Palletizing planning method and electronic device
By generating and analyzing the stability of candidate palletizing schemes, the target palletizing scheme is automatically determined, solving the problems of low accuracy and efficiency in palletizing planning in existing technologies, and achieving efficient and accurate palletizing planning and transportation safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FEIKETENG INTELLIGENT TECH (QINGDAO) CO LTD
- Filing Date
- 2026-04-16
- Publication Date
- 2026-06-23
AI Technical Summary
The stability testing accuracy of existing palletizing planning methods is low, resulting in low accuracy and efficiency in palletizing planning.
Candidate palletizing schemes are generated based on bag attribute parameters and carriage structure parameters. Stability analysis is performed by using the acceleration time history of preset transportation conditions. Simulation is conducted using multibody dynamics, discrete element method or finite element method to quantify the instability risk of the pallet during transportation and automatically determine the target palletizing scheme.
It achieves automatic quantification and accurate prediction of stack stability, improves the accuracy and efficiency of palletizing planning, reduces the risk of accidents during transportation, is applicable to a variety of materials and vehicle types, and has high scalability.
Smart Images
Figure CN122264458A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and in particular to a palletizing planning method and electronic device. Background Technology
[0002] Small-bag packaging is widely used due to its advantages such as accurate measurement, convenient handling, and adaptability to multi-level distribution and terminal retail.
[0003] Currently, traditional palletizing planning methods mainly rely on the experience of on-site operators or automated loading equipment to determine the palletizing scheme. After palletizing according to the scheme, the stability of the resulting stack is judged by visual inspection or simple manual shaking test by the operator. If it is unstable, the stack is manually adjusted and the stability test is repeated until the stack is stable.
[0004] However, the above-mentioned palletizing planning method has low accuracy in stability testing, which in turn leads to low accuracy and efficiency in palletizing planning. Summary of the Invention
[0005] This application provides a palletizing planning method and electronic device, which can improve the accuracy and efficiency of palletizing planning, thereby solving the problem of low accuracy and efficiency in palletizing planning in the prior art.
[0006] In a first aspect, embodiments of this application provide a palletizing planning method, which includes: generating multiple candidate palletizing schemes that satisfy preset loading constraints based on bag attribute parameters and carriage structure parameters; performing stability analysis on each candidate palletizing scheme based on bag attribute parameters, carriage structure parameters, material parameters inside the bag, and contact friction coefficient according to the acceleration time history of preset transportation conditions, to obtain the stack stability of the corresponding candidate palletizing scheme; determining the target palletizing scheme based on the stack stability of each candidate palletizing scheme, and displaying the target palletizing scheme.
[0007] In this embodiment, multiple candidate palletizing schemes that meet preset loading constraints are generated based on bag attribute parameters and carriage structure parameters. Then, according to the acceleration time history of preset transportation conditions, stability analysis is performed on each candidate palletizing scheme based on bag attribute parameters, carriage structure parameters, material parameters inside the bag, and contact friction coefficient. The stability of the palletized structure of the corresponding candidate palletizing scheme is obtained. This provides an automatic quantification method for palletized stability, eliminating the need for visual inspection or manual shaking tests by operators. It can accurately quantify the ability of the palletized structure corresponding to the candidate palletizing scheme to resist instability phenomena such as slippage, overturning, and local collapse under the acceleration time history of preset transportation conditions. In other words, it can accurately predict the instability risk of the palletized structure during actual transportation. This improves the accuracy and efficiency of stability analysis, enabling traceable and auditable stability assessments and providing an accurate data foundation for determining the target palletizing scheme. Subsequently, based on the pallet stability of each candidate palletizing scheme, the target palletizing scheme is determined and displayed. This allows for the accurate identification of the optimal target palletizing scheme, automating palletizing planning without relying on the experience of on-site operators or automated loading equipment. This improves the accuracy and efficiency of palletizing planning, providing an accurate data foundation for subsequent automated loading equipment operations. It effectively reduces the risk of accidents such as cargo collapse, bag damage, and spillage due to pallet instability during actual transportation, thus improving transportation safety. Furthermore, by simply changing the bag attribute parameters, carriage structure parameters, bag material parameters, and contact friction coefficient, the method of this application embodiment can be extended to various materials such as fertilizers, grains, feed, and fine chemicals. This allows the method of this application embodiment to serve palletizing planning scenarios across multiple factories, vehicle models, and routes, demonstrating high scalability and applicability.
[0008] Secondly, embodiments of this application provide a palletizing planning device, which includes: a generation module, used to generate multiple candidate palletizing schemes that meet preset loading constraints based on bag attribute parameters and carriage structure parameters; an analysis module, used to perform stability analysis on each candidate palletizing scheme according to the acceleration time history of preset transportation conditions, based on bag attribute parameters, carriage structure parameters, material parameters inside the bag, and contact friction coefficient, to obtain the stack stability of the corresponding candidate palletizing scheme; and a determination module, used to determine the target palletizing scheme based on the stack stability of each candidate palletizing scheme, and display the target palletizing scheme.
[0009] Thirdly, embodiments of this application provide an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the palletizing planning method of any embodiment of this application.
[0010] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the palletizing planning method as described in any embodiment of this application.
[0011] The descriptions of the second, third, and fourth aspects in this application can be referenced to the detailed description of the first aspect; and the beneficial effects described in the second, third, and fourth aspects can be referenced to the analysis of the beneficial effects in the first aspect, which will not be repeated here.
[0012] In this application, the name of the palletizing planning device does not limit the equipment or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.
[0013] These or other aspects of this application will become more readily apparent in the following description. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0015] Figure 1 This is a flowchart illustrating the palletizing planning method provided in an embodiment of this application; Figure 2 This is another schematic flowchart of the palletizing planning method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the palletizing planning device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.
[0017] It should be noted that the terms "first," "second," "target," and "original," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprising," "having," and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0018] Figure 1 This is a flowchart illustrating a palletizing planning method provided in this application embodiment. This embodiment can be applied to scenarios requiring palletizing planning of flexible small-bag packaged goods, and can be used in industries such as chemicals, grains, feed, and fine chemicals. The palletizing planning method provided in this embodiment can be executed by a palletizing planning device provided in this application embodiment, which can be implemented through software and / or hardware. In a specific embodiment, the palletizing planning device can be integrated into an electronic device. The executing entity of this method can be an electronic device. See also... Figure 1 The palletizing planning method in this embodiment includes, but is not limited to, the following steps: S110. Based on the bag attribute parameters and the carriage structure parameters, generate multiple candidate palletizing schemes that meet the preset loading constraints.
