Hoisting task control method and device of crane and electronic equipment

Abstract

The invention discloses a hoisting task control method and device of a crane and electronic equipment, and relates to the field of mechanical control. The method comprises the following steps: constructing a coupling model of a crane and a wind power blade according to hoisting task parameters; determining first control data of the crane according to the coupling model; obtaining current environment data, wherein the current environment data comprises wind speed and wind direction; determining wind load distribution corresponding to the current environment data in a preset wind load database; calculating position deviation and attitude deviation of a hoisting point of the crane according to the wind load distribution, and calculating a wind load compensation vector; superposing the wind load compensation vector into the first control data to generate second control data; and converting the second control data into a servo control instruction, and driving the crane to run according to the carrying track according to the servo control instruction. According to the technical scheme, the control instruction is dynamically adjusted by calculating the wind load compensation vector, and the accuracy of the hoisting process is improved.

Description

Technical Field

[0001] The present application relates to the field of mechanical control, and in particular to a method, device and electronic equipment for controlling a lifting task of a crane. Background Art

[0002] With the rapid development of the renewable energy industry, wind power generation, as a clean and efficient way to obtain energy, has an increasing demand for facility construction. In the construction of wind farms, cranes are key equipment for installing huge wind turbine blades. Wind turbine blades are usually large in size and mass, which places extremely high demands on the precise control and stability of lifting equipment. Therefore, cranes must not only operate in complex terrain and unstable weather conditions, but also ensure the safety and precise alignment of wind turbine blades during the lifting process.

[0003] At present, the relevant technology lifts the wind turbine motor through a fixed path. Under strong wind conditions, the large planar structure of the wind turbine blade may catch the wind like a sail, which may cause position deviation during the lifting process of the crane. Therefore, the existing technology is insufficient in accuracy during the lifting process of wind turbine blades.

[0004] Therefore, there is an urgent need for a crane lifting task control method, device and electronic equipment. Summary of the invention

[0005] The present application provides a method, device and electronic equipment for controlling a lifting task of a crane, which improves the accuracy of the lifting process by calculating a wind load compensation vector and dynamically adjusting control instructions.

[0006] In a first aspect of the present application, a method for controlling a lifting task of a crane is provided, the method comprising: obtaining lifting task parameters of the crane; constructing a coupling model of the crane and a wind turbine blade according to the lifting task parameters; determining first control data of the crane according to the coupling model; obtaining current environmental data, the current environmental data including wind speed and wind direction; determining the wind load distribution corresponding to the current environmental data in a preset wind load database; calculating the position deviation and attitude deviation of the crane lifting point according to the wind load distribution, and calculating a wind load compensation vector according to the position deviation and the attitude deviation; superimposing the wind load compensation vector onto the first control data to generate second control data, the second control data including a carrying instruction of the crane; converting the second control data into a servo control instruction, and driving the crane to operate according to the carrying trajectory according to the servo control instruction.

[0007] By adopting the above technical solution, by obtaining the lifting task parameters of the crane, and constructing the coupling model of the crane and the wind turbine blade according to the lifting task parameters, the dynamic relationship between the crane and the wind turbine blade can be accurately described, providing a reliable model basis for subsequent control decisions. On this basis, by solving the coupling model, the first control data of the crane, that is, the optimal control strategy of the crane under ideal working conditions, can be obtained. At the same time, the method also considers the influence of wind load on the lifting process. By obtaining wind speed data and wind direction data, and determining the wind load distribution under the current environmental data in the preset wind load database, the force of the wind load on the wind turbine blade can be accurately estimated. Furthermore, by calculating the position and posture deviation of the lifting point caused by the wind load and generating the corresponding wind load compensation vector, the control strategy of the crane can be optimized and corrected on the basis of the first control data, and the second control data after considering the influence of the wind load can be obtained. Finally, by converting the second control data into the servo control instruction of the crane, and driving the crane to operate accordingly, the precise control of the lifting process of the wind turbine blade can be achieved. This method can adapt to wind load disturbances and dynamically adjust control instructions, thereby improving the accuracy, safety, reliability and efficiency of the wind turbine blade lifting process and reducing the lifting risks in complex environments.

[0008] Optionally, constructing the coupling model of the crane and the wind turbine blade according to the lifting task parameters specifically includes: constructing a multi-rigid body dynamic equation of the crane based on the kinematic principle of the crane mechanism; obtaining three-dimensional size parameters of the wind turbine blade, and constructing a geometric model of the wind turbine blade according to the three-dimensional size parameters; embedding the geometric model into the multi-rigid body dynamic model to obtain the coupling model.

[0009] By adopting the above technical solution, when constructing the coupling model of the crane and the wind turbine blade, firstly, based on the kinematic principle of the crane mechanism, the crane multi-rigid body dynamic equation is established, which can accurately describe the motion constraints and dynamic characteristics between the crane actuators and lay the foundation for coupling modeling. Secondly, by obtaining the three-dimensional size parameters of the wind turbine blade and constructing the geometric model of the wind turbine blade accordingly, the digital expression of the wind turbine blade structure can be realized, providing a geometric basis for subsequent force analysis. Finally, the geometric model of the wind turbine blade is embedded in the crane dynamic model to obtain a coupling model that includes the two subsystems. This coupling model not only takes into account the dynamic characteristics of the crane body, but also the geometric characteristics and flexibility characteristics of the wind turbine blade. It can more realistically reflect the force interaction between the two during the lifting process, provide a more refined and reliable decision-making basis for lifting control, and help improve the pertinence and effectiveness of lifting control.

