angle predicted value comprises a second swing angle position predicted value and a second swing angle speed predicted value.
The linear relation of the swing angle observation values which are fitted by the swing angle observation values of a plurality of lifting appliances which are acquired successively can be utilized, and the linear relation of the swing angle observation values which are fitted in advance meets the following formula (5):
wherein θ is the observation value of the swing angle position,and t is a preset sampling time for the observed value of the swing angular velocity.
Substituting the swing angle observation value obtained at the current moment and the swing angle observation value obtained at the last moment into the formula to obtain a first second swing angle prediction value, and obtaining a second swing angle prediction value by using the first second swing angle prediction value and the swing angle observation value of the lifting appliance obtained at the current moment, and then analogizing the first swing angle prediction value to obtain second swing angle prediction values corresponding to different time points in the target time range.
And obtaining a second swing angle predicted value corresponding to different time points in the target time range by utilizing a pre-fitted swing angle observed value linear relation according to the preset predicted frequency in the target time range.
And S103, obtaining a target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value, so as to judge dangerous working conditions based on the target swing angle predicted value within a target time range.
In a possible implementation, referring to FIG. 4, step S103 includes sub-steps S103-1 to S103-4.
S103-1, constructing a dynamic error model of the first swing angle predicted value and a dynamic error model of the second swing angle predicted value.
Wherein the kinetic error model of the first pivot angle predictive value satisfies the following formula (6):
in the method, in the process of the invention,as the predicted value of the first swing angle at the time t, u t-1 For control input of crane at time t-1, x t-1 For the state value of the angle of oscillation of the spreader at time t-1 +.>An error of the first swing angle predicted value, A m And B m Is a coefficient matrix.
The kinetic error model of the second pivot angle predictive value satisfies the following formula (7):
in the method, in the process of the invention,a second swing angle predicted value at time t, u t-1 For control input of crane at time t-1, x t-1 For the state value of the angle of oscillation of the spreader at time t-1 +.>An error of the second swing angle predicted value, A l And B l Is a coefficient matrix.
S103-2, obtaining a first normal distribution according to a dynamic error model of the first swing angle predicted value.
Wherein the first normal distribution is a normal distribution to which an error of the first tilt angle predicted value obeys, satisfying the following formula (8):
wherein omega is m ～(μ m ，σ m ) Mu, as a first normal distribution m To expect, sigma m Standard deviation, H m Is a state matrix, P m As an error covariance matrix of the error,is the average value of the first swing angle predicted value.
S103-3, obtaining second normal distribution according to a dynamic error model of the second swing angle predicted value.
Wherein the second normal distribution is a normal distribution to which an error of the second tilt angle predicted value obeys, satisfying the following formula (9):
wherein omega is l ～(μ l ，σ l ) Mu, as second normal distribution l To expect, sigma l Standard deviation, H l Is a state matrix, P l As an error covariance matrix of the error,is the average value of the second swing angle predicted value.
S103-4, according to the first normal distribution and the second normal distribution, the first swing angle predicted value and the second swing angle predicted value are fused, and the target swing angle predicted value is obtained.
The target swing angle predicted value is obtained by performing Kalman filtering on the first swing angle predicted value and the second swing angle predicted value.
Alternatively, the implementation process of step S103-4 may be:
s103-4-1, obtaining fusion parameters according to the variance of the first normal distribution and the variance of the second normal distribution.
Wherein the fusion parameter is a Kalman gain when the Kalman filtering is simplified, and the following formula (10) is satisfied:
where K is the fusion parameter (i.e., kalman gain),is the variance of the first normal distribution, +.>Is the variance of the second normal distribution.
S103-4-3, fusing the first swing angle predicted value and the second swing angle predicted value by utilizing the fusion parameter to obtain a target swing angle predicted value.
In the embodiment of the invention, the second swing angle predicted value, the first swing angle predicted value and the fusion parameter are utilized to obtain the swing angle predicted gain, and then the sum value of the first swing angle predicted value and the swing angle predicted gain is used as the target swing angle predicted value.
