Hook posture calculation method and device, equipment, storage medium and product
By acquiring the crane hook attitude measurement value, using a preset algorithm to calculate and iteratively update the filter column vector, and correcting the hook attitude measurement value, the problem of inaccurate crane hook rope tilt measurement value is solved, achieving more accurate hook attitude calculation and reducing safety risks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANXI ELECTRIC POWER CONSTR CO LTD (CEEC)
- Filing Date
- 2024-11-22
- Publication Date
- 2026-05-15
AI Technical Summary
In existing technologies, the measurement value of the crane hook rope tilt is affected by environmental interference, resulting in large fluctuations in the angle measurement value, which cannot accurately reflect the current state of the hook rope and poses a safety hazard.
By acquiring the hook attitude measurement value of the crane, calculating the filter column vector using a preset algorithm and updating it iteratively, and correcting the hook attitude measurement value based on the filter column vector, the estimated hook attitude value is obtained.
This reduces noise in the calculation process, yields more accurate hook attitude calculation results, and reduces safety hazards.
Smart Images
Figure CN119714254B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, device, storage medium, and computer program product for calculating the attitude of a hook. Background Technology
[0002] In the construction of large-scale wind power projects, cranes are indispensable as commonly used equipment. However, crane hooks often tilt during operation. When the angle between the crane hook and the vertical line of the ground is too large, it can cause serious safety hazards if not adjusted in time. Therefore, it is essential to detect the tilting state of the crane hook. Currently, the tilting degree of the hook is mainly determined by inertial sensors during crane operation. However, due to the influence of environmental interference, the angle measurement value will fluctuate greatly, and the measurement value cannot accurately reflect the current hook state. Therefore, a real-time and accurate hook attitude calculation method is needed.
[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention
[0004] The main purpose of this application is to provide a method, device, equipment, storage medium, and computer program product for calculating the attitude of a hook, aiming to solve the technical problem in the related art where the angle measurement value cannot accurately reflect the current state of the hook rope due to the large oscillation caused by environmental interference when judging the tilt of the hook rope by inertial sensors.
[0005] To achieve the above objectives, this application proposes a method for calculating the attitude of a lifting hook, the method comprising:
[0006] Obtain the crane hook attitude measurement value;
[0007] The hook attitude measurement value is calculated and processed by a preset algorithm to obtain the filter column vector at the current time. The filter column vector is iteratively updated based on the hook attitude measurement value at each time.
[0008] Based on the filter column vector, the measured hook attitude value is corrected to obtain the estimated hook attitude value.
[0009] In one embodiment, the step of calculating and processing the hook attitude measurement value using a preset algorithm to obtain the filter column vector at the current moment includes:
[0010] Obtain the first attitude measurement value at the previous moment;
[0011] The first attitude measurement value is calculated using a preset algorithm to obtain the first observation value at the previous moment;
[0012] Based on the first observation value, the first attitude measurement value, and the hook attitude 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 attitude measurement value, and the hook attitude measurement value includes:
[0014] Perform matrix decomposition on the first attitude measurement value to obtain the first orthogonal matrix and the first upper triangular matrix of the previous time step;
[0015] Based on the first observation, the hook attitude measurement, the first orthogonal matrix, and the first upper triangular matrix, the sensor column vector for 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 attitude 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 attitude measurement value, the second orthogonal matrix and the second upper triangular matrix at the current moment are calculated;
[0018] Based on the second orthogonal matrix and the first observation value, the sensor observation value at the current time is calculated;
[0019] Based on the sensor observations 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 attitude measurement value based on the filter column vector to obtain the hook attitude estimate value includes:
[0021] The estimated hook attitude is calculated by multiplying the filter column vector by the hook attitude measurement value, wherein the calculation formula for the estimated hook attitude value is as follows:
[0022] Y = α T θ+e
[0023] Where, α T θ represents the hook attitude 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 attitude measurement value using a preset algorithm to obtain the filter column vector at the current moment, the method further includes:
[0025] The hook attitude measurement values are convolved to obtain the input convolution sum;
[0026] Based on the input convolution and 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 Let A represent the sensor observation at time n. n The vector representing the hook attitude measurement value, θ represents the filter column vector, and e n This represents noise data.
[0029] Furthermore, to achieve the above objectives, this application also proposes a hook attitude calculation device, which includes:
[0030] The acquisition module is used to acquire the measurement values of the crane hook posture;
[0031] The processing module is used to calculate and process the hook attitude measurement value through a preset algorithm to obtain the filter column vector at the current time, wherein the filter column vector is iteratively updated based on the hook attitude measurement value at each time.
