Methods, devices, equipment, and storage media for controlling loads on crane vessels

Abstract

This invention provides a method, apparatus, equipment, and storage medium for controlling loads on a crane vessel. It acquires real-time environmental parameters, load parameters, and crane vessel operational parameters via sensors. These parameters are input into a parameter generation model to obtain initial control parameters. The initial control parameters are then optimized using a fitness function to obtain target control parameters. Based on these target control parameters, the crane vessel is controlled to move the load to the target position. This invention effectively reduces positional errors during load operation and significantly improves the accuracy of load control by real-time acquisition of environmental parameters, load parameters, and crane vessel operational parameters, and by introducing a parameter generation model and a fitness function to optimize the control parameters. Furthermore, by evaluating the control parameters using a fitness function and incorporating vessel stability, load operation accuracy, and task completion into the optimization objectives, it effectively improves load movement efficiency, shortens operation time, and enhances operation quality.

Description

Technical Field

[0001] This invention relates to the field of crane vessel technology, and in particular to a method, apparatus, equipment and storage medium for controlling the hoisting of objects based on a crane vessel. Background Technology

[0002] Crane vessels, commonly used in marine engineering, are widely employed in lifting and transportation tasks. In actual operation, crane vessels face complex marine environments, including the influence of wind, waves, and currents, leading to lower precision in load control and impacting task efficiency and safety. Furthermore, the stability and accuracy of the loads lifted by the crane vessel directly affect the smooth progress of the project. Traditional load control methods rely on manual operation and simple feedback control, which are insufficient to effectively cope with complex dynamic environments, exhibiting shortcomings such as low precision and poor adaptability.

[0003] In summary, the problems existing in the current technology urgently need to be solved. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and storage medium for controlling suspended loads based on a crane vessel, in order to overcome the deficiencies in the prior art and improve the accuracy of suspended load control.

[0005] This invention provides a method for controlling the hoisting of objects based on a crane vessel, comprising:

[0006] Real-time environmental parameters, real-time load parameters, and real-time operational parameters of the crane vessel are obtained through sensors.

[0007] The real-time environmental parameters, the real-time load parameters, and the real-time operation parameters are input into the parameter generation model to obtain the initial control parameters;

[0008] The initial control parameters are optimized using a fitness function to obtain the target control parameters;

[0009] Based on the target control parameters, the crane vessel is controlled to move the load to the target position.

[0010] According to the lifting control method based on a crane vessel provided by the present invention, the parameter generation model is trained through the following steps:

[0011] Obtain the training dataset, including crane vessel operation parameters, load motion parameters, and operation environment parameters;

[0012] The parameters are set to generate the weight function of the model, and the value of the weight function is determined based on the iteration results;

[0013] Based on the weighting function, a parameter generation model is constructed using the particle swarm optimization algorithm.

[0014] The parameter generation model is trained based on the training dataset until a preset number of iterations is reached.

[0015] According to the present invention, a method for controlling the hoisting load based on a crane vessel, wherein the step of setting parameters to generate a weight function for the model, and determining the value of the weight function based on the iteration results, specifically includes:

[0016] Construct an adaptive adjustment function for inertia weights;

[0017] The adaptive adjustment function of inertia weight is transformed into a nonlinear inertia weight function, and the nonlinear inertia weight function is used as the weight function of the parameter generation model.

[0018] According to the lifting control method based on a crane vessel provided by the present invention, the step of converting the adaptive adjustment function of inertial weight into a nonlinear inertial weight function is achieved in the following way:

[0019]

[0020] Where, ω start ω is the initial inertia weight. end To terminate the inertia weights, t is the current iteration number; t max The initial maximum number of iterations is set, and k is a control factor.

