A design method for large packaging containers

Abstract

The present invention belongs to the fields of intelligent equipment and three-dimensional simulation technology, and proposes a design method for large packaging containers, specifically: importing the three-dimensional model of the packaging container into finite element analysis software, performing load simulation in the finite element analysis software to obtain load simulation data, calculating the defect state value according to the load simulation data, and finally identifying the damaged risk position of the inner packaging container through the defect state value and performing design processing. This method particularly focuses on providing a quantitative method for the risk of load imbalance caused by the layout of fixing devices with insufficient design considerations through a finely adjusted double-layer packaging structure, so as to provide stronger stability and higher reliability for the inner packaging protection of products during transportation. The core of this method lies in optimizing the design related to the fixing devices of the double-layer packaging, aiming to reduce the risk of damage to the inner packaging caused by the fixing devices due to external impacts and pressures during transportation, thereby enhancing the protection performance of large packaging containers for transported items.

Description

Technical Field

[0001] The present invention belongs to the fields of intelligent equipment and 3D simulation technology, and particularly relates to a design method for large packaging containers. Background Art

[0002] With the popularization of intelligent manufacturing and the development of the manufacturing industry, the demand for the preparation of high-performance production equipment and instruments is currently increasing continuously. High-performance manufacturing equipment includes automated production line equipment, precision machining equipment, large robotic arms, etc. However, the transportation operations after their preparation have always faced safety challenges. Especially in transportation scenarios such as overseas transportation, the safety of the transportation process is more emphasized. This is because this type of equipment often has characteristics such as high technology density, large size and weight, and high preparation cost, which are not commonly faced in ordinary transportation. Currently, double-layer packaging is usually adopted in this field, that is, the inner layer packaging is custom-designed according to the shape and size of the equipment, and foam plastic or EVA (ethylene-vinyl acetate) is usually used as the filling and buffering material between the inner layer packaging and the outer layer packaging.

[0003] Both the inner layer packaging and the outer layer packaging are made of strong materials such as wood, metal or high-strength plastic, so as to be able to withstand external physical pressure and impact. In addition to the filling and buffering material between the outer layer and the inner layer packaging, fixing devices such as straps, clamps and support frames are also required. In ordinary transportation, the fixing devices are usually set according to the experience of designers, which can meet the transportation needs of most small packaging containers. However, when the position of the fixing device for large packaging is selected improperly, it will lead to the risk of load imbalance. This is because the external forces generated during transportation, such as vibration and impact, will be transmitted to the inner layer packaging through the fixing device. The dynamic load caused by the external force will cause stress waves to propagate in the packaging container structure, resulting in local stress concentration, and this stress reaction often has the characteristics of high peak value and concentrated position. Therefore, the risk of load imbalance will easily cause damage to the position of the fixing device on the inner layer packaging. It should be noted that the inner layer packaging of high-performance manufacturing equipment usually has refined design requirements such as waterproof and anti-static. Therefore, the damage of the inner layer packaging will lead to a great safety risk in the transportation task, especially in the process of sea transportation, this safety risk is more prominent than land transportation. Therefore, based on the transportation of large production equipment, when designing the packaging container, it is necessary to analyze the position layout decision of the fixing device on the inner layer packaging to provide stronger protection and improve the safety performance of the equipment during transportation. Summary of the Invention

[0004] The purpose of the present invention is to propose a design method for large packaging containers to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0005] To achieve the above object, according to one aspect of the present invention, a design method for a large packaging container is provided, and the method includes the following steps:

[0006] S100, import the three-dimensional model of the packaging container into finite element analysis software;

[0007] S200, perform load simulation in the finite element analysis software to obtain load simulation data;

[0008] S300, calculate the defect state value according to the load simulation data;

[0009] S400, identify the damaged risk positions of the inner packaging container through the defect state value and perform design processing.

[0010] Further, in step S100, the method of importing the three-dimensional model of the packaging container into finite element analysis software is: obtain the three-dimensional model of the packaging container in SolidWorks three-dimensional modeling software, where the three-dimensional model includes the inner packaging, the outer packaging, and the fixing devices and cushioning materials between the inner packaging and the outer packaging, and the types of fixing devices include one or more of straps, clamps, and support frames; import the three-dimensional model into the finite element analysis software, and set the material properties of each component of the three-dimensional model in the finite element analysis software, where the finite element analysis software selects any one of ANSYS, ABAQUS, or MSC.MARC.