It is understood that the first yaw angle predicted value, the second yaw angle predicted value, and the target yaw angle predicted value satisfy the following formula (11):
x＝x pre_model +K×(x pre_liner -H×x pre_model ) (11)
wherein x is the target swing angle predicted value, x pre_model For the first swing angle predictive value, x pre_liner K is a fusion parameter, and H is a state matrix.
According to the embodiment of the invention, the target swing angle predicted value is generated according to the frequency of 100hz, so that a plurality of swing angle predicted values are generated between the swing angle observed values acquired at two different adjacent moments, as shown in fig. 5, one swing angle observed value is acquired according to the visual image acquired by the camera every 40ms, and one swing angle predicted value is generated every 10ms within the 40ms, so that the observation frequency based on the visual swing angle position and the swing angle speed is increased to 100hz from 25hz, further, the faster observation speed is provided, the dangerous working condition can be judged more quickly by the controller of the crane, and meanwhile, the calculation resource requirement is not greatly increased.
Because the swing angle feedback method provided by the embodiment of the invention mainly relates to generating a plurality of swing angle predicted values between two adjacent swing angle observed values, when the frequency of a camera is 25hz, the accumulated error in the swing angle predicting process lasts for 0.4 seconds at most, and after a new swing angle observed value is acquired, the accumulated error in the front is cleared.
The swing angle feedback method provided by the embodiment of the invention can be used for programming by adopting a C language, each numerical value adopts the calculation precision of a 16-bit floating point number (float 16), and the calculation time delay is less than 1ms when the swing angle feedback method runs on the CPU of i 5.
In order to perform the above method embodiments and corresponding steps in each possible implementation, an implementation of the swing angle feedback device 100 applied to a controller of a crane is given below.
Referring to fig. 6, the yaw angle feedback device 100 includes a first prediction module 101, a second prediction module 102, a processing module 103, and a construction module 104.
The first prediction module 101 is configured to obtain, for a swing angle observation value of the lifting appliance obtained at a current time, a first swing angle prediction value of the lifting appliance within a target time range by using a pre-constructed dynamics model for the crane, where the target time range is a time range from the current time to an obtaining time of a next swing angle observation value.
And the second prediction module 102 is configured to perform linear fusion on the swing angle observation value of the lifting appliance acquired at the current moment, and obtain a second swing angle prediction value of the lifting appliance in the target time range.
And the processing module 103 is used for obtaining a target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value so as to judge dangerous working conditions based on the target swing angle predicted value within a target time range.
A construction module 104, configured to determine an actual mass of the crane and an actual lifting height of the crane by using multiple sets of step response test data of the crane; and constructing a dynamic model according to the actual mass of the crane and the actual lifting height of the crane.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the container stacking device 100 applied to the controller of the automated crane described above may refer to the corresponding process in the foregoing method embodiment, and will not be described herein again.
Further, the embodiment of the invention also provides a crane, which comprises a lifting appliance and a controller, wherein the controller is burnt with a computer program, and the computer program can be used for executing related operations in the swing angle feedback method provided by the embodiment of the method when being executed.
The embodiment of the invention also provides a computer readable storage medium containing a computer program, which when executed can be used for executing the related operations in the swing angle feedback method provided by the method embodiment.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method of swing angle feedback, characterized by a controller applied to a crane, the crane further comprising a spreader, the method comprising:
obtaining a first swing angle predicted value of the lifting appliance in a target time range by utilizing a pre-constructed dynamic model aiming at the lifting appliance for the swing angle observed value of the lifting appliance obtained at the current moment, wherein the target time range is a time range from the current moment to the moment of obtaining the next swing angle observed value;
linear fusion is carried out on the swing angle observation value of the lifting appliance, which is obtained at the current moment, so as to obtain a second swing angle prediction value of the lifting appliance in the target time range;
and obtaining a target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value, so as to judge dangerous working conditions based on the target swing angle predicted value within the target time range.
