Lifting hook posture calculation method and device, equipment, storage medium and product

Abstract

The invention discloses a lifting hook attitude calculation method and device, equipment, a storage medium and a product, and relates to the technical field of data processing. The lifting hook attitude calculation method comprises the steps that a lifting hook attitude measurement value of a crane is obtained; the hook attitude measurement value is calculated through a preset algorithm, a filter column vector at the current moment is obtained, and the filter column vector is iteratively updated based on the hook attitude measurement value at each moment; and based on the filter column vector, correcting the hook attitude measurement value to obtain a hook attitude estimation value. According to the method, the lifting hook posture of the crane in the operation process can be accurately calculated.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment, storage medium and computer program product for calculating a hook posture. Background Art

[0002] In the construction of large-scale wind power projects, cranes are indispensable as common equipment. The hook rope of the crane often tilts during operation. When the angle between the hook rope and the vertical line of the ground is too large, it will cause serious safety hazards if it is not adjusted in time. Therefore, it is very necessary to detect the oblique state of the hook on the crane. At present, during the operation of the crane, the inclination of the hook rope is mainly judged by inertial sensors. Due to the influence of environmental interference, the oscillation of the angle measurement value will be relatively large, and the measurement value cannot accurately reflect the current hook rope state. Therefore, a real-time and accurate hook posture calculation method is needed.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, equipment, storage medium and computer program product for calculating the hook posture, aiming to solve the technical problem in the related art that the inclination degree of the hook rope is determined by an inertial sensor, and due to the influence of environmental interference, the oscillation of the angle measurement value will be relatively large, and the measurement value cannot accurately reflect the current hook rope state.

[0005] To achieve the above purpose, the present application proposes a method for calculating the hook posture, which comprises:

[0006] Get the measured value of the crane's hook posture;

[0007] The hook posture measurement value is calculated and processed by a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment;

[0008] Based on the filter column vector, the hook posture measurement value is corrected to obtain the hook posture estimation value.

[0009] In one embodiment, the step of calculating and processing the hook posture measurement value by a preset algorithm to obtain the filter column vector at the current moment includes:

[0010] Get the first posture measurement value of the previous moment;

[0011] Calculate the first posture measurement value by using a preset algorithm to obtain a first observation value at the previous moment;

[0012] Based on the first observation value, the first posture measurement value and the hook posture measurement value, the filter column vector at the current moment is calculated.

[0013] In one embodiment, the step of calculating the filter column vector at the current moment based on the first observation value, the first posture measurement value and the hook posture measurement value includes:

[0014] Performing matrix decomposition on the first posture measurement value to obtain a first orthogonal matrix and a first upper triangular matrix at the previous moment;

[0015] Based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix, the sensor column vector at the next moment is calculated.

[0016] In one embodiment, the step of calculating the sensor column vector at the next moment based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix includes:

[0017] Based on the first orthogonal matrix, the first upper triangular matrix and the hook posture measurement value, a second orthogonal matrix and a second upper triangular matrix at the current moment are calculated;

[0018] Based on the second orthogonal matrix and the first observation value, calculate the sensor observation value at the current moment;

[0019] Based on the sensor observation values ​​and the second upper triangular matrix, the sensor column vector at the current moment is calculated.

[0020] In one embodiment, the step of correcting the hook posture measurement value based on the filter column vector to obtain the hook posture estimation value includes:

[0021] The filter column vector is multiplied by the hook attitude measurement value to calculate the hook attitude estimation value, wherein the calculation formula of the hook attitude estimation value is as follows:

[0022] Y=α T θ+e

[0023] Among them, α T represents the hook posture measurement value, θ represents the filter column vector, and e represents Gaussian white noise.

[0024] In one embodiment, before the step of calculating and processing the hook posture measurement value by a preset algorithm to obtain the filter column vector at the current moment, the step further includes:

[0025] Performing convolution processing on the hook posture measurement value to obtain an input convolution sum;

[0026] Based on the input convolution and the sensor observation error, a preset algorithm is constructed, wherein the preset algorithm is expressed as:

[0027] y n =A n θ+e n

[0028] Among them, y n represents the sensor observation value at the nth moment, A n represents the vector corresponding to the hook posture measurement value, θ represents the filter column vector, e n represents noisy data.