[0010] Optionally, determining the first control data of the crane according to the coupling model specifically includes: constructing an objective function corresponding to energy optimization of the crane and constraint conditions for safe lifting of the wind turbine blades based on the coupling model; solving the objective function to obtain an optimal control sequence of each actuator of the crane while satisfying the constraint conditions; and discretizing the optimal control sequence to generate the first control data.

[0011] By adopting the above technical solution, when determining the first control data of the crane, the objective function of crane energy optimization is first constructed based on the coupling model to ensure that the energy consumption of the crane is minimized under the premise of meeting the constraints of safe lifting of wind turbine blades, so as to achieve energy saving and environmental protection. Secondly, by solving the objective function, the optimal control sequence of the crane actuator can be obtained to ensure that the operating state of each actuator achieves the optimal energy efficiency under the premise of meeting the constraints, and the cost is minimized while the wind turbine blades are safely and reliably lifted. Finally, the optimal control sequence is converted into the first control data through discretization processing, and the theoretical optimal control strategy can be converted into an instruction format acceptable to the crane control system, which is convenient for subsequent control execution.

[0012] Optionally, calculating the position deviation and attitude deviation of the lifting point according to the wind load distribution specifically includes: converting the wind load distribution into a concentrated force and moment acting on the center of gravity corresponding to the wind turbine blade; calculating the concentrated force component of the concentrated force at the lifting point, and calculating the moment component of the moment at the lifting point; and calculating the position deviation and the attitude deviation based on the concentrated force component and the moment component.

[0013] By adopting the above technical solution, the distributed wind load is first converted into concentrated force and moment acting on the center of gravity of the wind turbine blade, which simplifies the expression of the wind load and facilitates numerical analysis and processing by the computer. Secondly, by calculating the components of the concentrated force and moment at the lifting point, the direct load on the lifting point is obtained, revealing the influence mechanism of the wind load on the actuator of the crane, which is helpful to design a targeted load compensation scheme. Finally, the posture deviation is calculated based on the concentrated force and moment components, which can quantitatively describe the degree of deviation of the wind turbine blade caused by the wind load relative to the ideal lifting posture, evaluate the severity of the wind load interference, and facilitate the formulation of corresponding control correction measures. The above method converts the spatially distributed wind load into an equivalent load at the lifting point, and then directly calculates the position deviation and attitude deviation from the equivalent load. The mathematical model is clear and concise, and the calculation efficiency is high. It can realize real-time evaluation of wind load interference and enhance the response ability of the lifting control system to wind disturbances.

[0014] Optionally, calculating the wind load compensation vector based on the position deviation and the attitude deviation specifically includes: inputting the position deviation and the attitude deviation into a preset wind load compensation controller; obtaining the compensation amount of each degree of freedom of the crane output by the wind load compensation controller; and combining the compensation amounts of each degree of freedom to obtain the wind load compensation vector.

[0015] By adopting the above technical solution, when calculating the wind load compensation vector, the position deviation and attitude deviation are first input into the wind load compensation controller, and the compensation amount of each degree of freedom of the crane is directly generated according to the size and direction of the attitude deviation, which simplifies the control model of wind load compensation and facilitates engineering implementation. Secondly, by obtaining the compensation amount of each degree of freedom output by the controller, the specific manifestation of wind load compensation in each control channel can be clarified, which is conducive to the subsequent coordinated control of the actuator. Finally, the compensation amount of each degree of freedom is combined to obtain the wind load compensation vector, realizing the vector superposition of the compensation effects of different degrees of freedom, making the wind load compensation strategy more comprehensive and systematic, and being able to suppress attitude deviations from multiple dimensions and improve the accuracy of the lifting trajectory of wind turbine blades.

[0016] Optionally, superimposing the wind load compensation vector to the first control data to generate the second control data specifically includes: vector superimposing the wind load compensation vector with a control instruction of a corresponding degree of freedom in the first control data to obtain the second control data.

[0017] By adopting the above technical solution, by superimposing the wind load compensation vector into the first control data, the wind load compensation effect can be introduced on the basis of the original energy optimal control strategy, so as to realize the optimization correction of the crane control instruction. This method adopts the form of vector superposition to merge the two control effects on the corresponding degrees of freedom, taking into account both the energy optimization goal and the wind load compensation requirements, so that during the execution of the second control data, the energy consumption of the crane can be minimized while ensuring the safe and stable lifting of the wind turbine blades, achieving a balance between energy saving and safety, and reflecting the systematic and comprehensive nature of strategy formulation. At the same time, due to the use of vector superposition, the correction of the original control data by wind load compensation is reversible. When the external wind load conditions change, the compensation vector can be easily adjusted, so that the control strategy can be adaptively adjusted with environmental changes, thereby enhancing the flexibility of the lifting process.

[0018] Optionally, after converting the second control data into a servo control instruction and driving the crane to operate along the transport trajectory according to the servo control instruction, the method further includes: acquiring the position potential energy of the wind turbine blade during the descent of the wind turbine blade; adjusting the hydraulic unloading valve of the crane to convert the position potential energy into hydraulic energy, and storing the hydraulic energy in an accumulator.

[0019] By adopting the above technical solution, during the descent of the wind turbine blades, the position potential energy information is collected. By adjusting the hydraulic unloading valve of the crane, the potential energy released during the descent of the wind turbine blades can be converted into hydraulic energy and stored in the accumulator instead of being directly dissipated. This hydraulic energy storage method can effectively recover the gravitational potential energy during the descent of the wind turbine blades and reduce energy waste. The stored hydraulic energy is supplied to the crane hydraulic system, which can reduce the output power of the hydraulic pump and reduce the energy consumption of the crane during the ascent of the wind turbine blades.