[0032] The correction module is used to correct the hook attitude measurement value based on the filter column vector to obtain the hook attitude estimate value.
[0033] In addition, to achieve the above objectives, this application also proposes a hook attitude calculation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the hook attitude calculation method as described above.
[0034] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the hook attitude calculation method described above.
[0035] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the hook attitude calculation method described above.
[0036] This application proposes a method, apparatus, device, storage medium, and computer program product for calculating the attitude of a crane hook. This application obtains the hook attitude measurement value of the crane, processes the hook attitude measurement value based on a preset algorithm to obtain the filter column vector at the current moment, and then corrects the hook attitude measurement value using the filter column vector to obtain the estimated hook attitude value. Since the angle of the hook is constantly changing 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 fixed filter coefficients. Therefore, it can reduce the noise generated during the calculation process and obtain a more accurate hook attitude calculation result. Attached Figure Description
[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is a flowchart illustrating an embodiment of the hook attitude calculation method of this application.
[0040] Figure 2 This is a flowchart illustrating Embodiment 2 of the hook attitude calculation method of this application;
[0041] Figure 3 This is a schematic diagram of the module structure of the hook attitude calculation device according to an embodiment of this application;
[0042] Figure 4 This is a schematic diagram of the hardware operating environment involved in the hook attitude calculation method in this application embodiment.
[0043] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0044] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0045] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0046] The main solution in this application embodiment is:
[0047] Obtain the crane hook attitude measurement value;
[0048] The hook attitude measurement value is calculated and processed by a preset algorithm to obtain the filter column vector at the current time. The filter column vector is iteratively updated based on the hook attitude measurement value at each time.
[0049] Based on the filter column vector, the measured hook attitude value is corrected to obtain the estimated hook attitude value.
[0050] Since existing technologies mainly rely on inertial sensors to determine the tilt of the hook rope, the angle measurement values can oscillate significantly due to environmental interference, and the measured values cannot accurately reflect the current state of the hook rope. Therefore, a real-time and accurate hook attitude calculation method is needed.
[0051] This application proposes a method, apparatus, device, storage medium, and computer program product for calculating the attitude of a crane hook. This application obtains the hook attitude measurement value of the crane, processes the hook attitude measurement value based on a preset algorithm to obtain the filter column vector at the current moment, and then corrects the hook attitude measurement value using the filter column vector to obtain the estimated hook attitude value. Since the angle of the hook is constantly changing 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 fixed filter coefficients. Therefore, it can reduce the noise generated during the calculation process and obtain a more accurate hook attitude calculation result.
[0052] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or hook attitude calculation device capable of performing the above functions. The following description uses a hook attitude calculation device as an example to illustrate this embodiment and the subsequent embodiments.
[0053] Based on this, the embodiments of this application provide a method for calculating the attitude of a hook, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the hook attitude calculation method of this application.
[0054] In this embodiment, the hook attitude calculation method includes steps S10 to S30:
[0055] Step S10: Obtain the hook attitude measurement value of the crane;
[0056] It should be noted that the hook attitude measurement value can be the hook angle data measured by the sensor. The hook angle data includes pitch angle, roll angle, yaw angle, etc., and there is no specific limitation.
[0057] Additionally, it should be noted that due to the influence of environmental noise, the values measured by the sensors are not very accurate at present, so further processing of the data measured by the sensors is required. 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) the excessive load will cause the crane to be overloaded, which may damage the crane; (2) the oblique pull of the hook rope will generate a lateral component force, and the load is prone to displacement or even large swing along the lateral component force, which will bring safety risks; (3) the oblique pull of the hook rope will cause the wire rope to come out of the drum groove, causing the wire rope to fall off the track.
[0058] It should be noted that the method provided in this application mainly consists of 7 steps. Step 1: Establish the input-output model of the sensor. Step 2: Solve for the filter column vector composed of the filter coefficients at time n. Step 3: Calculate R. n Matrix (upper triangular matrix). Step 4: Construct a new upper triangular matrix. Step 5: Calculation Matrix (orthogonal matrix at time n+1) and R n+1 Matrix. Step 6: Calculate y n+1 (Sensor observation at time n+1). Step 7: Solve the upper triangular matrix equation R. n+1 θ n+1 =y n+1 , to obtain θ n+1 By iterating through steps 3 to 7, the filter column vector θ = [θ0, θ1, ... θ] at any given time can be obtained. p ] T The method will be explained in detail below.