[0019] Among them, the bag body attribute parameters are used to describe the physical characteristics of the flexible bag packaging itself, including bag body geometric parameters and bag body material parameters, etc.; bag body geometric parameters include the length, width and height of the bag body, or the bag body geometric parameters include the three-dimensional contour or point cloud data of the bag body; bag body material parameters include the elastic modulus, Poisson's ratio and other material mechanical parameters of the bag material.
[0020] The structural parameters of the cargo compartment refer to the space and structural features inside the transport vehicle that can be used for loading, including the geometric parameters and constraints of the cargo compartment. The geometric parameters of the cargo compartment include the length, width and height of the interior of the cargo compartment. The constraints of the cargo compartment include the height and stiffness of the front panel, side panels and tailgate, as well as whether there are anti-slip mats, etc.
[0021] Preset loading constraints are pre-defined loading rules and restrictions that must be followed, including the cargo width-to-height ratio and safe distance from the edge of the cargo compartment.
[0022] The candidate palletizing schemes are a variety of bagged cargo placement layouts generated based on bag attribute parameters and carriage structure parameters, under the premise of meeting preset loading constraints.
[0023] Specifically, the bag attribute parameters of the flexible pouch packaging of the goods to be loaded and the vehicle compartment structure parameters can be obtained. That is, the operator can input the bag attribute parameters of the flexible pouch packaging of the goods to be loaded and the vehicle compartment structure parameters into the electronic device. Then, based on the bag attribute parameters and the vehicle compartment structure parameters, multiple candidate palletizing schemes that meet the preset loading constraints are generated. That is, a genetic algorithm can be used to encode the palletizing parameters into genes, and multiple candidate palletizing schemes that meet the preset loading constraints are randomly generated based on the bag geometry parameters and the vehicle compartment geometry parameters. At this time, a preset number of candidate palletizing schemes can be generated.
[0024] The palletizing parameters may include at least one of the following: total number of layers in the pallet, number of bags per layer, arrangement of each layer, misalignment between adjacent layers, and edge clearance distance. Misalignment between adjacent layers is the horizontal offset distance between the edge of the upper layer of goods and the edge of the lower layer of goods within the same pallet. Edge clearance distance is the reserved gap between the outer edge of the entire pallet and the side wall, door, column, and other structures of the carriage.
[0025] S120. Based on the acceleration time history of the preset transportation conditions, the stability analysis of each candidate palletizing scheme is performed on the bag attribute parameters, carriage structure parameters, bag material parameters and contact friction coefficient to obtain the pallet stability of the corresponding candidate palletizing scheme.
[0026] Among them, the preset transportation conditions are the vehicle transportation conditions set in advance for stability analysis of the palletizing scheme, and the acceleration time history is the time series curve of the vehicle acceleration over time under the preset transportation conditions, reflecting the magnitude, direction and change law of acceleration at different times.
[0027] The parameters of the material inside the bag are used to describe the physical characteristics of the material itself loaded inside the bag, including the density, filling rate and compressibility coefficient of the material loaded inside the bag.
[0028] The coefficient of contact friction is a mechanical parameter used to characterize the resistance to relative sliding or rolling between the contact surfaces of two objects in contact, including the static / dynamic coefficients of contact pairs such as bag to bag, bag to the floor of a carriage, and bag to the side wall of a carriage.
[0029] Stack stability refers to the overall ability of the stack to resist instability phenomena such as slippage, overturning, and local collapse under the acceleration time history of a preset transportation condition.
[0030] Specifically, after obtaining multiple candidate palletizing schemes, the acceleration time history of the preset transportation conditions can be obtained. That is, the operator can select the preset transportation conditions used for stability analysis and input the acceleration time history of the preset transportation conditions into the electronic equipment.
[0031] Next, based on the acceleration time history of the preset transportation conditions, stability analysis is performed on each candidate palletizing scheme according to bag attribute parameters, carriage structure parameters, bag material parameters, and contact friction coefficient to obtain the pallet stability of the corresponding candidate palletizing scheme. For example, simulation methods such as multibody dynamics, discrete element method, finite element method, or coupled method can be used to simulate each candidate palletizing scheme according to the acceleration time history of the preset transportation conditions, based on bag attribute parameters, carriage structure parameters, bag material parameters, and contact friction coefficient to obtain the simulation results of the corresponding candidate palletizing scheme. The simulation results may include the pallet tilt angle sequence and the number of slipped bags sequence. Data analysis is then performed on the simulation results of each candidate palletizing scheme to determine the pallet stability of the corresponding candidate palletizing scheme.
[0032] For example, each candidate palletizing scheme is selected as the current candidate palletizing scheme. The maximum tilt angle is determined from the pallet tilt angle sequence of the current candidate palletizing scheme. Then, the maximum number of sliding bags is determined from the number of sliding bags sequence of the current candidate palletizing scheme. The ratio of the maximum number of sliding bags to the total number of bags in the current candidate palletizing scheme is calculated to obtain the maximum sliding ratio of the current candidate palletizing scheme. The total number of bags is the total number of bags included in the pallet corresponding to the current candidate palletizing scheme. Then, based on the maximum tilt angle and the maximum sliding ratio of the current candidate palletizing scheme, a preset mapping relationship is queried to obtain the pallet stability corresponding to the current candidate palletizing scheme.
[0033] The stack tilt angle sequence includes the stack tilt angle at each moment within the preset simulation duration. The stack tilt angle is the angle at which the entire stack deviates from the vertical direction at a specific moment, and the maximum tilt angle is the maximum value among all stack tilt angles included in the stack tilt angle sequence. The sliding bag number sequence includes the number of sliding bags at each moment within the preset simulation duration. The sliding bag number is the number of bags that slide at a specific moment, and the maximum sliding bag number is the maximum value among all sliding bag numbers included in the sliding bag number sequence. The preset simulation duration is the total duration of the acceleration time history of the preset transportation conditions, and the time interval between the above adjacent moments is the same as the time interval between adjacent moments in the acceleration time history.
[0034] The preset mapping relationship includes the correspondence between the maximum tilt angle, the maximum slip ratio and the stability of the stack, and the preset mapping relationship can be determined in advance based on multiple calibration tests.
