Hardware digital production line layout optimization method

Abstract

The invention relates to the field of computer aided design, in particular to a hardware digital production line layout optimization method. The method comprises the following steps: carrying out functional region division on the hardware production line to obtain production line functional regions; constructing a production line basic layout three-dimensional model for the hardware production line based on the production line function area; carrying out precision demand sequence generation on the production line function area and carrying out numerical control node optimization positioning on the production line basic layout three-dimensional model; performing dust distribution analysis on the production line function area based on the production line basic layout three-dimensional model, and constructing a dust concentration influence model for numerical control node optimization positioning data and the production line function area; and optimizing the production line layout of the hardware production line based on the numerical control node optimization positioning data, the production line dust distribution data, the dust concentration influence model and the production line basic layout three-dimensional model. Layout optimization is carried out by obtaining related data of the production line, and the part machining quality can be improved.

Description

Technical Field

[0001] The invention relates to the field of computer-aided design, and in particular to a layout optimization method for a hardware digital production line. Background Art

[0002] At present, the hardware industry is undergoing a digital transformation, which has brought revolutionary changes to traditional hardware manufacturing. More and more hardware companies are beginning to apply digital production lines in order to improve production efficiency and product quality, so as to maintain their competitive advantage in the market. The digitalization of hardware production lines can improve production efficiency and product quality by automating production processes, accurately controlling production parameters, and reducing production defects and waste; at the same time, it is also necessary to plan the digital hardware production line to ensure that it can shorten the production cycle, reduce material waste and reduce energy consumption; however, there is a lack of data analysis on the environment of the hardware production line and the transmission signal of the CNC node of the production line, which leads to the imperfect optimization scheduling strategy of the production line, which affects the high-precision demand products of the production line, affecting production efficiency and product quality. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for optimizing the layout of a hardware digital production line to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the layout optimization method of the hardware digital production line includes the following steps:

[0005] Step S1: perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space; perform dense point cloud data collection on the three-dimensional laser data of the production space to obtain a production space point cloud set; construct a three-dimensional model of the production space of the hardware production line based on the production space point cloud set to obtain a three-dimensional model of the production space.

[0006] Step S2: Divide the hardware production line into functional areas to obtain functional areas of the production line; perform spatial partitioning on the three-dimensional model of the production space to obtain three-dimensional partitions of the production space; generate a basic layout model of the production line for the three-dimensional model of the production space based on the three-dimensional partitions of the production space and the functional areas of the production line to obtain a three-dimensional model of the basic layout of the production line.

[0007] Step S3: Perform regional machining tolerance analysis on the functional areas of the production line to obtain machining tolerance data for each area; generate precision requirement sequences for the functional areas of the production line based on the machining tolerance data for each area to obtain precision requirement sequences for each area; perform CNC node optimization positioning on the three-dimensional model of the basic layout of the production line based on the precision requirement sequences for each area to obtain CNC node optimization positioning data.

[0008] Step S4: Based on the three-dimensional model of the basic layout of the production line, perform dust distribution analysis on the functional areas of the production line to obtain dust distribution data of the production line; based on the CNC node optimization positioning data, perform data transmission path analysis on the three-dimensional model of the basic layout of the production line to obtain optimized data transmission lines; based on the dust distribution data of the production line, construct dust concentration influence models for the optimized data transmission lines and functional areas of the production line to obtain a dust concentration influence model; based on the CNC node optimization positioning data, the production line dust distribution data, the dust concentration influence model and the three-dimensional model of the basic layout of the production line, optimize the layout of the hardware production line to obtain the production line layout optimization result.

[0009] The present invention acquires 3D laser data of the production space through spatial 3D laser scanning technology, constructs a point cloud set of the production space using a dense point cloud data set, and constructs a 3D model of the production space based on the point cloud set, which can accurately present the spatial layout and structure of the hardware production line, which is helpful for the comprehensive analysis and evaluation of the existing production line, and provides basic information and visual reference for subsequent layout optimization; the hardware production line is divided into functional areas, and the zoning processing is combined with the 3D model of the production space, which can intuitively display the spatial distribution and mutual relationship of different functional areas, and generate a basic production line layout model based on the functional areas and spatial partitions, which can help designers Preliminary evaluation of the production line process and efficiency, and optimization of equipment and regional layout, lay the foundation for subsequent precision demand analysis and CNC node positioning; machining tolerance analysis of the functional areas of the production line can obtain the requirements of each area for equipment accuracy and machining accuracy. Based on the tolerance data, the accuracy requirement sequence can be generated to determine the accuracy requirements of CNC equipment and sensors in different areas. By optimizing the positioning of CNC nodes on the basic layout model of the production line, the accuracy requirements of each functional area can be met to ensure that the accuracy and quality of the production line meet the standards; machining tolerance analysis of the functional areas of the production line can obtain the requirements of each area for equipment accuracy and machining accuracy. Based on the tolerance data, the accuracy requirement sequence can be generated to determine the accuracy requirements of CNC equipment and sensors in different areas. By optimizing the positioning of CNC nodes on the basic layout model of the production line, the accuracy requirements of each functional area can be met to improve the optimization scheduling strategy of the production line and ensure that the accuracy and quality of the production line meet the standards.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space;

[0012] Step S12: Calculating the laser travel phase difference of the three-dimensional laser data in the production space to obtain laser travel phase difference data;

[0013] Step S13: acquiring the three-dimensional coordinates of the space surface point based on the laser travel phase difference data to obtain the three-dimensional coordinates of the production space surface point;

[0014] Step S14: performing a dense point cloud data set on the three-dimensional coordinates of the production space surface points to obtain a production space point cloud set;

[0015] Step S15: constructing a three-dimensional model of the production space of the hardware production line based on the production space point cloud set to obtain a three-dimensional model of the production space.

[0016] The present invention uses advanced spatial three-dimensional laser scanning technology to perform an all-round scan of the hardware production line to obtain high-precision three-dimensional laser data, laying a data foundation for the subsequent construction of an accurate digital twin model; by calculating the laser travel phase difference of the collected three-dimensional laser data, the phase change information generated by the laser in the process of spatial propagation is extracted, providing a key basis for the subsequent accurate restoration of the three-dimensional coordinates of the spatial surface points; based on the laser travel phase difference data, combined with the principle of triangulation, the three-dimensional coordinates of each surface point in the production space are accurately calculated, providing accurate spatial position information for the construction of a high-precision point cloud model; based on the laser travel phase difference data, combined with the principle of triangulation, the three-dimensional coordinates of each surface point in the production space are accurately calculated, providing accurate spatial position information for the construction of a high-precision point cloud model; based on the constructed production space point cloud set, reverse engineering and three-dimensional modeling technology are used to construct a high-precision, high-fidelity three-dimensional model of the hardware production line, realizing the digital restoration of the actual production environment, providing an intuitive and accurate digital twin platform for subsequent layout optimization, and being able to analyze and operate the production space in a virtual environment, thereby more effectively planning and optimizing the production line layout.

[0017] Preferably, step S2 comprises the following steps:

[0018] Step S21: Acquire the functional characteristics of the hardware production line to obtain the functional characteristics of the hardware production line;

[0019] Step S22: dividing the hardware production line into functional areas based on the functional characteristics of the hardware production line to obtain functional areas of the production line;

[0020] Step S23: performing spatial partition processing on the three-dimensional model of the production space to obtain three-dimensional partitions of the production space;

[0021] Step S24: Analyze the basic attribute data of the three-dimensional partitions of the production space to obtain the basic attribute data of the three-dimensional partitions of the production space;

[0022] Step S25: Based on the basic attribute data of the three-dimensional partition of the production space, the functional area of ​​the production line is positioned to obtain the positioning data of the functional area of ​​the production space;

[0023] Step S26: Generate a basic layout model of the production line for the three-dimensional model of the production space based on the functional area positioning data of the production space, and obtain a three-dimensional model of the basic layout of the production line.