[0059] Step S20: The hook attitude measurement value is calculated and processed by a preset algorithm to obtain the filter column vector at the current time. The filter column vector is iteratively updated based on the hook attitude measurement value at each time.
[0060] It should be noted that the preset algorithm can be a pre-set AI model or an algorithm. The preset algorithm can be constructed in the following ways:
[0061] y n =A n θ+e n
[0062] Among them, y n Let A represent the sensor observation at time n. n The vector representing the hook attitude measurement value, θ represents the filter column vector, and e n This represents noise 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 updated iteratively, thereby achieving an accurate estimation of the estimated value.
[0064] In one feasible implementation, before the step of calculating and processing the hook attitude measurement value using a preset algorithm to obtain the filter column vector at the current moment, the method further includes:
[0065] The hook attitude measurement values are convolved to obtain the input convolution sum;
[0066] Based on the input convolution and 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 Let A represent the sensor observation at time n. n The vector representing the hook attitude measurement value, θ represents the filter column vector, and e n This represents noise data.
[0069] It should be noted that before calculating and processing the hook attitude measurement values using a preset algorithm, an algorithm / model needs to be built, as follows:
[0070] Establish an input-output model y for observing the sensor. n =A n θ+e n
[0071] At time k, the sensor's observed value y(k) consists of two parts: the convolution of the input values and the observation error, i.e.
[0072]
[0073] Where the operator * represents the discrete convolution operation, x k =[x(k),x(k-1),...,x(kp)] T Let θ = [θ0, θ1, ..., θp] be the column vector of inputs from time k to kp. p ] TLet x(k) represent the filter column vector consisting of filter coefficients, y(k) represent the sensor input at time k, y(k) represent the sensor observation at time k, and e(k) represent the observation error at time k. This formula represents the discrete convolution of the observation at time k with the filter coefficients at time k, where the observation at time k is the input at time k (including the column vector consisting of inputs from time k to kp; depending on the kernel size, the input for convolution calculation involves not only time k but also all inputs up to time kp). The specific convolution calculation is as follows: Then sum this sum with the observation error e(k) at time k.
[0074] Construct a column vector from all observations at n time points, denoted as y. n Then the matrix representation of the input-output model is: y n =A n θ+e n A n =[x1,x2,...,x n ] T e n =[e(1),e(2),...,e(n)] T
[0075] In one feasible implementation, step S20, which calculates and processes the hook attitude measurement value using a preset algorithm to obtain the filter column vector at the current moment, includes:
[0076] Obtain the first attitude measurement value at the previous moment;
[0077] The first attitude measurement value is calculated using a preset algorithm to obtain the 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 based on the filter column vector at the previous moment. Since the filter column vector at the previous moment is also updated based on the hook attitude measurement value at the previous moment, the filter measurement value is updated here based on historical data and the current hook attitude measurement value. In this embodiment, the Nth moment is the previous moment, and the N+1th moment is the current moment.
[0079] It should be noted that the first attitude measurement value is the hook attitude measurement value at the previous moment. The first attitude measurement value is calculated by a preset algorithm to obtain the first observation value at the previous moment. The calculation method is as shown in the algorithm formula of the input-output model.
[0080] It should be noted that the first observation at the previous time step can also be calculated by solving for the filter column vector formed by the filter coefficients at time n: θ = [θ0, θ1, ... θ p ]T The objective is to solve for The solution method is as follows: (1) According to the normal equation A n T A n θ n =A n T y n Calculate A n T A n Cholesky decomposition yields A n T A n =GG T (2) Solve the triangular matrix equation G using the back substitution method. T θ n =G -1 A n T A n θ n This yields the filter column vector θ = [θ0, θ1, ... θ p ] T The solution method is a common method in this field. For the sake of completeness of the overall calculation steps, only a brief explanation is given here.
[0081] Based on the first observation value, the first attitude measurement value, and the hook attitude measurement value, the filter column vector at the current moment is calculated.
[0082] It should be noted that the first observation value is y from the previous time step. n The first attitude measurement value is the hook attitude measurement value of the previous moment, and the hook attitude measurement value is the sensor measurement value of the current moment. The filter column vector is iteratively updated using the sensor measurement value of the current moment.