[0021] According to the lifting control method based on a crane vessel provided by the present invention, the step of constructing a parameter generation model based on the particle swarm optimization algorithm according to the weight function is achieved in the following way:

[0022] v i (t+1)=ω·v i (t)+c1r1(t)[p i (t)-x i (t)]+c2r2(t)[p g -x i [(t)]+α·δ(t)

[0023] Among them, v i (t+1) represents the velocity of particle i at time t+1, ω is the inertial weight, which controls the inertial effect on the particle's velocity, and v i (t) represents the velocity of particle i at time t, c1 is the first learning factor used to adjust the velocity of the particle moving towards its optimal position, c2 is the second learning factor used to adjust the velocity of the particle moving towards the global optimal position, r1 and r2 are random numbers with values ​​ranging from [0, 1], used to introduce randomness to increase the diversity of the search, p i (t) represents the historical best position of particle i at time t, p gFor the optimal position of the group globally, x i (t) represents the position of particle i at time t, α is the adjustment coefficient of the escape factor, which is usually increased after the local optimum is identified to enhance randomness, δ(t) is the random perturbation term used to enhance the exploratory nature of the particle, r3 is a random number between [0, 1], and x max and x min These are the upper and lower boundaries of the particle search space, respectively.

[0024] According to the lifting control method based on a crane vessel provided by the present invention, the step of optimizing the initial control parameters through a fitness function to obtain the target control parameters is achieved in the following way:

[0025] S(x i )=ω1·S stability (V wind H wave T wave V flow θ flow θ pitch θ roll θ heave )+ω2·S operation (V wind H wave T wave V flow θ flow h lifting )+ω3·S task (x lifting x horizontal x target )

[0026] Among them, S stability To address the impact of the marine environment on ship stability, S operation To ensure the operational precision of lifting, S target V represents the completion rate of the hoisting task. wind For wind speed, H wave Wave height, T wave For period, V flow For flow velocity, θ flow For the direction of flow, θ pitc h is the ship's pitch angle, θ roll For the ship's roll angle and θ Heave h is the ship's heave angle. lifting The lifting height of the suspended object, (x lifting y lifting ) represents the current position of the suspended object and (x) represents the position of the suspended object. target y target () indicates the target location of the suspended object.

[0027] According to a crane vessel-based load control method provided by the present invention, after the step of controlling the crane vessel to move the load to the target position according to the target control parameters, the method further includes:

[0028] Obtain the actual position of the suspended object;

[0029] Determine the error information between the actual position of the suspended object and the target position;

[0030] Based on the error information, feedback information is generated, which is used to adjust the parameter generation accuracy of the parameter generation model.

[0031] The present invention also provides a lifting control device based on a crane vessel, comprising:

[0032] The data acquisition module is used to acquire real-time environmental parameters, real-time load parameters, and real-time operating parameters of the crane vessel through sensors.

[0033] The parameter generation module is used to input the real-time environmental parameters, the real-time suspended object parameters, and the real-time operation parameters into the parameter generation model to obtain the initial control parameters;

[0034] The parameter optimization module is used to optimize the initial control parameters using a fitness function to obtain the target control parameters;

[0035] The load-moving module is used to control the crane vessel to move the load to the target position according to the target control parameters.

[0036] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the lifting control method based on the crane ship as described above.

[0037] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lifting control method based on a crane ship as described above.

[0038] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the lifting control method based on a crane ship as described above.

[0039] This invention provides a method, apparatus, equipment, and storage medium for controlling loads on a crane vessel. It acquires real-time environmental parameters, load parameters, and crane vessel operational parameters via sensors. These parameters are then input into a parameter generation model to obtain initial control parameters. A fitness function is used to optimize the initial control parameters, yielding target control parameters. Based on these target control parameters, the crane vessel is controlled to move the load to the target position. This invention effectively reduces positional errors during load handling and significantly improves control accuracy by real-time acquisition of environmental, load, and crane vessel operational parameters and the introduction of a parameter generation model and fitness function to optimize the control parameters. Furthermore, by evaluating the control parameters using a fitness function and incorporating vessel stability, load handling accuracy, and task completion into the optimization objectives, it effectively improves load movement efficiency, shortens operation time, and enhances operational quality. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the lifting control method based on a crane vessel provided by the present invention.

