Highway shipping container stowage plan assistance system

Abstract

The application discloses a highway transportation container loading plan auxiliary system, adopts B / S framework design, and develops main function design based on a SpringMVC framework.The system module is mainly divided into: input and output module, plan calculation module, data management module, man-machine interface module.According to manual input data or reading user data document, selecting loading material category, material quantity and loading requirement, calculating corresponding data, and automatically generating the optimal loading plan, according to user demand, the loading plan is presented in the form of loading plan table or three-dimensional visual model, providing simple, clear and specific loading design scheme for users.Based on three space segmentation algorithm and improved PSO algorithm, the data is calculated.The user can output locally or upload to the server according to the specific requirements, and realize the rapid generation and output of the optimal loading plan in the user-defined loading environment.

Description

Technical Field

[0001] The present invention mainly relates to the field of three-dimensional containerized material loading algorithms for road transportation, and in particular to a loading plan generation tool and an information management system. Background Art

[0002] Road transport has always been the most important means of transportation in my country's logistics. Among them, the problem of material loading is an extremely important link in the logistics process. A reasonable loading plan can not only effectively improve the loading efficiency of goods, but also save transportation costs, and even ensure the transportation safety of transported goods and transport vehicles. Therefore, a mature algorithm system is needed to calculate and optimize the loading plan of goods, and to load the goods with the optimal plan to achieve the purpose of improving loading efficiency and reducing logistics costs. In the general sense, the function of loading system design is to optimize the configuration for mixed cargo loading or loading of the same type of goods, so that the final generated loading plan is the best, and the goal of achieving the highest container volume utilization or the largest amount of cargo filling in the container is achieved, thereby increasing the utilization rate of loading and unloading equipment and improving transportation efficiency.

[0003] Currently, there are relatively mature loading software systems both domestically and internationally that address material loading issues. These loading systems primarily target objects with certain regularities as loading targets, designing loading plans for cargo in containers, pallets, and other containers. They can design cargo loading plans in the shortest possible time based on the loading requirements of the cargo and containers, while simultaneously achieving the optimization goals of maximizing container volume utilization or maximizing the amount of cargo filled within the container, and generating corresponding loading plans. This type of loading system is primarily designed for general logistics loading scenarios, and is developed primarily based on the algorithm's versatility, simplicity, and efficiency. Therefore, the primary application scenarios for this type of system are generally suitable for loading scenarios with fewer loading restrictions, a more user-friendly loading environment, relatively regular loading materials, and a low complexity of material types. However, for more complex loading scenarios, the loading algorithm design of this type of loading system does not well meet specific loading requirements. For example, loading restrictions in ordinary commercial logistics contexts are relatively loose, and the loading plans for loading materials can be solved using general algorithms to meet low-restrictive loading requirements; however, in special logistics contexts, and even in military transportation contexts, many loading materials themselves have their own special characteristics, which leads to the need for more loading restrictions when loading these materials to ensure the safety of the transportation process. Summary of the Invention

[0004] The present invention studies the road transport loading problems arising from special loading environments with special loading requirements, such as personnel loading, mixed loading of people, and loading restrictions of multiple types of materials, and designs and invents a road transport decision-making support software system.

[0005] The software system adopts a B / S framework design, and the main function design is developed based on a Spring MVC framework. The system modules are mainly divided into: an input / output module, a scheme calculation module, a data management module and a man-machine interactive interface module. The input / output module is interactively connected with the man-machine interactive interface module and the scheme calculation module, and the data management module is interactively connected with the scheme calculation module.

[0006] The main functions of the software system are as follows: the input / output module reads user data documents or manually inputs data according to user requirements; the scheme calculation module selects the material category, the material quantity and the loading requirements, calculates the corresponding data, and automatically generates an optimal loading scheme; and the data management module presents the loading scheme in the form of a loading scheme table or a three-dimensional visual model through the man-machine interactive interface module, so as to provide a simple and clear specific loading design scheme for the user.