[0020] In a second aspect of the present application, a lifting task control device for a crane is provided, the device comprising: an acquisition module and a processing module; the acquisition module is used to acquire the lifting task parameters of the crane; the processing module is used to construct a coupling model of the crane and the wind turbine blade according to the lifting task parameters; the processing module is also used to determine the first control data of the crane according to the coupling model; the acquisition module is also used to acquire current environmental data, the current environmental data including wind speed and wind direction; the processing module is also used to determine the wind load distribution corresponding to the current environmental data in a preset wind load database; the processing module is also used to calculate the position deviation and attitude deviation of the crane lifting point according to the wind load distribution, and calculate the wind load compensation vector according to the position deviation and the attitude deviation; the processing module is also used to superimpose the wind load compensation vector on the first control data to generate second control data, the second control data including the carrying instruction of the crane; the processing module is also used to convert the second control data into a servo control instruction, and drive the crane to operate according to the carrying trajectory according to the servo control instruction.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the lifting task parameters of the crane and constructing the coupling model of the crane and the wind turbine blade according to the lifting task parameters, the dynamic relationship between the crane and the wind turbine blade can be accurately described, providing a reliable model basis for subsequent control decisions. On this basis, by solving the coupling model, the first control data of the crane can be obtained, that is, the optimal control strategy of the crane under ideal working conditions. At the same time, the method also considers the influence of wind load on the lifting process. By obtaining wind speed data and wind direction data, and determining the wind load distribution under the current environmental data in the preset wind load database, the force of wind load on the wind turbine blade can be accurately estimated. Furthermore, by calculating the position and posture deviation of the lifting point caused by the wind load and generating the corresponding wind load compensation vector, the control strategy of the crane can be optimized and corrected on the basis of the first control data, and the second control data after considering the influence of wind load can be obtained. Finally, by converting the second control data into the servo control instruction of the crane and driving the crane to operate accordingly, the precise control of the lifting process of the wind turbine blade can be achieved. This method can adapt to wind load disturbances and dynamically adjust control instructions, thereby improving the safety, reliability and efficiency of the wind turbine blade lifting process and reducing the lifting risks in complex environments.

[0024] 2. When constructing the coupling model of the crane and the wind turbine blade, firstly, based on the kinematic principle of the crane mechanism, the multi-rigid body dynamic equation of the crane is established, which can accurately describe the motion constraints and dynamic characteristics between the crane actuators and lay the foundation for coupling modeling. Secondly, by obtaining the three-dimensional size parameters of the wind turbine blade and constructing the geometric model of the wind turbine blade accordingly, the digital expression of the wind turbine blade structure can be realized, providing a geometric basis for subsequent force analysis. Finally, the geometric model of the wind turbine blade is embedded in the dynamic model of the crane to obtain a coupling model that includes the two subsystems. This coupling model not only takes into account the dynamic characteristics of the crane body, but also the geometric characteristics and flexibility characteristics of the wind turbine blade. It can more realistically reflect the force interaction between the two during the lifting process, provide a more refined and reliable decision-making basis for lifting control, and help improve the pertinence and effectiveness of lifting control.

[0025] 3. By superimposing the wind load compensation vector into the first control data, the wind load compensation effect can be introduced on the basis of the original energy optimal control strategy to achieve the optimization correction of the crane control command. This method adopts the form of vector superposition to merge the two control effects on the corresponding degrees of freedom, taking into account both the energy optimization goal and the wind load compensation requirements. During the execution of the second control data, it can ensure the safe and stable lifting of the wind turbine blades while minimizing the energy consumption of the crane, achieving a balance between energy saving and safety, and reflecting the systematic and comprehensive nature of strategy formulation. At the same time, due to the use of vector superposition, the correction of the original control data by wind load compensation is reversible. When the external wind load conditions change, the compensation vector can be easily adjusted, so that the control strategy can be adaptively adjusted with environmental changes, enhancing the flexibility of the lifting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for controlling a lifting task of a crane disclosed in an embodiment of the present application; Figure 2 It is a module schematic diagram of a lifting task control device of a crane disclosed in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0027] Explanation of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0028] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0029] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0030] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0031] The present application provides a method, device and electronic equipment for controlling a lifting task of a crane, referring to Figure 1 , Figure 1 1 is a flow chart of a method for controlling a lifting task of a crane provided in an embodiment of the present application. The method is applied to a controller of a crane and includes steps S101 to S108, which are as follows: Step S101: Obtain the lifting task parameters of the crane.

[0032] In step S101, the crane controller accesses the task management module of the crane, which stores the basic information of each lifting task in advance, including the model of the wind turbine blade to be lifted, the expected lifting height, and the lifting time. The crane controller sends a data query request to the task management module. After receiving the request, the task management module retrieves the task information corresponding to the task identification number from its own storage area, extracts the expected lifting height parameters, and sends them to the controller through the internal communication bus. After obtaining the lifting height, the crane controller continues to obtain the weight and size of the wind turbine blade. The weight and size of the wind turbine blade will be written into the RFID electronic tag installed on the wind turbine blade by the manufacturer when the wind turbine blade leaves the factory. The RFID electronic tag records the factory number, model specification, weight parameters, and dimensional parameters such as length, width, height, and torsion angle of the wind turbine blade. The crane controller establishes a data link with the RFID tag on the wind turbine blade through the wireless radio frequency communication module. The controller sends a data reading instruction to the RFID tag. After receiving the instruction, the RFID tag sends the wind turbine blade parameter data stored in itself to the controller through a radio frequency signal. The control unit parses and extracts the received wind turbine blade parameter data, and saves it together with the previously acquired lifting height parameters to form complete lifting task parameters, which include wind turbine blade weight, wind turbine blade size and lifting height.