[0083] In one feasible implementation, the step of calculating the filter column vector at the current moment based on the first observation value, the first attitude measurement value, and the hook attitude measurement value includes:
[0084] Perform matrix decomposition on the first attitude measurement value to obtain the first orthogonal matrix and the first upper triangular matrix of the previous time step;
[0085] It should be noted that the matrix factorization process can be as follows:
[0086] For the first attitude measurement value A n =[x1,x2,...,x n ] T Perform QR decomposition to obtain the orthogonal matrix Q from the previous time step. n and the upper triangular matrix R nThe QR decomposition of a matrix is an important numerical linear algebra method that decomposes a matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). The matrix decomposition can be achieved through Schmidt orthogonal normalization, which involves orthogonalizing and normalizing the column vectors of the matrix to obtain the orthogonal matrix Q and the upper triangular matrix R. That is, the first orthogonal matrix and the first upper triangular matrix.
[0087] Based on the first observation, the hook attitude measurement, the first orthogonal matrix, and the first upper triangular matrix, the sensor column vector for the next moment is calculated.
[0088] In one feasible implementation, the step of calculating the sensor column vector at the next moment based on the first observation value, the hook attitude 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 attitude measurement value, the second orthogonal matrix and the second upper triangular matrix at the current moment are calculated;
[0090] It should be noted that the second upper triangular matrix is obtained by augmenting the first upper triangular matrix, and the construction method can be as follows:
[0091] For R n The matrix is augmented to obtain in For R n Remove the first line and change x n+1 =[x(n+1),x(n),...,x(n+1-p)] T The transpose of is taken as the last row of the matrix, resulting in
[0092] Based on the second orthogonal matrix and the first observation value, the sensor observation value at the current time is calculated;
[0093] It should be noted that the calculation Matrix and R n+1 Matrix, pair Perform QR decomposition to obtain in It is an orthogonal matrix, R n+1 It is an upper triangular matrix of p*p, O is the zero matrix of (np)*(np), 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 through block matrix Among them, y p*1 It is a column vector with rows of p and columns of 1, y (n-p)*1It is an np-row, 1-column column vector that is concatenated together to form an n-row, 1-column column vector.
[0095] Based on the sensor observations 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 , to obtain θ n+1 This is the filter column vector at time n+1. At this point, the new filter column vector has been updated, allowing iterative processing to continue to time n+2 based on the chronological order.
[0097] By iterating through steps 3 to 7, the filter column vector θ = [θ0, θ1, ... θ] at any given time can be obtained. p ] T .
[0098] Step S30: Based on the filter column vector, correct the hook attitude measurement value to obtain the hook attitude estimate value.
[0099] It should be noted that when the filter column vector θ = [θ0, θ1, ... θ] is obtained... p ] T Then, the sensor continuously samples p pitch angles, denoted as α = [α0, α1, ... α p ] T Then the estimated value of the true value of the p-th pitch angle is α. T The value obtained by θ+e is the hook attitude estimate, where e is Gaussian white noise.
[0100] This application proposes a method, apparatus, device, storage medium, and computer program product for calculating the attitude of a crane hook. This application obtains the hook attitude measurement value of the crane, processes the hook attitude measurement value based on a preset algorithm to obtain the filter column vector at the current moment, and then corrects the hook attitude measurement value using the filter column vector to obtain the estimated hook attitude value. Since the angle of the hook is constantly changing 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 fixed filter coefficients. Therefore, it can reduce the noise generated during the calculation process and obtain a more accurate hook attitude calculation result.
[0101] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2The step S30, which corrects the hook attitude measurement value based on the filter column vector to obtain the hook attitude estimate, includes:
[0102] Step S31: Multiply the filter column vector by the hook attitude measurement value to calculate the hook attitude estimate, wherein the calculation formula for the hook attitude estimate is as follows:
[0103] Y = α T θ+e
[0104] Where, α T θ represents the hook attitude 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 based on a preset algorithm. Through the iterative update of the sensor filter coefficients, the value of θ = [θ0, θ1, ... θ] can be reduced. p ] T The computational load, especially when A n T A n When the condition number is large, directly calculate θ = [θ0, θ1, ... θ] p ] T The difficulty is extremely high. By iteratively updating the filter column vectors, the computational load can be reduced, making it easier to run on processors with low computing power.
[0106] In this embodiment, the filter column vector θ = [θ0, θ1, ... θ p ] T It can correct the sensor sampling values to obtain more accurate estimates of pitch angle, roll angle, and yaw angle.
[0107] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the hook attitude calculation method of this application. Any simple transformations based on this technical concept are within the protection scope of this application.