[0007] The software system scheme generation algorithm module is the main module of the software system. The system mainly solves data based on a three-space segmentation algorithm and an improved PSO algorithm. According to different loading requirements, the system algorithm constructs a variety of constraint condition models and material and personnel loading standard algorithms to meet the user-defined loading requirements and loading environment, and calculates the optimal scheme under different constraint condition combinations.

[0008] For the generated scheme results, considering that only the table form is used for presentation, the specific details and expected effects of the scheme cannot be specifically and intuitively displayed, the software system is developed based on Unity WebGL, the scheme results are presented in the form of a three-dimensional visual model in the user interface, and the user can control and adjust the specific conditions of the scheme.

[0009] Finally, the user can output the scheme locally or upload and save the scheme to the server according to specific requirements, so as to realize the rapid generation and output of the optimal loading scheme in the user-defined loading environment. DETAILED DESCRIPTION

[0010] Figure 1 The figure shows the overall system block diagram of the application.

[0011] Figure 2 The figure shows the function schematic view of the data input / output module of the application.

[0012] Figure 3 The figure shows the function schematic view of the scheme calculation module of the application.

[0013] Figure 4 The figure shows the function schematic view of the data management module of the application.

[0014] Figure 5Shown is an example diagram of data statistics of the present invention.

[0015] Figure 6 Shown is an example diagram of the user login interface of the present invention.

[0016] Figure 7 Shown is an example diagram of the system operation interface of the present invention.

[0017] Figure 8 The following is an example diagram of a scenario data table.

[0018] Figure 9 The following is an example of a three-dimensional interactive interface. DETAILED DESCRIPTION

[0019] The system of the present invention will be further described below with reference to the accompanying drawings.

[0020] The overall system block diagram of the present invention is as follows: Figure 1 As shown in the figure, the system primarily includes a data input / output module, a human-computer interaction interface, a solution calculation module, and a data management module. The data input / output module primarily handles data access, parsing, and transmission, and is also responsible for exporting and saving the generated data. The solution generation results are handled by the human-computer interaction interface, which is responsible for displaying the generated solution content to the user and providing a three-dimensional display interface for intuitive display of the results. The solution calculation module and data management module are the core data processing modules of this software system, responsible for data access and calculation.

[0021] The main tasks of the data input and output module are as follows:

[0022] like Figure 2 As shown, the data input and output module is mainly divided into three main functions: data reading and parsing function, Json data stream generation function, and solution data export function.

[0023] Data reading and parsing. Data input methods include manual user input and user-uploaded data documents. In the manual data input method, the user selects and inputs data in the interactive interface, and the system directly caches the data through the interface. In the document data upload method, the user exports an Excel format template. The template content includes data items such as loading material type, quantity, loading method, loading requirements, personnel requirements, and vehicle conditions. After the Excel template file is uploaded, the system parses the uploaded document content item by item, compares the corresponding data items, extracts data row by row for newly added, modified, and deleted data, and updates the data cache.

[0024] Generate a JSON data stream. This software system's data transmission module primarily uses JSON data streams for data processing and transmission. The system processes the extracted cached data through an interface program, generating a fixed-format JSON data stream. The data is then categorized by tag for subsequent data transmission. Data flow direction depends on subsequent user needs, primarily to the database and solution calculation modules.

[0025] Export of solution data. For the generated solutions, the system generates solution data tables and three-dimensional displays in the user interface. The software system has preset report templates, and the template content includes basic task information, user information, task information, special loading requirements, material information, vehicle information, personnel information, three-dimensional expected diagrams, etc. Based on user needs, the system calls the Json data stream of the corresponding label, parses the corresponding items, extracts the corresponding data, and uses the report generation interface module to overwrite the corresponding input into the template table position. The default format of the template is word format. Then, according to user needs, the solution report can be converted into a word or pdf format document and saved locally.

[0026] The main workflow of the solution calculation module is as follows:

[0027] like Figure 3 As shown in Figure 3, the workflow of the solution calculation module is mainly divided into five parts.