[0033] Step S102: constructing a coupling model of the crane and the wind turbine blade according to the lifting task parameters.

[0034] In step S102, based on the kinematic principle of the crane mechanism, the multi-rigid body dynamic equation of the crane is constructed; the three-dimensional size parameters of the wind turbine blade are obtained, and the geometric model of the wind turbine blade is constructed according to the three-dimensional size parameters; the geometric model is embedded in the multi-rigid body dynamic model to obtain the coupling model.

[0035] Specifically, the crane controller constructs the multi-rigid body dynamic equations of the crane based on the kinematic principle of the crane's mechanism. The storage unit of the crane controller pre-stores the mechanism parameters of the crane, including the degrees of freedom, connecting rod length, mass, and moment of inertia of each joint. The controller reads the mechanism parameters of the crane from the storage unit and substitutes them into the calculation templates of the kinematic equations and dynamic equations. The crane is composed of multiple components such as a tower, a luffing arm, a lifting arm, and a hook connected by hinges, sliding pairs, and other kinematic pairs. Based on the mechanism parameters, the controller establishes the coordinate system of each component, and obtains the position change matrix and attitude transformation matrix of each component according to the connection sequence. The controller uses the forward kinematic equation to solve the kinematic parameters of each component of the crane, such as velocity and acceleration. For a crane composed of multiple structures connected by kinematic pairs, its kinematic equation is recursively solved through the position transformation matrix and the velocity transformation matrix. Assume that the crane consists of n components, and the position transformation matrix of the i-th component is A i , the velocity transformation matrix is ​​B i , the joint variable is q i , then: A i =A1·A2·...·A i-1 ·T i (q i ); B i =B i-1 ·J i (q i ); Among them, T i (q i ) is the position transformation matrix of the i-th joint, J i (q i ) is the velocity transformation matrix of the i-th joint, and they are all joint variables q i A1 and B0 are the position transformation matrix and velocity transformation matrix from the base coordinate system to the first component coordinate system, respectively. According to the above recursive formula, the position transformation matrix A of the crane end component (such as the hook) relative to the base is obtained: n and velocity transformation matrix B n , and then find the position vector P of the end component n and velocity vector V n : P n=A n ·P0; V n =B n V0; Among them, P0 and V0 are the position vector and velocity vector of the end component respectively. n and velocity vector V n By taking the derivatives respectively, we can get the velocity vector V of the end component n and the acceleration vector A n : V n =JAC·Q'; A n =JAC·Q''+JAC'·Q'; Among them, Q' and Q'' are the first-order derivatives (joint velocity) and second-order derivatives (joint acceleration) of the joint variables, respectively, and JAC is the Jacobian matrix of the crane, satisfying: JAC=[∂P n / ∂q1,∂P n / ∂q2,…,∂P n / ∂q n]； On this basis, the controller further applies the dynamics theory to establish the force balance equation and torque balance equation for each component; and these equations are combined to obtain the multi-rigid body dynamics model of the whole machine. The multi-rigid body dynamics model takes the driving force and torque of each actuator of the crane as input and the speed and acceleration of each component as output.

[0036] After obtaining the multi-rigid body dynamics model of the crane, the crane controller constructs a geometric model of the wind turbine blade according to the three-dimensional size parameters of the wind turbine blade. The controller reads the geometric parameters of the wind turbine blade from the RFID electronic tag, such as the chord length, twist angle, thickness of the airfoil, and the length, width and other dimensions of the wind turbine blade. Based on these geometric parameters, the controller generates a series of airfoil curves in the three-dimensional modeling space. The controller uses a three-dimensional interpolation algorithm, such as spline interpolation, to fit the airfoil curve piece by piece to generate a continuous surface model of the wind turbine blade, that is, the geometric model of the wind turbine blade.

[0037] Finally, the crane controller embeds the geometric model of the wind turbine blade into the multi-rigid body dynamics model to form a coupled model of the wind turbine blade and the crane. The controller first searches for the lifting point position adjacent to the root of the wind turbine blade in the multi-rigid body dynamics model, and defines a connection coordinate system that coincides with the root of the wind turbine blade at the lifting point position. The controller constrains the root node of the geometric model of the wind turbine blade with the connection coordinate system, so that the geometric model of the wind turbine blade can move with the movement of the crane lifting point.

[0038] Step S103: Determine first control data of the crane according to the coupling model.

[0039] In step S103, based on the coupling model, an objective function of energy optimization of the crane and constraints for safe lifting of the wind turbine blades are constructed; the objective function is solved to obtain an optimal control sequence of each actuator of the crane while satisfying the constraints; the optimal control sequence is discretized to generate the first control data.

[0040] Specifically, the server constructs the energy optimization objective function of the crane based on the coupling model. The energy consumption of the crane mainly comes from actuators such as the luffing motor, lifting motor, and slewing motor, and the energy consumption weights of different mechanisms are different. The server establishes their energy consumption models based on the rated power and actual load of each mechanism to obtain the energy consumption function per unit time. The server weightedly sums the energy consumption functions of each mechanism to obtain the energy consumption objective function of the entire crane system. The objective function uses the speed and acceleration of each joint of the crane as independent variables, and the total energy consumption during the lifting cycle as the dependent variable, reflecting the mapping relationship between energy consumption and control input. The objective function is as follows: J=∫[w1·E1(v1, a1)+w2·E2(v2, a2)+...+w m ·E m (v m , a m )]dt Among them, the integration interval is the lifting cycle [0, T], the objective function J represents the weighted sum of the energy consumption functions of each actuator over the entire lifting cycle, that is, the total energy consumption, m is the number of actuators of the crane, E i (v i , a i ) is the energy consumption function of the ith mechanism, where v i is the velocity variable of the ith mechanism, a i is the acceleration of the ith mechanism, w i is the energy consumption weight of the i-th mechanism. The optimization goal is to solve the optimal speed curve v i (t) and acceleration curve a i( t), so that the objective function J is minimized.