[0011] Further, in step S200, the method of performing load simulation in the finite element analysis software to obtain load simulation data is: identify each fixing device from the three-dimensional model and record them as fixing units respectively; construct a dynamic load model using digital twin technology according to the prototype observation data;

[0012] Read the dynamic load model in the finite element analysis software, and record the stress values obtained from the dynamic load simulation of the fixing units in real time in any dynamic load model; the stress values obtained from the simulation of each fixing unit in any dynamic load model constitute the load simulation data corresponding to the dynamic load model, and the time length of the simulation data is TC, where TC ∈ [1, 24] hours.

[0013] Further, in step S300, the method of calculating the defect state value according to the load simulation data is:

[0014] Denote ROAM as the monitoring period, where ROAM ∈ [1, 5] hours; assume that the number of fixing units monitored within the current ROAM period is N, and the interval for obtaining stress values is t;

[0015] For any fixing unit, X i is the i-th stress value measured during the monitoring period ROAM, and T iTo obtain the moment corresponding to the i-th stress value, denote the maximum stress value obtained during the monitoring period as the stress peak Peak, and the minimum stress value as the stress deviation Devi;

[0016] Construct the first stress set Str_Fs = {(T i , X i ) | 0 ≤ T i ≤ ROAM / t, Devi ≤ X i ≤ Peak}, and use the polyfit function to perform polynomial fitting on the first stress set Str_Fs. The resulting fitted function is denoted as the original loss function X = f(T i );

[0017] Arrange all stress values within the same fixed unit in ascending order. Denote the upper quartile of the stress values as Str(75%), the lower quartile as Str(25%). Denote the stress upper bound Str_Sup = Str(75%) + 1.5(Str(75%) - Str(25%)), and the stress lower bound Str_Inf = Str(25%) - 1.5(Str(75%) - Str(25%));

[0018] Denote the average value of the stress values as the stress mean Ave_Str. If X i ≤ Str_Inf or X i > Str_Sup, then X i is called the stress loss value. The set composed of stress loss values is the first loss set. Denote the loss rate characteristic vector of the stress loss value as Loss_va = (|X i - X i－1 | / t, |X i+1 - X i | / t). The mean vector of the loss rate characteristic vectors corresponding to each stress loss value in the first loss set is denoted as Loss_ave. Calculate the Mahalanobis distance between all stress loss values and the mean vector as the differential loss DL. When the differential loss at any moment does not exist, inherit the first differential loss obtained by its reverse-time search; Denote the set composed of the differential losses of all fixed units at any moment as T_DL, then the percentile value of the differential loss of any fixed unit in the set T_DL is denoted as the set rank Rank;

[0019] If the differential loss corresponding to any stress loss value X i is greater than the average value of all differential losses, then this stress loss value X i is called the stress defect value. Replace the obtained stress defect value with the stress mean Ave_Str to form the second stress set Str_Sc = {(T i , X i ) | 0 ≤ T i≤ROAM / t, Devi≤X i ≤Peak}, and use the polyfit function to perform polynomial fitting on the second stress set Str_Sc, and the obtained function is denoted as the corrected loss function X * = g(T i ); For the current fixed unit, calculate the defect state value Vali:

[0020]

[0021] where hs<> is the harmonic mean function, Rank i and DL i respectively represent the set rank and differential loss of the current fixed unit at the i-th moment.

[0022] Since the above defect state value is calculated depending on the stress value, and the change degree of the stress value in a certain fixed interval reflects the loss rate eigenvector of the stress value in the fixed unit, it is possible to identify the breakage risk caused by the load imbalance risk due to unreasonable fixation in the inner packaging container and perform effective quantification. However, due to the problem that the situation simulation ability is insufficient in the dynamic load model read by the finite element analysis software, resulting in an unclear risk accumulation effect, especially when it is necessary to statistically analyze the differential loss to measure the loss rate eigenvector, the problem of insufficient sensitivity is particularly obvious. To solve this problem of insufficient data sensitivity and improve the accuracy of defect state value calculation, the present invention proposes a more preferred solution.