[0024] The present invention extracts key features that can characterize its production functions, such as processing type, equipment size, and material flow, by deeply analyzing the process flow and equipment composition of the hardware production line, and provides a basis for the subsequent functional area division; functional area division based on the functional characteristics of the hardware production line is a key step in layout optimization. During this period, the production line is divided into multiple functional areas, such as assembly area, processing area, quality inspection area, and storage area. Each functional area has its own unique purpose and requirements. This division helps to organize and optimize the layout of the production line and ensure effective flow and interaction between different functional areas. According to the division of functional areas, the constructed three-dimensional model of the production space is spatially partitioned, and the three-dimensional model is divided into three-dimensional partitions corresponding to the functional areas, providing an independent digital space for each functional area; basic attribute data analysis of the three-dimensional partitions of the production space helps to understand and define the characteristics of each partition. This step includes analyzing the size, shape, capacity, and surrounding environment of each partition. Understanding these basic attribute data is crucial for evaluating and optimizing the partition layout, ensuring that the partition can meet the requirements of the functional areas of the production line; basic attribute data analysis of the three-dimensional partitions of the production space helps to understand and define the characteristics of each partition. This step includes analyzing the size, shape, capacity, and surrounding environment of each partition. Understanding these basic attribute data is crucial for evaluating and optimizing the partition layout, ensuring that the partition can meet the requirements of the functional areas of the production line; based on the positioning data of the functional areas of the production space, each functional area is integrated into the three-dimensional model of the production space to generate a preliminary three-dimensional model of the basic layout of the production line, providing a basis for subsequent refined layout optimization.

[0025] Preferably, step S24 comprises the following steps:

[0026] Step S241: Calculate the available space volume of the three-dimensional partitions of the production space to obtain available space volume data of the partitions;

[0027] Step S242: Identify the partition air circulation openings of the three-dimensional partitions of the production space to obtain the partition air circulation opening areas;

[0028] Step S243: acquiring air pressure remote sensing data of the partitioned air circulation opening area to obtain circulation air pressure remote sensing data;

[0029] Step S244: analyzing the air circulation area of ​​the partitioned air circulation openings to obtain the partitioned air circulation area;

[0030] Step S245: Calculate the air circulation degree of the three-dimensional partitions of the production space based on the air pressure remote sensing data and the partition air circulation area to obtain the partition air circulation degree;

[0031] Step S246: basic attribute data collection is performed on the available spatial volume data of the partition and the air circulation degree of the partition to obtain basic attribute data of the three-dimensional partition of the production space.

[0032] The present invention calculates the available space volume by three-dimensional partitioning of the production space, which is an important step because it provides a measure of the available space in each partition. By calculating the available space volume within the partition, the capacity and size of the partition can be evaluated, which is crucial for determining whether the partition can accommodate the required equipment, personnel and other elements. The data generated thereby provides key information for subsequent layout decisions; by analyzing the three-dimensional partitioning of the production space, the doors, windows and vents that affect air circulation are identified, providing key area positioning for subsequent analysis of air circulation conditions; remote sensing technology is used to obtain air pressure data in the partition air circulation outlet area to achieve real-time monitoring of air circulation conditions and provide data support for evaluating air circulation efficiency; analysis of the partition The circulation area of ​​the air ventilation opening area can be used to further quantitatively analyze the air ventilation characteristics. The effectiveness of air ventilation can be evaluated by calculating the total circulation area of ​​the ventilation opening. A larger circulation area indicates better air ventilation, while a smaller area indicates that improvement is needed to ensure sufficient air flow within the partition. Taking into account the air pressure remote sensing data and circulation area of ​​the ventilation opening, the air circulation degree of each three-dimensional partition of the production space is calculated, and the air circulation efficiency is quantitatively evaluated to provide a scientific basis for improving the production environment. The calculated usable space volume data and air circulation degree are integrated into the attribute data of the three-dimensional partition of the production space to form a more comprehensive and detailed description of the partition attributes, providing richer data support for the subsequent functional area layout and environmental optimization.

[0033] Preferably, step S3 comprises the following steps:

[0034] Step S31: Acquire regional production dimension tolerances for functional areas of the production line to obtain production dimension tolerance data for each area;

[0035] Step S32: acquiring the product roughness of the functional areas of the production line to obtain product roughness data of each area;

[0036] Step S33: performing data fitting on the production dimension tolerance data of each region and the product roughness data of each region to obtain the processing tolerance data of each region;

[0037] Step S34: Calculate the functional area machining accuracy for the machining tolerance data of each area to obtain the machining accuracy data of each area;

[0038] Step S35: Generate the accuracy requirement sequence for the functional areas of the production line based on the processing accuracy data of each area to obtain the accuracy requirement sequence for each area;

[0039] Step S36: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the accuracy requirement sequence of each area to obtain CNC node optimization positioning data.

[0040] The present invention obtains the dimensional tolerance data of the products in each functional area, such as length, width, height, and aperture, according to the production process requirements of each functional area, so as to provide a direct basis for the subsequent processing accuracy analysis; obtains the surface roughness data of the products in each functional area, which is an important indicator for measuring the surface quality of the product and an important factor in determining the processing accuracy requirements; by performing data fitting on the dimensional tolerance data and product roughness data of each area, comprehensive regional processing tolerance data can be obtained. This step involves analyzing and summarizing different parameters to describe and define the processing accuracy requirements of each functional area. The processing tolerance data set thus generated provides a comprehensive overview of the functional areas and provides important input for subsequent accuracy calculation and optimization; the processing accuracy of each functional area is calculated. The key aspect of optimizing the layout is to determine the processing accuracy required for each area by analyzing the dimensional tolerance and product roughness data, which includes dimensional accuracy, surface roughness, and shape tolerance. The processing accuracy data set obtained guides the accuracy requirements of equipment and processes in the area; based on the calculated processing accuracy data of each functional area, all functional areas are sorted in order from high to low accuracy requirements to generate a precision requirement sequence to clarify the sensitivity of different functional areas to processing accuracy; based on the generated precision requirement sequence, the three-dimensional model of the basic layout of the production line is adjusted to prioritize the layout of functional areas with high processing accuracy requirements in areas with stable environment and less interference, and optimize the positioning of CNC nodes to minimize the impact of dust factors on processing accuracy.

[0041] Preferably, step S36 includes the following steps:

[0042] Step S361: Perform data transmission line analysis on the three-dimensional model of the basic layout of the production line to obtain data transmission line data;

[0043] Step S362: performing data transmission response priority sequence analysis on each regional accuracy requirement sequence to obtain a regional data priority response sequence;

[0044] Step S363: Calculate the priority weight of the regional data priority response sequence to obtain the regional data priority response weight;

[0045] Step S364: reconstructing the data transmission line data with priority weights based on the regional data priority response weights to obtain a data priority response line;

[0046] Step S365: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the data priority response line to obtain CNC node optimization positioning data.

