A large gantry crane shifting and vehicle allocation optimization method and system based on spmt

Abstract

This invention discloses a method and system for optimizing the relocation and allocation of large gantry cranes based on SPMT (Specialized Spatial Management Machine), relating to the field of SPMT vehicle allocation technology. The method includes: establishing an iterative optimization framework connecting upper, middle, and lower layers; feeding back the dynamic risk value output by the lower-layer risk assessment model to the upper-layer optimization model to generate a new candidate configuration set; subsequently, performing simulation prediction by the middle-layer model and risk assessment by the lower-layer model on the new candidate configuration set again, until the generated candidate configuration set meets the preset convergence conditions. This achieves integrated dynamic and static optimization of the SPMT grouping scheme, overcoming the shortcomings of traditional empirical methods that rely on static estimation and cannot assess dynamic risks. It can significantly reduce vehicle configuration redundancy and operating costs while ensuring absolute safety, ultimately outputting a safe, reliable, and economically optimal relocation and allocation scheme.

Description

Technical Field

[0001] This invention relates to the field of SPMT vehicle matching technology, and in particular to an optimized method and system for the relocation and vehicle matching of large gantry cranes based on SPMT. Background Technology

[0002] As a key heavy equipment in ports, docks, shipyards, and large equipment manufacturing bases, the overall relocation of large gantry cranes is a complex and costly project. Traditional relocation methods often use track sliding or a large number of hydraulic lifting modules in conjunction with temporary tracks. These methods have drawbacks such as cumbersome procedures, long cycles, high foundation requirements, and high risks.

[0003] In recent years, with the maturity of Self-Propelled Modular Transporter (SPMT) technology, the use of multiple SPMTs working in parallel to achieve the overall handling and relocation of large structures by "carrying" has become the mainstream method for moving large gantry cranes. This method has significant advantages such as flexibility, high efficiency, minimal damage to the ground, and the ability to plan paths.

[0004] However, in the practical application of SPMT-based large gantry crane relocation solutions, a key technical bottleneck and optimization challenge is encountered: how to scientifically and efficiently determine the optimal configuration of SPMT vehicles. Specifically, existing methods often rely on conservative and rough mechanical estimations or analogies to past project experience to determine the number of SPMT trains, axle load distribution, and overall layout. They lack detailed consideration of the dynamic working conditions of the vehicles. Dynamic conditions such as starting, braking, steering, and road unevenness during the relocation process will cause a redistribution of loads among the SPMT axle groups. Existing experience-based vehicle matching methods based on static estimation are difficult to accurately predict and cope with these dynamic effects, and cannot assess the impact of dynamic load impacts on the SPMT and gantry crane structures, resulting in insufficient robustness of the solution.

[0005] Therefore, there is an urgent need for a scientific vehicle matching optimization method that can comprehensively consider SPMT performance parameters and dynamic shifting conditions to achieve safe, reliable, cost-effective, efficient and convenient shifting operations. Summary of the Invention

[0006] This invention provides an optimized method for the relocation and vehicle matching of large gantry cranes based on SPMT (Special Power Module), comprising:

[0007] Step 1: Obtain the gantry crane structural parameters, predetermined displacement path parameters, and SPMT vehicle performance parameters;

[0008] Step 2: Based on the structural parameters of the gantry crane and the performance parameters of the SPMT vehicle, construct an upper-level optimization model to generate a candidate configuration set that satisfies the static support stability and axle load limit constraints and minimizes the total configuration cost.

[0009] Step 3: Construct a mid-level dynamic coupling model based on the generated candidate configuration set, and predict the dynamic load time history of each SPMT shaft group by simulating the dynamic working conditions generated by the predetermined displacement path parameters.

[0010] Step 4: Construct a lower-level risk assessment model based on the predicted dynamic load time history to quantify the dynamic risk of the candidate configuration set;

[0011] Step 5: Establish an iterative optimization framework that connects the upper, middle, and lower layers: Feed the dynamic risk value output by the lower-layer risk assessment model back to the upper-layer optimization model to generate a new candidate configuration set. Then, perform simulation prediction by the middle-layer model and risk assessment by the lower-layer model on the new candidate configuration set again until the generated candidate configuration set meets the preset convergence conditions.