[0023] Preferably, in step S300, the method for calculating the defect state value according to the load simulation data is as follows: The time length of the simulation data is TC, and TC ∈ [1, 24] hours; within the TC time, record the moment when the fixed unit obtains the stress value as the acquisition point; take any fixed unit as the current fixed unit; in the current fixed unit, obtain the maximum stress value and denote it as the macro peak value, and subtract the stress value at any acquisition point from the macro peak value to obtain the difference denoted as the macro subsidence Maro;

[0024] Calculate the average value of the stress values at all acquisition points of the current fixed unit and denote it as the stress level estimate EST; the ratio of the stress level estimate to the macro subsidence is denoted as the distance value scalar Dcal; if the distance value scalar of an acquisition point is larger than that of its previous acquisition point, then define this acquisition point as a growth site, construct the distance value scalars at each growth site into a sequence denoted as the distance value sequence Diva.Ls, and denote the growth site corresponding to all the minimum values in the distance value sequence as the low touch site; denote the time interval between any low touch site and the first low touch site obtained by searching in the reverse time direction as the sub-sensitive domain corresponding to this low touch site;

[0025] In the sub-sensitive domain, obtain the maximum value of the macro-settlement amount and the maximum value of the distance scalar value at each growth site, and denote them as the sub-settlement degree and the sub-scale value respectively. The sub-settlement degree and the sub-scale value are constructed into a binary tuple denoted as the sensitive array. Calculate the average value of the sensitive arrays of each sub-sensitive domain and denote it as the mean array; Compare to obtain the sensitive array with the smallest Euclidean distance from the mean array and denote it as the reference array. The elements of the reference array are denoted as the reference settlement degree and the reference scale value respectively; Calculate the slope of any sensitive array and the reference array and denote it as the defect sensitivity value Desn. Denote the maximum value among the various defect sensitivity values as max.Desn;

[0026] Obtain and calculate the average value of the macro-settlement amount at each growth site in the sub-sensitive domain and denote it as the sub-average settlement degree Asie; Obtain the defect state value Deff through the sub-sensitive domain. Its calculation method is:

[0027]

[0028] where j1 is the accumulation variable, the max<> function is the maximum value function, SE is the number of sub-sensitive domains under the current fixed unit, Desn j1 and Maro j1 are the defect sensitivity value and the macro-settlement amount of the j1-th sub-sensitive domain respectively, and Dcal j1 is the distance scalar value corresponding to the growth site of the j1-th sub-sensitive domain. The lg() function is the logarithmic function with base 10, and the exp() function is the exponential function with base e, the natural constant.

[0029] Since the defect state value is calculated based on the simulation data under feature extraction, there is a corresponding degree of risk accumulation for different types of navigation features with different intensities and durations. Thus, it effectively quantifies the load imbalance risk caused by the unreasonable layout of fixing devices in the packaging container, providing a mathematical support for further determining the risk location.

[0030] Further, in step S400, the method for identifying the breakage risk position of the inner packaging container through the defect state value and performing design processing is as follows: Calculate the weighted average of the defect state values obtained by the same fixed unit under different dynamic load models and denote it as the defect state level DSMR. The weight value of the weighted average is BIC×exp(-RANK), where BIC represents the Bayesian information criterion corresponding to the dynamic load model, and RANK(N) represents the serial number value of the defect state value of the current fixed unit in the descending order sorting of the current dynamic load model; The sequence of defect state levels corresponding to each fixed unit is denoted as DSMR.Ls, and its mean value and minimum value are denoted as Ls.avg and Ls.btt respectively; Remove the elements greater than its average value from DSMR.Ls and arrange them from largest to smallest. Denote the difference between any element in DSMR.Ls and its next element as Ls.G, and denote the ratio of the maximum value in each Ls.G to the average value of DSMR.Ls as the small value overflow degree SMO; When the defect state level of a fixed unit satisfies DSMR≥(1 + SMO)×(2Ls.avg-Ls.btt), then define this fixed unit as a risk unit, and consider that there is a risk of transportation breakage of the fixing device corresponding to the risk unit on the inner packaging. Thicken the position of the inner packaging corresponding to the risk unit, and the thickening ratio is any value within the range of 10% - 20%.

[0031] Preferably, in step S400, the method for identifying the breakage risk position of the inner packaging container through the defect state value and performing design processing is as follows: If it is determined that a fixed unit is a risk unit, then send the serial number corresponding to this risk unit to the client to warn that the position of the fixing device corresponding to the risk unit on the inner packaging needs to be replaced.