[0047] The present invention can identify and define the data transmission path by analyzing the data transmission line of the basic layout three-dimensional model of the production line, analyze the data flow and connection between different functional areas in the basic layout model, and the transmission line data set obtained thereby provides insights into the data transmission requirements, distances and potential bottlenecks, and provides basic information for subsequent optimization and positioning; according to the accuracy requirement sequence of each area, the sensitivity of different functional areas to the data transmission response speed is analyzed, and the areas with high requirements for data response speed, such as high-precision processing areas and real-time monitoring areas, are identified to form a regional data priority response sequence; the regional data priority response sequence is quantitatively evaluated, and different priority weights are assigned to each area, such as a higher weight is assigned to the high-precision processing area to reflect its higher requirements for data transmission response speed; based on the regional data priority response weight, the existing data transmission lines are replanned and adjusted, such as setting dedicated lines for high-priority areas, optimizing line paths, and increasing transmission bandwidth, and constructing data priority response lines to ensure that the data transmission requirements of high-precision areas are met first; according to the optimized data priority response line, the layout position of the numerical control node is adjusted to shorten the data transmission distance of the high-priority area as much as possible, reduce transmission delay, improve the real-time and reliability of data transmission, and provide stable and reliable data support for high-precision processing.

[0048] Preferably, step S4 comprises the following steps:

[0049] Step S41: performing dust distribution analysis on the functional areas of the production line based on the three-dimensional model of the basic layout of the production line to obtain dust distribution data of the production line;

[0050] Step S42: performing data transmission path analysis on the three-dimensional model of the basic layout of the production line based on the numerical control node optimization positioning data to obtain an optimized data transmission line;

[0051] Step S43: performing dust content analysis on the optimized data transmission line based on the dust distribution data of the production line to obtain the dust content in the line area;

[0052] Step S44: Drawing a dust impact response time curve for the optimized data transmission line based on the dust content in the line area to obtain a dust impact response time curve;

[0053] Step S45: Drawing a dust content wear curve for the functional area of ​​the production line based on the dust content of the line area to obtain a dust wear curve;

[0054] Step S46: constructing a model for the dust impact response time curve and the dust wear curve to obtain a dust concentration impact model;

[0055] Step S47: Optimizing the layout of the basic layout three-dimensional model of the production line based on the numerical control node optimization positioning data, the production line dust distribution data, and the dust concentration influence model to obtain an optimized production line three-dimensional model;

[0056] Step S48: Optimizing the layout of the hardware production line based on the optimized production line three-dimensional model to obtain a production line layout optimization result.

[0057] The present invention can determine and quantify the dust concentration and distribution in the functional area of ​​the production line by performing dust distribution analysis on the three-dimensional model of the basic layout of the production line, and use simulation or measured data to identify dust sources, air flow patterns and dust settling rates. The dust distribution data set obtained thereby provides insights into the dust environment of the production line, and provides information for evaluating and alleviating the potential impact of dust on production line efficiency and product quality. According to the CNC node optimization positioning data, the path of the data transmission line in three-dimensional space is analyzed, the line direction and length are optimized, the area with high dust concentration is avoided, and the interference of dust on data transmission is reduced. The optimized data transmission line is spatially superimposed and analyzed with the dust distribution data, the dust concentration in the area where the data transmission line is located is calculated, and the impact of dust on data transmission is evaluated. Based on the dust content data of the line area, a relationship curve between dust concentration and data transmission response time is established, and the dust is quantitatively evaluated. The impact of dust on data transmission performance provides a basis for formulating dust prevention measures; analyzes the wear of equipment and components under different dust concentrations, and draws a curve of the relationship between dust content and equipment wear degree to provide a reference for selecting appropriate dust prevention levels and maintenance strategies; integrates the dust impact response time curve and the dust wear curve, constructs a dust concentration impact model, comprehensively evaluates the impact of dust on data transmission and equipment life, and provides a decision-making basis for layout optimization; based on CNC node optimization positioning data, production line dust distribution data and dust concentration impact model, adjusts the basic layout 3D model of the production line, optimizes equipment placement, plans clean areas, and minimizes the impact of dust on production; applies the optimized production line 3D model to the actual production line layout, and ultimately achieves the goal of improving data transmission stability, extending equipment life, and improving the production environment by optimizing equipment placement, area division, and logistics routes.

[0058] Preferably, step S41 includes the following steps:

[0059] Step S411: injecting standard detection light into the functional area of ​​the production line to obtain regional standard detection light;

[0060] Step S412: acquiring a functional regional spectrum diagram of the regional standard detection light to obtain a functional regional spectrum diagram;

[0061] Step S413: performing production dust reflection analysis on the functional area spectrum to obtain production dust reflection spectrum data;

[0062] Step S414: performing regional production dust content analysis on the production dust reflectance spectrum data to obtain the regional production dust content;

[0063] Step S415: Perform dust distribution analysis on regional production dust content based on the three-dimensional model of the basic layout of the production line to obtain production line dust distribution data.

[0064] The present invention can establish an initial and unified detection basis by injecting standard detection light into the functional area of ​​the production line. This step can ensure that the intensity and wavelength factors of the detected light are unified, providing a reliable basis for subsequent analysis, which can reduce the randomness in the process of obtaining the spectrum and make the detection result more accurate; by obtaining the functional area spectrum, the components of the light in the area can be analyzed to provide a data basis for identifying and separating the dust reflection spectrum, thereby realizing accurate detection of production dust; by analyzing the functional area spectrum, the reflection spectrum data of the production dust can be identified and separated, the interference of other substances on dust detection can be eliminated, and the accuracy of dust content analysis can be improved; according to the intensity and characteristics of the production dust reflection spectrum data, the regional production dust content is analyzed, the concentration changes of dust in the production environment can be monitored in real time, and data support can be provided for timely taking dust reduction measures; combined with the basic layout three-dimensional model of the production line, the spatial distribution analysis of the regional production dust content can be carried out, which can intuitively present the distribution of production line dust, help identify dust pollution sources and diffusion paths, and provide a scientific basis for optimizing the production environment.

[0065] Preferably, step S44 includes the following steps:

[0066] Step S441: acquiring a transmission signal from a data transmission line to obtain line transmission signal data;

[0067] Step S442: performing spectrum conversion on the line transmission signal data to obtain a transmission signal spectrum;

[0068] Step S443: performing signal phase change rate analysis on the transmission signal spectrum to obtain the signal spectrum phase change rate;

[0069] Step S444: plotting a change rate curve for the signal spectrum phase change rate to obtain a change rate curve;

[0070] Step S445: Calculate the slope of the change rate curve to obtain the slope of the curve;

[0071] Step S446: performing response time analysis on the slope of the curve to obtain response time data;

[0072] Step S447: Plotting a dust impact response time curve for the response time data based on the dust content in the line area to obtain a dust impact response time curve.

[0073] The present invention can monitor the operation status of the transmission line in real time by acquiring the sensor signal of the data transmission line. By collecting and analyzing the data of the sensor signal, any abnormal situation in the transmission line, such as signal interference, data loss or transmission delay, can be detected, which helps to timely discover and solve potential problems in the data transmission process and ensure the integrity and accuracy of the data; the spectrum conversion of the transmitted sensor signal data can observe the signal characteristics more clearly from the frequency domain, and provide data for analyzing the impact of dust on signals of different frequencies; the phase change rate of the transmission signal spectrum can capture the subtle delay and distortion caused by the influence of dust in the transmission process of the signal, and then quantify the impact of dust on signal transmission; the signal spectrum phase The change rate curve can intuitively show the trend of the impact of dust on signal transmission, providing a visual basis for subsequent analysis; calculating the slope of the change rate curve can quantitatively describe the speed of change of the impact of dust on signal transmission, providing a more accurate indicator for evaluating the degree of dust impact; by analyzing the slope of the curve, the change in signal response time can be inferred, reflecting the impact of dust on signal transmission delay, providing a basis for evaluating the real-time and stability of data transmission; combined with the dust content data of the transmission line, a response time and dust content relationship curve is drawn, which can intuitively show the influence of dust concentration on signal transmission response time, provide a scientific basis for predicting and warning the impact of dust on data transmission, and provide data support for formulating corresponding dust control measures.