[0012] The SPMT-based optimization method for large gantry crane relocation and vehicle allocation, as described above, involves constructing an upper-level optimization model based on the gantry crane's structural parameters and SPMT vehicle performance parameters. This model generates a candidate configuration set that satisfies static support stability and axle load limits while minimizing total configuration cost. The specific steps are as follows:

[0013] Based on the obtained gantry crane structural parameters and SPMT vehicle performance parameters, a parameter set that can be directly called by the optimization model is constructed.

[0014] Define the decision variables of the optimization model and construct the objective function based on the optimization objective;

[0015] The engineering requirements for gantry crane relocation and vehicle allocation are transformed into identifiable mathematical constraints;

[0016] The improved genetic algorithm is used as the solver to iterate over the decision variables. During the iteration process, the parameter set is called to calculate the objective function and search for the optimal solution that satisfies the aforementioned mathematical constraints, which is then output as the current candidate configuration set.

[0017] The SPMT-based large gantry crane shifting and vehicle matching optimization method described above involves constructing a mid-level dynamic coupling model based on the generated candidate configuration set. By simulating the dynamic working conditions generated by the predetermined shifting path parameters, the dynamic load time history of each SPMT axle group is predicted. Specifically, it consists of the following sub-steps:

[0018] Based on the structural parameters and candidate configuration set of the gantry crane, a dynamic digital prototype coupling the gantry crane and the SPMT vehicle fleet was constructed;

[0019] Dynamic operating condition input data is generated based on the predetermined shift path parameters;

[0020] Based on the constructed dynamic digital prototype and dynamic working condition input data, a dynamic solver is configured and a displacement simulation operation is performed;

[0021] After the simulation is completed, the dynamic load time history data of each SPMT axis group is extracted from the solver results.

[0022] The SPMT-based optimization method for moving and matching large gantry cranes, as described above, involves constructing a lower-level risk assessment model based on the predicted dynamic load time history to quantify the dynamic risk of the candidate configuration set. This process is divided into the following sub-steps:

[0023] The quantitative results of three indicators—instantaneous overload, load fluctuation, and coordinated imbalance—are calculated based on the predicted dynamic load time history.

[0024] The weights are dynamically assigned based on the degree of adverseness of the indicators themselves, and the three indicators are then merged into a dynamic risk value according to the assigned weights.

[0025] The present invention also provides a large gantry crane shifting and vehicle allocation optimization system based on SPMT, including: a parameter acquisition module, a candidate configuration set generation module, a dynamic coupling module, a risk assessment module, and a cross-layer iteration module;

[0026] The parameter acquisition module is used to acquire the gantry crane structural parameters, the predetermined displacement path parameters, and the SPMT vehicle performance parameters;

[0027] The candidate configuration set generation module is used to build an upper-level optimization model based on the gantry crane structural parameters and SPMT vehicle performance parameters, and generate a candidate configuration set that meets the constraints of static support stability and axle load limit and has the lowest total configuration cost.

[0028] The dynamic coupling module is used to build a mid-level dynamic coupling model based on the generated candidate configuration set. It predicts the dynamic load time history of each SPMT shaft group by simulating the dynamic working conditions generated by the predetermined displacement path parameters.

[0029] The risk assessment module is used to build a lower-level risk assessment model based on the predicted dynamic load time history, and to quantify the dynamic risk of the candidate configuration set.

[0030] The cross-layer iterative module is used to establish an iterative optimization framework that connects the upper, middle, and lower layers: the dynamic risk value output by the lower-layer risk assessment model is fed back to the upper-layer optimization model to generate a new candidate configuration set. Then, the simulation prediction of the middle-layer model and the risk assessment of the lower-layer model are performed again on the new candidate configuration set until the generated candidate configuration set meets the preset convergence conditions.