[0042] Figure 2 This is a schematic diagram of the lifting control device based on a crane ship provided by the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0045] To address the problems in existing technologies, this invention proposes a crane vessel-based load control method to improve the accuracy of load control. The crane vessel-based load control method is described below, as follows: Figure 1 As shown, including but not limited to the following steps:

[0046] Step 110: Obtain real-time environmental parameters, real-time load parameters, and real-time operating parameters of the crane vessel through sensors.

[0047] In step 110, the sensors may include, but are not limited to, environmental monitoring sensors, load position sensors, and ship motion sensors.

[0048] Environmental monitoring sensors are used to collect real-time marine environmental parameters, such as wind speed, wave height, wave period, ocean current speed, and ocean current direction.

[0049] The suspended object position sensor is used to detect the real-time position coordinates of the suspended object, the height of the suspended object, and other parameters.

[0050] Hull motion sensors are used to monitor the real-time motion status of the crane vessel, such as the ship's pitch angle, roll angle, and heave angle.

[0051] Step 120: Input the real-time environmental parameters, the real-time load parameters, and the real-time operation parameters into the parameter generation model to obtain the initial control parameters.

[0052] In this step, the parameter generation model is constructed as follows:

[0053] The training dataset includes crane vessel operation parameters, load motion parameters, and operation environment parameters.

[0054] The parameter generation model is trained using the particle swarm optimization algorithm, and a weight function is set. The weight function is adjusted by an adaptive inertia weight function to improve the model's optimization ability and convergence speed.

[0055] By inputting real-time environmental parameters, load parameters, and operational parameters into the trained parameter generation model, initial control parameters can be obtained.

[0056] Step 130: Optimize the initial control parameters using the fitness function to obtain the target control parameters.

[0057] In this step, the fitness function is used to evaluate the quality of the initial control parameters, and is implemented using the following formula:

[0058] F fitness =w1S stability +w2S operation +w3S target

[0059] Among them, S stability To address the impact of the marine environment on ship stability, S operation To ensure the operational precision of lifting, S target The task completion rate of the hoisting is represented by ω1, ω2, and ω3, which are the weight values ​​of each indicator.

[0060] The fitness function takes into account the impact of environmental factors such as wind speed, wave height, and ship motion angle on the stability of the crane vessel, as well as operational accuracy such as the positional deviation of the hoisted object, to ensure that the target control parameters can meet the actual operation requirements.

[0061] Step 140: According to the target control parameters, control the crane vessel to move the load to the target position.

[0062] In step 140, the optimized target control parameters are sent to the actuators of the crane vessel via the control system, controlling the crane vessel's operating device to smoothly move the suspended load to the target position along the set path. Specifically, the control system monitors the current position (x, y) of the suspended load in real time. lifting y lifting ) and target position (x target y target The error between the two is dynamically adjusted based on the error information to ensure the smooth movement and high-precision positioning of the load.

[0063] This invention effectively improves the operational accuracy and stability of crane vessels by acquiring operational parameters in real time, establishing a parameter generation model, and introducing a fitness function to optimize control parameters. It also exhibits good robustness and adaptability, especially in complex marine environments.

[0064] As a further optional embodiment, the parameter generation model is trained through the following steps:

[0065] Obtain the training dataset, including crane vessel operation parameters, load motion parameters, and operation environment parameters;

[0066] The parameters are set to generate the weight function of the model, and the value of the weight function is determined based on the iteration results;

[0067] Based on the weighting function, a parameter generation model is constructed using the particle swarm optimization algorithm.

[0068] The parameter generation model is trained based on the training dataset until a preset number of iterations is reached.

[0069] In this embodiment, a training dataset is obtained from historical job data. The dataset includes the following:

[0070] Crane vessel operating parameters: the movement of the crane vessel in different marine environments;

[0071] Motion parameters of the suspended object: the real-time motion status of the suspended object;

[0072] Operating environment parameters: wind speed, wave height, wave period, ocean current speed, and ocean current direction, etc.