[0074] Preferably, step S45 includes the following steps:

[0075] Step S451: Performing capacitive sensing processing on the processing surface of the functional area of ​​the production line to obtain capacitive sensing data of the processing surface;

[0076] Step S452: performing dust coverage ratio analysis on the processing surface capacitance sensing data based on the dust content in the line area to obtain the processing surface dust coverage ratio;

[0077] Step S453: Obtaining the processing surface friction coefficient of the functional area of ​​the production line to obtain the processing surface friction coefficient;

[0078] Step S454: analyzing the roughness of the processed surface based on the friction coefficient of the processed surface to obtain the surface roughness;

[0079] Step S455: Draw a dust content wear curve for the surface roughness based on the dust coverage ratio of the processed surface to obtain a dust wear curve.

[0080] The present invention can sensitively capture the dust coverage on the processing surface by performing capacitive sensing processing on the functional area of ​​the production line, obtain capacitive sensing data reflecting the degree of dust coverage, and provide a data basis for subsequent analysis of the impact of dust on processing quality; by analyzing the capacitive sensing data of the processing surface, the proportion of the processing surface covered by dust can be calculated. Dust has unique capacitive characteristics. Compared with the surface not covered with dust, the capacitance value of the surface covered with dust will change. By comparing the capacitance sensing data with the capacitance data of the standard surface, the dust coverage ratio of the processed surface in a certain area can be quantitatively analyzed. This step helps to determine the degree of dust contamination on the surface of the functional area of ​​the production line; obtaining the friction coefficient of the processed surface in the functional area of ​​the production line can reflect the smoothness of the processed surface and provide a reference indicator for analyzing the degree of dust wear on the processed surface; by analyzing the friction coefficient of the processed surface, the roughness of the processed surface can be evaluated to provide control data for studying the degree of dust wear; combining the dust coverage ratio of the processed surface and the surface roughness data to draw a dust content wear curve can intuitively show the relationship between the dust coverage and the degree of wear of the processed surface, thereby helping to identify dust wear risks and providing data support for optimizing production processes and formulating dust control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0082] Figure 1 It is a schematic diagram of the steps of the method for optimizing the layout of a hardware digital production line of the present invention;

[0083] Figure 2 for Figure 1 Detailed step flow diagram of step S3;

[0084] Figure 3 for Figure 2 Detailed step flow chart of step S36 in FIG. DETAILED DESCRIPTION

[0085] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0086] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0087] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0088] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for optimizing the layout of a hardware digital production line, the method comprising the following steps:

[0089] Step S1: perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space; perform dense point cloud data collection on the three-dimensional laser data of the production space to obtain a production space point cloud set; construct a three-dimensional model of the production space of the hardware production line based on the production space point cloud set to obtain a three-dimensional model of the production space.

[0090] Step S2: Divide the hardware production line into functional areas to obtain functional areas of the production line; perform spatial partitioning on the three-dimensional model of the production space to obtain three-dimensional partitions of the production space; generate a basic layout model of the production line for the three-dimensional model of the production space based on the three-dimensional partitions of the production space and the functional areas of the production line to obtain a three-dimensional model of the basic layout of the production line.

[0091] Step S3: Perform regional machining tolerance analysis on the functional areas of the production line to obtain machining tolerance data for each area; generate precision requirement sequences for the functional areas of the production line based on the machining tolerance data for each area to obtain precision requirement sequences for each area; perform CNC node optimization positioning on the three-dimensional model of the basic layout of the production line based on the precision requirement sequences for each area to obtain CNC node optimization positioning data.

[0092] Step S4: Based on the three-dimensional model of the basic layout of the production line, perform dust distribution analysis on the functional areas of the production line to obtain dust distribution data of the production line; based on the CNC node optimization positioning data, perform data transmission path analysis on the three-dimensional model of the basic layout of the production line to obtain optimized data transmission lines; based on the dust distribution data of the production line, construct dust concentration influence models for the optimized data transmission lines and functional areas of the production line to obtain a dust concentration influence model; based on the CNC node optimization positioning data, the production line dust distribution data, the dust concentration influence model and the three-dimensional model of the basic layout of the production line, optimize the layout of the hardware production line to obtain the production line layout optimization result.

[0093] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the method for optimizing the layout of a hardware digital production line of the present invention. In this example, the method for optimizing the layout of a hardware digital production line includes the following steps:

[0094] Step S1: perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space; perform dense point cloud data collection on the three-dimensional laser data of the production space to obtain a production space point cloud set; construct a three-dimensional model of the production space of the hardware production line based on the production space point cloud set to obtain a three-dimensional model of the production space.

[0095] In an embodiment of the present invention, a high-precision three-dimensional laser scanner is used to perform an all-round scan of the hardware production line and obtain the laser data by the laser travel phase difference, the point cloud data of the production space is obtained based on the laser travel phase difference, and the point cloud data is denoised, spliced, and simplified to generate a dense point cloud data set; based on the point cloud data set, a three-dimensional model of the production space including production equipment and logistics channel elements is constructed using three-dimensional modeling software.

[0096] Step S2: Divide the hardware production line into functional areas to obtain functional areas of the production line; perform spatial partitioning on the three-dimensional model of the production space to obtain three-dimensional partitions of the production space; generate a basic layout model of the production line for the three-dimensional model of the production space based on the three-dimensional partitions of the production space and the functional areas of the production line to obtain a three-dimensional model of the basic layout of the production line.

[0097] In an embodiment of the present invention, the process flow and functional requirements of the hardware production line are extracted as feature data, and the production line is divided into multiple functional areas including a stamping area, a welding area, an assembly area, and a spraying area according to the feature data of the hardware production line. In combination with the production process, the three-dimensional model of the production space is partitioned accordingly to divide the three-dimensional space areas corresponding to the functional areas. Based on the results of the three-dimensional partitioning of the production space and the division of the functional areas, the equipment and workstation elements of each functional area are laid out according to the actual production process to generate a three-dimensional model of the basic layout of the production line containing detailed equipment information.

[0098] Step S3: Perform regional machining tolerance analysis on the functional areas of the production line to obtain machining tolerance data for each area; generate precision requirement sequences for the functional areas of the production line based on the machining tolerance data for each area to obtain precision requirement sequences for each area; perform CNC node optimization positioning on the three-dimensional model of the basic layout of the production line based on the precision requirement sequences for each area to obtain CNC node optimization positioning data.

[0099] In an embodiment of the present invention, the machining tolerance of each area is analyzed according to the machining accuracy requirements of different functional areas, the key machining dimensions and tolerance ranges of each area are determined, and according to the machining tolerance data of each area and in combination with the production process, each functional area is sorted according to the level of demand for machining accuracy, and an accuracy requirement sequence is generated. Based on the accuracy requirement sequence of each area, the three-dimensional model of the basic layout of the production line is analyzed using simulation software, the spatial positioning of key CNC nodes is optimized, the cumulative errors in the machining process are minimized, and finally the optimized positioning data of the CNC nodes are obtained.