[0028] Step 1: Obtaining Task Data. The system parses the received JSON data to read the task tag, loading material tag, and custom loading requirement tag. The material tag data needs to be parsed and extracted, and cached for subsequent sorting of loaded materials. Furthermore, the loading requirement tag data needs to be parsed and extracted, and data identification and classification performed for subsequent constraint determination.

[0029] Step 2: Construct constraints. Process the loading requirements, extract the tag item data content, determine the loading constraints based on the user's selected loading requirements, and construct a mathematical model of the loading environment for the current task based on the constraints. Given a vehicle type, loading control parameters, and rectangular packaging materials, the standard load can be calculated as follows:

[0030] a) Calculate the unit volume mass V of materials g = g / (l·b·h) and the carriage loading volume mass V G =G / (L·B·H), if V g ≥2V G, the standard load is N = int (G / g), end the calculation; otherwise, continue to the next step; l, b, h are the length, width and height of the material; L, B, H are the length, width and height of the carriage respectively. g is the weight of the material; G is the weight of the carriage;

[0031] b) Determine the loading calculation gap Δ and the maximum loading height H according to the packaging quality, load reduction and loading height control regulations. s , maximum number of loading floors and maximum number of unloading floors;

[0032] c) Calculate the plane loading parameters and loading capacity according to the material stacking method requirements:

[0033] 1) If materials are only allowed to be loaded horizontally, calculate the horizontal loading parameter N according to formula (A.1) cc 、N cr 、N lc 、N lr , go to d); where: N cc N is the number of horizontal rows of materials; cr N is the number of loading rows on the horizontal loading column of materials; lc N is the number of columns of materials loaded in sequence; lr The number of loading rows on the material loading column.

[0034]

[0035] 2) If the materials can be loaded horizontally or longitudinally but at least one row is loaded horizontally, use the "left horizontal and right longitudinal" stacking method and calculate the plane loading parameter N according to the following steps: cc 、N cr 、N lc 、N lr :

[0036] Calculate relevant parameters:

[0037] Among them: K1 is the transverse box fit coefficient of the package; K3 is the loading modulus of the package; X max The maximum number of horizontal columns.

[0038] Calculate in sequence: f(1) = deci(K1-K3), f(2) = deci(K1-K3×2), ..., f(X) = deci(K1-K3×X), ..., f(X max )=deci(K1-K3×X max ), the X value corresponding to the smallest value is the optimal number of horizontal rows N cc ;

[0039] Calculate other loading parameters N according to formula (A.2) cr 、N lc 、N lr ;

[0040]

[0041]

[0042] 3) If the material allows full loading, calculate the plane loading parameter N according to the following steps cc 、N cr 、N lc 、N lr :Calculate the plane loading parameters of "left horizontal right straight" stacking according to step 2) in: When the stacking method is "left horizontally and right sequentially", the number of horizontal rows of materials; When the stacking method is "left horizontally and right sequentially", the number of rows loaded on the horizontal loading column of materials; When the "left horizontal and right sequential" stacking method is adopted, the number of columns of materials loaded sequentially; When the stacking method of "left horizontal and right sequential" is adopted, the number of rows of materials loaded sequentially;

[0043] Calculate relevant parameters:

[0044] Among them: K2 is the longitudinal box-fitting coefficient of the package;

[0045] Calculate in sequence: f(1) = deci(K2-K3), f(2) = deci(K2-K3×2), ..., f(Y) = deci(K2-K3×Y), ..., f(Y max )=deci(K2-K3×Y max ), the Y value corresponding to the smallest value is the optimal number of rows for loading

[0046] Calculate other loading parameters according to formula (A.3) in: When the stacking method is "front horizontal and back sequential", the number of columns of materials loaded sequentially; When the stacking method is "front horizontal and back vertical", the number of rows loaded on the horizontal loading column of materials; When the stacking method is "horizontally in front and sequentially in back", the number of horizontal rows of materials;

[0047]

[0048]