[0035] S130. Determine the target palletizing scheme based on the pallet stability of each candidate palletizing scheme, and display the target palletizing scheme.
[0036] The target palletizing scheme is the optimal palletizing scheme among multiple candidate palletizing schemes.
[0037] Specifically, after determining the stack stability of each candidate palletizing scheme, the target palletizing scheme can be determined based on the stack stability of each candidate palletizing scheme. For example, the candidate palletizing scheme with the highest stack stability can be determined as the target palletizing scheme. Then, the target palletizing scheme is displayed, that is, the palletizing parameters of the target palletizing scheme are displayed on the screen of the electronic device.
[0038] The technical solution of this application generates multiple candidate palletizing schemes that meet preset loading constraints based on bag attribute parameters and carriage structure parameters. Then, according to the acceleration time history of preset transportation conditions, a stability analysis is performed on each candidate palletizing scheme based on bag attribute parameters, carriage structure parameters, material parameters inside the bag, and contact friction coefficient to obtain the stack stability of the corresponding candidate palletizing scheme. This provides an automatic quantification method for stack stability without relying on visual inspection or manual shaking tests by operators. It can accurately quantify the ability of the stack corresponding to the candidate palletizing scheme to resist instability phenomena such as slippage, overturning, and local collapse under the action of acceleration time history of preset transportation conditions, that is, it can accurately predict the instability risk of the stack during actual transportation. This approach improves the accuracy and efficiency of stability analysis, enabling traceable and auditable stability assessments and providing a precise data foundation for determining the target palletizing scheme. Subsequently, based on the pallet stability of each candidate palletizing scheme, the target palletizing scheme is determined and displayed. This allows for the accurate identification of the optimal target palletizing scheme, automating palletizing planning without relying on the experience of on-site operators or automated loading equipment. This improves the accuracy and efficiency of palletizing planning, providing a precise data foundation for subsequent automated loading equipment operations. It effectively reduces the risk of accidents such as cargo collapse, bag damage, and spillage caused by pallet instability during actual transportation, thus enhancing transportation safety. Furthermore, by simply changing the bag attribute parameters, carriage structure parameters, bag material parameters, and contact friction coefficient, the method of this application embodiment can be extended to various materials such as fertilizers, grains, feed, and fine chemicals. This allows the method of this application embodiment to serve palletizing planning scenarios across multiple factories, vehicle models, and routes, demonstrating high scalability and applicability.
[0039] The following further describes a palletizing planning method provided by an embodiment of this application. Figure 2 This is another schematic flowchart of the palletizing planning method provided in this application. This application's embodiment is an optimization based on the above embodiments. See also... Figure 2 The method in this embodiment includes, but is not limited to, the following steps: S210. Based on the bag attribute parameters and the carriage structure parameters, generate multiple candidate palletizing schemes that meet the preset loading constraints.
[0040] Optionally, after obtaining multiple candidate palletizing schemes, the candidate palletizing schemes can be displayed on the screen of the electronic device to support operators in interactively fine-tuning the palletizing parameters in the candidate palletizing schemes. The palletizing parameters that operators can interactively fine-tune include the placement orientation and order of each layer of bags, edge clearance distance, etc.
[0041] S220. Based on the bag body attribute parameters, carriage structure parameters, bag internal material parameters and contact friction coefficient, model each candidate palletizing scheme to obtain the vehicle model of the corresponding candidate palletizing scheme.
[0042] Specifically, after obtaining multiple candidate palletizing schemes, modeling tools can be used to model each candidate palletizing scheme based on bag attribute parameters, vehicle structure parameters, bag material parameters, and contact friction coefficient, thereby obtaining the vehicle model of the corresponding candidate palletizing scheme. The modeling tools can be existing modeling tools in the prior art.
[0043] S230. Simulate the vehicle model of each candidate palletizing scheme according to the acceleration time history of the preset transportation conditions, and obtain the simulation results of the corresponding candidate palletizing scheme.
[0044] The simulation results may include a stack tilt angle sequence, a sliding bag number sequence, and a bag displacement sequence for each bag. The bag displacement sequence includes the bag displacement of a specific bag at each moment within a preset simulation duration. The bag displacement is the distance between the position of the specific bag at a specific moment and its position at the initial moment, where the initial moment is the start of the simulation.
[0045] Specifically, after obtaining the vehicle models of each candidate palletizing scheme, simulation tools can be used to apply the acceleration time history of the preset transportation conditions to the vehicle models of each candidate palletizing scheme, thereby simulating the vehicle models of each candidate palletizing scheme. Simultaneously, the tilt angle of the pallet corresponding to each candidate palletizing scheme, the number of sliding bags, and the displacement of each bag are recorded at each moment within the preset simulation duration. Then, based on the tilt angle of the pallet corresponding to each candidate palletizing scheme at each moment within the preset simulation duration, a sequence of the tilt angles of the pallet corresponding to each candidate palletizing scheme is constructed; based on the number of sliding bags of the pallet corresponding to each candidate palletizing scheme at each moment within the preset simulation duration, a sequence of the number of sliding bags of the pallet corresponding to each candidate palletizing scheme is constructed; and based on the displacement of each bag in the pallet corresponding to each candidate palletizing scheme at each moment within the preset simulation duration, a sequence of the displacements of each bag in the pallet corresponding to each candidate palletizing scheme is constructed. This constitutes the simulation results for each candidate palletizing scheme. The simulation tools used can be existing simulation tools in the prior art.
[0046] S240. Determine the stack stability of the corresponding candidate stacking scheme based on the simulation results of each candidate stacking scheme.
[0047] Specifically, after obtaining the simulation results of each candidate palletizing scheme, the maximum tilt angle of the corresponding candidate palletizing scheme can be determined based on the sequence of pallet tilt angles of each candidate palletizing scheme; then, the maximum number of sliding bags in the corresponding candidate palletizing scheme can be determined based on the sequence of the number of sliding bags in each candidate palletizing scheme; next, the maximum displacement of the corresponding candidate palletizing scheme can be determined based on the sequence of bag displacements of each bag in each candidate palletizing scheme, wherein the maximum displacement is the maximum bag displacement in the sequence of bag displacements of all bags; finally, the pallet stability of the corresponding candidate palletizing scheme can be determined based on the maximum tilt angle, the maximum number of sliding bags, and the maximum displacement of each candidate palletizing scheme.