[0031] The beneficial effects achieved by this invention are as follows: By constructing a three-layer coupled model of static configuration, dynamic simulation and risk assessment and a closed-loop iterative optimization framework, the integrated optimization of the SPMT grouping scheme is realized. This overcomes the shortcomings of traditional empirical methods that rely on static estimation and cannot assess dynamic risks. Under the premise of ensuring absolute safety, it can significantly reduce vehicle configuration redundancy and operating costs, and finally output a safe, reliable and economically optimal vehicle relocation scheme. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present 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 only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0033] Figure 1 This is a flowchart of a method for optimizing the relocation and vehicle allocation of a large gantry crane based on SPMT, provided in Embodiment 1 of this application;

[0034] Figure 2 This is a schematic diagram of a large gantry crane relocation and vehicle matching optimization system based on SPMT provided in Embodiment 2 of this application. Detailed Implementation

[0035] The technical solutions of the embodiments of the present 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 the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1

[0037] like Figure 1 As shown, Embodiment 1 of this application provides a method for optimizing the relocation and vehicle allocation of large gantry cranes based on SPMT, including:

[0038] Step S10: Obtain the gantry crane structural parameters, predetermined displacement path parameters, and SPMT vehicle performance parameters;

[0039] The process for obtaining and processing the structural parameters of the gantry crane is as follows:

[0040] The total mass of the gantry crane and its three-dimensional centroid coordinates in the structural coordinate system are obtained through design drawings, calculation sheets, or on-site weighing and measurement.

[0041] Obtain the spatial coordinates of the support points preset for the main support structure (usually the outriggers) of the gantry crane;

[0042] Obtain the structural stiffness parameters of key load-bearing components such as the main beam and outriggers of the gantry crane;

[0043] The above parameters are normalized and the coordinate system is unified, transforming all spatial coordinates into a global coordinate system with the ground as the reference, laying the foundation for subsequent layout optimization and mechanical calculations.

[0044] The process for obtaining and processing the predetermined shift path parameters is as follows:

[0045] Obtain the starting and ending coordinates of the shift path, as well as the trajectory of the path's centerline;

[0046] Sampling is performed at fixed intervals along the centerline of the path to obtain the plane coordinates and elevation of each sampling point, thereby determining the longitudinal and transverse slopes of the path.

[0047] Identify all turning segments in the path and extract their turning radii;

[0048] Assess the smoothness information of the road surface during the relocation operation and quantify it as the power spectral density (PSD) of road surface roughness.

[0049] SPMT vehicle performance parameters are obtained from suppliers or extracted from equipment manuals, including:

[0050] Basic parameters of a single vehicle: model number, rated load per axle, number of standard axles per SPMT, axle spacing (longitudinal and transverse), and platform dimensions;

[0051] Driving and driving parameters: maximum speed, maximum traction / braking force, acceleration and deceleration range;

[0052] Steering system parameters: supported steering modes (such as straight, diagonal, lateral, center turn, etc.), minimum steering radius, and maximum steering angle of each axle group;

[0053] Suspension system parameters: static stiffness and damping coefficient of hydraulic suspension, maximum stroke, response time constant of load balancing system, and balancing accuracy;

[0054] Tire parameters: tire vertical stiffness and damping;

[0055] Standardize and validate the data in the parameter library to ensure consistent units and identify the variable range of each parameter (such as different configurations achieved by adding or removing axis modules).

[0056] Step S20: Based on the structural parameters of the gantry crane and the performance parameters of the SPMT vehicle, construct an upper-level optimization model to generate a candidate configuration set that satisfies the static support stability and axle load limit constraints and minimizes the total configuration cost;

[0057] The process involves receiving the structural parameters of the gantry crane and the performance parameters of the SPMT vehicle, transforming them into a structured mathematical optimization model, and then using a numerical solution algorithm to obtain a candidate configuration set that meets the static constraints. The process is divided into the following sub-steps:

[0058] Step S21: Based on the obtained gantry crane structural parameters and SPMT vehicle performance parameters, construct a parameter set that can be directly called by the optimization model;

[0059] The parameter set includes the following four core parameters:

[0060] 1. Geometric and mass parameters: Total mass of the gantry crane 3D centroid coordinates The set of coordinates of the pre-set contact support points of the gantry crane in the global coordinate system. , The number of support points;

[0061] 2. Discretized mesh parameters: The area at the bottom of the gantry crane where vehicles can be arranged is divided into... Each grid The center coordinates are represented as , , ;

[0062] 3. A subset of SPMT model parameters, represented as follows:

[0063] ,in Let the unit cost of the k-th model be... For the cost of a single shaft, Let be the number of axles for the k-th type of vehicle. , Let be the platform length and width for the k-th vehicle type. This refers to the rated load-bearing capacity of a single shaft. This represents the total number of SPMT models.