[0049] Compare and select the best loading method. The stacking method of "left horizontal and right vertical" is adopted, and the plane loading parameter N is determined according to formula (A.2) cc 、N cr 、N lc 、Nlr Otherwise, adopt the stacking method of "front horizontal and back straight" and determine the plane loading parameter N according to formula (A.3) cc 、N cr 、N lc 、N lr ;

[0050] d) Calculate the number of loading layers N according to formula (A.4) h ;

[0051] N h =min{int(H s / h), maximum number of loading layers} (A.4)

[0052] e) If N h ×h>Hb+0.5h and the maximum number of load shedding layers>0, calculate the number of load shedding layers Z and the load shedding amount N according to formulas (A.6) and (A.5) d , otherwise let N d =0;

[0053]

[0054] Z=min{N h -int(0.5+H b / h), maximum number of load shedding layers} (A.6)

[0055] f) Calculate the standard loading capacity N of materials according to formula (A.7) pm , pieces, packages, boxes, etc.

[0056]

[0057] Step 3: Sort the loaded materials and plan the remaining space. During the loading process, first load the single-type cargo that cannot be mixed. Then, sort the cargo that can be mixed by stacking layer from smallest to largest. If the number of stacking layers is the same, sort by cargo volume in descending order. The purpose is to prioritize loading larger cargo to occupy more loading space, preventing smaller cargo from occupying space for larger cargo and hindering the loading of larger cargo. For cargo of the same volume, prioritize those with larger bottom areas. Remaining space planning uses a three-space partitioning algorithm, abstracting the container loading process into a hierarchical structure. This method is similar to the actual container loading process. The floor can generally be divided into vertical and horizontal layers. A horizontal layer involves first filling the container floor with cargo, then filling it along the depth of the container. A vertical layer involves first filling the container's side with cargo, then filling it layer by layer along the width or length. The floor depth is determined by the deepest level of cargo in the floor. When loading, three sub-spaces to be loaded will be generated in the carriage. The cargo will be loaded in sequence according to the determined cargo sorting rules, and each layer of cargo will be filled layer by layer until the loading is completed.

[0058] Step 4: Optimal calculation of loading position. This software system improves and optimizes the particle swarm algorithm and is used to perform optimal calculation of loading position. Particle swarm algorithm, also known as particle swarm optimization algorithm or bird flock foraging algorithm (PSO), is a type of evolutionary algorithm and a parallel algorithm. It starts from a random solution, searches for the optimal solution through iteration, evaluates the quality of the solution through the fitness function, and searches for the global optimal solution by following the currently searched optimal value. The algorithm is initialized as a group of random particles (random solution), and then finds the optimal solution through iteration. In each iteration, the particles update themselves by tracking two extreme values ​​(pbest and gbest). After finding these two optimal values, the particles update their speed and position using the following formula.

[0059] v i =ω×v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i ) (B.1)

[0060] x i =x i +v i (B.2)

[0061] In formulas (B.1) and (B.2), i = 1, 2, ..., N, where N is the total number of particles in the group; v i is the speed of the particle; rand() is a random number between (0, 1); xi is the current position of the particle; c1 and c2 are learning factors that determine the effect of the two extreme values ​​in the velocity update process; v i The maximum value is V max (greater than 0), if v i Greater than V max , then v i =V max .

[0062] The original PSO algorithm is primarily used for solving extreme value problems involving continuous functions. However, the solution to the cargo packing problem is discrete. Therefore, the results directly obtained using the PSO algorithm are not applicable to the actual packing problem. To adapt the PSO algorithm to the three-dimensional packing problem and maximize the diversity of solutions, a correction strategy is used to optimize the algorithm. First, the cargo items are sorted and numbered. Then, the particle swarm optimization algorithm is initialized. The position of each particle represents a set of feasible solutions. Each feasible solution is a sequence corresponding to the cargo item number. The cargo items are packed according to the solution sequence, and the vehicle utilization rate is used as the fitness function to obtain the fitness function value for each solution sequence. The next iteration is performed, and a new particle swarm is generated, i.e., a new solution sequence. The solution sequence is then corrected to obtain a feasible solution. The fitness function is recalculated, and the global and individual optima are updated. This iterative cycle continues until the maximum number of iterations is met or the fitness function reaches a set value. Ultimately, the global optimal solution in the particle swarm becomes the final optimal solution sequence. The highest vehicle utilization can be achieved by packing in the order of the optimal solution sequence.