[0032] Preferably, all variables not defined in the present invention, if not clearly defined, can be manually set thresholds.

[0033] The present invention also provides a design system for large packaging containers. The design system for large packaging containers includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the design method for large packaging containers. The design system for large packaging containers can run on computing devices such as desktop computers, laptop computers, palm computers, and cloud data centers. The operable system can include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs in the following units of the system:

[0034] A model acquisition unit, configured to import the three-dimensional model of the packaging container into finite element analysis software;

[0035] A load simulation unit for performing load simulation in finite element analysis software to obtain load simulation data;

[0036] A deviation analysis and construction unit for calculating the defect state value based on the load simulation data;

[0037] A design processing unit for identifying the damaged risk positions of the inner packaging container through the defect state value and performing design processing.

[0038] The beneficial effects of the present invention are as follows: The present invention provides a design method for large packaging containers, taking into account the special characteristics of high-performance production equipment in terms of shape, size, and weight. Through customized double-layer packaging, the position layout of the fixing devices in the large packaging container is analyzed and optimized. The present invention effectively quantifies the load imbalance risk caused by improper fixing device layout, enhancing the overall stability and balance of the packaging. It can more precisely independently analyze the load imbalance risk under the design requirements of packaging containers for different devices, prevent the occurrence of damage problems induced by the load imbalance risk in the inner packaging, and significantly reduce the risk of damage to the inner packaging caused by the fixing devices due to external impact and pressure during transportation, thereby providing a safer protection performance for the transported objects by the large packaging container and providing a safer transportation solution for high-performance production equipment. Description of the Drawings

[0039] By elaborating on the embodiments shown in conjunction with the drawings, the above and other features of the present invention will become more apparent. The same reference numerals in the drawings of the present invention represent the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings:

[0040] Figure 1 Shown is a flowchart of a design method for large packaging containers;

[0041] Figure 2 Shown is a structural diagram of a design system for large packaging containers. Detailed Embodiments

[0042] The following will clearly and completely describe the concept, specific structure, and technical effects generated by the present invention in conjunction with the embodiments and drawings to fully understand the purpose, solution, and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0043] As Figure 1 Shown is a flowchart of a design method for large packaging containers. The following will be described in conjunction with Figure 1To describe a design method for large packaging containers according to an embodiment of the present invention, the method includes the following steps:

[0044] S100, import the three-dimensional model of the packaging container into finite element analysis software;

[0045] S200, perform load simulation in the finite element analysis software to obtain load simulation data;

[0046] S300, calculate the defect state value according to the load simulation data;

[0047] S400, identify the breakage risk positions of the inner packaging container through the defect state value and perform design processing.

[0048] Further, in step S100, the method of importing the three-dimensional model of the packaging container into finite element analysis software is: obtain the three-dimensional model of the packaging container in SolidWorks three-dimensional modeling software, where the three-dimensional model includes the inner packaging, the outer packaging, and the fixing devices and cushioning materials between the inner packaging and the outer packaging, and the types of fixing devices include one or more of straps, clamps, and support frames; import the three-dimensional model into the finite element analysis software, and set the material properties of each component of the three-dimensional model in the finite element analysis software, where the finite element analysis software is ABAQUS.

[0049] Further, in step S200, the method of performing load simulation in the finite element analysis software to obtain load simulation data is: identify each fixing device from the three-dimensional model and record them as fixing units respectively; construct a dynamic load model using digital twin technology based on the prototype observation data;

[0050] Read the dynamic load model in the finite element analysis software, and record the stress values obtained from the dynamic load simulation of the fixing units in any dynamic load model in real time; the stress values obtained from the simulation of each fixing unit in any dynamic load model constitute the load simulation data corresponding to the dynamic load model, and the time length of the simulation data is TC, and TC takes a value of 1 hour.

[0051] Where the prototype observation data includes acceleration data in the X, Y, and Z axis directions, and the prototype observation data is collected through an acceleration sensor; the dynamic load model is the simulation data of the prototype observation data; the simulation data is constructed by extracting the features of the prototype observation data and using digital twin technology, and each simulation data is used as the dynamic load model.