[0064] 4. Optimize weights and coefficients: Cost coefficient of a single SPMT unit Cost coefficient of a single shaft Load imbalance penalty weight Constraining the penalty weight for violations Static safety factor These weights and coefficients are all preset values ​​of the system.

[0065] Step S22: Define the decision variables of the optimization model and construct the objective function based on the optimization objective;

[0066] The decision variables of the optimization model are defined as Boolean type, represented as: ,in , , , Indicates in the grid ( Deploy a model of at the geometric center of ) SPMT, This indicates that SPMT will not be deployed at this grid location;

[0067] Based on the optimization objective of "satisfying static support stability and axle load limits while minimizing total configuration cost", the objective function is constructed as follows:

[0068]

[0069] in The return value of the objective function. To calculate the total cost of the vehicle model based on the current decision variables, , The total number of axes calculated based on the current decision variables. = , The total cost of the axis calculated based on the current decision variables, , , Let m be the static load on the m-th axis. The average value of the static load across all axes, where m ranges from 1 to... Static loads on each axis Solving by establishing static equilibrium equations: Treating the gantry crane as a rigid body, all activated SPMTs (i.e., The platform stiffness together forms a support system. Based on the balance of force and moment, and considering the distribution model of the support point flexibility, the load distributed to each axis is calculated.

[0070] Step S23: Transform the engineering requirements for gantry crane relocation and vehicle allocation into identifiable mathematical constraints;

[0071] The transformed identifiable mathematical constraints include:

[0072] Uniqueness constraint, meaning that at most one SPMT can be placed at each grid location, is expressed as: ;

[0073] Coverage constraint, meaning that each support point of the gantry crane must be covered by at least one SPMT platform, is expressed as: ;

[0074] Axle load safety constraints, namely, the static load on each axle must not exceed the product of the rated bearing capacity and the static safety factor, are expressed as follows: .

[0075] Step S24: Use the improved genetic algorithm as a solver to iterate over the decision variables. During the iteration process, call the parameter set to calculate the objective function and search for the optimal solution that satisfies the aforementioned mathematical constraints, which is then output as the current candidate configuration set.

[0076] Since the objective function contains high-order nonlinear terms (fourth-order terms and logarithmic terms), an improved genetic algorithm (IGA) is used for numerical solution.

[0077] The length of each chromosome in the algorithm is Each gene locus corresponds to a decision variable. Gene value and The values ​​are in a one-to-one correspondence; the following process is executed in each iteration:

[0078] Decode the chromosomes to obtain the candidate configuration set (all) (the value of ); call the static equilibrium equations to calculate and ; Call the parameters in the parameter set, and combine them with the calculated and Calculate the objective function value J; iterate through the SPMT layout schemes, verifying whether the placement position and axis load of each SPMT satisfy all mathematical constraints, define a counter term V (initially 0), increment the counter term by 1 for each SPMT or axis that does not satisfy the constraints; combine the counter term V with the objective function value J using the formula: Fusion into fitness Verify the results generated in this iteration Compared with the previous iteration If the difference is less than a preset threshold, the iteration stops and the candidate configuration set at this time is output as the optimal solution.

[0079] Step S30: Construct a mid-level dynamic coupling model based on the generated candidate configuration set, and predict the dynamic load time history of each SPMT shaft group by simulating the dynamic working conditions generated by the predetermined displacement path parameters.

[0080] To generate candidate static configuration schemes, a dynamic virtual prototype coupling the gantry crane, SPMT fleet, and road surface is established. Numerical simulation is used to model the dynamic process during actual relocation operations, predicting and outputting the dynamic load time history of each SPMT axis. This provides a data foundation for subsequent dynamic risk assessment. The process is divided into the following sub-steps:

[0081] Step S31: Based on the gantry crane structural parameters and candidate configuration set, construct a dynamic digital prototype that couples the gantry crane and the SPMT vehicle fleet;