[0041] While establishing the energy objective function, the server establishes the constraint conditions for the safe lifting of wind turbine blades. Since wind turbine blades are flexible structures, they are prone to deformations such as bending, torsion, and vibration during the lifting process. Excessive deformation may cause blade damage or collision accidents. In order to ensure the safety of the lifting of wind turbine blades, the server establishes the constraint inequality for blade deformation based on the allowable stress and allowable strain of the blade material. At the same time, based on the safety distance requirements between wind turbine blades and obstacles such as towers and the ground, the constraint conditions for the safe lifting of wind turbine blades are established. Assume that there are n key points on the wind turbine blade, the displacement of the jth point during the lifting process is dj, the stress is σj, and the strain is εj. Then the deformation constraint of the wind turbine blade can be expressed as: |dj|≤[d]; |σj|≤[σ]; |εj|≤[ε] Among them, [d], [σ], and [ε] are the allowable displacement, allowable stress, and allowable strain of the wind turbine blade, respectively. These inequalities indicate that the actual displacement, stress, and strain of each point on the wind turbine blade must be controlled within the allowable range to ensure the structural safety of the blade.

[0042] Secondly, the server solves the energy optimization objective function to obtain the optimal control sequence that meets the constraints. The server adopts the idea of ​​model predictive control (MPC) to divide the entire lifting cycle into several time windows. In each time window, based on the prediction of the current state and future working conditions, the optimal control sequence in the limited time domain is solved, and then rolling optimization is performed to continuously update the control sequence. Specifically, in each optimization window, the server discretizes the wind turbine blade-crane coupling model and converts it into a set of nonlinear differential algebraic equations. On this basis, the discretized energy objective function is used as the performance indicator function, and the discretized constraints are used as equality or inequality constraints to construct a nonlinear optimization problem. Then, the server uses the sequential quadratic programming (SQP) algorithm to iteratively solve the problem and obtain the optimal control sequence in the current window. As the time window rolls, the server continuously updates the initial conditions and constraint parameters of the optimization problem, repeatedly calls the SQP algorithm, and generates the optimal control sequence in the entire lifting cycle.

[0043] Finally, the server discretizes the optimal control sequence and generates the first control data. Since the optimal control sequence is defined in the continuous time domain, and the actual crane control is based on discrete time steps, it is necessary to convert the continuous control sequence into discrete control instructions. The server takes the average value of the optimal control sequence in each control cycle as the control input of the actuator in that cycle. In this way, the optimal control sequence is discretized into a series of control instructions, namely the first control data. Each control instruction contains the speed and acceleration setting values ​​of each actuator of the crane in a control cycle. Through the combination of these setting values, the coordinated movement and energy-saving optimization of the crane can be achieved. The first control data is a discrete time series, and each time point corresponds to a set of control instructions for the actuator. The interval of the time series is the control period, which is usually determined according to the sampling frequency and computing power of the crane control system. The typical control period can be tens to hundreds of milliseconds, which is not limited in this application.

[0044] Step S104: Acquire current environmental data, where the current environmental data includes wind speed and wind direction.

[0045] In step S104, the server establishes a data communication link with the environmental monitoring system of the wind farm, and the server sends a data request instruction to the environmental monitoring system, requesting the current wind speed and wind direction data of the wind farm. After receiving the request, the environmental monitoring system queries the latest wind speed and wind direction records from the database, packages them into a data packet in a standard format, and sends them to the server through the communication interface.

[0046] Step S105: determining the wind load distribution corresponding to the current environmental data in a preset wind load database.

[0047] In step S105, the server accesses the preset wind load database. The preset wind load database stores wind load distribution samples of wind turbine blades, which are obtained through previous simulation calculations and experimental tests. Each wind load distribution sample corresponds to specific environmental conditions, such as wind speed and wind direction parameters, and contains load data such as pressure distribution, shear distribution, and bending moment distribution of various parts of the wind turbine blade. The preset wind load database is indexed according to wind speed and wind direction data, which facilitates the server to quickly find the corresponding wind load distribution according to real-time wind conditions. The server uses the current wind speed and wind direction data obtained in step S104 as query conditions, and searches for matching wind load distribution samples in the preset wind load database.

[0048] Step S106: Calculate the position deviation and attitude deviation of the crane lifting point according to the wind load distribution, and calculate the wind load compensation vector according to the position deviation and the attitude deviation.

[0049] In step S106, the wind load distribution is converted into concentrated force and moment acting on the corresponding center of gravity of the wind turbine blade; the concentrated force component of the concentrated force at the lifting point is calculated, and the moment component of the moment at the lifting point is calculated; the position deviation and the attitude deviation are calculated according to the concentrated force component and the moment component; the position deviation and the attitude deviation are input into a preset wind load compensation controller; the compensation amount of each degree of freedom of the crane output by the wind load compensation controller is obtained; the compensation amounts of each degree of freedom are combined to obtain the wind load compensation vector.

[0050] Specifically, the server numerically integrates the wind load distribution to obtain the magnitude and direction of the resultant force of the wind load in the span and chord directions of the wind turbine blade, and then performs matrix transformation on these resultant forces according to the geometric dimensions of the wind turbine blade to obtain the concentrated force of the wind load in the blade coordinate system. The calculation formula for the concentrated force is F=∬f(x, y)dxdy; wherein f(x, y) is the wind load distribution on the wind turbine blade, x is the span coordinate of the blade, and y is the chord coordinate of the blade.