[0052] Further, in step S300, the method of calculating the defect state value according to the load simulation data is: record ROAM as the monitoring period, and ROAM takes a value of 1 hour; assume that the number of fixing units monitored within the current ROAM period is N, and the interval for obtaining stress values is t;

[0053] For any fixed unit, X i is the i-th stress value measured by ROAM during the monitoring period T, i is the time corresponding to the i-th stress value. Denote the maximum stress value obtained during the monitoring period as the stress peak Peak, and the minimum stress value as the stress deviation Devi;

[0054] Construct the first stress set Str_Fs = {(T i , X i ) | 0 ≤ T i ≤ ROAM / t, Devi ≤ X i ≤ Peak}. Use the polyfit function to perform polynomial fitting on the first stress set Str_Fs, and denote the obtained fitting function as the original loss function X = f(T i );

[0055] Arrange all the stress values within the same fixed unit in ascending order. Denote the upper quartile of the stress values as Str(75%), and the lower quartile as Str(25%). Denote the stress upper bound Str_Sup = Str(75%) + 1.5(Str(75%) - Str(25%)), and the stress lower bound Str_Inf = Str(25%) - 1.5(Str(75%) - Str(25%));

[0056] Denote the average value of the stress values as the stress mean Ave_Str. If X i ≤ Str_Inf or X i > Str_Sup, then X i is called the stress loss value. The set composed of stress loss values is the first loss set. Denote the loss rate characteristic vector of the stress loss value as Loss_va = (|X i - X i－1 | / t, |X i+1 - X i | / t). The mean vector of the loss rate characteristic vectors corresponding to each stress loss value in the first loss set is denoted as Loss_ave. Calculate the Mahalanobis distance between all stress loss values and the mean vector as the differential loss DL. When the differential loss at any moment does not exist, inherit the first differential loss obtained by its reverse-time search; Denote the set composed of the differential losses of all fixed units at any moment as T_DL, then the percentile value of the differential loss of any fixed unit in the set T_DL is denoted as the set rank Rank;

[0057] If the differential loss corresponding to any stress loss value X i is greater than the average value of all differential losses, then the stress loss value X iis the stress defect value. The obtained stress defect value is replaced with the stress mean Ave_Str to form the second stress set Str_Sc = {(T i , X i ) | 0 ≤ T i ≤ ROAM / t, Devi ≤ X i ≤ Peak}. The polyfit function is used to perform polynomial fitting on the second stress set Str_Sc, and the obtained function is denoted as the corrected loss function X * = g(T i ); For the current fixed unit, the defect state value Vali is calculated as follows:

[0058]

[0059] where hs<> is the harmonic mean function, Rank i and DL i represent the set rank and differential loss of the current fixed unit at the i-th moment respectively.

[0060] The call data set within the harmonic mean function includes the set rank and differential loss obtained at all moments during the ROAM period of i.

[0061] Among them, the polyfit function is called through the numpy package in python. When the differential loss DL, X i－1 or X i+1 does not exist, the data corresponding to that moment is ignored, that is, the data at that moment is not used for calculating the defect state value.

[0062] Furthermore, in step S300, the method for calculating the defect state value according to the load simulation data is as follows: The time length of the simulation data is TC, and TC takes a value of 1 hour; within the TC time, the moments when the fixed unit obtains stress values are recorded as acquisition points; any fixed unit is taken as the current fixed unit; in the current fixed unit, the maximum stress value obtained is recorded as the macro peak value, and the difference obtained by subtracting the stress value at any acquisition point from the macro peak value is recorded as the macro sinkage Maro;

[0063] Calculate the average value of the stress values at all acquisition points of the current fixed unit and record it as the stress level estimate EST; The ratio of the stress level estimate to the macro sinkage is recorded as the distance scalar Dcal; If the distance scalar of an acquisition point is larger than that of its previous acquisition point, then this acquisition point is defined as a growth site, and the distance scalars at each growth site are constructed into a sequence denoted as the distance sequence Diva.Ls, and the growth sites corresponding to all minimum values within the distance sequence are recorded as low touch sites; The time interval between any low touch site and the first low touch site obtained by searching in the reverse time direction is recorded as the sub-sensitive domain corresponding to this low touch site;