[0082] First, a flexible multibody model of the gantry crane is established: a hybrid modeling method of lumped mass and flexible beam is adopted. The total mass and moment of inertia of the gantry crane are assigned to several rigid mass blocks according to the actual geometric distribution. Massless flexible beam elements are introduced between the mass blocks (such as between main beam segments and at the connection between the main beam and the outriggers). The bending stiffness (EI) and torsional stiffness (GJ) parameters are derived from the structural stiffness parameters obtained in step S10, which are used to simulate the elastic deformation of the structure under dynamic loads. Then, a sub-model containing a mechanical subsystem and a control subsystem is established for each SPMT in the scheme. The mechanical subsystem includes a rigid body (size taken from the parameter library), hydraulic suspension (modeled as a nonlinear spring-damping-actuator unit, parameters derived from the suspension system parameters), and tires (modeled as a combination of vertical spring damping and lateral brush models, parameters...). (Data source: tire parameters); Control subsystem: a centralized collaborative controller model that simulates the logic function of the actual controller. It is responsible for calculating and generating distributed control commands for each SPMT drive, braking, and steering subsystem in real time based on the received macroscopic motion commands, and running the load balance control algorithm to dynamically adjust the suspension pressure. Finally, the coupling relationship between the models is defined: Mechanical coupling: the gantry crane model and the corresponding SPMT submodel are connected through three-way (vertical, longitudinal, and lateral) linear / nonlinear force elements according to the SPMT layout in the candidate configuration set. The stiffness value is set according to the characteristics of the actual connection tooling to simulate the flexibility of the contact surface; Control coupling: the output of the centralized collaborative controller model is used as control commands to act on the drive, braking, steering, and suspension actuators of each SPMT.

[0083] Step S32: Generate dynamic operating condition input data based on the predetermined shift path parameters;

[0084] Based on the planar coordinates, slope, and turning radius of each sampling point on the shift path in step S10, a global reference trajectory describing the motion of the group's centroid is generated, including the expected position, heading angle, velocity, and acceleration in the time series; based on the evaluated road surface roughness power spectral density (PSD), a corresponding three-dimensional random road surface elevation grid is generated.

[0085] Step S33: Configure the dynamic solver based on the constructed dynamic digital prototype and dynamic working condition input data, and execute the displacement simulation operation;

[0086] The dynamics solver employs an implicit rigid integration algorithm for differential-algebraic equations (such as a backward difference method based on variable-order, variable-step-size finite difference). The dynamics solver is configured and a shift simulation job is executed, specifically including:

[0087] The topology and all physical parameters of the dynamic digital prototype are imported into the dynamic solver. A global reference trajectory and road surface elevation grid are applied to the solver. That is, macroscopic motion commands are input to the control subsystem according to the global reference trajectory. The road surface elevation grid is set as the elevation input of all SPMT tire contact points to control the dynamic digital prototype to simulate the displacement process. The solver is started to perform time-domain numerical integration to calculate the change of the entire dynamic digital prototype state from the initial to the end time.

[0088] Step S34: After the simulation job is completed, extract the dynamic load time history data of each SPMT axis group from the solver results;

[0089] After the simulation is completed, extract the time history data of the vertical dynamic support reaction force of each SPMT axis throughout the entire simulation duration from the solver results. This refers to the dynamic load time history data, which is output, where m is the linear axis index and t is the time sequence index in the global reference trajectory.

[0090] Step S40: Construct a lower-level risk assessment model based on the predicted dynamic load time history to quantify the dynamic risk of the candidate configuration set;

[0091] The core objective of the lower-level risk assessment model is to define and calculate a set of risk quantification indicators directly targeting the dynamic displacement process. From three dimensions—instantaneous overload, load fluctuation, and coordinated imbalance—it accurately assesses the robustness of candidate configurations in real-world operations, providing scientific risk feedback for upper-level optimization. Its specific process consists of the following sub-steps:

[0092] Step S41: Calculate the quantitative results of three indicators—instantaneous overload, load fluctuation, and coordinated imbalance—based on the predicted dynamic load time history;

[0093] The instantaneous overload index The calculation formula is:

[0094]

[0095] in Return dynamic load time history data The peak value in the time history refers to the maximum value of all axis loads over the entire time history (the entire simulation operation), where m is the axis index and t is the time sequence index in the global reference trajectory. The rated load for a single shaft. The preset dynamic safety factor (>1);

[0096] Load fluctuation index The calculation formula is:

[0097]

[0098] in , , , These represent the standard deviation, mean, maximum, and minimum values ​​of the load on the m-th axis over the entire time history, where m ranges from 1 to... , This represents the total number of axes used in the current candidate configuration set. The preset peak band penalty coefficient;

[0099] The formula for calculating the group coordination imbalance index is:

[0100]

[0101] in This represents the total average value of all axis loads over the entire time history. , These are the average loads on the left and right axes of the train's centerline over the entire time period. This is the preset penalty coefficient for left-right imbalance.