[0051] Similarly, the server numerically integrates the moment of the wind load distribution to obtain the moment of the wind load on the center of gravity of the wind turbine blade. The moment calculation formula is M=∬r(x, y)×f(x, y)dxdy; where r(x, y) is the position vector of the wind load action point.

[0052] The server converts the concentrated force and moment acting on the center of gravity of the wind turbine blade into the force and moment at the lifting point of the crane. This is because the crane is connected to the wind turbine blade through the lifting point, and the force at the lifting point directly determines the motion response of each degree of freedom of the crane. The server first calculates the component of the concentrated force at the lifting point based on the position vector of the lifting point relative to the center of gravity of the wind turbine blade. Since the wind turbine blade is a moving carrier during the lifting process, the component of the concentrated force at the lifting point also changes with time. The server needs to calculate the concentrated force component at the lifting point in real time according to the instantaneous position and posture of the wind turbine blade in each control cycle. Concentrated force component F1=F-(F·R / |R|^2)*R; where R is the position vector of the lifting point relative to the center of gravity of the wind turbine blade, and |R| represents the modulus of R, that is, the distance from the lifting point to the center of gravity of the wind turbine blade.

[0053] Similarly, the server also calculates the torque component of the torque at the lifting point. The server uses the moment of inertia of the wind turbine blade and the position vector of the lifting point. Since the effect of the torque is related to the position of the lifting point, the component of the torque at the lifting point depends not only on the torque vector itself, but also on the position of the lifting point relative to the point of action of the torque (i.e., the center of gravity of the wind turbine blade). The server cross-multiplies the torque vector at the center of gravity of the wind turbine blade and the lifting point position vector to obtain the torque component of the torque at the lifting point. The torque component is M1=M+R×(I·α); where I is the moment of inertia of the wind turbine blade and α is the angular acceleration vector of the wind turbine blade.

[0054] Based on the concentrated force component and moment component at the lifting point, the server can calculate the position deviation and attitude deviation of the wind turbine blade under the action of wind load. The position deviation indicates the offset of the actual position of the wind turbine blade relative to the ideal position, which can be obtained by dividing the concentrated force component at the lifting point by the stiffness coefficient of the wind turbine blade. The attitude deviation indicates the deflection angle of the actual attitude of the wind turbine blade relative to the ideal attitude, which can be obtained by dividing the moment component at the lifting point by the rotational stiffness coefficient of the wind turbine blade.

[0055] Finally, the server needs to calculate the wind load compensation vector based on the position deviation and attitude deviation. The purpose of wind load compensation is to keep the wind turbine blades in the ideal position and attitude as much as possible under the action of wind load, and reduce the deviation during the lifting process. The server inputs the position deviation and attitude deviation into the preset wind load compensation controller. The wind load compensation controller is a feedback-based control algorithm, such as PID control and adaptive control. The wind load compensation controller can calculate the compensation amount of each degree of freedom of the crane (such as amplitude variation, lifting, rotation, etc.) according to the deviation signal.

[0056] Specifically, the wind load compensation controller inputs the position deviation and attitude deviation into different control channels respectively, and calculates the compensation amount of each degree of freedom. For example, the position deviation is mainly caused by the amplitude change and lifting motion, so the corresponding compensation amount is mainly reflected in the amplitude change and lifting degrees of freedom. Similarly, the attitude deviation is mainly caused by the rotational motion, so the corresponding compensation amount is mainly reflected in the rotational degree of freedom.

[0057] After obtaining the compensation amount of each degree of freedom, the server combines them into a complete wind load compensation vector. The wind load compensation vector contains the compensation control instructions of each actuator of the crane (such as luffing motor, hoisting motor, slewing motor, etc.), such as speed and acceleration. The server generates the compensation control instructions of each actuator by mapping the compensation amount of each degree of freedom to the corresponding actuator, and packages them into a control vector in a standard format.

[0058] For example, based on the wind load distribution, the server calculates that the concentrated force at the center of gravity of the wind turbine blade at a certain moment is 5kN, and the concentrated moment is 10kN·m. It is known that the lifting point is located at the root of the wind turbine blade, 2m away from the center of gravity. The server first decomposes the concentrated force at the lifting point into two components, horizontal and vertical, with sizes of 3kN and 4kN respectively. Then, the server calculates the component of the moment at the lifting point, which is equal to the cross product of the concentrated moment and the lifting point position vector, with a size of 8kN·m. Based on the concentrated force and moment components at the lifting point, the server calculates that the position deviation of the wind turbine blade is 0.2m and the attitude deviation is 0.5°. The server inputs these two deviations into the PID controller and obtains the speed compensation of the variable amplitude motor as 1.5rad / s, the speed compensation of the lifting motor as 0.8m / s, and the angular velocity compensation of the rotary motor as 0.2rad / s. Finally, the server converts these compensations into corresponding control instructions to generate a complete wind load compensation vector.

[0059] Step S107: superimposing the wind load compensation vector into the first control data to generate second control data, wherein the second control data includes a carrying instruction of the crane.

[0060] In step S107, the wind load compensation vector is vector-superimposed with the control instruction of the corresponding degree of freedom in the first control data to obtain the second control data.

[0061] Specifically, the server matches the wind load compensation vector with the first control data. The first control data is generated in step S103 according to the objective function and constraint conditions of energy optimization, and represents the optimal control sequence of each degree of freedom of the crane under ideal working conditions. The wind load compensation vector is generated in step S106 according to the posture deviation of the wind turbine blade and the wind load effect, and represents the compensation control amount of each degree of freedom of the crane under wind load interference. Since the sources and purposes of these two control data are different, their data formats and the degrees of freedom contained may not be completely consistent.