2. The method according to claim 1, wherein before obtaining the first tilt angle predicted value of the spreader in the target time range using the pre-constructed dynamics model for the crane, the method further comprises:
determining the actual mass of the crane and the actual lifting height of the crane by utilizing the multi-group step response test data of the crane;
and constructing the dynamic model according to the actual mass of the crane and the actual lifting height of the crane.
3. The method of claim 2, wherein the step of determining the actual mass of the crane and the actual lift height of the crane using the plurality of sets of step responses of the crane comprises:
obtaining a deviation function aiming at the crane by utilizing the least square error of the multi-group step response test data, the nominal value of the mass of the crane and the nominal value of the lifting height of the crane;
and carrying out nonlinear optimization on the deviation function to obtain the actual mass of the crane and the actual lifting height of the crane.
4. The method according to claim 1, wherein the step of obtaining the second tilt angle predicted value of the spreader in the target time range by linearly fusing the tilt angle observed values of the spreader obtained at the current time comprises:
and generating a second swing angle predicted value of the lifting appliance in the target time range according to the preset sampling time and the preset predicted frequency by utilizing a linear relation of the pre-fitted swing angle observed value for the swing angle observed value of the lifting appliance acquired at the current moment.
5. The method of claim 1, wherein the step of obtaining a target tilt angle prediction value for the spreader based on the first tilt angle prediction value and the second tilt angle prediction value comprises:
constructing a dynamic error model of the first swing angle predicted value and a dynamic error model of the second swing angle predicted value;
obtaining a first normal distribution according to a dynamic error model of the first swing angle predicted value, wherein the first normal distribution is a normal distribution obeyed by errors of the first swing angle predicted value;
obtaining a second normal distribution according to the dynamic error model of the second swing angle predicted value, wherein the second normal distribution is a normal distribution obeyed by errors of the second swing angle predicted value;
and according to the first normal distribution and the second normal distribution, fusing the first swing angle predicted value and the second swing angle predicted value to obtain the target swing angle predicted value.
6. The method of claim 5, wherein the step of fusing the first tilt angle prediction value and the second tilt angle prediction value based on the first normal distribution and the second normal distribution to obtain the target tilt angle prediction value comprises:
obtaining fusion parameters according to the variance of the first normal distribution and the variance of the second normal distribution;
and fusing the first swing angle predicted value and the second swing angle predicted value by using the fusion parameter to obtain the target swing angle predicted value.
7. The method of claim 6, wherein the step of fusing the first tilt angle prediction value and the second tilt angle prediction value using the fusion parameter to obtain the target tilt angle prediction value comprises:
obtaining a swing angle prediction gain by using the second swing angle prediction value, the first swing angle prediction value and the fusion parameter;
and taking the sum value of the first swing angle predicted value and the swing angle predicted gain as the target swing angle predicted value.
8. A swing angle feedback device, characterized by a controller for a crane, the crane further comprising a spreader, the device comprising:
the first prediction module is used for obtaining a first swing angle predicted value of the lifting appliance in a target time range from the current moment to the moment of obtaining the next swing angle observed value by utilizing a pre-constructed dynamic model aiming at the lifting appliance for the swing angle observed value of the lifting appliance at the current moment;
the second prediction module is used for carrying out linear fusion on the swing angle observation value of the lifting appliance, which is acquired at the current moment, so as to obtain a second swing angle prediction value of the lifting appliance in the target time range;
and the processing module is used for obtaining a target swing angle predicted value of the lifting appliance according to the first swing angle predicted value and the second swing angle predicted value so as to judge dangerous working conditions based on the target swing angle predicted value within the target time range.
9. Crane, characterized in that it comprises a spreader and a controller, which is burnt with a computer program, which when executed implements the swing angle feedback method according to any of claims 1-7.
10. A computer readable storage medium, characterized in that it stores a computer program, which when executed by a processor implements the yaw angle feedback method according to any one of claims 1-7.
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