[0102] Step S42: Dynamically assign weights based on the degree of badness of the indicators themselves, and merge the three indicators into a dynamic risk value according to the assigned weights;

[0103] First, calculate the weight of each indicator's degree of non-compliance: , , ; , , The deterioration weights are assigned to three indicators: instantaneous overload, load fluctuation, and coordinated imbalance. , , The reference baseline values ​​for three indicators—instantaneous overload, load fluctuation, and coordinated imbalance—are respectively (preset by historical data or engineering specifications); then, the three weight values ​​are normalized; finally, the formula is used: The three indicators are integrated into a dynamic risk value. , used to characterize the risk assessment results of the current candidate configuration set, where , , This represents the normalized weight of the degree of undesirability.

[0104] Step S50: Establish an iterative optimization framework that connects the upper, middle and lower layers: Feed the dynamic risk value output by the lower layer risk assessment model back to the upper layer optimization model to generate a new candidate configuration set. Then, perform simulation prediction by the middle layer model and risk assessment by the lower layer model on the new candidate configuration set again until the generated candidate configuration set meets the preset convergence conditions.

[0105] The lower-level risk assessment model feeds back the output dynamic risk value to the upper-level optimization model, which then replaces the objective function J with a total objective function that incorporates the dynamic risk value. And the axle load safety constraints were modified as follows: ,in Let m be the static load on the m-th axis. Return the maximum dynamic load of the m-th spool over the entire time period. This is the dynamic load reduction factor (0.8 in this embodiment). The rated load for a single shaft. Static safety factor; overall objective function Where J is the objective function value calculated in the previous round. This refers to the dynamic risk value fed back from the current lower-level risk assessment model. , These are the normalization factors for the objective function value and the dynamic risk value, respectively. As a dynamic risk weighting factor, , The basic weight is 0.3 in this embodiment. The maximum adjustment range is 0.4. This represents the desired dynamic risk target value. The preset sensitivity adjustment coefficient, This is the hyperbolic tangent function, used to smoothly map the input to the interval [-1, 1].

[0106] After the upper-level optimization model completes the above adjustments, it continues to run the improved genetic algorithm to generate a new candidate configuration set, which is then transmitted to the middle-level dynamic coupling model to predict the dynamic load time history of each SPMT axis group. The lower-level risk assessment model then performs dynamic risk assessment based on the predicted dynamic load time history. The assessed dynamic risk value is then fed back to the upper-level optimization model, which continues to generate a new candidate configuration set. This process is repeated iteratively. During the iteration process, the candidate configuration set with the smallest objective function return value and dynamic risk value is continuously selected as the optimal solution. When the optimal solution has not been updated for three consecutive iterations, the iteration stops and the current optimal solution is output.

[0107] Example 2

[0108] like Figure 2 As shown, Embodiment 2 of this application provides a large gantry crane shifting and vehicle allocation optimization system based on SPMT, including: parameter acquisition module 21, candidate configuration set generation module 22, dynamic coupling module 23, risk assessment module 24, and cross-layer iteration module 25;

[0109] Parameter acquisition module 21 is used to acquire gantry crane structural parameters, predetermined displacement path parameters and SPMT vehicle performance parameters;

[0110] The candidate configuration set generation module 22 is used to construct an upper-level optimization model based on the gantry crane structural parameters and SPMT vehicle performance parameters, and generate a candidate configuration set that satisfies static support stability and axle load limit constraints while minimizing the total configuration cost; specifically, it includes:

[0111] 1. The parameter set construction submodule is used to construct an optimization model based on the obtained gantry crane structural parameters and SPMT vehicle performance parameters. It is a parameter set that can be directly called.

[0112] 2. The objective function construction submodule is used to define the decision variables of the optimization model and construct the objective function based on the optimization objective;

[0113] 3. Mathematical constraint construction submodule, used to transform the engineering requirements for gantry crane relocation and vehicle allocation into identifiable mathematical constraints;

[0114] 4. The candidate configuration set solution submodule is used to use the improved genetic algorithm as a solver to iterate on the decision variables. During the iteration process, the parameter set is called to calculate the objective function and search for the optimal solution that satisfies the aforementioned mathematical constraints, which is then output as the current candidate configuration set.