[0100] Step S4: Based on the three-dimensional model of the basic layout of the production line, perform dust distribution analysis on the functional areas of the production line to obtain dust distribution data of the production line; based on the CNC node optimization positioning data, perform data transmission path analysis on the three-dimensional model of the basic layout of the production line to obtain optimized data transmission lines; based on the dust distribution data of the production line, construct dust concentration influence models for the optimized data transmission lines and functional areas of the production line to obtain a dust concentration influence model; based on the CNC node optimization positioning data, the production line dust distribution data, the dust concentration influence model and the three-dimensional model of the basic layout of the production line, optimize the layout of the hardware production line to obtain the production line layout optimization result.

[0101] In an embodiment of the present invention, by analyzing the distribution of dust in various areas of the production line based on the basic layout three-dimensional model of the production line and combining sensor monitoring means, the dust distribution data of the production line is obtained, the data transmission path is analyzed according to the CNC node optimization positioning data, and the dust concentration influence model is constructed in combination with the dust distribution data of the production line to quantify the influence of dust concentration on the data transmission quality. The CNC node optimization positioning data, the production line dust distribution data, the dust concentration influence model and the three-dimensional model of the basic layout of the production line are comprehensively considered, and the production line layout is adjusted by using an optimization algorithm to optimize the equipment placement, logistics routes, and data transmission paths. Finally, the production line layout optimization result that can reduce the impact of dust, improve data transmission quality, and optimize production efficiency is obtained.

[0102] The present invention acquires 3D laser data of the production space through spatial 3D laser scanning technology, constructs a point cloud set of the production space using a dense point cloud data set, and constructs a 3D model of the production space based on the point cloud set, which can accurately present the spatial layout and structure of the hardware production line, which is helpful for the comprehensive analysis and evaluation of the existing production line, and provides basic information and visual reference for subsequent layout optimization; the hardware production line is divided into functional areas, and the zoning processing is combined with the 3D model of the production space, which can intuitively display the spatial distribution and mutual relationship of different functional areas, and generate a basic production line layout model based on the functional areas and spatial partitions, which can help designers Preliminary evaluation of the production line process and efficiency, and optimization of equipment and regional layout, lay the foundation for subsequent precision demand analysis and CNC node positioning; machining tolerance analysis of the functional areas of the production line can obtain the requirements of each area for equipment accuracy and machining accuracy. Based on the tolerance data, the accuracy requirement sequence can be generated to determine the accuracy requirements of CNC equipment and sensors in different areas. By optimizing the positioning of CNC nodes on the basic layout model of the production line, the accuracy requirements of each functional area can be met to ensure that the accuracy and quality of the production line meet the standards; machining tolerance analysis of the functional areas of the production line can obtain the requirements of each area for equipment accuracy and machining accuracy. Based on the tolerance data, the accuracy requirement sequence can be generated to determine the accuracy requirements of CNC equipment and sensors in different areas. By optimizing the positioning of CNC nodes on the basic layout model of the production line, the accuracy requirements of each functional area can be met to improve the optimization scheduling strategy of the production line and ensure that the accuracy and quality of the production line meet the standards.

[0103] Preferably, step S1 comprises the following steps:

[0104] Step S11: Perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space;

[0105] In an embodiment of the present invention, a high-precision three-dimensional laser scanner is used to perform an all-round scan of the hardware production line. During the scanning process, the laser scanner emits a laser beam according to a preset scanning path and angle, and receives the laser signal reflected from the surface of the object. At the same time, the position and posture information of the laser scanner is recorded to obtain complete three-dimensional laser data of the production space.

[0106] Step S12: Calculating the laser travel phase difference of the three-dimensional laser data in the production space to obtain laser travel phase difference data;

[0107] In an embodiment of the present invention, by analyzing the phase difference between the emitted laser beam and the received reflected laser beam based on the laser data, utilizing the principle of laser ranging, combining the geometric parameters of the scanner and the laser wavelength information, and calculating the phase difference during the laser travel process, the distance information of each scanning point is obtained, that is, the laser travel phase difference data.

[0108] Step S13: acquiring the three-dimensional coordinates of the space surface point based on the laser travel phase difference data to obtain the three-dimensional coordinates of the production space surface point;

[0109] In an embodiment of the present invention, by combining the laser travel phase difference data, as well as the position and posture information of the laser scanner at each scanning moment, triangulation or other spatial geometry calculation methods are used to infer the precise coordinates of each scanning point in three-dimensional space, and ultimately obtain the three-dimensional coordinate data of the surface points of the production space.

[0110] Step S14: performing a dense point cloud data set on the three-dimensional coordinates of the production space surface points to obtain a production space point cloud set;

[0111] In an embodiment of the present invention, the three-dimensional coordinate data of the production space surface points are denoised and filtered to remove redundant data and noise points. Then, according to the scanning density and accuracy requirements, the point cloud data are spliced ​​and fused to generate a uniform and dense production space point cloud set to fully express the surface geometric information of the production space.

[0112] Step S15: constructing a three-dimensional model of the production space of the hardware production line based on the production space point cloud set to obtain a three-dimensional model of the production space.

[0113] In an embodiment of the present invention, by processing point cloud data based on a point cloud set of the production space using three-dimensional modeling software, a three-dimensional model of the production space including production equipment and logistics channel elements is constructed through point cloud segmentation, surface reconstruction, and texture mapping technology.

[0114] The present invention uses advanced spatial three-dimensional laser scanning technology to perform an all-round scan of the hardware production line to obtain high-precision three-dimensional laser data, laying a data foundation for the subsequent construction of an accurate digital twin model; by calculating the laser travel phase difference of the collected three-dimensional laser data, the phase change information generated by the laser in the process of spatial propagation is extracted, providing a key basis for the subsequent accurate restoration of the three-dimensional coordinates of the spatial surface points; based on the laser travel phase difference data, combined with the principle of triangulation, the three-dimensional coordinates of each surface point in the production space are accurately calculated, providing accurate spatial position information for the construction of a high-precision point cloud model; based on the laser travel phase difference data, combined with the principle of triangulation, the three-dimensional coordinates of each surface point in the production space are accurately calculated, providing accurate spatial position information for the construction of a high-precision point cloud model; based on the constructed production space point cloud set, reverse engineering and three-dimensional modeling technology are used to construct a high-precision, high-fidelity three-dimensional model of the hardware production line, realizing the digital restoration of the actual production environment, providing an intuitive and accurate digital twin platform for subsequent layout optimization, and being able to analyze and operate the production space in a virtual environment, thereby more effectively planning and optimizing the production line layout.

[0115] Preferably, step S2 comprises the following steps:

[0116] Step S21: Acquire the functional characteristics of the hardware production line to obtain the functional characteristics of the hardware production line;

[0117] In an embodiment of the present invention, by referring to the process flow documents, equipment manuals and technical data of the hardware production line and combining with on-site investigations, the functional requirements of each link of the production line are recorded in detail, such as raw material processing, stamping, welding assembly, surface treatment, packaging and transportation, and finally the functional characteristics of the hardware production line are formed.

[0118] Step S22: dividing the hardware production line into functional areas based on the functional characteristics of the hardware production line to obtain functional areas of the production line;

[0119] In an embodiment of the present invention, the production line is divided into several functional areas, such as stamping area, welding area, assembly area, and spraying area, based on the acquired functional characteristics of the hardware production line and combined with the actual production process flow, and each functional area corresponds to a specific production link.