[0073] Next, a weighting function is introduced into the parameter generation model to balance the importance of different parameters in the model. The initial value of the weighting function is determined through empirical data and dynamically adjusted based on the results of subsequent iterations.

[0074] The weighting function is defined as:

[0075] w k =f(ΔE) k )

[0076] ω1 is the weight of the k-th class parameter, ΔE k Let f(·) represent the error contribution of the k-th class of parameters to the model performance, and let f(·) be the weight update function, which is optimized by the gradient descent algorithm of the error.

[0077] Subsequently, the particle swarm optimization algorithm is used to optimize the structure and weights of the parameter generation model. This algorithm is implemented through the following steps:

[0078] Set the initial position x for each particle i (0) and initial velocity y i (0), randomly distributed in the parameter space;

[0079] Using the training dataset, calculate the fitness function for each particle;

[0080] Based on the particle's historical best position p i and the global optimal position p g Adjust the position and velocity of the particles, and update the formula as follows:

[0081] v i (t+1)=ω·v i (t)+c1r1(t)[p i (t)-x i (t)]+c2r2(t)[p g -x i (t)]

[0082] x i (t+1)=x i (t)+v i (t+1)

[0083] Among them, v i (t+1) represents the velocity of particle i at time t+1, ω is the inertial weight, which controls the inertial effect on the particle's velocity, and v i (t) represents the velocity of particle i at time t, c1 is the first learning factor used to adjust the velocity of the particle moving towards its optimal position, c2 is the second learning factor used to adjust the velocity of the particle moving towards the global optimal position, r1 and r2 are random numbers with values ​​ranging from [0, 1], used to introduce randomness to increase the diversity of the search, pi (t) represents the historical best position of particle i at time t, p g For the optimal position of the group globally, x i (t) represents the position of particle i at time t, α is the adjustment coefficient of the escape factor, which is usually increased after the local optimum is identified to enhance randomness, δ(t) is the random perturbation term used to enhance the exploratory nature of the particle, r3 is a random number between [0, 1], and x max and x min These are the upper and lower boundaries of the particle search space, respectively.

[0084] The particle swarm optimization process stops when the fitness function reaches a preset threshold or the number of iterations reaches its upper limit. By optimizing the parameter generation model using the particle swarm optimization algorithm, the model can quickly converge to the global optimum in a complex parameter space.

[0085] The parameter generation model is trained based on the training dataset.

[0086] The acquired training dataset is input into the parameter generation model, and the model parameters are gradually optimized using batch training. During model training, the error is calculated using the following objective function:

[0087]

[0088] Where: L is the loss function; y i The actual value; is the model's predicted value; N is the total number of samples.

[0089] During training, the model parameters are continuously optimized until the preset number of iterations is reached or the loss function value is less than a set threshold.

[0090] The parameter generation model trained through the above steps can generate initial control parameters more efficiently, significantly improving the accuracy and stability of load control and providing a solid foundation for subsequent optimization and operation.

[0091] This embodiment combines particle swarm optimization (PSO) with a dynamic weight adjustment mechanism to make the parameter generation model more efficient and accurate, significantly improving the model's adaptability to complex operating environments. In practical applications, it can quickly generate control parameters that meet operational requirements, improving the crane vessel's operational efficiency and safety.

[0092] As a further optional embodiment, the step of setting parameters to generate the weight function of the model, wherein the value of the weight function is determined based on the iteration results, specifically includes:

[0093] Construct an adaptive adjustment function for inertia weights;

[0094] The adaptive adjustment function of inertia weight is transformed into a nonlinear inertia weight function, and the nonlinear inertia weight function is used as the weight function of the parameter generation model.