[0115] The dynamic coupling module 23 is used to construct a mid-level dynamic coupling model based on the generated candidate configuration set, and predict the dynamic load time history of each SPMT shaft group by simulating the dynamic working conditions generated by the predetermined displacement path parameters; specifically, it includes:

[0116] 1. Digital Prototype Construction Submodule, used to construct a dynamic digital prototype that couples the gantry crane and the SPMT fleet based on the gantry crane's structural parameters and candidate configuration set;

[0117] 2. The working condition data construction submodule is used to generate dynamic working condition input data based on the predetermined shift path parameters;

[0118] 3. The displacement simulation submodule is used to configure the dynamic solver based on the constructed dynamic digital prototype and dynamic working condition input data, and to execute displacement simulation operations;

[0119] 4. Dynamic load time history data output submodule, used to extract the dynamic load time history data of each SPMT axis group from the solver results after the simulation job is completed.

[0120] Risk assessment module 24 is used to construct a lower-level risk assessment model based on the predicted dynamic load time history, quantifying the dynamic risk of the candidate configuration set; specifically, it includes:

[0121] 1. The index calculation submodule is used to calculate the quantitative results of three indicators: instantaneous overload, load fluctuation, and coordinated imbalance based on the predicted dynamic load time history.

[0122] 2. The indicator fusion submodule is used to dynamically allocate weights based on the degree of badness of the indicators themselves, and to merge the three indicators into a dynamic risk value according to the allocated weights.

[0123] The cross-layer iteration module 25 is used to establish an iterative optimization framework that connects the upper, middle and lower layers: the dynamic risk value output by the lower layer risk assessment model is fed back to the upper layer optimization model to generate a new candidate configuration set. Then, the simulation prediction of the middle layer model and the risk assessment of the lower layer model are performed again on the new candidate configuration set until the generated candidate configuration set meets the preset convergence conditions.

[0124] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0125] The memory is used to store one or more program instructions;

[0126] A processor for running one or more program instructions to execute a SPMT-based optimization method for moving and matching large gantry cranes.

[0127] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide a SPMT-based optimization method for moving and matching large gantry cranes.

[0128] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer performs the above-described SPMT-based method for optimizing the movement and matching of large gantry cranes.

[0129] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0130] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0131] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0132] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0133] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0134] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0135] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0136] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the relocation and vehicle allocation of large gantry cranes based on SPMT, characterized in that, include: Step 1: Obtain the gantry crane structural parameters, predetermined displacement path parameters, and SPMT vehicle performance parameters; Step 2: Based on the gantry crane structural parameters and SPMT vehicle performance parameters, construct an upper-level optimization model to generate a candidate configuration set that satisfies static support stability and axle load limit constraints while minimizing total configuration cost. This involves the following sub-steps: Based on the obtained gantry crane structural parameters and SPMT vehicle performance parameters, a parameter set that can be directly called by the optimization model is constructed. Define the decision variables of the optimization model and construct the objective function based on the optimization objective; The engineering requirements for gantry crane relocation and vehicle allocation are transformed into identifiable mathematical constraints; The improved genetic algorithm is used as the solver to iterate over the decision variables. During the iteration process, the parameter set is called to calculate the objective function and search for the optimal solution that satisfies the aforementioned mathematical constraints, which is then output as the current candidate configuration set. Step 3: Construct a mid-level dynamic coupling model based on the generated candidate configuration set. Predict the dynamic load time history of each SPMT shaft group by simulating the dynamic working conditions generated by the predetermined displacement path parameters. This is specifically divided into the following sub-steps: Based on the structural parameters and candidate configuration set of the gantry crane, a dynamic digital prototype coupling the gantry crane and the SPMT vehicle fleet was constructed; Dynamic operating condition input data is generated based on the predetermined shift path parameters; Based on the constructed dynamic digital prototype and dynamic working condition input data, a dynamic solver is configured and a displacement simulation operation is performed; After the simulation is completed, the dynamic load time history data of each SPMT axis group is extracted from the solver results; Step 4: Construct a lower-level risk assessment model based on the predicted dynamic load time history to quantify the dynamic risk of the candidate configuration set. This involves the following sub-steps: The quantitative results of three indicators—instantaneous overload, load fluctuation, and coordinated imbalance—are calculated based on the predicted dynamic load time history. The weights are dynamically assigned based on the degree of adverseness of the indicators themselves, and the three indicators are then merged into a dynamic risk value according to the assigned weights. Step 5: Establish an iterative optimization framework that connects the upper, middle, and lower layers: Feed the dynamic risk value output by the lower-layer risk assessment model back to the upper-layer optimization model to generate a new candidate configuration set. Then, perform simulation prediction by the middle-layer model and risk assessment by the lower-layer model on the new candidate configuration set again until the generated candidate configuration set meets the preset convergence conditions.