[0048] Furthermore, in one implementation, the stack stability of the corresponding candidate stacking scheme is determined based on the maximum tilt angle, maximum number of sliding bags, and maximum displacement of each candidate stacking scheme, including Sa1-Sa5: Sa1: Obtain the tilt angle weight, sliding bag number weight, and displacement weight.
[0049] Among them, the tilt angle weight is the degree of influence of the tilt angle of the stack on the stability of the stack, the sliding bag number weight is the degree of influence of the number of sliding bags on the stability of the stack, and the displacement weight is the degree of influence of the bag displacement on the stability of the stack. The tilt angle weight, sliding bag number weight and displacement weight can be set according to actual usage requirements, and the sum of the tilt angle weight, sliding bag number weight and displacement weight can be 1.
[0050] Sa2. Determine the tilt stability coefficient of the corresponding candidate palletizing scheme based on the preset tilt angle threshold and the maximum tilt angle of each candidate palletizing scheme.
[0051] The preset tilt angle threshold is the maximum allowed tilt angle of the stack.
[0052] The tilt stability coefficient is used to quantify the stability of candidate palletizing schemes in the tilt angle dimension under the action of acceleration time history under preset transportation conditions. Furthermore, the larger the tilt stability coefficient, the smaller the corresponding maximum tilt angle, and the higher the stability of the pallet.
[0053] Specifically, each candidate palletizing scheme is selected as the current candidate palletizing scheme. The ratio of the maximum tilt angle of the current candidate palletizing scheme to the preset tilt angle threshold can be calculated, and the difference between 1 and the ratio can be calculated to obtain the tilt stability coefficient of the current candidate palletizing scheme.
[0054] Sa3. Based on the preset slip ratio threshold and the total number of bags and the maximum number of slipped bags for each candidate palletizing scheme, determine the slip stability coefficient of the corresponding candidate palletizing scheme.
[0055] The preset slip ratio threshold is the maximum slip ratio that is allowed for each bag in the stack to slip.
[0056] The slip stability coefficient is used to quantify the stability of candidate palletizing schemes in the dimension of the number of slipped bags under the action of acceleration time history under preset transportation conditions. Furthermore, the larger the slip stability coefficient, the smaller the corresponding maximum number of slipped bags, and the higher the stability of the pallet.
[0057] Specifically, each candidate palletizing scheme is selected as the current candidate palletizing scheme. The ratio of the maximum number of slipped bags to the total number of bags in the current candidate palletizing scheme can be calculated to obtain the actual maximum slip ratio of the current candidate palletizing scheme. Then, the ratio of the actual maximum slip ratio of the current candidate palletizing scheme to the preset slip ratio threshold is calculated, and the difference between 1 and the ratio is calculated to obtain the slip stability coefficient of the current candidate palletizing scheme.
[0058] Sa4. Determine the displacement stability coefficient of the corresponding candidate palletizing scheme based on the preset displacement threshold and the maximum displacement of each candidate palletizing scheme.
[0059] The preset displacement threshold is the maximum bag displacement allowed for each bag in the stack.
[0060] The displacement stability coefficient is used to quantify the stability of candidate palletizing schemes in the dimension of bag displacement under the action of acceleration time history under preset transportation conditions. Furthermore, the larger the displacement stability coefficient, the smaller the corresponding maximum displacement, and the higher the stability of the pallet.
[0061] Specifically, each candidate palletizing scheme is selected as the current candidate palletizing scheme. The ratio of the maximum displacement of the current candidate palletizing scheme to the preset displacement threshold can be calculated, and the difference between 1 and the ratio can be calculated to obtain the displacement stability coefficient of the current candidate palletizing scheme.
[0062] Sa5. Based on the tilt angle weight, the sliding bag number weight, and the displacement weight, the tilt stability coefficient, sliding stability coefficient, and displacement stability coefficient of each candidate palletizing scheme are weighted and fused to obtain the pallet stability of the corresponding candidate palletizing scheme.
[0063] Specifically, the stack stability of the current candidate palletizing schemes Where ω1 is the tilt angle weight, θ1 is the maximum tilt angle of the current candidate palletizing scheme, and θ0 is the preset tilt angle threshold. ω1 is the tilt stability coefficient of the current candidate palletizing scheme; ω2 is the weight of the number of sliding bags; N1 is the maximum number of sliding bags in the current candidate palletizing scheme; N2 is the total number of bags in the current candidate palletizing scheme; and N0 is the preset sliding ratio threshold. ω3 is the slip stability coefficient of the current candidate palletizing scheme; L1 is the maximum displacement of the current candidate palletizing scheme; and L0 is the preset displacement threshold. is the displacement stability coefficient of the current candidate palletizing scheme.
[0064] Optionally, to limit the range of stack stability values for candidate stacking schemes to [0,10], the stack stability... To limit the range of stack stability values for candidate stacking schemes to [0, 100], the stack stability... .
[0065] In this embodiment, by using tilt angle weight, sliding bag number weight, and displacement weight, the quantitative standard for stack stability can be made more suitable for actual use scenarios, and the calculation efficiency can be improved, the implementation complexity can be reduced, thereby improving the calculation efficiency and accuracy of stack stability.
[0066] In another implementation, each candidate palletizing scheme is selected as the current candidate palletizing scheme, and the sum of the tilt stability coefficient, slip stability coefficient and displacement stability coefficient of the current candidate palletizing scheme is calculated to obtain the pallet stability of the current candidate palletizing scheme.
[0067] S250. Determine the car space utilization rate of each candidate palletizing scheme.
[0068] Specifically, the volume of the stack corresponding to each candidate stacking scheme can be determined, where the stack volume is the volume of the stack formed by the candidate stacking scheme, and the ratio of the stack volume corresponding to each candidate stacking scheme to the effective volume of the carriage can be calculated to obtain the carriage space utilization rate of the corresponding candidate stacking scheme.
[0069] S260. Determine the target palletizing scheme based on the pallet stability and carriage space utilization of each candidate palletizing scheme.
[0070] Specifically, in one implementation, from multiple candidate palletizing schemes, a candidate palletizing scheme whose pallet stability meets a preset stability constraint is selected, and the candidate palletizing scheme with the highest carriage space utilization rate among the selected candidate palletizing schemes is determined as the target palletizing scheme; wherein, the preset stability constraint includes that the pallet stability is greater than a preset stability threshold, and the preset stability threshold is a pre-set minimum pallet stability for selecting a candidate palletizing scheme as the target palletizing scheme.