[0063] Step 5: Output the loaded result data. Calculate the data according to the algorithm, package the results, send them to the data interface for parsing, generate a fixed-format JSON data stream, and send it to the database or the interactive interface for data display.

[0064] The main tasks of the data management module are as follows:

[0065] like Figure 4 As shown, the data management module is mainly divided into three main functions: loading information management function, data statistics function, and user information management function.

[0066] Loading information management function. This function mainly includes operations such as information entry, modification, deletion, retrieval, update, and output, as well as the management of loading plans. According to user needs, loading data and the loading plans generated by calculation can be saved. The system packages the loading task data, loading material data, and loading plan data, and exports the data package to the database through the output interface for storage. At the same time, the corresponding tags are configured. The next time the user creates a new task, the saved historical data and historical plans can be viewed in the interface list. The user can directly call the material data, loading requirements, loading plans, and other contents.

[0067] Data statistics function. Statistics complete the historical data of loading and stowage schemes for road transport equipment and materials, providing data support for scientific analysis of the working rules of road transport loading and stowage. This function loads specific loading scheme data from the database according to user needs, and performs multi-project and multi-category data statistics in multiple dimensions according to time, unit, task, equipment, vehicle, materials and other data, and provides various visualization methods such as pie charts, trend charts, and bar charts. Data statistics example diagram is as follows Figure 5 shown.

[0068] User information management function. According to the security requirements of the subsystem, you need to enter the user name and password to log in when using the software system. The database has a user information management field for storing user login information. The user login interface is as follows Figure 6 shown.

[0069] The main functions of the human-computer interaction interface are as follows:

[0070] The human-computer interaction interface mainly carries two main functions: operation interface interaction and solution data display function, and three-dimensional interactive interface display function.

[0071] Operation interface interaction and solution data display function. This system is based on Java to realize the development of the front-end web interface. Users can log in and interact with the system through the web interface. The main operation interface mainly carries the selection and input functions of configuration items such as mission information, vehicle information, material information, personnel information, and loading requirements. After the user configures the corresponding items in the interface according to needs, click the Save button to store the configuration data, and then click Generate Plan. The data enters the solution calculation module for solution calculation and generates a loading solution data table. Example of system operation interface and solution data table Figure 7 、 Figure 8 shown.

[0072] 3D interactive interface display function. In order to achieve realistic and accurate loading plans, Cinema4D software is selected as the modeling tool in this system, and Unity WebGL is used as the technical route for development to simulate and display the loading plan, and provide a simple and intuitive interface interaction method for users to fine-tune the loading plan. WebGL (full name Web Graphics Library) is a 3D drawing protocol. This drawing technology standard allows JavaScript and OpenGL ES 2.0 to be combined together. By adding a JavaScript binding to OpenGL ES 2.0, WebGL can provide hardware 3D accelerated rendering for HTML5 Canvas. After generating the loading plan, the user can click the 3D display button to view the 3D display model of the plan as needed. At the same time, the user can click to view detailed information on the loading materials as needed, drag the loading materials, and fine-tune the plan. The example figure of the 3D interactive interface is as follows Figure 9 shown.

[0073] The system of the present invention is introduced above, and the principles and implementation methods of the present invention are explained. The description of the above embodiments is used to help understand the method of the present invention and its core concept.