[0029] In addition, to achieve the above purpose, the present application also proposes a hook posture calculation device, the hook posture calculation device comprising:

[0030] An acquisition module is used to obtain the measured value of the crane hook posture;

[0031] A processing module, used for calculating and processing the hook posture measurement value by a preset algorithm to obtain a filter column vector at a current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment;

[0032] A correction module is used to correct the hook posture measurement value based on the filter column vector to obtain a hook posture estimation value.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a hook posture calculation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the hook posture calculation method as described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the hook posture calculation method described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the hook posture calculation method as described above are implemented.

[0036] The present application proposes a hook attitude calculation method, device, equipment, storage medium and computer program product. The present application obtains the hook attitude measurement value of the crane, calculates and processes the hook attitude measurement value based on a preset algorithm, obtains the filter column vector at the current moment, and then corrects the hook attitude measurement value through the filter column vector to obtain the hook attitude estimation value. Since the angle of the hook is changing all the time and the filter column vector is updated at every moment, the filter column vector is more suitable for parameter correction at the current moment than the fixed filter coefficient. Therefore, it can reduce the noise generated in the calculation process and obtain a more accurate hook attitude calculation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the method for calculating the hook posture of the present application;

[0040] Figure 2 A flow chart of the second embodiment of the hook posture calculation method of the present application;

[0041] Figure 3 This is a schematic diagram of the module structure of the hook posture calculation device according to an embodiment of the present application;

[0042] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the hook posture calculation method in the embodiment of the present application.

[0043] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0045] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0046] The main solutions of the embodiments of this application are:

[0047] Get the measured value of the crane's hook posture;

[0048] The hook posture measurement value is calculated and processed by a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment;

[0049] Based on the filter column vector, the hook posture measurement value is corrected to obtain the hook posture estimation value.

[0050] Since the existing technology mainly uses inertial sensors to determine the inclination of the hook rope, due to the influence of environmental interference, the oscillation of the angle measurement value will be relatively large, and the measurement value cannot accurately reflect the current hook rope state. Therefore, a real-time and accurate hook posture calculation method is needed.

[0051] The present application proposes a hook attitude calculation method, device, equipment, storage medium and computer program product. The present application obtains the hook attitude measurement value of the crane, calculates and processes the hook attitude measurement value based on a preset algorithm, obtains the filter column vector at the current moment, and then corrects the hook attitude measurement value through the filter column vector to obtain the hook attitude estimation value. Since the angle of the hook is changing all the time and the filter column vector is updated at every moment, the filter column vector is more suitable for parameter correction at the current moment than the fixed filter coefficient. Therefore, it can reduce the noise generated in the calculation process and obtain a more accurate hook attitude calculation result.

[0052] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a hook posture calculation device, etc. The following takes the hook posture calculation device as an example to illustrate this embodiment and the following embodiments.

[0053] Based on this, the embodiment of the present application provides a method for calculating the hook posture, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the hook posture calculation method of the present application.

[0054] In this embodiment, the hook posture calculation method includes steps S10 to S30:

[0055] Step S10, obtaining the hook posture measurement value of the crane;

[0056] It should be noted that the hook attitude measurement value may be the hook angle data measured by the sensor, wherein the hook angle data includes pitch angle, roll angle, yaw angle, etc., without specific limitation.

[0057] In addition, it should be noted that due to the influence of environmental noise, the current values ​​measured by the sensor are not very accurate, so the data measured by the sensor needs to be further processed. When the angle between the crane hook rope and the vertical line of the ground is too large, if it is not adjusted in time, it will cause serious safety hazards, such as: (1) excessive load causes crane overload, which may damage the crane; (2) the hook rope is inclined to produce a lateral force, and the load is prone to displacement or even swinging along the lateral force, bringing safety risks; (3) the hook rope is inclined to cause the wire rope to slip out of the rope groove of the drum, causing the wire rope to fall off the track.

[0058] It should be noted that the method provided in the embodiment of the present application is mainly divided into 7 steps. Step 1: Establish the input and output model of the sensor. Step 2: Solve the filter column vector composed of the filter coefficients at the nth moment. Step 3: Calculate R n Matrix (upper triangular matrix). Step 4: Construct a new upper triangular matrix Step 5: Calculation Matrix (orthogonal matrix at the n+1th moment) and R n+1 Matrix. Step 6: Calculate y n+1 (Sensor observation value at time n+1). Step 7: Solve the upper triangular matrix equation R n+1 θ n+1 =y n+1 , we get θ n+1 By iterating from step 3 to step 7, we can obtain the filter column vector θ = [θ 0 ,θ 1 ,...θ p ] T , the method is described in detail below.