[0120] Step S23: performing spatial partition processing on the three-dimensional model of the production space to obtain three-dimensional partitions of the production space;

[0121] In an embodiment of the present invention, based on the constructed three-dimensional model of the production space, the production space is virtually divided using three-dimensional modeling software according to the factory structure and area division information, and three-dimensional space partitions corresponding to the actual production areas, such as workshops and passages, are divided.

[0122] Step S24: Analyze the basic attribute data of the three-dimensional partitions of the production space to obtain the basic attribute data of the three-dimensional partitions of the production space;

[0123] In an embodiment of the present invention, by analyzing the basic attribute data of each three-dimensional partition of the production space, such as area, height, load-bearing capacity, ventilation conditions, and power supply configuration, these data can be obtained by consulting design drawings and on-site measurements and stored in a database.

[0124] Step S25: Based on the basic attribute data of the three-dimensional partition of the production space, the functional area of ​​the production line is positioned to obtain the positioning data of the functional area of ​​the production space;

[0125] In an embodiment of the present invention, by combining the divided functional areas of the production line and the acquired basic attribute data of the three-dimensional partitions of the production space, a layout planning algorithm, such as rule-based layout planning and genetic algorithm-based layout planning, is used to locate each functional area within a suitable three-dimensional partition of the production space, and finally the production space functional area positioning data is obtained.

[0126] Step S26: Generate a basic layout model of the production line for the three-dimensional model of the production space based on the functional area positioning data of the production space, and obtain a three-dimensional model of the basic layout of the production line.

[0127] In an embodiment of the present invention, the production equipment and logistics channel elements in each functional area are laid out according to the actual production process based on the obtained production space functional area positioning data, and added to the constructed three-dimensional model of the production space, and finally a basic layout three-dimensional model of the production line containing detailed equipment information is generated.

[0128] The present invention extracts key features that can characterize its production functions, such as processing type, equipment size, and material flow, by deeply analyzing the process flow and equipment composition of the hardware production line, and provides a basis for the subsequent functional area division; functional area division based on the functional characteristics of the hardware production line is a key step in layout optimization. During this period, the production line is divided into multiple functional areas, such as assembly area, processing area, quality inspection area, and storage area. Each functional area has its own unique purpose and requirements. This division helps to organize and optimize the layout of the production line and ensure effective flow and interaction between different functional areas. According to the division of functional areas, the constructed three-dimensional model of the production space is spatially partitioned, and the three-dimensional model is divided into three-dimensional partitions corresponding to the functional areas, providing an independent digital space for each functional area; basic attribute data analysis of the three-dimensional partitions of the production space helps to understand and define the characteristics of each partition. This step includes analyzing the size, shape, capacity, and surrounding environment of each partition. Understanding these basic attribute data is crucial for evaluating and optimizing the partition layout, ensuring that the partition can meet the requirements of the functional areas of the production line; basic attribute data analysis of the three-dimensional partitions of the production space helps to understand and define the characteristics of each partition. This step includes analyzing the size, shape, capacity, and surrounding environment of each partition. Understanding these basic attribute data is crucial for evaluating and optimizing the partition layout, ensuring that the partition can meet the requirements of the functional areas of the production line; based on the positioning data of the functional areas of the production space, each functional area is integrated into the three-dimensional model of the production space to generate a preliminary three-dimensional model of the basic layout of the production line, providing a basis for subsequent refined layout optimization.

[0129] Preferably, step S24 includes the following steps:

[0130] Step S241: Calculate the available space volume of the three-dimensional partitions of the production space to obtain available space volume data of the partitions;

[0131] In an embodiment of the present invention, the volume of each partition is calculated by using three-dimensional modeling software based on the divided production space, taking into account the space occupied by existing equipment and structural columns in the partition, and eliminating these unavailable spaces through Boolean operations, ultimately obtaining the actual available space volume data for each partition.

[0132] Step S242: Identify the partition air circulation openings of the three-dimensional partitions of the production space to obtain the partition air circulation opening areas;

[0133] In an embodiment of the present invention, by analyzing the three-dimensional model of the production space, the openings that connect each partition to the outside world, such as windows, doors, and vents, are identified. These opening areas are the partition air circulation openings, and their position and size information can be extracted from the model and corrected in combination with on-site measurement data.

[0134] Step S243: acquiring air pressure remote sensing data of the partitioned air circulation opening area to obtain circulation air pressure remote sensing data;

[0135] In an embodiment of the present invention, by deploying an air pressure sensor network in each partitioned air circulation area, the air pressure data of each area is collected in real time. The sensor network can transmit data wirelessly and aggregate the data to a central server for storage and analysis, thereby forming circulation air pressure remote sensing data covering all air circulation outlets.

[0136] Step S244: analyzing the air circulation area of ​​the partitioned air circulation openings to obtain the partitioned air circulation area;

[0137] In an embodiment of the present invention, the area of ​​each air circulation opening is calculated based on the identified partitioned air circulation opening areas using three-dimensional modeling software or image processing technology. For irregularly shaped air circulation openings, they can be divided into multiple regular shapes for approximate calculation, and ultimately the total area of ​​air circulation in each partition is obtained.

[0138] Step S245: Calculate the air circulation degree of the three-dimensional partitions of the production space based on the air pressure remote sensing data and the partition air circulation area to obtain the partition air circulation degree;

[0139] In an embodiment of the present invention, the air circulation degree of each partition is calculated based on the circulation air pressure remote sensing data, combined with the air circulation area of ​​each partition, using the principles of fluid mechanics and CFD simulation methods. The air circulation degree can be expressed as the volume of air passing through the partition per unit time, reflecting the ventilation capacity of the partition.

[0140] Step S246: basic attribute data collection is performed on the available spatial volume data of the partition and the air circulation degree of the partition to obtain basic attribute data of the three-dimensional partition of the production space.

[0141] In an embodiment of the present invention, by integrating the available space volume data of each partition and the air circulation degree of each partition, a basic attribute data set of three-dimensional partitions of the production space is formed, which comprehensively describes the space capacity and ventilation conditions of each partition.

[0142] The present invention calculates the available space volume by three-dimensional partitioning of the production space, which is an important step because it provides a measure of the available space in each partition. By calculating the available space volume within the partition, the capacity and size of the partition can be evaluated, which is crucial for determining whether the partition can accommodate the required equipment, personnel and other elements. The data generated thereby provides key information for subsequent layout decisions; by analyzing the three-dimensional partitioning of the production space, the doors, windows and vents that affect air circulation are identified, providing key area positioning for subsequent analysis of air circulation conditions; remote sensing technology is used to obtain air pressure data in the partition air circulation outlet area to achieve real-time monitoring of air circulation conditions and provide data support for evaluating air circulation efficiency; analysis of the partition The circulation area of ​​the air ventilation opening area can be used to further quantitatively analyze the air ventilation characteristics. The effectiveness of air ventilation can be evaluated by calculating the total circulation area of ​​the ventilation opening. A larger circulation area indicates better air ventilation, while a smaller area indicates that improvement is needed to ensure sufficient air flow within the partition. Taking into account the air pressure remote sensing data and circulation area of ​​the ventilation opening, the air circulation degree of each three-dimensional partition of the production space is calculated, and the air circulation efficiency is quantitatively evaluated to provide a scientific basis for improving the production environment. The calculated usable space volume data and air circulation degree are integrated into the attribute data of the three-dimensional partition of the production space to form a more comprehensive and detailed description of the partition attributes, providing richer data support for the subsequent functional area layout and environmental optimization.