[0095] In this embodiment, inertia weight is an important parameter in the particle swarm optimization algorithm, used to balance global and local search capabilities. To enable the inertia weight to dynamically adapt to different stages of the optimization process, this embodiment constructs an adaptive adjustment function for the inertia weight, the expression of which is:

[0096]

[0097] Among them, T max ω represents the maximum number of generations; max ω represents the maximum inertia weight. min t represents the minimum inertia weight; t represents the current iteration number.

[0098] Through this adjustment function, the inertia weight can be dynamically adjusted according to the changes in the current fitness function. In the early stage of optimization, a larger weight value is maintained to enhance the global search capability; in the later stage of optimization, the weight value is reduced to improve the accuracy and stability of the local search.

[0099] In practical applications, adjusting linear inertia weights may not meet the optimization requirements of complex parameter spaces. Therefore, the adaptive adjustment function of inertia weights is further transformed into a nonlinear inertia weight function to improve the flexibility and efficiency of the optimization process. The expression for the nonlinear inertia weight function is:

[0100]

[0101] Where, ω start ω is the initial inertia weight. end To terminate the inertia weights, t is the current iteration number; t max The initial maximum number of iterations is set, and k is a control factor.

[0102] This nonlinear function optimizes the initial slow decrease rate of the inertia weight, allowing for a more thorough exploration of the global search space; in the later stages of optimization, the decrease rate of the inertia weight accelerates, thus focusing on the local search.

[0103] As a further optional embodiment, the step of converting the adaptive adjustment function of inertia weight into a nonlinear inertia weight function is achieved in the following way:

[0104]

[0105] Where, ω start ω is the initial inertia weight. end To terminate the inertia weights, t is the current iteration number; tmax The initial maximum number of iterations is set, and k is a control factor.

[0106] This embodiment achieves refined control over the dynamic adjustment of weights by transforming the adaptive inertia weight adjustment function into a nonlinear inertia weight function and applying it to the parameter generation model. During the optimization process, the model can dynamically adjust the weight ratio of global search and local search according to the needs of different stages, thereby significantly improving the convergence speed and final accuracy of the model. In practical applications, this embodiment can improve the generation efficiency of control parameters for crane vessel operations, further enhancing the safety and stability of crane operations.

[0107] As a further optional embodiment, the step of constructing a parameter generation model based on the particle swarm optimization algorithm according to the weight function is implemented in the following way:

[0108] v i (t+1)=ω·v i (t)+c1r1(t)[p i (t)-x i (t)]+c2r2(t)[p g -x i [(t)]+α·δ(t)

[0109] Among them, v i (t+1) represents the velocity of particle i at time t+1, ω is the inertial weight, which controls the inertial effect on the particle's velocity, and v i (t) represents the velocity of particle i at time t, c1 is the first learning factor used to adjust the velocity of the particle moving towards its optimal position, c2 is the second learning factor used to adjust the velocity of the particle moving towards the global optimal position, r1 and r2 are random numbers with values ​​ranging from [0, 1], used to introduce randomness to increase the diversity of the search, p i (t) represents the historical best position of particle i at time t, p g For the optimal position of the group globally, x i (t) represents the position of particle i at time t, α is the adjustment coefficient of the escape factor, which is usually increased after the local optimum is identified to enhance randomness, δ(t) is the random perturbation term used to enhance the exploratory nature of the particle, r3 is a random number between [0, 1], and x max and x min These are the upper and lower boundaries of the particle search space, respectively.

[0110] In this embodiment, when the particle swarm is determined to be trapped in a local optimum, the probability of its escape is increased by adjusting the particle velocity update formula. The escape mechanism is implemented by enhancing the randomness factor; therefore, an escape factor α is added to the particle swarm optimization algorithm, where α is an adjustment coefficient for the escape factor, which is typically increased after the local optimum is identified to enhance randomness. δ(t) is a random perturbation term used to enhance the exploratory nature of the particles; it can be generated using random numbers, where r3 is a random number between [0, 1], and x... max and x min These are the upper and lower boundaries of the particle search space, respectively.