[0108] This application also provides a hook attitude calculation device, please refer to... Figure 3 The hook attitude calculation device includes:
[0109] The acquisition module 10 is used to acquire the hook posture measurement value of the crane;
[0110] Processing module 20 is used to calculate and process the hook attitude measurement value through a preset algorithm to obtain the filter column vector at the current time, wherein the filter column vector is iteratively updated based on the hook attitude measurement value at each time.
[0111] The correction module 30 is used to correct the hook attitude measurement value based on the filter column vector to obtain the hook attitude estimate value.
[0112] Optionally, the processing module includes:
[0113] The acquisition unit is used to acquire the first attitude measurement value at the previous moment;
[0114] The first calculation unit is used to calculate the first attitude measurement value using a preset algorithm to obtain the first observation value at the 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 attitude measurement value, and the hook attitude measurement value.
[0116] Optionally, the second computing unit includes:
[0117] The decomposition subunit is used to perform matrix decomposition on the first attitude measurement value to obtain the first orthogonal matrix and the first upper triangular matrix of the previous time step.
[0118] The calculation subunit is used to calculate the sensor column vector at the next moment based on the first observation value, the hook attitude measurement value, the first orthogonal matrix, and the first upper triangular matrix.
[0119] Optionally, the computing subunit includes:
[0120] The first calculation component is used to calculate the second orthogonal matrix and the second upper triangular matrix at the current moment based on the first orthogonal matrix, the first upper triangular matrix and the hook attitude measurement value;
[0121] The second calculation component is used to calculate the sensor observation value at the current time based on the second orthogonal matrix and the first observation value;
[0122] The third calculation component is used to calculate the sensor column vector at the current time based on the sensor observations 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 with the hook attitude measurement value to calculate the hook attitude estimate, wherein the calculation formula for the hook attitude estimate is as follows:
[0125] Y = α T θ+e
[0126] Where, α T θ represents the hook attitude measurement value, θ represents the filter column vector, and e represents Gaussian white noise.
[0127] Optionally, the device further includes:
[0128] The convolution processing module is used to perform convolution processing on the hook attitude measurement values to obtain the input convolution sum;
[0129] The construction module is used to construct a preset algorithm based on the input convolution and sensor observation error, wherein the preset algorithm is expressed as:
[0130] y n =A n θ+e n
[0131] Among them, y n Let A represent the sensor observation at time n. n The vector representing the hook attitude measurement value, θ represents the filter column vector, and e n This represents noise data.
[0132] The hook attitude calculation device provided in this application, employing the hook attitude calculation method in the above embodiments, can solve the technical problem of hook attitude calculation. Compared with the prior art, the beneficial effects of the hook attitude calculation device provided in this application are the same as those of the hook attitude calculation method provided in the above embodiments, and other technical features in the hook attitude calculation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
[0133] This application provides a hook attitude calculation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the hook attitude calculation method in the above embodiment 1.
[0134] The following is for reference. Figure 4 The diagram illustrates a structural schematic of a hook attitude calculation device suitable for implementing embodiments of this application. The hook attitude calculation device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The hook attitude calculation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0135] like Figure 4 As shown, the hook attitude calculation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the hook attitude calculation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the hook attitude calculation device to communicate wirelessly or wiredly with other devices to exchange data. Although hook attitude calculation devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.
[0136] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0137] The hook attitude calculation device provided in this application, employing the hook attitude calculation method in the above embodiments, can solve the technical problem of hook attitude calculation. Compared with the prior art, the beneficial effects of the hook attitude calculation device provided in this application are the same as those of the hook attitude calculation method provided in the above embodiments, and other technical features in this hook attitude calculation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.
[0138] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.
[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0140] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the hook attitude calculation method in the above embodiments.
[0141] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. 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 conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0142] The aforementioned computer-readable storage medium may be included in the hook attitude calculation device; or it may exist independently and not assembled into the hook attitude calculation device.
[0143] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the hook attitude calculation device, cause the hook attitude calculation device to:
[0144] Obtain the crane hook attitude measurement value;
[0145] The hook attitude measurement value is calculated and processed by a preset algorithm to obtain the filter column vector at the current time. The filter column vector is iteratively updated based on the hook attitude measurement value at each time.
[0146] Based on the filter column vector, the measured hook attitude value is corrected to obtain the estimated hook attitude value.
[0147] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0148] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0149] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0150] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described hook attitude calculation method, thereby solving the technical problem of hook attitude calculation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the hook attitude calculation method provided in the above embodiments, and will not be repeated here.
[0151] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the hook attitude calculation method described above.
[0152] The computer program product provided in this application can solve the technical problem of hook attitude calculation. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the hook attitude calculation method provided in the above embodiments, and will not be repeated here.