[0059] Step S20, calculating and processing the hook posture measurement value by a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment;

[0060] It should be noted that the preset algorithm can be a pre-set AI model or an algorithm, wherein the preset algorithm can be constructed in the following manner:

[0061] y n =A n θ+e n

[0062] Among them, y n represents the sensor observation value at the nth moment, A n represents the vector corresponding to the hook posture measurement value, θ represents the filter column vector, e n represents noisy data.

[0063] It should be noted that the filter column vector is a vector coefficient used to correct the hook attitude measurement value. When the hook attitude measurement value is updated, the filter column vector will also be iteratively updated to achieve accurate estimation of the estimated value.

[0064] In a feasible implementation manner, before the step of calculating and processing the hook posture measurement value by a preset algorithm to obtain the filter column vector at the current moment, the step further includes:

[0065] Performing convolution processing on the hook posture measurement value to obtain an input convolution sum;

[0066] Based on the input convolution and the sensor observation error, a preset algorithm is constructed, wherein the preset algorithm is expressed as:

[0067] y n =A n θ+e n

[0068] Among them, y n represents the sensor observation value at the nth moment, A n represents the vector corresponding to the hook posture measurement value, θ represents the filter column vector, e n represents noisy data.

[0069] It should be noted that before calculating and processing the hook posture measurement value through the preset algorithm, it is necessary to build an algorithm / model. The process is as follows:

[0070] Establish the input-output model y for sensor observation n =A n θ+e n

[0071] At the kth moment, the sensor's observation value is y(k), which consists of two parts: the convolution of the input value and the observation error, that is,

[0072]

[0073] The operator * represents the discrete convolution operation, x k =[x(k),x(k-1),...,x(kp)] T Represents the column vector of input from time k to kp, θ=[θ 0 ,θ 1 ,...θ p ] Trepresents the filter column vector composed of filter coefficients, x(k) represents the sensor input at time k, y(k) represents the sensor observation value at time k, and e(k) represents the observation error at time k. This formula indicates that the observation value at time k is the input at time k (including: the column vector composed of the input from time k to kp. According to the size of the convolution kernel, the input of the convolution calculation involves not only time k, but also all inputs until time kp) and the filter coefficient at time k for discrete convolution. The specific convolution calculation is: Then sum it with the observation error e(k) at time k.

[0074] All observations at n moments form a column vector, denoted as y n , then the matrix representation of the input-output model is: n =A n θ+e n , where A n =[x 1 ,x 2 ,...,x n ] T , e n =[e(1),e(2),...,e(n)] T

[0075] In a feasible implementation manner, the step S20 of calculating and processing the hook posture measurement value by a preset algorithm to obtain the filter column vector at the current moment includes:

[0076] Get the first posture measurement value of the previous moment;

[0077] Calculate the first posture measurement value by using a preset algorithm to obtain a first observation value at the previous moment;

[0078] It should be noted that when calculating the filter column vector at the current moment, it is necessary to update it according to the filter column vector at the previous moment. Since the filter column vector at the previous moment is also updated according to the hook posture measurement value at the previous moment, the filter measurement value is updated here according to the historical data and the current hook posture measurement value, wherein the Nth moment in this embodiment is the previous moment, and the N+1th moment is the current moment.

[0079] It should be noted that the first posture measurement value is the hook posture measurement value at the previous moment. The first posture measurement value is calculated by a preset algorithm to obtain the first observation value at the previous moment. The calculation method is shown in the algorithm formula of the input-output model.

[0080] It should be noted that the method for calculating the first observation value at the previous moment can also be: solving the filter column vector composed of the filter coefficients at moment n: θ = [θ0 ,θ 1 ,...θ p ] T , the solution goal is The solution method is: (1) According to the normal equation A n T A n θ n =A n T y n , calculate A n T A n The Cholesky decomposition of n T A n =GG T , (2) Use the back substitution method to solve the triangular matrix equation G T θ n =G -1 A n T A n θ n , we get the filter column vector θ = [θ 0 ,θ 1 ,...θ p ] T The solution method is a common method in this field. For the completeness of the overall calculation steps, only a brief description is given here.