[0111] As a further optional embodiment, the step of optimizing the initial control parameters using a fitness function to obtain the target control parameters is achieved in the following way:

[0112] S(x i )=ω1·S stability (V wind H wave T wave V flow θ flow θ pitch θ roll θ heave )+ω2·S operation (V wind H wave T wave V flow θ flow h lifting )+ω3·S task (x lifting x horizontal x target )

[0113] Among them, S stability To address the impact of the marine environment on ship stability, S operation To ensure the operational precision of lifting, S target V represents the completion rate of the hoisting task. wind For wind speed, H wave Wave height, T wave For period, V flow For flow velocity, θ flow For the direction of flow, θ pitch For the ship's pitch angle, θ roll For the ship's roll angle and θ Heave h is the ship's heave angle. lifting The lifting height of the suspended object, (x lifting y lifting ) represents the current position of the suspended object and (x) represents the position of the suspended object. target y target() indicates the target location of the suspended object.

[0114] During actual lifting operations, the IPSO algorithm calculates the appropriate steering angle and lifting operation commands based on real-time collected marine environmental parameters and the crane vessel's attitude information. Input marine environmental parameters include wind speed V. wind Wave height H wave Period T wave Flow velocity V flow and the direction of flow θ flow Etc. Ship data is derived from the ship's position P. location and attitude, including pitch angle θ pitc h, roll angle θ roll and heave angle θ Heave Composition. The suspended object is raised or lowered by a height h. lifting The current position of the suspended object (x lifting y lifting ) and the target position of the suspended object (x target y target )composition.

[0115] The fitness function needs to be designed to comprehensively consider ship stability, lifting operation accuracy, and task completion rate. Using marine environment and ship attitude information as input, the optimization results are calculated using the following fitness function.

[0116] S(x i )=ω1·S stability (V wind H wave T wave V flow θ flow θ pitch θ roll θ heave )+ω2·S operation (V wind H wave T wave V flow θ flow h lifting )+ω3·S task (x lifting x horizontal x target )

[0117] Among them, S stability Based on ship attitude (pitch angle, roll angle), wind speed, wave height, wave period, current speed and direction, the impact of the marine environment on ship stability is considered. operation The lifting and lateral speeds of the suspended load are optimized for operational precision based on wind speed, current velocity, wave period, wave height, and the height of the suspended load. targetThe task completion rate of the suspended object is calculated based on the error between the current position and the target position.

[0118] In the particle swarm optimization algorithm, the position of the suspended particle is represented as:

[0119] x i =[θ rudder v lifting v horizontal ]

[0120] Where θ rudder It is the steering angle; v lifting It is the lifting speed of the suspended object; v horizontal It is the lateral velocity of the suspended object.

[0121] Next, the particle velocity is updated by improving the particle swarm optimization formula, and the particle position is updated by improving the position update formula. Based on the optimized optimal particle positions, the steering angle, hoisting / lowering commands, and lateral velocity are calculated according to the position values ​​of each particle. Assume the optimal particle position is:

[0122]

[0123] This updates the calculations for the steering angle, lifting speed, and lateral speed, as shown in the following formulas:

[0124]

[0125] By changing the marine environmental parameters from wind speed V wind Wave height H wave Period T wave Flow velocity V flow and the direction of flow θ flow By incorporating these parameters into the Particle Swarm Optimization (IPSO) algorithm, the control parameters of the crane vessel can be effectively optimized based on these marine environmental parameters, thereby calculating appropriate steering angles and lifting operation commands, and achieving efficient operation under complex sea conditions.

[0126] This embodiment significantly improves the optimization effect of control parameters for crane vessel operations by constructing a multi-factor fitness function that comprehensively considers the impact of the marine environment, the accuracy of load handling, and the degree of task completion. The dynamic optimization of the fitness function ensures that the target control parameters can adapt to the complex and ever-changing marine environment while meeting the precise positioning requirements of the load, thereby improving the safety and efficiency of crane vessel operations.