[0064] In the sub-sensitive domain, obtain the maximum value of the macro settlement amount and the maximum value of the distance scalar at each growth site, and denote them as the sub-settlement degree and the sub-scale value respectively. The sub-settlement degree and the sub-scale value are constructed into a binary group and denoted as the sensitive array. Calculate the average value of the sensitive arrays of each sub-sensitive domain and denote it as the mean array; compare and obtain the sensitive array with the smallest Euclidean distance from the mean array and denote it as the reference array. The elements of the reference array are denoted as the reference settlement degree and the reference scale value respectively; calculate the slope of any sensitive array and the reference array and denote it as the defect sensitivity value Desn, and denote the maximum value among all the defect sensitivity values as max.Desn;

[0065] Obtain and calculate the average value of the macro settlement amount at each growth site in the sub-sensitive domain and denote it as the sub-average settlement degree Asie; obtain the defect state value Deff through the sub-sensitive domain, and its calculation method is:

[0066]

[0067] where j1 is the cumulative variable, the max<> function is the maximum value function, SE is the number of sub-sensitive domains under the current fixed unit, Desn j1 and Maro j1 are the defect sensitivity value and the macro settlement amount of the j1-th sub-sensitive domain respectively, and Dcal j1 is the distance scalar corresponding to the growth site of the j1-th sub-sensitive domain, lg() is the logarithmic function with base 10, and exp() is the exponential function with base e, the natural constant.

[0068] Among them, the reference array and its corresponding acquisition points are not involved in the calculation of the defect state value.

[0069] Further, in step S400, the method for identifying the damaged risk position of the inner packaging container through the defect state value and performing design processing is as follows: Calculate the weighted average of the defect state values obtained by the same fixed unit under different dynamic load models and denote it as the defect state level DSMR. The weight value of the weighted average is BIC×exp(-RANK), where BIC represents the Bayesian information criterion corresponding to the dynamic load model, exp() is the exponential function with the natural constant e as the base, and RANK(N) represents the serial number value of the defect state value of the current fixed unit in the descending order sorting of the current dynamic load model; The sequence record of the defect state levels corresponding to each fixed unit is denoted as DSMR.Ls, and its mean value and minimum value are denoted as Ls.avg and Ls.btt respectively; Remove the elements greater than its average value from DSMR.Ls and arrange them from largest to smallest. Denote the difference between any element in DSMR.Ls and its next element as Ls.G, and denote the ratio of the maximum value in each Ls.G to the average value of DSMR.Ls as the small value overflow degree SMO; When the defect state level of a fixed unit satisfies DSMR≥(1 + SMO)×(2Ls.avg-Ls.btt), then define this fixed unit as a risk unit, and consider that there is a risk of transportation damage on the inner packaging for the fixing device corresponding to the risk unit. Perform a thickening treatment on the position of the inner packaging corresponding to the risk unit, and the thickening ratio is 10%.

[0070] Preferably, in step S400, the method for identifying the damaged risk position of the inner packaging container through the defect state value and performing design processing is as follows: If it is determined that a fixed unit is a risk unit, then send the serial number corresponding to this risk unit to the client to warn that the position of the fixing device corresponding to the risk unit on the inner packaging needs to be changed.

[0071] An embodiment of the present invention provides a design method for a large packaging container, such as Figure 2 shown in the structural diagram of a design system for a large packaging container of the present invention. The design system for a large packaging container in this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned embodiment of the design method for a large packaging container.

[0072] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it runs in the following units of the system:

[0073] A model acquisition unit, configured to import the three-dimensional model of the packaging container into finite element analysis software;

[0074] A load simulation unit, configured to perform load simulation in the finite element analysis software to obtain load simulation data;

[0075] A deviation analysis and construction unit for calculating the defect state value according to the load simulation data;

[0076] A design processing unit for identifying the damaged risk positions of the inner packaging container through the defect state value and performing design processing.

[0077] The design system for large packaging containers can run on computing devices such as desktop computers, laptop computers, palmtop computers, and cloud servers. The design system for large packaging containers that can run may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above examples are only examples of the design system for large packaging containers, and do not constitute a limitation on the design system for large packaging containers. It may include more or fewer components than the examples, or combine certain components, or different components. For example, the design system for large packaging containers may also include input / output devices, network access devices, buses, etc.

[0078] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the operating system of the design system for large packaging containers, and connects various parts of the entire operating system of the design system for large packaging containers through various interfaces and lines.