[0143] Preferably, step S3 comprises the following steps:

[0144] Step S31: Acquire regional production dimension tolerances for functional areas of the production line to obtain production dimension tolerance data for each area;

[0145] Step S32: acquiring the product roughness of the functional areas of the production line to obtain product roughness data of each area;

[0146] Step S33: performing data fitting on the production dimension tolerance data of each region and the product roughness data of each region to obtain the processing tolerance data of each region;

[0147] Step S34: Calculate the functional area machining accuracy for the machining tolerance data of each area to obtain the machining accuracy data of each area;

[0148] Step S35: Generate the accuracy requirement sequence for the functional areas of the production line based on the processing accuracy data of each area to obtain the accuracy requirement sequence for each area;

[0149] Step S36: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the accuracy requirement sequence of each area to obtain CNC node optimization positioning data.

[0150] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S3 in the embodiment, step S3 includes the following steps:

[0151] Step S31: Acquire regional production dimension tolerances for functional areas of the production line to obtain production dimension tolerance data for each area;

[0152] In an embodiment of the present invention, by consulting product design drawings and process standard technical documents for each functional area of ​​the production line, collecting dimensional tolerance requirements for key components, determining the allowable dimensional deviation range in the production process of each area, and finally obtaining production dimensional tolerance data for each area.

[0153] Step S32: acquiring the product roughness of the functional areas of the production line to obtain product roughness data of each area;

[0154] In an embodiment of the present invention, the surface roughness target value of the product of each functional area is determined according to the design requirements and surface quality standards, the processing capability of the production equipment, the type of tool, and the cutting parameter factors are considered, the surface roughness level that can actually be achieved in each area is estimated, and finally the roughness data of the product in each area is obtained.

[0155] Step S33: performing data fitting on the production dimension tolerance data of each region and the product roughness data of each region to obtain the processing tolerance data of each region;

[0156] In an embodiment of the present invention, by utilizing data fitting techniques, such as the least squares method and neural networks, correlation analysis is performed on the obtained production dimensional tolerance data of each region and the obtained product roughness data of each region, and a mathematical model between the two is established. The model can comprehensively reflect the impact of different processing accuracies on product size and surface quality, and obtain the processing tolerance data of each region.

[0157] Step S34: Calculate the functional area machining accuracy for the machining tolerance data of each area to obtain the machining accuracy data of each area;

[0158] In an embodiment of the present invention, the machining accuracy of each functional area is calculated based on the obtained machining tolerance data of each area, combined with statistical principles and error propagation theory. The machining accuracy can be quantified using standard deviation and tolerance grade indicators, and data for each functional area is marked.

[0159] Step S35: Generate the accuracy requirement sequence for the functional areas of the production line based on the processing accuracy data of each area to obtain the accuracy requirement sequence for each area;

[0160] In the embodiment of the present invention, all functional areas are sorted from high to low according to the machining accuracy requirements based on the calculated machining accuracy data of each functional area, so as to form an accuracy requirement sequence.

[0161] Step S36: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the accuracy requirement sequence of each area to obtain CNC node optimization positioning data.

[0162] In an embodiment of the present invention, the three-dimensional model of the basic layout of the production line is analyzed using simulation software based on the accuracy requirement sequence of each area. By adjusting the spatial position and processing path of the key CNC nodes for data processing, the functional areas with high precision requirements are brought closer to reduce the cumulative error in the processing process, and finally the optimized positioning data of the CNC nodes are obtained.

[0163] The present invention obtains the dimensional tolerance data of the products in each functional area, such as length, width, height, and aperture, according to the production process requirements of each functional area, so as to provide a direct basis for the subsequent processing accuracy analysis; obtains the surface roughness data of the products in each functional area, which is an important indicator for measuring the surface quality of the product and an important factor in determining the processing accuracy requirements; by performing data fitting on the dimensional tolerance data and product roughness data of each area, comprehensive regional processing tolerance data can be obtained. This step involves analyzing and summarizing different parameters to describe and define the processing accuracy requirements of each functional area. The processing tolerance data set thus generated provides a comprehensive overview of the functional areas and provides important input for subsequent accuracy calculation and optimization; the processing accuracy of each functional area is calculated. The key aspect of optimizing the layout is to determine the processing accuracy required for each area by analyzing the dimensional tolerance and product roughness data, which includes dimensional accuracy, surface roughness, and shape tolerance. The processing accuracy data set obtained guides the accuracy requirements of equipment and processes in the area; based on the calculated processing accuracy data of each functional area, all functional areas are sorted in order from high to low accuracy requirements to generate a precision requirement sequence to clarify the sensitivity of different functional areas to processing accuracy; based on the generated precision requirement sequence, the three-dimensional model of the basic layout of the production line is adjusted to prioritize the layout of functional areas with high processing accuracy requirements in areas with stable environment and less interference, and optimize the positioning of CNC nodes to minimize the impact of dust factors on processing accuracy.

[0164] Preferably, step S36 includes the following steps:

[0165] Step S361: Perform data transmission line analysis on the three-dimensional model of the basic layout of the production line to obtain data transmission line data;

[0166] Step S362: performing data transmission response priority sequence analysis on each regional accuracy requirement sequence to obtain a regional data priority response sequence;

[0167] Step S363: Calculate the priority weight of the regional data priority response sequence to obtain the regional data priority response weight;

[0168] Step S364: reconstructing the data transmission line data with priority weights based on the regional data priority response weights to obtain a data priority response line;

[0169] Step S365: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the data priority response line to obtain CNC node optimization positioning data.

[0170] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 5 is a schematic diagram of a detailed step flow chart of step S36 in FIG. 5 . In this embodiment, step S36 includes the following steps:

[0171] Step S361: Perform data transmission line analysis on the three-dimensional model of the basic layout of the production line to obtain data transmission line data;

[0172] In an embodiment of the present invention, based on the three-dimensional model of the basic layout of the production line, all nodes that require data transmission, such as control systems and data processing centers, are identified, the data interaction relationship between the nodes is analyzed, and the data transmission path and method, such as wired network, wireless network, and field bus, are determined, and finally complete data transmission line data is formed, including node information, connection method, and transmission distance.

[0173] Step S362: performing data transmission response priority sequence analysis on each regional accuracy requirement sequence to obtain a regional data priority response sequence;

[0174] In an embodiment of the present invention, the degree of influence of data transmission delay on the processing accuracy of each region is analyzed according to the accuracy requirement sequence of each region. The higher the accuracy requirement of the region, the higher the real-time requirement for data transmission. Therefore, a higher priority needs to be given. Finally, a regional data priority response sequence is obtained to guide the subsequent data transmission line optimization.

[0175] Step S363: Calculate the priority weight of the regional data priority response sequence to obtain the regional data priority response weight;

[0176] In the embodiment of the present invention, the priorities of different regions are quantified according to the regional data priority response sequence, and the regional data priority response weight is obtained by adopting the hierarchical analysis method and the expert scoring method. The higher the weight value, the more urgent the data transmission demand of the region.

[0177] Step S364: reconstructing the data transmission line data with priority weights based on the regional data priority response weights to obtain a data priority response line;

[0178] In an embodiment of the present invention, the data transmission line is optimized and reconstructed based on the data transmission line data and the obtained regional data priority response weight. For example, by adjusting the network topology, optimizing the routing algorithm, and configuring QoS parameters, a shorter transmission distance can be allocated to the high-priority area to ensure the real-time and reliability of data transmission, and finally obtain a data priority response line.