[0127] As a further optional embodiment, after the step of controlling the crane vessel to move the load to the target position according to the target control parameters, the method further includes:

[0128] Obtain the actual position of the suspended object;

[0129] Determine the error information between the actual position of the suspended object and the target position;

[0130] Based on the error information, feedback information is generated, which is used to adjust the parameter generation accuracy of the parameter generation model.

[0131] After the suspended load is moved to the target location, its actual position (x) is obtained in real time using high-precision sensors (such as GPS sensors, laser rangefinders, inertial navigation systems, etc.). actual y actual ).

[0132] Based on the target position and the actual position of the suspended object, calculate the error information between the two. The error formula is as follows:

[0133] Δx=x actual -x target

[0134] Δy=y actual -y target

[0135]

[0136] Where Δx and Δy are the errors in the horizontal and vertical directions between the actual position and the target position, respectively, and Δd is the Euclidean distance error between the actual position and the target position.

[0137] Based on the aforementioned error information Δx, Δy, and Δd, feedback information is generated to dynamically adjust the generation accuracy of the parameter generation model. The specific steps for generating the feedback information include:

[0138] If the error Δd is less than the preset threshold (e.g., 5 cm), the accuracy of the parameter generation model is considered to have met the requirements and no adjustment is needed.

[0139] If the error Δd exceeds the preset threshold, the error information is recorded and the parameter adjustment step is initiated.

[0140] Based on the error information, the generation accuracy of the parameter generation model is adjusted, specifically in the following ways:

[0141] Adjusting the weight function: Fine-tuning the weight coefficients (e.g., inertia weights) of the weight function in the parameter generation model to enhance the model's ability to optimize specific parameters;

[0142] Optimize the training dataset: Add the current error information and actual location data as new training data to the training dataset of the parameter generation model to improve the model's adaptability to similar environmental conditions.

[0143] Increase the number of iterations: Appropriately increase the number of iterations in model training to improve the model's generalization ability.

[0144] Based on the generated feedback information, the parameter generation model is dynamically updated to generate more accurate control parameters. The updated parameter generation model can be used for the next lifting operation, achieving adaptive optimization of the system.

[0145] The following describes the lifting control device based on a crane vessel provided by the present invention, such as... Figure 2 As shown, the crane vessel-based load control device described below and the crane vessel-based load control method described above can be referred to in correspondence.

[0146] A crane vessel-based hoisting control device includes:

[0147] The data acquisition module 210 is used to acquire real-time environmental parameters, real-time load parameters, and real-time operating parameters of the crane vessel through sensors.

[0148] The parameter generation module 220 is used to input the real-time environmental parameters, the real-time suspended object parameters, and the real-time operation parameters into the parameter generation model to obtain the initial control parameters;

[0149] The parameter optimization module 230 is used to optimize the initial control parameters through a fitness function to obtain the target control parameters;

[0150] The hoisting object moving module 240 is used to control the crane vessel to move the hoisting object to the target position according to the target control parameters.

[0151] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a crane-ship-based load control method, which includes:

[0152] Real-time environmental parameters, real-time load parameters, and real-time operational parameters of the crane vessel are obtained through sensors.

[0153] The real-time environmental parameters, the real-time load parameters, and the real-time operation parameters are input into the parameter generation model to obtain the initial control parameters;

[0154] The initial control parameters are optimized using a fitness function to obtain the target control parameters;

[0155] Based on the target control parameters, the crane vessel is controlled to move the load to the target position.

[0156] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0157] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the crane-based load control method provided by the above methods, the method comprising:

[0158] Real-time environmental parameters, real-time load parameters, and real-time operational parameters of the crane vessel are obtained through sensors.

[0159] The real-time environmental parameters, the real-time load parameters, and the real-time operation parameters are input into the parameter generation model to obtain the initial control parameters;

[0160] The initial control parameters are optimized using a fitness function to obtain the target control parameters;

[0161] Based on the target control parameters, the crane vessel is controlled to move the load to the target position.