2. A large gantry crane relocation and vehicle allocation optimization system based on SPMT, characterized in that, The method for optimizing the movement and allocation of large gantry cranes based on SPMT as described in claim 1 includes: a parameter acquisition module, a candidate configuration set generation module, a dynamic coupling module, a risk assessment module, and a cross-layer iteration module. The parameter acquisition module is used to acquire the gantry crane structural parameters, the predetermined displacement path parameters, and the SPMT vehicle performance parameters; The candidate configuration set generation module is used to build an upper-level optimization model based on the gantry crane structural parameters and SPMT vehicle performance parameters, and generate a candidate configuration set that meets the constraints of static support stability and axle load limit and has the lowest total configuration cost. The dynamic coupling module is used to build a mid-level dynamic coupling model based on the generated candidate configuration set. It predicts the dynamic load time history of each SPMT shaft group by simulating the dynamic working conditions generated by the predetermined displacement path parameters. The risk assessment module is used to build a lower-level risk assessment model based on the predicted dynamic load time history, and to quantify the dynamic risk of the candidate configuration set. The cross-layer iterative module is used to establish an iterative optimization framework that connects the upper, middle, and lower layers: the dynamic risk value output by the lower-layer risk assessment model is fed back to the upper-layer optimization model to generate a new candidate configuration set. Then, the simulation prediction of the middle-layer model and the risk assessment of the lower-layer model are performed again on the new candidate configuration set until the generated candidate configuration set meets the preset convergence conditions.

3. The SPMT-based large gantry crane shifting and vehicle allocation optimization system according to claim 2, characterized in that, The candidate configuration set generation module specifically includes: The parameter set construction submodule is used to construct a parameter set that can be directly called based on the obtained gantry crane structural parameters and SPMT vehicle performance parameters; The objective function construction submodule is used to define the decision variables of the optimization model and construct the objective function based on the optimization objective. The mathematical constraint construction submodule is used to transform the engineering requirements for gantry crane relocation and vehicle allocation into identifiable mathematical constraints; The candidate configuration set solving submodule is used to use the improved genetic algorithm as a solver to iterate over the decision variables. During the iteration process, it calls the parameter set to calculate the objective function and searches for the optimal solution that satisfies the aforementioned mathematical constraints, which is then output as the current candidate configuration set.

4. The SPMT-based large gantry crane relocation and vehicle allocation optimization system according to claim 2, characterized in that, The dynamic coupling module specifically includes: The digital prototype construction submodule is used to build a dynamic digital prototype that couples the gantry crane and the SPMT fleet based on the gantry crane's structural parameters and candidate configuration set; The working condition data construction submodule is used to generate dynamic working condition input data based on the predetermined shift path parameters; The displacement simulation submodule is used to configure the dynamic solver based on the constructed dynamic digital prototype and dynamic working condition input data, and to execute displacement simulation operations. The dynamic load time history data output submodule is used to extract the dynamic load time history data of each SPMT axis group from the solver results after the simulation job is completed.

5. The SPMT-based large gantry crane shifting and vehicle allocation optimization system according to claim 2, characterized in that, The risk assessment module specifically includes: The indicator calculation submodule is used to calculate the quantitative results of three indicators: instantaneous overload, load fluctuation, and coordinated imbalance, based on the predicted dynamic load time history. The indicator fusion submodule is used to dynamically allocate weights based on the severity of each indicator and to merge the three indicators into a dynamic risk value according to the allocated weights.

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