[0153] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A method for calculating the attitude of a lifting hook, characterized in that, The method includes: Obtain the crane hook attitude measurement value; The hook attitude measurement value is calculated and processed by a preset algorithm to obtain the filter column vector at the current time. The filter column vector is iteratively updated based on the hook attitude measurement value at each time. Based on the filter column vector, the hook attitude measurement value is corrected to obtain the hook attitude estimate value; The step of calculating and processing the hook attitude measurement value using a preset algorithm to obtain the filter column vector at the current moment includes: Obtain the first attitude measurement value at the previous moment; The first attitude measurement value is calculated to obtain the first observation value at the previous moment; By performing Schmidt orthogonal normalization, the column vectors of the matrix corresponding to the first attitude measurement value are orthogonalized and normalized to obtain the first orthogonal matrix and the first upper triangular matrix at the previous time step. Delete the first row of the first upper triangular matrix, and take the transpose of the hook state measurement values at the current time and the previous P-1 times as the last row of the first upper triangular matrix to obtain the augmented upper triangular matrix; Perform QR decomposition on the augmented upper triangular matrix to obtain the second orthogonal matrix and the second upper triangular matrix at the current time. The expressions for the second orthogonal matrix and the second upper triangular matrix at the current time are: in, It is the second orthogonal matrix. It is the augmented upper triangular matrix. To delete the matrix after the first row of the first upper triangular matrix, It is the second upper triangular matrix. Yes (np) The zero matrix of (np), Forgetting factor; Based on the second orthogonal matrix and the first observation value, the sensor observation value at the current time is calculated; Based on the sensor observations and the second upper triangular matrix, the sensor column vector at the current moment is calculated.
2. The method as described in 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 estimate value includes: The estimated hook attitude is calculated by multiplying the filter column vector by the hook attitude measurement value, wherein the calculation formula for the estimated hook attitude value is as follows: Y=a T θ+e Where, α T θ represents the hook attitude measurement value, θ represents the filter column vector, and e represents Gaussian white noise.
3. The method as described in claim 1, characterized in that, Before the step of calculating and processing the hook attitude measurement value using a preset algorithm to obtain the filter column vector at the current moment, the method further includes: The hook attitude measurement values are convolved to obtain the input convolution sum; Based on the input convolution and 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 Let A represent the sensor observation at time n. n The vector representing the hook attitude measurement value, θ represents the filter column vector, and e n This represents noise data.
4. A hook attitude calculation device, characterized in that, The device includes: The acquisition module is used to acquire the measurement values of the crane hook posture; The processing module is used to calculate and process the hook attitude measurement value through a preset algorithm to obtain the filter column vector at the current time, wherein the filter column vector is iteratively updated based on the hook attitude measurement value at each time. The correction module is used to correct the hook attitude measurement value based on the filter column vector to obtain the hook attitude estimate value; The correction module includes: The acquisition unit is used to acquire the first attitude measurement value at the previous moment; The first calculation unit is used to calculate the first attitude measurement value to obtain the first observation value at the previous moment; The second calculation unit includes: The decomposition sub-unit is used to orthogonalize and normalize the column vectors of the matrix corresponding to the first attitude measurement value through Schmitt orthogonal normalization to obtain the first orthogonal matrix and the first upper triangular matrix at the previous time step. The computational subunit includes: The first calculation component is used to delete the first row of the first upper triangular matrix, take the transpose of the hook state measurement values at the current time and the previous P-1 times as the last row of the first upper triangular matrix to obtain the augmented upper triangular matrix, and perform QR decomposition on the augmented upper triangular matrix to obtain the second orthogonal matrix and the second upper triangular matrix at the current time. The expressions for the second orthogonal matrix and the second upper triangular matrix at the current time are: in, It is the second orthogonal matrix. It is the augmented upper triangular matrix. To delete the matrix after the first row of the first upper triangular matrix, It is the second upper triangular matrix. Yes (np) The zero matrix of (np), Forgetting factor; The second calculation component is used to calculate the sensor observation value at the current time based on the second orthogonal matrix and the first observation value; The third calculation component is used to calculate the sensor column vector at the current time based on the sensor observations and the second upper triangular matrix.
5. A hook attitude calculation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the hook attitude calculation method as described in any one of claims 1 to 3.
6. 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, it implements the steps of the hook attitude calculation method as described in any one of claims 1 to 3.
7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the hook attitude calculation method as described in any one of claims 1 to 3.