[0062] In order to achieve effective superposition of control data, the server matches the degrees of freedom of the wind load compensation vector and the first control data. Specifically, the server first extracts the various components in the wind load compensation vector, such as amplitude compensation, lifting compensation, and rotation compensation, and matches them one by one with the control instructions of the corresponding degrees of freedom in the first control data. For example, the amplitude compensation is matched with the amplitude angular velocity instruction in the first control data, the lifting compensation is matched with the lifting speed instruction in the first control data, the rotation compensation is matched with the rotation angular velocity instruction in the first control data, and so on.

[0063] After completing the degree of freedom matching, the server performs vector superposition on the control instructions for each degree of freedom. Vector superposition is a calculation method based on vector space, which can combine multiple physical quantities of the same type into a composite quantity. In this application, the server regards the control instruction of a certain degree of freedom in the first control data as a reference vector, and regards the corresponding compensation amount in the wind load compensation vector as a correction vector. Through the vector addition method, the two vectors are added to obtain the corrected control instruction of the degree of freedom.

[0064] It should be noted that since the wind load compensation vector is a time variable and the first control data is a time series, the server needs to consider the synchronization of the two in the time dimension when performing vector superposition. Specifically, the server discretizes the wind load compensation vector according to the time step of the first control data to obtain a compensation sequence of the same length as the first control data. The server performs vector superposition of each element in the compensation sequence with the control instruction at the corresponding moment in the first control data to obtain a corrected control sequence.

[0065] By vector superposition of control instructions for all degrees of freedom, a complete second control data is obtained. Compared with the first control data, the second control data not only takes into account the optimization of crane energy consumption, but also takes into account the compensation correction of wind load interference, so it can better adapt to the actual lifting conditions and ensure the lifting accuracy and stability under complex wind conditions.

[0066] Step S108: converting the second control data into a servo control instruction, and driving the crane to operate along the transport trajectory according to the servo control instruction.

[0067] In step S108, the motor control instructions of each actuator of the crane are calculated according to the displacement and speed instructions of each degree of freedom in the second control data; the motor control instructions are converted into PWM signals, and the PWM signals are output to the servo drive system of the crane; the servo drive system controls the movement of the crane actuator according to the PWM signals to realize the wind disturbance resistant lifting of the wind turbine blades.

[0068] In a possible implementation, after step S108, the method further includes: acquiring the position potential energy of the wind turbine blade during the descent of the wind turbine blade; adjusting the hydraulic unloading valve of the crane to convert the position potential energy into hydraulic energy, and storing the hydraulic energy in an accumulator.

[0069] Specifically, the server monitors the height of the wind turbine blades in real time through sensors to calculate the current position potential energy. The calculation of position potential energy can be simplified to P=mgh, where m is the mass, g is the acceleration of gravity, and h is the height of the wind turbine blade relative to the starting point. Subsequently, this part of energy is converted into hydraulic energy by controlling the hydraulic system of the crane, that is, the adjustment of the hydraulic unloading valve. The hydraulic unloading valve adjusts the speed and direction of the hydraulic oil flow so that the potential energy is converted into hydraulic energy through the action of the hydraulic pump. The converted hydraulic energy is then stored in the accumulator.

[0070] Reference Figure 2 The present application also provides a lifting task control device for a crane, which is a server. The server includes an acquisition module 201 and a processing module 202. The acquisition module 201 is used to acquire the lifting task parameters of the crane; the processing module 202 is used to construct a coupling model of the crane and the wind turbine blade according to the lifting task parameters; the processing module 202 is also used to determine the first control data of the crane according to the coupling model; the acquisition module 201 is also used to acquire current environmental data, and the current environmental data includes wind speed and wind direction; the processing module 202 is also used to obtain the current environmental data in a preset wind load database The wind load distribution corresponding to the current environmental data is determined; the processing module 202 is also used to calculate the position deviation and posture deviation of the crane lifting point according to the wind load distribution, and calculate the wind load compensation vector according to the position deviation and the posture deviation; the processing module 202 is also used to superimpose the wind load compensation vector into the first control data to generate second control data, and the second control data includes the carrying instruction of the crane; the processing module 202 is also used to convert the second control data into a servo control instruction, and drive the crane to operate according to the carrying trajectory according to the servo control instruction.

[0071] In a possible implementation, the processing module 202 constructs a coupling model of the crane and the wind turbine blade according to the lifting task parameters, specifically including: the processing module 202 constructs the multi-rigid body dynamics equation of the crane based on the kinematic principle of the crane mechanism; the acquisition module 201 acquires the three-dimensional size parameters of the wind turbine blade, and constructs the geometric model of the wind turbine blade according to the three-dimensional size parameters; the processing module 202 embeds the geometric model into the multi-rigid body dynamics model to obtain the coupling model.

[0072] In a possible implementation, the processing module 202 determines the first control data of the crane according to the coupling model, specifically including: the processing module 202 constructs the objective function of the crane corresponding to the energy optimization and the constraint conditions for the safe lifting of the wind turbine blades based on the coupling model; the processing module 202 solves the objective function and obtains the optimal control sequence of each actuator of the crane while satisfying the constraint conditions; the processing module 202 discretizes the optimal control sequence to generate the first control data.