[0081] Based on the first observation value, the first posture measurement value and the hook posture measurement value, the filter column vector at the current moment is calculated.

[0082] It should be noted that the first observation value is y at the previous moment. n The first attitude measurement value is the hook attitude measurement value at the previous moment, and the hook attitude measurement value is the sensor measurement value at the current moment. The filter column vector is iteratively updated through the sensor measurement value at the current moment.

[0083] In a feasible implementation manner, the step of calculating the filter column vector at the current moment based on the first observation value, the first posture measurement value and the hook posture measurement value includes:

[0084] Performing matrix decomposition on the first posture measurement value to obtain a first orthogonal matrix and a first upper triangular matrix at the previous moment;

[0085] It should be noted that the process of matrix decomposition can be:

[0086] For the first posture measurement value A n =[x 1 ,x 2,...,x n ] T Perform QR decomposition to obtain the orthogonal matrix Q at the previous moment n and the upper triangular matrix R n , the QR decomposition of a matrix is ​​an important numerical linear algebra method, which decomposes a matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). The matrix decomposition method can be: Schmidt orthogonal normalization, by orthogonalizing and normalizing the column vectors of the matrix, the orthogonal matrix Q and the upper triangular matrix R are obtained. That is, the first orthogonal matrix and the first upper triangular matrix.

[0087] Based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix, the sensor column vector at the next moment is calculated.

[0088] In a feasible implementation manner, the step of calculating the sensor column vector at the next moment based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix includes:

[0089] Based on the first orthogonal matrix, the first upper triangular matrix and the hook posture measurement value, a second orthogonal matrix and a second upper triangular matrix at the current moment are calculated;

[0090] It should be noted that the first upper triangular matrix is ​​augmented to obtain the second upper triangular matrix, and the construction method thereof may be:

[0091] R n The matrix is ​​expanded to obtain in For R n Remove the first line and replace x n+1 =[x(n+1),x(n),...,x(n+1-p)] T As the last row of the matrix, we get

[0092] Based on the second orthogonal matrix and the first observation value, calculate the sensor observation value at the current moment;

[0093] It should be noted that the calculation Matrices and R n+1 Matrix, yes Perform QR decomposition and get in is an orthogonal matrix, R n+1 is a p*p upper triangular matrix, O is a (np)*(np) zero matrix, and λ is the forgetting factor, which is between 0 and 1 and is usually selected according to the actual working conditions.

[0094] Then, calculate y n+1 , calculated by block matrix Among them, y p*1 is a column vector with p rows and 1 column, y (n-p)*1 It is a column vector with np rows and 1 column, which are spliced ​​together to form a column vector with n rows and 1 column.

[0095] Based on the sensor observation values ​​and the second upper triangular matrix, the sensor column vector at the current moment is calculated.

[0096] It should be noted that solving the upper triangular matrix equation R n+1 θ n+1 =y n+1 , we get θ n+1 , which is the filter column vector at time n+1. At this point, the new filter column vector is updated so that the n+2 moment can be iterated according to the order of time.

[0097] By iterating from step 3 to step 7, we can obtain the filter column vector θ = [θ 0 ,θ 1 ,...θ p ] T .

[0098] Step S30: Based on the filter column vector, the hook posture measurement value is corrected to obtain a hook posture estimation value.

[0099] It should be noted that when the filter column vector θ=[θ 0 ,θ 1 ,...θ p ] T After that, the sensor continuously samples p pitch angles, denoted as α = [α 0 ,α 1 ,...α p ] T , then the estimated value of the true value of the pth pitch angle is α T θ+e, the obtained value is the estimated value of the hook posture, where e is Gaussian white noise.

[0100] The present application proposes a hook attitude calculation method, device, equipment, storage medium and computer program product. The present application obtains the hook attitude measurement value of the crane, calculates and processes the hook attitude measurement value based on a preset algorithm, obtains the filter column vector at the current moment, and then corrects the hook attitude measurement value through the filter column vector to obtain the hook attitude estimation value. Since the angle of the hook is changing all the time and the filter column vector is updated at every moment, the filter column vector is more suitable for parameter correction at the current moment than the fixed filter coefficient. Therefore, it can reduce the noise generated in the calculation process and obtain a more accurate hook attitude calculation result.