[0079] The memory can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the design system for large packaging containers. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0080] Although the description of the present invention has been quite detailed and several of the described embodiments have been described in particular, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above in terms of embodiments foreseeable by the inventor for the purpose of providing a useful description, and non-substantive modifications to the present invention that are not currently foreseeable may still represent equivalent modifications of the present invention.

Claims

1. A design method for a large packaging container, characterized in that: The method comprises the following steps: S100, importing the three-dimensional model of the packaging container into finite element analysis software; S200, load simulation is performed in finite element analysis software to obtain load simulation data; S300, calculating the defect effectiveness value according to the load simulation data; S400, identify the risk location of damage to the inner packaging container through the defect effectiveness value and perform design treatment; In step S300, the method for calculating the defect effectiveness value according to the load simulation data is as follows: ROAM is the monitoring period, ROAM∈[1,5] hours; the number of fixed units monitored in the current ROAM period is N, and the interval for obtaining stress values ​​is t; for any fixed unit, X i is the i-th stress value measured during the monitoring period ROAM, T i To obtain the time corresponding to the i-th stress value, the maximum value of the stress value obtained during the monitoring period is recorded as the stress peak value Peak, and the minimum value is the stress deviation value Devi; construct the first stress set Str_Fs = {(T i ,X i )|0≤T i ≤ROAM / t,Devi≤X i ≤Peak}, the polyfit function is used to perform polynomial fitting on the first stress set Str_Fs, and the fitted function is recorded as the original loss function X=f(T i ); Arrange all stress values ​​in the same fixed unit in ascending order, record the upper quartile of the stress value as Str(75%), the lower quartile as Str(25%), record the upper limit of stress Str_Sup = Str(75%) + 1.5(Str(75%) - Str(25%), and the lower limit of stress Str_Inf = Str(25%) - 1.5(Str(75%) - Str(25%)); The average value of stress is called stress mean Ave_Str. i ≤Str_Inf or X i >Str_Sup, then X i is the stress loss value, the set consisting of stress loss values ​​is the first loss set, and the loss rate characteristic vector of the stress loss value is Loss_va=(|X i -X i－1 | / t,|X i＋1 -X i | / t), the mean vector of the loss rate feature vector corresponding to each stress loss value in the first loss set is denoted as Loss_ave, and the Mahalanobis distance between all stress loss values ​​and the mean vector is calculated as the differential loss DL. When the differential loss at any moment does not exist, the first differential loss obtained by the reverse time search is inherited; the differential loss set of all fixed units at any moment is denoted as T_DL, and the percentile value of the differential loss of any fixed unit in the set T_DL is denoted as the set rank Rank; If any stress loss value X i The corresponding differential loss is greater than the average value of all differential losses, then the stress loss value X i is the stress defect value, and the obtained stress defect value is replaced by the stress mean value Ave_Str to form the second stress set Str_Sc={(T i ,X i )| 0≤T i ≤ROAM / t,Devi≤X i ≤Peak}, use the polyfit function to perform polynomial fitting on the second stress set Str_Sc, and the obtained function is recorded as the modified loss function X * =g(T i ); For the current fixed unit, the defect effectiveness value Vali is calculated: ； Where hs<> is the harmonic mean function, Rank i and DL i They represent the set order and differential loss of the current fixed unit at the i-th moment respectively.

2. A design method for a large packaging container according to claim 1, characterized in that: In step S100, the method of importing the three-dimensional model of the packaging container into the finite element analysis software is: obtaining the three-dimensional model of the packaging container in the SolidWorks three-dimensional modeling software, wherein the three-dimensional model includes an inner packaging, an outer packaging, and a fixing device and a cushioning material between the inner packaging and the outer packaging, and the type of the fixing device includes one or more of a strap, a clamp, and a support frame; The three-dimensional model is imported into the finite element analysis software, and the material properties of each component of the three-dimensional model are set in the finite element analysis software, wherein the finite element analysis software is selected from any one of ANSYS, ABAQUS or MSC.MARC.

3. A design method for a large packaging container according to claim 1, characterized in that: In step S200, load simulation is performed in finite element analysis software, and the method for obtaining load simulation data is: identifying each fixture from the three-dimensional model and recording it as a fixture unit; constructing a dynamic load model using digital twin technology based on prototype observation data; The dynamic load model is read in the finite element analysis software, and the stress value obtained by the dynamic load simulation of the fixed unit is recorded in real time in any dynamic load model; the stress values ​​obtained by simulating each fixed unit in any dynamic load model constitute the load simulation data corresponding to the dynamic load model, and the time length of the simulation data is TC, TC∈[1,24] hours.