[0162] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the crane-based load control method provided by the above methods, the method comprising:

[0163] Real-time environmental parameters, real-time load parameters, and real-time operational parameters of the crane vessel are obtained through sensors.

[0164] The real-time environmental parameters, the real-time load parameters, and the real-time operation parameters are input into the parameter generation model to obtain the initial control parameters;

[0165] The initial control parameters are optimized using a fitness function to obtain the target control parameters;

[0166] Based on the target control parameters, the crane vessel is controlled to move the load to the target position.

[0167] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0168] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the lifting of loads based on a crane vessel, characterized in that, include: Real-time environmental parameters, real-time load parameters, and real-time operational parameters of the crane vessel are obtained through sensors. The real-time environmental parameters, the real-time load parameters, and the real-time operation parameters are input into the parameter generation model to obtain the initial control parameters; The initial control parameters are optimized using a fitness function to obtain the target control parameters; Based on the target control parameters, the crane vessel is controlled to move the load to the target position; The error between the current position of the suspended object and the target position is monitored in real time, and the control commands of the crane vessel are dynamically adjusted based on the error information. The step of optimizing the initial control parameters using a fitness function to obtain the target control parameters is achieved in the following way: in, The impact of the marine environment on ship stability, To ensure the precision of lifting operations, V represents the completion rate of the hoisting task. wind For wind speed, H wave Wave height, T wave For period, V flow For flow rate, For flow direction, For ship pitch angle, For the ship's roll angle and h is the ship's heave angle. lifting The lifting height of the suspended object, (x) lifting y lifting ) represents the current position of the suspended object and (x) represents the position of the suspended object. target y target () indicates the target location of the suspended object. These are the weight values ​​for each indicator; The parameter generation model is trained through the following steps: Obtain the training dataset, including crane vessel operation parameters, load motion parameters, and operation environment parameters; The parameters are set to generate the weight function of the model, and the value of the weight function is determined based on the iteration results; Based on the weighting function, a parameter generation model is constructed using the particle swarm optimization algorithm. The parameter generation model is trained based on the training dataset until a preset number of iterations is reached; The step of setting parameters to generate the model's weight function, and determining the value of the weight function based on the iteration results, specifically includes: Construct an adaptive adjustment function for inertia weights; The adaptive adjustment function of inertia weight is transformed into a nonlinear inertia weight function, and the nonlinear inertia weight function is used as the weight function of the parameter generation model. The step of transforming the adaptive adjustment function of inertia weight into a nonlinear inertia weight function is achieved in the following way: in, As the initial inertia weight, To terminate the inertia weight, This represents the current iteration number; The initial maximum number of iterations. As a control factor; After the step of controlling the crane vessel to move the load to the target position according to the target control parameters, the method further includes: Obtain the actual position of the suspended object; Determine the error information between the actual position of the suspended object and the target position; Based on the error information, feedback information is generated, which is used to adjust the parameter generation accuracy of the parameter generation model.

2. A lifting control device based on a crane vessel, characterized in that, The method for controlling the hoisted load based on a crane vessel as described in claim 1 includes: The data acquisition module is used to acquire real-time environmental parameters, real-time load parameters, and real-time operating parameters of the crane vessel through sensors. The parameter generation module is used to input the real-time environmental parameters, the real-time suspended object parameters, and the real-time operation parameters into the parameter generation model to obtain the initial control parameters; The parameter optimization module is used to optimize the initial control parameters using a fitness function to obtain the target control parameters; The load-moving module is used to control the crane vessel to move the load to the target position according to the target control parameters; it is also used to monitor the error between the current position of the load and the target position in real time, and dynamically adjust the control commands of the crane vessel according to the error information.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the lifting control method based on the crane ship as described in claim 1.

4. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the lifting control method based on the crane ship as described in claim 1.

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