[0101] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 The step S30 of correcting the hook posture measurement value based on the filter column vector to obtain the hook posture estimation value comprises:

[0102] Step S31, multiplying the filter column vector by the hook posture measurement value to calculate the hook posture estimation value, wherein the calculation formula of the hook posture estimation value is as follows:

[0103] Y=α T θ+e

[0104] Among them, α T represents the hook posture measurement value, θ represents the filter column vector, and e represents Gaussian white noise.

[0105] It should be noted that the use of the filter column vector is calculated based on a preset algorithm. By recursively updating the sensor filter coefficients, θ=[θ 0 ,θ 1 ,...θ p ] T The amount of calculation, especially when A n T A n When the condition number is relatively large, directly calculate θ = [θ 0 ,θ 1 ,...θ p ] T By iteratively updating the filter column vector, the amount of calculation can be reduced, making it easier to run on a processor with low computing power.

[0106] In this embodiment, the filter column vector θ=[θ 0 ,θ 1 ,...θ p ] TThe sensor sampling values ​​can be corrected to obtain more accurate estimates of the pitch angle, roll angle, and yaw angle.

[0107] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the hook posture calculation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0108] This application also provides a hook posture calculation device, please refer to Figure 3 , the hook posture calculation device comprises:

[0109] An acquisition module 10 is used to obtain a hook posture measurement value of the crane;

[0110] A processing module 20 is used to calculate and process the hook posture measurement value through a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment;

[0111] The correction module 30 is used to correct the hook posture measurement value based on the filter column vector to obtain the hook posture estimation value.

[0112] Optionally, the processing module includes:

[0113] An acquisition unit, used for acquiring a first posture measurement value at a previous moment;

[0114] A first calculation unit, used to calculate the first posture measurement value by a preset algorithm to obtain a first observation value at a previous moment;

[0115] The second calculation unit is used to calculate the filter column vector at the current moment based on the first observation value, the first posture measurement value and the hook posture measurement value.

[0116] Optionally, the second computing unit includes:

[0117] A decomposition subunit, used for performing matrix decomposition on the first posture measurement value to obtain a first orthogonal matrix and a first upper triangular matrix at a previous moment;

[0118] The calculation subunit is used to calculate the sensor column vector at the next moment based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix.

[0119] Optionally, the computing subunit includes:

[0120] A first calculation component is used to calculate a second orthogonal matrix and a second upper triangular matrix at a current moment based on the first orthogonal matrix, the first upper triangular matrix and the hook posture measurement value;

[0121] A second calculation component, used for calculating the sensor observation value at the current moment based on the second orthogonal matrix and the first observation value;

[0122] The third computing component is used to calculate the sensor column vector at the current moment based on the sensor observation value and the second upper triangular matrix.

[0123] Optionally, the correction module includes:

[0124] The third calculation unit is used to multiply the filter column vector by the hook posture measurement value to calculate the hook posture estimation value, wherein the calculation formula of the hook posture estimation value is as follows:

[0125] Y=α T θ+e

[0126] Among them, α T represents the hook posture measurement value, θ represents the filter column vector, and e represents Gaussian white noise.

[0127] Optionally, the device further comprises:

[0128] A convolution processing module, used for performing convolution processing on the hook posture measurement value to obtain an input convolution sum;

[0129] A construction module is used to construct a preset algorithm based on the input convolution and the sensor observation error, wherein the preset algorithm is expressed as:

[0130] y n =A n θ+e n

[0131] Among them, y n represents the sensor observation value at the nth moment, A n represents the vector corresponding to the hook posture measurement value, θ represents the filter column vector, e n represents noisy data.

[0132] The hook posture calculation device provided by the present application adopts the hook posture calculation method in the above embodiment, which can solve the technical problem of hook posture calculation. Compared with the prior art, the beneficial effects of the hook posture calculation device provided by the present application are the same as the beneficial effects of the hook posture calculation method provided by the above embodiment, and other technical features in the hook posture calculation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0133] The present application provides a hook posture calculation device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the hook posture calculation method in the above-mentioned embodiment one.

[0134] Reference below Figure 4 , which shows a schematic diagram of the structure of a hook posture calculation device suitable for implementing the embodiment of the present application. The hook posture calculation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The hook posture calculation device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0135] like Figure 4As shown, the hook posture calculation device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the hook posture calculation device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the hook posture calculation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a hook posture calculation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0136] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0137] The hook posture calculation device provided by the present application adopts the hook posture calculation method in the above embodiment, which can solve the technical problem of hook posture calculation. Compared with the prior art, the beneficial effects of the hook posture calculation device provided by the present application are the same as the beneficial effects of the hook posture calculation method provided by the above embodiment, and other technical features in the hook posture calculation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0138] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0139] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0140] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the hook posture calculation method in the above-mentioned embodiment.