[0073] In a possible implementation, the processing module 202 calculates the position deviation and the attitude deviation of the lifting point according to the wind load distribution, specifically including: the processing module 202 converts the wind load distribution into a concentrated force and a moment acting on the corresponding center of gravity of the wind turbine blade; the processing module 202 calculates the concentrated force component of the concentrated force at the lifting point, and calculates the moment component of the moment at the lifting point; the processing module 202 calculates the position deviation and the attitude deviation according to the concentrated force component and the moment component.

[0074] In a possible implementation, the processing module 202 calculates the wind load compensation vector based on the position deviation and the attitude deviation, specifically including: the processing module 202 inputs the position deviation and the attitude deviation into a preset wind load compensation controller; the acquisition module 201 acquires the compensation amount of each degree of freedom of the crane output by the wind load compensation controller; the processing module 202 combines the compensation amounts of each degree of freedom to obtain the wind load compensation vector.

[0075] In a possible implementation, the processing module 202 superimposes the wind load compensation vector into the first control data to generate second control data, which specifically includes: the processing module 202 performs vector superposition of the wind load compensation vector and the control instruction of the corresponding degree of freedom in the first control data to obtain the second control data.

[0076] In a possible implementation, after the processing module 202 converts the second control data into a servo control instruction and drives the crane to operate according to the transport trajectory according to the servo control instruction, the method further includes: during the descent of the wind turbine blade, the acquisition module 201 acquires the position potential energy of the wind turbine blade; the processing module 202 adjusts the hydraulic unloading valve of the crane, converts the position potential energy into hydraulic energy, and stores the hydraulic energy in an accumulator.

[0077] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0078] The present application also provides an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0079] The communication bus 302 is used to realize the connection and communication between these components.

[0080] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0081] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0082] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0083] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a crane lifting task control method.

[0084] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a crane lifting task control method stored in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0085] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments.

[0086] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0091] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0092] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A crane lifting task control method, characterized in that: The method comprises: Get the lifting task parameters of the crane; Constructing a coupling model of the crane and the wind turbine blade according to the lifting task parameters; determining first control data of the crane according to the coupling model; Acquiring current environmental data, wherein the current environmental data includes wind speed and wind direction; Determine the wind load distribution corresponding to the current environmental data in a preset wind load database; Calculating the position deviation and attitude deviation of the crane lifting point according to the wind load distribution, and calculating the wind load compensation vector according to the position deviation and the attitude deviation; superimposing the wind load compensation vector to the first control data to generate second control data, wherein the second control data includes a carrying instruction of the crane; The second control data is converted into a servo control instruction, and the crane is driven to operate according to the transport trajectory according to the servo control instruction.

2. The method according to claim 1, characterized in that The step of constructing the coupling model of the crane and the wind turbine blade according to the lifting task parameters specifically includes: Based on the kinematic principle of the crane mechanism, construct the multi-rigid body dynamic equation of the crane; Acquiring three-dimensional size parameters of the wind turbine blade, and constructing a geometric model of the wind turbine blade according to the three-dimensional size parameters; The geometric model is embedded into the multi-rigid body dynamics model to obtain the coupling model.

3. The method according to claim 1, characterized in that: Determining the first control data of the crane according to the coupling model specifically includes: Based on the coupling model, constructing an objective function corresponding to energy optimization of the crane and constraint conditions for safe lifting of the wind turbine blades; Solving the objective function to obtain an optimal control sequence of each actuator of the crane while satisfying the constraint conditions; The optimal control sequence is discretized to generate the first control data.

4. The method according to claim 1, characterized in that: The calculating the position deviation and attitude deviation of the lifting point according to the wind load distribution specifically includes: Converting the wind load distribution into concentrated force and moment acting on the corresponding center of gravity of the wind turbine blade; Calculating the concentrated force component of the concentrated force at the lifting point, and calculating the moment component of the moment at the lifting point; The position deviation and the posture deviation are calculated based on the concentrated force component and the moment component.

5. The method according to claim 4, characterized in that The calculating of the wind load compensation vector according to the position deviation and the attitude deviation specifically includes: inputting the position deviation and the attitude deviation into a preset wind load compensation controller; Obtaining the compensation amount of each degree of freedom of the crane output by the wind load compensation controller; The compensation amounts of the various degrees of freedom are combined to obtain the wind load compensation vector.

6. The method according to claim 5, characterized in that The step of superimposing the wind load compensation vector onto the first control data to generate the second control data specifically includes: The wind load compensation vector is vector-superimposed with the control instruction of the corresponding degree of freedom in the first control data to obtain the second control data.

7. The method according to claim 1, characterized in that After converting the second control data into a servo control instruction and driving the crane to operate according to the servo control instruction, the method further includes: During the descending process of the wind turbine blade, obtaining the position potential energy of the wind turbine blade; The hydraulic unloading valve of the crane is adjusted to convert the position potential energy into hydraulic energy, and the hydraulic energy is stored in an accumulator.

8. A crane lifting task control device, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire the lifting task parameters of the crane; The processing module (202) is used to construct a coupling model of the crane and the wind turbine blade according to the lifting task parameters; The processing module (202) is further used to determine first control data of the crane according to the coupling model; The acquisition module (201) is further used to acquire current environmental data, wherein the current environmental data includes wind speed and wind direction; The processing module (202) is further used to determine the wind load distribution corresponding to the current environmental data in a preset wind load database; The processing module (202) is further used to calculate the position deviation and attitude deviation of the crane lifting point according to the wind load distribution, and calculate the wind load compensation vector according to the position deviation and the attitude deviation; The processing module (202) is further used to superimpose the wind load compensation vector into the first control data to generate second control data, wherein the second control data includes a carrying instruction of the crane; The processing module (202) is further used to convert the second control data into a servo control instruction, and drive the crane to operate according to the transport trajectory according to the servo control instruction.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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