[0071] In another implementation, an optimization algorithm is used, with the palletizing parameters as the optimization variables, and the objective of satisfying the preset stability constraints of the pallet and maximizing the utilization rate of the carriage space. The palletizing parameters in each candidate palletizing scheme are iteratively optimized to obtain the target palletizing scheme. The optimization algorithm can be a genetic algorithm, a heuristic search algorithm, a simulated annealing algorithm, or other intelligent optimization methods.
[0072] Furthermore, using a genetic algorithm, with the palletizing parameters as optimization variables and the objective of satisfying preset stability constraints on pallet stability while maximizing the utilization rate of the carriage space, the palletizing parameters in each candidate palletizing scheme are iteratively optimized to obtain the target palletizing scheme, including Sb1-Sb8: Sb1. Construct a palletizing scheme set based on multiple candidate palletizing schemes.
[0073] Sb2. Based on the stability of the stack, preset stability constraints, and the utilization rate of the carriage space, determine a first set number of elite stacking schemes from the stacking scheme set.
[0074] The first set quantity is the number of elite individuals that need to be retained, as preset.
[0075] Specifically, a fitness function is constructed based on the premise that the stack stability meets the preset stability constraints and maximizes the utilization rate of the carriage space. This function is F = η - P × (1 - S), where F is the fitness function value, η is the carriage space utilization rate, P is the penalty coefficient (a positive number greater than 1), and S is the stability constraint satisfaction flag. S = 1 when the stack stability meets the preset stability constraints, and S = 0 when the stack stability does not meet the preset stability constraints. Therefore, using the stacking parameters as the basis and maximizing the fitness as the optimization objective, the stacking scheme set is iteratively optimized.
[0076] Specifically, based on preset stability constraints and the stack stability and carriage space utilization of each stacking scheme in the stacking scheme set, the fitness of the corresponding stacking scheme is calculated. Then, all stacking schemes in the stacking scheme set are sorted in descending order of fitness, and the stacking schemes in the first set number of slots are determined as elite stacking schemes.
[0077] Sb3. Select the non-elite palletizing schemes in the palletizing scheme set to obtain multiple first palletizing schemes, and perform crossover and mutation operations on the palletizing parameters in the multiple first palletizing schemes to obtain multiple second palletizing schemes.
[0078] Among them, non-elite palletizing schemes are palletizing schemes that are not elite palletizing schemes in the palletizing scheme set.
[0079] Specifically, the selection strategy in the genetic algorithm can be used to select non-elite palletizing schemes in the palletizing scheme set based on fitness, so as to obtain multiple first palletizing schemes. The number of first palletizing schemes can be set to a fixed value, and the selection strategy can be a tournament selection strategy, a truncation selection strategy, etc.
[0080] Next, the crossover strategy in the genetic algorithm can be used to perform crossover operations on the stacking parameters of multiple first stacking schemes, and the mutation strategy in the genetic algorithm can be used to perform mutation operations on the multiple stacking schemes obtained by the crossover operation to obtain multiple second stacking schemes. The number of second stacking schemes can be set to a fixed value. The crossover strategy can be single-point crossover, two-point crossover, uniform crossover, etc., and the mutation strategy can be uniform mutation, Gaussian mutation, etc.
[0081] Sb4. Based on the acceleration time history of the preset transportation conditions, the stability analysis of each second palletizing scheme is carried out based on the bag attribute parameters, carriage structure parameters, bag material parameters and contact friction coefficient. The pallet stability of the corresponding second palletizing scheme is obtained, and the carriage space utilization rate of each second palletizing scheme is determined.
[0082] The specific steps for stability analysis are the same as those for S220 to S240, and can be referred to the above description; the specific steps for determining the utilization rate of the carriage space are the same as those for S250, and can be referred to the above description.
[0083] Sb5. Based on the stability of the stack, preset stability constraints, and the utilization rate of the carriage space, select a second set number of stacking schemes from the first set number of elite stacking schemes and multiple second stacking schemes. Update the stacking scheme set based on the second set number of stacking schemes and determine the current iteration number.
[0084] Specifically, the number of palletizing schemes included in the palletizing scheme set is set to a fixed value, namely the second preset number, which is the same as the preset number in S110.
[0085] Specifically, the fitness of each second palletizing scheme can be determined based on the stability of the pallet, preset stability constraints, and the utilization rate of the carriage space. Then, according to the fitness from high to low, the first set number of elite palletizing schemes and multiple second palletizing schemes are sorted, and the palletizing schemes in the first second set number replace the original palletizing schemes in the palletizing scheme set, thereby updating the palletizing scheme set.
[0086] Then, when performing the selection, crossover, and mutation operations for the first time, the current iteration number is determined to be 1; otherwise, the iteration number determined in the previous iteration is incremented by 1 to obtain the current iteration number.
[0087] Sb6. Determine whether the current iteration count has reached the preset iteration count.
[0088] The preset iteration count is the number of iterations to terminate the iteration optimization process.
[0089] Specifically, if the current iteration count has not reached the preset iteration count, execute Sb7; if the current iteration count has reached the preset iteration count, execute Sb8.
[0090] Sb7. If the current iteration count has not reached the preset iteration count, return to execute the first set number of elite palletizing schemes determined from the palletizing scheme set based on the stability of the pallet, preset stability constraints, and the utilization rate of the carriage space.
[0091] Specifically, if the current iteration count has not reached the preset iteration count, return to execute Sb2 to continue iteratively optimizing the updated palletizing scheme set.
[0092] Sb8. When the current iteration number reaches the preset iteration number, the target palletizing scheme is determined from the updated palletizing scheme set based on the stability of the pallet, the preset stability constraints, and the utilization rate of the carriage space.
[0093] Specifically, when the current iteration count reaches the preset iteration count, the palletizing scheme with the highest fitness in the updated palletizing scheme set is determined as the target palletizing scheme. That is, the palletizing scheme with the highest palletizing stability that satisfies the preset stability constraint and the highest utilization rate of the carriage space in the updated palletizing scheme set is determined as the target palletizing scheme.
[0094] In this embodiment, the target palletizing scheme can be the optimal solution that achieves a reasonable trade-off between pallet stability and carriage space utilization, thereby improving the efficiency and accuracy of determining the target palletizing scheme.