Claims

1. Highway transport loading and distributing plan auxiliary system, characterized by: The system is designed with B / S architecture and developed based on SpringMVC framework; the system is divided into: input and output module, solution calculation module, data management module, and human-computer interaction interface module; The input and output module receives data manually input by the user or reads user data documents. The solution calculation module selects the loading material category, material quantity and loading requirements, calculates the corresponding data, and automatically generates the optimal loading solution. The data management module presents the loading solution in the form of a loading solution table or a 3D visual model through the human-computer interaction interface module according to user needs. The workflow of the solution calculation module is as follows: Step 1: Obtaining task data: Parse the received JSON data to read the task tag, loading material tag, and custom loading requirement tag. On the one hand, the material tag data needs to be parsed and extracted, and the data cached for subsequent sorting of loaded materials. On the other hand, the loading requirement tag data needs to be parsed and extracted, and data identification and classification are performed for subsequent determination of constraint conditions. Step 2: Construct constraints; The loading requirements are processed, the tag item data content is extracted, and the loading constraints are determined based on the loading requirements selected by the user. Based on the constraints, a mathematical model of the loading environment for the current task is constructed. Given a vehicle type, loading control parameters, and rectangular packaging materials, the standard loading capacity is calculated according to the following steps: a) Calculate the unit volume mass V of materials g = g / (l·b·h) and the carriage loading volume mass V G =G / (L·B·H), if V g ≥2V G , the standard loading capacity is N = int (G / g), end the calculation; otherwise, continue downward; l, b, h are the length, width and height of the material; L, B, H are the length, width and height of the carriage respectively; g is the weight of the material; G is the weight of the carriage; b) Determine the loading calculation gap Δ and the maximum loading height H according to the packaging quality, load reduction and loading height control regulations. s , maximum number of loading floors and maximum number of unloading floors; c) Calculate the plane loading parameters and loading capacity according to the material stacking method requirements: d) Calculate the number of loading layers N according to formula (A.4) h ; N h =min{int(H s / h), maximum number of loading layers} (A.4) e) If N h ×h>H b +0.5h and the maximum number of load shedding layers > 0, calculate the number of load shedding layers Z and the load shedding amount N according to formulas (A.6) and (A.5) d , otherwise let N d =0; Z=min{N h -int(0.5+H b / h), maximum number of load reduction layers} (A.6) f) Calculate the standard loading capacity N of materials according to formula (A.7) pm , pieces, packages, boxes; Step 3: Sort the loaded materials and plan the remaining space; during the cargo loading process, first load the single type of cargo that cannot be mixed, and then sort the cargo that can be mixed according to the number of stacking layers from small to large. If the number of stacking layers is the same, sort them in descending order according to the cargo volume; cargo with the same volume is sorted according to the principle of giving priority to the one with the larger bottom area; the remaining space planning adopts a three-space segmentation algorithm to abstract the container loading process into a hierarchical structure, which is similar to the actual container loading process; the ground layer is divided into vertical layer and horizontal layer; the so-called horizontal layer means that the ground of the container is first filled with cargo, and then filled with cargo along the depth direction of the container; the vertical layer means that the cargo is first spread on one side of the container, and then filled with the container layer by layer along the width or length direction; the ground layer depth is determined by the deepest level of the cargo in the ground layer; when loading, three sub-spaces to be loaded are generated in the carriage, and the cargo is loaded in sequence according to the determined cargo sorting rules, and each layer of cargo must be filled layer by layer until the loading is completed; Step 4: Optimal calculation of loading position; Step 5: Output the loaded result data; perform data calculation according to the algorithm, package the results after the calculation is completed, send them to the data interface for parsing, generate a Json data stream in a fixed format, and send it to the database or to the interactive interface for data display.