[0141] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0142] The computer-readable storage medium may be included in the hook posture calculation device; or may exist independently without being assembled into the hook posture calculation device.

[0143] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the hook posture calculation device, the hook posture calculation device:

[0144] Get the measured value of the crane's hook posture;

[0145] The hook posture measurement value is calculated and processed by a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment;

[0146] Based on the filter column vector, the hook posture measurement value is corrected to obtain the hook posture estimation value.

[0147] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0148] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0149] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0150] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned hook posture calculation method, and can solve the technical problem of hook posture calculation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the hook posture calculation method provided in the above-mentioned embodiment, and will not be repeated here.

[0151] The present application also provides a computer program product, comprising a computer program, which implements the steps of the hook posture calculation method as described above when executed by a processor.

[0152] The computer program product provided by this application can solve the technical problem of hook posture calculation. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the hook posture calculation method provided by the above embodiment, which will not be repeated here.

[0153] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for calculating hook posture, characterized in that: The method comprises: Get the measured value of the crane's hook posture; The hook posture measurement value is calculated and processed by a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment; Based on the filter column vector, the hook posture measurement value is corrected to obtain the hook posture estimation value.

2. The method according to claim 1, characterized in that The step of calculating and processing the hook posture measurement value by a preset algorithm to obtain the filter column vector at the current moment includes: Get the first posture measurement value of the previous moment; Calculate the first posture measurement value by using a preset algorithm to obtain a first observation value at the previous moment; Based on the first observation value, the first posture measurement value and the hook posture measurement value, the filter column vector at the current moment is calculated.

3. The method according to claim 2, characterized in that The step of calculating the filter column vector at the current moment based on the first observation value, the first posture measurement value and the hook posture measurement value comprises: Performing matrix decomposition on the first posture measurement value to obtain a first orthogonal matrix and a first upper triangular matrix at the previous moment; Based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix, the sensor column vector at the next moment is calculated.

4. The method according to claim 3, characterized in that The step of calculating the sensor column vector at the next moment based on the first observation value, the hook posture measurement value, the first orthogonal matrix and the first upper triangular matrix includes: Based on the first orthogonal matrix, the first upper triangular matrix and the hook posture measurement value, a second orthogonal matrix and a second upper triangular matrix at the current moment are calculated; Based on the second orthogonal matrix and the first observation value, calculate the sensor observation value at the current moment; Based on the sensor observation values ​​and the second upper triangular matrix, the sensor column vector at the current moment is calculated.

5. The method according to claim 1, characterized in that The step of correcting the hook attitude measurement value based on the filter column vector to obtain the hook attitude estimation value comprises: The filter column vector is multiplied by the hook attitude measurement value to calculate the hook attitude estimation value, wherein the calculation formula of the hook attitude estimation value is as follows: Y=a T θ+e Among them, α T represents the hook posture measurement value, θ represents the filter column vector, and e represents Gaussian white noise.

6. The method according to claim 1, characterized in that Before the step of calculating and processing the hook posture measurement value by a preset algorithm to obtain the filter column vector at the current moment, the method further includes: Performing convolution processing on the hook posture measurement value to obtain an input convolution sum; Based on the input convolution and the sensor observation error, a preset algorithm is constructed, wherein the preset algorithm is expressed as: y n =A n θ+e n Among them, y n represents the sensor observation value at the nth moment, A n represents the vector corresponding to the hook posture measurement value, θ represents the filter column vector, e n represents noisy data.

7. A hook posture calculation device, characterized in that: The device comprises: An acquisition module is used to obtain the measured value of the crane hook posture; A processing module, used for calculating and processing the hook posture measurement value through a preset algorithm to obtain a filter column vector at the current moment, wherein the filter column vector is iteratively updated based on the hook posture measurement value at each moment; A correction module is used to correct the hook posture measurement value based on the filter column vector to obtain a hook posture estimation value.

8. A hook posture calculation device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the hook posture calculation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the hook posture calculation method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the hook posture calculation method according to any one of claims 1 to 6 are implemented.

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