[0095] Optionally, the compartment space utilization rate in S260 can be replaced with the total number of bags in the corresponding candidate palletizing scheme.
[0096] S270, Show the target palletizing solution.
[0097] Optionally, after determining the target palletizing scheme based on the pallet stability of each candidate palletizing scheme, the target palletizing scheme is converted into a palletizing scheme that can be recognized by the automated loading equipment, and the converted palletizing scheme is sent to the automated loading equipment so that the automated loading equipment can perform palletizing operations based on the target palletizing scheme.
[0098] Specifically, the target palletizing scheme can be analyzed to obtain the palletizing parameters, coordinates, placement posture, and palletizing order of each bag. At this point, the coordinates of each bag are in the simulation coordinate system. Next, the coordinate transformation matrix between the simulation coordinate system and the carriage coordinate system is used to transform the coordinates of each bag in the simulation coordinate system to obtain the coordinates of the corresponding bag in the carriage coordinate system. Then, the relative position of the corresponding bag to the carriage structure is determined based on the coordinates of each bag in the carriage coordinate system. Then, the palletizing parameters, the placement posture of each bag, the palletizing order of each bag, the coordinates of each bag in the carriage coordinate system, and the relative position of each bag to the carriage structure are encapsulated to obtain a palletizing scheme that can be recognized by the automated loading equipment. After that, the transformed palletizing scheme is sent to the automated loading equipment so that the automated loading equipment can perform palletizing operations based on the target palletizing scheme.
[0099] Among them, the coordinate transformation matrix between the simulation coordinate system and the vehicle coordinate system can be determined in advance through vehicle coordinate calibration test. The coordinate transformation matrix includes the transformation matrix between the simulation coordinate system and the radar coordinate system, and the transformation matrix between the radar coordinate system and the vehicle coordinate system.
[0100] In this embodiment, sending the converted palletizing scheme to the automated loading equipment can form a technical closed loop from pre-simulation verification to on-site automatic execution, effectively reducing the risk of accidents such as cargo collapse, bag damage, and spillage caused by pallet instability during actual transportation, and improving transportation safety.
[0101] Optionally, after obtaining the target palletizing scheme, the bag attribute parameters, carriage structure parameters, bag material parameters, contact friction coefficient, preset transportation conditions, transportation vehicle type, and target palletizing scheme can be saved to provide reusable templates for similar materials, similar routes, and vehicle types.
[0102] The technical solution of this application, based on bag attribute parameters and vehicle structure parameters, generates multiple candidate palletizing schemes that meet preset loading constraints. It then models each candidate palletizing scheme based on bag attribute parameters, vehicle structure parameters, material parameters inside the bag, and contact friction coefficient, obtaining a vehicle model for each candidate palletizing scheme. Next, it simulates the vehicle models of each candidate palletizing scheme according to the acceleration time history of preset transportation conditions, obtaining simulation results for each candidate palletizing scheme. Based on the simulation results of each candidate palletizing scheme, it determines the pallet stability of the corresponding candidate palletizing scheme. This provides an automatic quantification method for pallet stability, eliminating the need for visual inspection or manual shaking tests by operators. It accurately quantifies the ability of the pallet corresponding to a candidate palletizing scheme to resist instability phenomena such as slippage, overturning, and local collapse under the acceleration time history of preset transportation conditions. In other words, it can accurately predict the instability risk of the pallet during actual transportation, achieving traceable and auditable stability assessment. Modeling and simulation reduce the number of offline physical trials, lowering the cost of trial palletizing and trial transportation, and further improving the accuracy and efficiency of stability analysis. This provides an accurate data foundation for determining the target palletizing scheme. Then, the utilization rate of the carriage space for each candidate palletizing scheme is determined, and the target palletizing scheme is determined based on the stack stability and carriage space utilization of each candidate scheme. Demonstrating the target palletizing scheme allows for a comprehensive consideration of stack stability and carriage space utilization, making the target palletizing scheme more suitable for actual usage scenarios. This improves the accuracy of palletizing planning and automates the process, eliminating reliance on the experience of on-site operators or automated loading equipment. This improves the efficiency of palletizing planning and provides an accurate data foundation for subsequent automated loading equipment palletizing operations. This effectively reduces the risk of accidents such as cargo collapse, bag damage, and spillage caused by stack instability during actual transportation, thus improving transportation safety. Furthermore, by simply changing the bag body attribute parameters, carriage structure parameters, bag internal material parameters, and contact friction coefficient, the method of this application embodiment can be extended to various materials such as fertilizers, grains, feeds, and fine chemicals. This allows the method of this application embodiment to serve palletizing planning scenarios involving multiple factories, multiple vehicle models, and multiple routes, demonstrating high scalability and applicability.
[0103] Figure 3 This is a structural schematic diagram of the palletizing planning device provided in an embodiment of this application, referring to... Figure 3 The palletizing planning device may include: The generation module 310 is used to generate multiple candidate palletizing schemes that meet preset loading constraints based on bag attribute parameters and carriage structure parameters. The analysis module 320 is used to perform stability analysis on each candidate palletizing scheme based on the acceleration time history of the preset transportation conditions, bag attribute parameters, carriage structure parameters, bag material parameters and contact friction coefficient, and obtain the pallet stability of the corresponding candidate palletizing scheme. The determination module 330 is used to determine the target palletizing scheme based on the pallet stability of each candidate palletizing scheme and to display the target palletizing scheme.
[0104] In one embodiment, the analysis module 320 is specifically used to: model each candidate palletizing scheme based on bag attribute parameters, carriage structure parameters, bag material parameters and contact friction coefficient to obtain the vehicle model of the corresponding candidate palletizing scheme; simulate the vehicle model of each candidate palletizing scheme according to the acceleration time history of the preset transportation conditions to obtain the simulation results of the corresponding candidate palletizing scheme; and determine the pallet stability of the corresponding candidate palletizing scheme based on the simulation results of each candidate palletizing scheme.