2. The road transport loading and distributing plan auxiliary system according to claim 1 is characterized in that: The data input and output module is divided into: data reading and parsing function, Json data stream generation function, and solution data export function; Data reading and parsing function, data input methods are divided into manual data input by users and user uploaded data documents; among them, the manual data input method is that users select and input data in the interactive interface, and the data is directly cached through the interface; the document data upload method is that users export Excel format templates, and the Excel format template content includes the type of loading materials, quantity, loading method, loading requirements, personnel requirements, and vehicle conditions; after the Excel template file is uploaded, the uploaded document content is parsed item by item, and the corresponding data items are compared. For newly added, modified, and deleted data, data is extracted row by row, and the data cache is updated; Generate Json data stream function. The internal data transmission mode uses Json data stream to process and transmit data. Through the interface program, the extracted cache data is processed to generate a fixed-format Json data stream. The corresponding data is then classified according to the label for subsequent data transmission. The data stream transmission direction flows to the database and solution calculation module according to the user's subsequent needs. The solution data export function generates a solution data table and a three-dimensional display for the generated solution in the user interface; the system stores a preset report template, and the template content includes basic task information, user information, task information, special loading requirements, material information, vehicle information, personnel information, and three-dimensional expected map content; based on user needs, the system calls the Json data stream of the corresponding label, parses the corresponding items, extracts the corresponding data, and uses the report generation interface module to overwrite the corresponding input into the template table position; the default format of the template is word format, and then according to user needs, the solution report is converted into a word or pdf format document and saved locally.

3. The road transport loading and distributing plan auxiliary system according to claim 1 is characterized in that: The workflow of the solution calculation module is as follows: Calculate the plane loading parameters and loading capacity according to the material stacking method requirements: 1) If materials are only allowed to be loaded horizontally, calculate the horizontal loading parameter N according to formula (A.1) cc 、N cr 、N lc 、N lr , go to d); where: N cc N is the number of horizontal rows of materials; cr N is the number of loading rows on the horizontal loading column of materials; lc N is the number of columns of materials loaded in sequence; lr The number of loading rows on the material loading column; 2) If the materials can be loaded horizontally or longitudinally but at least one row is loaded horizontally, use the "left horizontally and right longitudinally" stacking method and calculate the plane loading parameter N according to the following steps: cc 、N cr 、N lc 、N lr : Calculate relevant parameters: K3=l / b, Among them: K1 is the transverse box fit coefficient of the package; K3 is the loading modulus of the package; X max is the maximum number of horizontal rows; Calculate in sequence: f(1) = deci(K1-K3), f(2) = deci(K1-K3×2), ..., f(X) = deci(K1-K3×X), ..., f(X max )=deci(K1-K3×X max ), the X value corresponding to the smallest value is the optimal number of horizontal rows N cc ; Calculate other loading parameters N according to formula (A.2) cr 、N lc 、N lr ; 3) If the material allows full loading, calculate the plane loading parameter N according to the following steps cc 、N cr 、N lc 、N lr : Calculate the plane loading parameters for "left horizontal right straight" stacking according to step 2) in: When the stacking method is "left horizontally and right sequentially", the number of horizontal rows of materials; When the stacking method is "left horizontally and right sequentially", the number of rows loaded on the horizontal loading column; When the "left horizontal and right sequential" stacking method is adopted, the number of columns of materials loaded sequentially; When the "left horizontal and right sequential" stacking method is adopted, the number of rows loaded on the sequential loading column; Calculate relevant parameters: Among them: K2 is the longitudinal box-fitting coefficient of the package; Calculate in sequence: f(1) = deci(K2-K3), f(2) = deci(K2-K3×2), ..., f(Y) = deci(K2-K3×Y), ..., f(Y max )=deci(K2-K3×Y max ), the Y value corresponding to the smallest value is the optimal number of rows for loading Calculate other loading parameters according to formula (A.3) in: When the stacking method is "front horizontal and back sequential", the number of columns of materials loaded sequentially; When the stacking method is "front horizontal and back vertical", the number of rows loaded on the horizontal loading column; When the stacking method is "horizontally in front and sequentially in back", the number of horizontally loaded columns of materials; Compare and select the best loading method; The stacking method of "left horizontal and right vertical" is adopted, and the plane loading parameter N is determined according to formula (A.2) cc 、N cr 、N lc 、N lr Otherwise, adopt the "front horizontal and back straight" stacking method and determine the plane loading parameter N according to formula (A.3) cc 、N cr 、N lc 、N lr .

Images