4. A design method for a large packaging container according to claim 1, characterized in that: In step S300, the method for calculating the defect effective state value according to the load simulation data is: the time length of the simulation data is TC, TC∈[1,24] hours; within the TC time, the moment when the fixed unit obtains the stress value is recorded as the acquisition point; any fixed unit is taken as the current fixed unit; in the current fixed unit, the maximum value of the stress value is obtained and recorded as the macro peak value, and the difference obtained by subtracting the macro peak value from the stress value at any acquisition point is recorded as the macro sinking amount Maro; Calculate the average stress value of the current fixed unit at all acquisition points and record it as the stress level estimate EST; the ratio of the stress level estimate to the macro-sinking amount is recorded as the distance scalar Dcal; if a collection point has a larger distance scalar than the previous collection point, define the collection point as a growth point, construct the distance scalars under each growth point into a sequence and record it as the distance sequence Diva.Ls, and record the growth points corresponding to all the minimum values ​​in the distance sequence as low touch points; the time interval between any low touch point and the first low touch point obtained by searching in the reverse time direction is recorded as the sub-sensitive domain corresponding to the low touch point; In the sub-sensitive domain, the maximum value of the macro-sinking amount and the maximum value of the distance scalar in each growth site are obtained, and they are recorded as the sub-sinking degree and the sub-scalar value respectively. The sub-sinking degree and the sub-scalar value are constructed into a two-tuple recorded as a sensitive array. The average value of the sensitive array of each sub-sensitive domain is calculated and recorded as a mean array; the sensitive array with the smallest Euclidean distance to the mean array is obtained by comparison and recorded as a benchmark array. The elements of the benchmark array are recorded as benchmark sinking degrees and benchmark values ​​respectively; Calculate the slope of any sensitive array and the reference array and record it as the defect sensitivity value Desn, and record the maximum value of each defect sensitivity value as max.Desn; obtain the average value of the macro-sinking amount of each growth site in the calculated sub-sensitive domain and record it as the sub-average sinking amount Asie; calculate the defect effectiveness value based on the defect sensitivity value and macro-sinking amount under the sub-sensitive domain.

5. A design method for a large packaging container according to claim 1, characterized in that: In step S400, the method for identifying the damage risk position of the inner packaging container by the defect effectiveness state value and performing design processing is: calculating the weighted average of the defect effectiveness state values ​​obtained by the same fixed unit under different dynamic load models and recording it as the defect effectiveness state level DSMR, the weight of the weighted average value is BIC×exp(-RANK), BIC represents the Bayesian information degree corresponding to the dynamic load model ， RANK(N) represents the serial number value of the defective effective state value of the current fixed unit in the descending order of the current dynamic load model; the defective effective state level write sequence of each fixed unit is recorded as DSMR.Ls, and its average and minimum values ​​are recorded as Ls.avg and Ls.btt respectively; the elements greater than its average value are removed from DSMR.Ls and arranged from large to small, and the difference between any element in DSMR.Ls and its next element is recorded as Ls.G, and the ratio of the maximum value in each Ls.G to the average value of DSMR.Ls is recorded as the small value overflow degree SMO; when the defective effective state level of a fixed unit satisfies DSMR≥(1+SMO)×(2Ls.avg-Ls.btt), the fixed unit is defined as a risk unit, and it is considered that the fixture corresponding to the risk unit has a risk of damage during transportation on the inner packaging, and the position of the inner packaging corresponding to the risk unit is thickened, and the thickening ratio is any value in the range of 10%-20%.

6. A design method for a large packaging container according to claim 5, characterized in that: In step S400, the method of identifying the risk position of damage of the inner packaging container through the defect effectiveness state value and performing design processing is: if a fixed unit is determined to be a risk unit, the corresponding serial number of the risk unit is sent to the client, and an early warning is given that the position of the fixing device corresponding to the risk unit on the inner packaging needs to be replaced.

7. A design system for large packaging containers, characterized in that: The design system for large packaging containers includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the design method for large packaging containers described in any one of claims 1 to 6 are implemented. The design system for large packaging containers runs on a desktop computer, a laptop computer, a PDA, and a computing device in a cloud data center.

Images