[0105] In one embodiment, the simulation results include a stack tilt angle sequence, a sliding bag number sequence, and a bag displacement sequence for each bag. The analysis module 320 determines the stack stability of the corresponding candidate stacking scheme based on the simulation results of each candidate stacking scheme, including: determining the maximum tilt angle of the corresponding candidate stacking scheme based on the stack tilt angle sequence of each candidate stacking scheme; determining the maximum number of sliding bags of the corresponding candidate stacking scheme based on the sliding bag number sequence of each candidate stacking scheme; determining the maximum displacement of the corresponding candidate stacking scheme based on the bag displacement sequence of each bag of each candidate stacking scheme; and determining the stack stability of the corresponding candidate stacking scheme based on the maximum tilt angle, maximum number of sliding bags, and maximum displacement of each candidate stacking scheme.
[0106] In one embodiment, the analysis module 320 determines the stack stability of each candidate palletizing scheme based on the maximum tilt angle, maximum number of sliding bags, and maximum displacement of each candidate palletizing scheme. This includes: obtaining tilt angle weights, sliding bag number weights, and displacement weights; determining the tilt stability coefficient of each candidate palletizing scheme based on a preset tilt angle threshold and the maximum tilt angle of each candidate palletizing scheme; determining the sliding stability coefficient of each candidate palletizing scheme based on a preset sliding ratio threshold and the total number of bags and the maximum number of sliding bags of each candidate palletizing scheme; determining the displacement stability coefficient of each candidate palletizing scheme based on a preset displacement threshold and the maximum displacement of each candidate palletizing scheme; and weighting and fusing the tilt stability coefficient, sliding stability coefficient, and displacement stability coefficient of each candidate palletizing scheme based on the tilt angle weights, sliding bag number weights, and displacement weights to obtain the stack stability of the corresponding candidate palletizing scheme.
[0107] In one embodiment, the determining module 330 determines the target palletizing scheme based on the pallet stability of each candidate palletizing scheme, including: determining the carriage space utilization rate of each candidate palletizing scheme; and determining the target palletizing scheme based on the pallet stability and carriage space utilization rate of each candidate palletizing scheme.
[0108] In one embodiment, the determining module 330 determines the target palletizing scheme based on the stability of the pallet body and the utilization rate of the carriage space of each candidate palletizing scheme, including: taking the palletizing parameters as optimization variables, taking the stability of the pallet body satisfying the preset stability constraint and maximizing the utilization rate of the carriage space as the optimization objective, iteratively optimizing the palletizing parameters in each candidate palletizing scheme to obtain the target palletizing scheme.
[0109] In one embodiment, the determining module 330 uses palletizing parameters as optimization variables and aims to optimize the palletizing parameters of each candidate palletizing scheme by satisfying preset stability constraints and maximizing the utilization rate of the carriage space. This optimization process includes: constructing a palletizing scheme set based on multiple candidate palletizing schemes; determining a first set number of elite palletizing schemes from the set based on palletizing stability, preset stability constraints, and carriage space utilization; selecting non-elite palletizing schemes from the set to obtain multiple first palletizing schemes, and performing crossover and mutation operations on the palletizing parameters of the multiple first palletizing schemes to obtain multiple second palletizing schemes; and optimizing each palletizing scheme according to the acceleration time history of a preset transportation condition, based on bag attribute parameters, carriage structure parameters, bag material parameters, and contact friction coefficient. The stability of the second palletizing scheme is analyzed to obtain the stability of the pallet body and determine the car space utilization rate of each second palletizing scheme. Based on the pallet body stability, preset stability constraints, and car space utilization rate, a second set number of palletizing schemes are selected from the first set number of elite palletizing schemes and multiple second palletizing schemes. The palletizing scheme set is updated based on the second set number of palletizing schemes, and the current iteration number is determined. If the current iteration number has not reached the preset iteration number, the process returns to determine the first set number of elite palletizing schemes from the palletizing scheme set based on the pallet body stability, preset stability constraints, and car space utilization rate. If the current iteration number has reached the preset iteration number, the target palletizing scheme is determined from the updated palletizing scheme set based on the pallet body stability, preset stability constraints, and car space utilization rate.
[0110] In one embodiment, the palletizing parameters in the determining module 330 include at least one of the following: total number of pallet layers, number of bags per layer, arrangement of each layer, misalignment between adjacent layers, and edge clearance distance.
[0111] In one embodiment, the palletizing planning device further includes a sending module, which is used to convert the target palletizing scheme into a palletizing scheme that can be recognized by the automatic loading equipment after determining the target palletizing scheme based on the pallet stability of each candidate palletizing scheme, and send the converted palletizing scheme to the automatic loading equipment so that the automatic loading equipment can perform palletizing operations based on the target palletizing scheme.
[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0113] The palletizing planning device provided in this embodiment can be applied to the palletizing planning method provided in any of the above embodiments, and has corresponding functions and beneficial effects.
[0114] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 A block diagram is shown of an exemplary electronic device 11 suitable for implementing embodiments of the present application. Figure 4 The electronic device 11 shown is merely an example and should not impose any limitations on the functionality and scope of use of this embodiment.
[0115] like Figure 4 As shown, the electronic device 11 is represented in the form of a general-purpose computing electronic device. The components of the electronic device 11 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).
[0116] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, industry-standard architecture buses, microchannel architecture buses, enhanced industry-standard architecture buses, Video Electronics Standards Association (VESA) local buses, and peripheral component interconnect buses.
[0117] Electronic device 11 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 11, including volatile and non-volatile media, removable and non-removable media.
[0118] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory 30 and / or cache memory 32. Electronic device 11 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.
[0119] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this application.
[0120] Electronic device 11 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 11, and / or with any device that enables electronic device 11 to communicate with one or more other computing devices (e.g., network interface card and modem, etc.). Such communication can be performed through input / output interface 22. Furthermore, electronic device 11 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network) through network adapter 20.
[0121] like Figure 4 As shown, network adapter 20 communicates with other modules of electronic device 11 via bus 18. It should be understood that, although... Figure 4 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 11, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, tape drives, and data backup storage systems.
[0122] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, such as implementing a palletizing planning method provided in any embodiment of this application.
[0123] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a palletizing planning method, such as that provided in any embodiment of this application.
[0124] The computer storage medium of this embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0125] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.
[0126] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, radio frequency, etc., or any suitable combination thereof.
[0127] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0128] Those skilled in the art will understand that the modules or steps described above in this application can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computing device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0129] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the inventive concept of this application, and the scope of this application is determined by the scope of the appended claims.
Claims
1. A palletizing planning method, characterized in that, The method includes: Based on the bag's attribute parameters and the carriage's structural parameters, multiple candidate palletizing schemes that meet the preset loading constraints are generated. Based on the acceleration time history of the preset transportation conditions, the stability analysis of each candidate palletizing scheme is performed on the bag attribute parameters, the carriage structure parameters, the material parameters inside the bag and the contact friction coefficient to obtain the pallet stability of the corresponding candidate palletizing scheme. The target palletizing scheme is determined based on the pallet stability of each candidate palletizing scheme, and the target palletizing scheme is displayed.
2. The palletizing planning method according to claim 1, characterized in that, Based on the acceleration time history of the preset transportation conditions, stability analysis is performed on each candidate palletizing scheme according to the bag attribute parameters, the carriage structure parameters, the material parameters inside the bag, and the contact friction coefficient, to obtain the pallet stability of the corresponding candidate palletizing scheme, including: Based on the bag body attribute parameters, the carriage structure parameters, the material parameters inside the bag, and the contact friction coefficient, the candidate palletizing schemes are modeled to obtain the vehicle model of the corresponding candidate palletizing scheme; The vehicle models of each candidate palletizing scheme are simulated according to the acceleration time history of the preset transportation conditions to obtain the simulation results of the corresponding candidate palletizing schemes. The stack stability of each candidate stacking scheme is determined based on the simulation results of each candidate stacking scheme.
3. The palletizing planning method according to claim 2, characterized in that, The simulation results include a stack tilt angle sequence, a sliding bag number sequence, and a bag displacement sequence for each bag. Based on the simulation results of each candidate stacking scheme, the stack stability of the corresponding candidate stacking scheme is determined, including: The maximum tilt angle of the corresponding candidate palletizing scheme is determined based on the sequence of pallet tilt angles of each candidate palletizing scheme. The maximum number of sliding bags for each candidate palletizing scheme is determined based on the sequence of sliding bag numbers for each candidate palletizing scheme. The maximum displacement of the corresponding candidate palletizing scheme is determined based on the bag displacement sequence of each bag in each candidate palletizing scheme. The stack stability of each candidate palletizing scheme is determined based on the maximum tilt angle, maximum number of sliding bags, and maximum displacement.
4. The palletizing planning method according to claim 3, characterized in that, The stack stability of each candidate palletizing scheme is determined based on its maximum tilt angle, maximum number of sliding bags, and maximum displacement, including: Obtain the tilt angle weight, the number of sliding bags weight, and the displacement weight; The tilt stability coefficient of the corresponding candidate palletizing scheme is determined based on the preset tilt angle threshold and the maximum tilt angle of each candidate palletizing scheme. Based on a preset slip ratio threshold and the total number of bags and the maximum number of slipped bags for each candidate palletizing scheme, the slip stability coefficient of the corresponding candidate palletizing scheme is determined. The displacement stability coefficient of the corresponding candidate palletizing scheme is determined based on the preset displacement threshold and the maximum displacement of each candidate palletizing scheme. Based on the tilt angle weight, the sliding bag number weight, and the displacement weight, the tilt stability coefficient, sliding stability coefficient, and displacement stability coefficient of each candidate palletizing scheme are weighted and fused to obtain the pallet stability of the corresponding candidate palletizing scheme.
5. The palletizing planning method according to claim 1, characterized in that, The target palletizing scheme is determined based on the pallet stability of each candidate palletizing scheme, including: Determine the carriage space utilization rate of each candidate palletizing scheme; The target palletizing scheme is determined based on the pallet stability and carriage space utilization of each candidate palletizing scheme.
6. The palletizing planning method according to claim 5, characterized in that, The target palletizing scheme is determined based on the pallet stability and carriage space utilization of each candidate palletizing scheme, including: Using the palletizing parameters as optimization variables, and with the objective of satisfying the preset stability constraints on the pallet stability and maximizing the utilization rate of the carriage space, the palletizing parameters in each candidate palletizing scheme are iteratively optimized to obtain the target palletizing scheme.
7. The palletizing planning method according to claim 6, characterized in that, Using palletizing parameters as optimization variables, and with the objective of satisfying preset stability constraints on pallet stability and maximizing the utilization rate of the carriage space, the palletizing parameters in each candidate palletizing scheme are iteratively optimized to obtain the target palletizing scheme, including: Construct a palletizing scheme set based on the multiple candidate palletizing schemes; Based on the stability of the stack, the preset stability constraints, and the utilization rate of the carriage space, a first set number of elite stacking schemes are determined from the set of stacking schemes. A selection operation is performed on the non-elite palletizing schemes in the palletizing scheme set to obtain multiple first palletizing schemes, and crossover and mutation operations are performed on the palletizing parameters in the multiple first palletizing schemes to obtain multiple second palletizing schemes. Based on the acceleration time history of the preset transportation conditions, stability analysis is performed on each second palletizing scheme according to the bag attribute parameters, the carriage structure parameters, the material parameters inside the bag and the contact friction coefficient, to obtain the pallet stability of the corresponding second palletizing scheme and determine the carriage space utilization rate of each second palletizing scheme. Based on the stability of the pallet, the preset stability constraints, and the utilization rate of the carriage space, a second set number of palletizing schemes are selected from the first set number of elite palletizing schemes and the multiple second palletizing schemes. The palletizing scheme set is updated based on the second set number of palletizing schemes, and the current iteration number is determined. If the current iteration count has not reached the preset iteration count, return to execute the first set number of elite palletizing schemes determined from the palletizing scheme set based on the pallet stability, the preset stability constraint, and the carriage space utilization rate; When the current iteration number reaches the preset iteration number, a target palletizing scheme is determined from the updated palletizing scheme set based on the pallet stability, the preset stability constraint, and the carriage space utilization rate.
8. The palletizing planning method according to claim 6, characterized in that, The palletizing parameters include at least one of the following: total number of layers in the pallet, number of bags per layer, arrangement of each layer, misalignment between adjacent layers, and edge clearance distance.
9. The palletizing planning method according to claim 1, characterized in that, After determining the target palletizing scheme based on the pallet stability of each candidate palletizing scheme, the process further includes: The target palletizing scheme is converted into a palletizing scheme that can be recognized by the automated loading equipment, and the converted palletizing scheme is sent to the automated loading equipment so that the automated loading equipment performs palletizing operations based on the target palletizing scheme.
10. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the palletizing planning method according to any one of claims 1 to 9.