[0179] Step S365: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the data priority response line to obtain CNC node optimization positioning data.

[0180] In an embodiment of the present invention, the transmission distance and data interaction efficiency between each CNC node are re-evaluated based on the optimized data priority response line. By adjusting the spatial position of the CNC nodes, for example, by bringing nodes that require frequent data interaction closer together, the transmission distance can be further shortened, the delay can be reduced, and the data transmission efficiency can be improved, ultimately obtaining CNC node optimized positioning data based on data priority response.

[0181] The present invention can identify and define the data transmission path by analyzing the data transmission line of the basic layout three-dimensional model of the production line, analyze the data flow and connection between different functional areas in the basic layout model, and the transmission line data set obtained thereby provides insights into the data transmission requirements, distances and potential bottlenecks, and provides basic information for subsequent optimization and positioning; according to the accuracy requirement sequence of each area, the sensitivity of different functional areas to the data transmission response speed is analyzed, and the areas with high requirements for data response speed, such as high-precision processing areas and real-time monitoring areas, are identified to form a regional data priority response sequence; the regional data priority response sequence is quantitatively evaluated, and different priority weights are assigned to each area, such as a higher weight is assigned to the high-precision processing area to reflect its higher requirements for data transmission response speed; based on the regional data priority response weight, the existing data transmission lines are replanned and adjusted, such as setting dedicated lines for high-priority areas, optimizing line paths, and increasing transmission bandwidth, and constructing data priority response lines to ensure that the data transmission requirements of high-precision areas are met first; according to the optimized data priority response line, the layout position of the numerical control node is adjusted to shorten the data transmission distance of the high-priority area as much as possible, reduce transmission delay, improve the real-time and reliability of data transmission, and provide stable and reliable data support for high-precision processing.

[0182] Preferably, step S4 comprises the following steps:

[0183] Step S41: performing dust distribution analysis on the functional areas of the production line based on the three-dimensional model of the basic layout of the production line to obtain dust distribution data of the production line;

[0184] In an embodiment of the present invention, by analyzing the distribution of dust in various areas of the production line based on a three-dimensional model of the basic layout of the production line, sensor monitoring, and dust diffusion simulation, the production space can be divided into grids, the dust concentration in each grid can be predicted, and ultimately the dust distribution data of the production line can be obtained.

[0185] Step S42: performing data transmission path analysis on the three-dimensional model of the basic layout of the production line based on the numerical control node optimization positioning data to obtain an optimized data transmission line;

[0186] In an embodiment of the present invention, by optimizing positioning data according to CNC nodes, analyzing the data transmission paths between nodes, considering the reliability and efficiency of data transmission, selecting appropriate transmission media and routing strategies, ultimately an optimized data transmission line is obtained and marked in the basic layout three-dimensional model of the production line.

[0187] Step S43: performing dust content analysis on the optimized data transmission line based on the dust distribution data of the production line to obtain the dust content in the line area;

[0188] In an embodiment of the present invention, by superimposing and analyzing the optimized data transmission line and the dust distribution data of the production line, the average dust concentration in the area where each data transmission line is located is calculated, and the dust content in the line area is obtained to evaluate the impact of dust on the data transmission quality.

[0189] Step S44: Drawing a dust impact response time curve for the optimized data transmission line based on the dust content in the line area to obtain a dust impact response time curve;

[0190] In an embodiment of the present invention, based on the obtained dust content in the line area and the influence mechanism of dust on data transmission, such as signal attenuation and interference enhancement, a relationship curve between dust concentration and data transmission response time is established using experimental data and simulation methods to obtain a dust influence response time curve.

[0191] Step S45: Drawing a dust content wear curve for the functional area of ​​the production line based on the dust content of the line area to obtain a dust wear curve;

[0192] In an embodiment of the present invention, by analyzing the wear of dust on equipment based on the dust content in the line area and combining the operating environment factors of the production equipment, an accelerated wear simulation experiment can be performed to establish a relationship curve between dust concentration and equipment wear life, and obtain a dust wear curve.

[0193] Step S46: constructing a model for the dust impact response time curve and the dust wear curve to obtain a dust concentration impact model;

[0194] In the embodiment of the present invention, the obtained dust impact response time curve and the obtained dust wear curve are integrated, and the relationship between the dust concentration and the two parameters is analyzed through a linear model to construct a dust concentration impact model through the analyzed relationship.

[0195] Step S47: Optimizing the layout of the basic layout three-dimensional model of the production line based on the numerical control node optimization positioning data, the production line dust distribution data, and the dust concentration influence model to obtain an optimized production line three-dimensional model;

[0196] In an embodiment of the present invention, the basic layout three-dimensional model of the production line is adjusted and optimized by optimizing the positioning data of the CNC nodes, the dust distribution data of the production line, and the dust concentration influence model, using a multi-objective optimization algorithm. The optimization objectives include minimizing the impact of dust on data transmission and equipment wear, and finally obtaining an optimized three-dimensional model of the production line.

[0197] Step S48: Optimizing the layout of the hardware production line based on the optimized production line three-dimensional model to obtain a production line layout optimization result.

[0198] In the embodiment of the present invention, the equipment layout and data transmission lines of the hardware production line are adjusted according to the optimized production line three-dimensional model, and finally the production line layout optimization result is obtained.

[0199] The present invention can determine and quantify the dust concentration and distribution in the functional area of ​​the production line by performing dust distribution analysis on the three-dimensional model of the basic layout of the production line, and use simulation or measured data to identify dust sources, air flow patterns and dust settling rates. The dust distribution data set obtained thereby provides insights into the dust environment of the production line, and provides information for evaluating and alleviating the potential impact of dust on production line efficiency and product quality; according to the CNC node optimization positioning data, the path of the data transmission line in three-dimensional space is analyzed, the line direction and length are optimized, and areas with high dust concentration are avoided as much as possible to reduce the interference of dust on data transmission; the optimized data transmission line and the dust distribution data are spatially superimposed and analyzed, the dust concentration in the area where the data transmission line is located is calculated, and the impact of dust on data transmission is evaluated; based on the line area dust content data, a relationship curve between dust concentration and data transmission response time is established, and the dust is quantitatively evaluated. The impact of dust on data transmission performance provides a basis for formulating dust prevention measures; analyzes the wear of equipment and components under different dust concentrations, and draws a curve showing the relationship between dust content and equipment wear, providing a reference for selecting appropriate dust prevention levels and maintenance strategies; integrates the dust impact response time curve and the dust wear curve, constructs a dust concentration impact model, comprehensively evaluates the impact of dust on data transmission and equipment life, and provides a decision-making basis for layout optimization; based on CNC node optimization positioning data, production line dust distribution data, and dust concentration impact model, adjusts the basic layout 3D model of the production line, optimizes equipment placement, plans clean areas, and minimizes the impact of dust on production; applies the optimized 3D model of the production line to the actual production line layout, and ultimately achieves the goal of improving data transmission stability, extending equipment life, and improving the production environment by optimizing equipment placement, area division, and logistics routes.

[0200] Preferably, step S41 includes the following steps:

[0201] Step S411: injecting standard detection light into the functional area of ​​the production line to obtain regional standard detection light;

[0202] In an embodiment of the present invention, appropriate measurement points are selected in the functional area of ​​the production line, a standard light source is used to illuminate the target area to ensure that the light covers the entire area evenly and stably, and a spectrometer is used to monitor and record the spectral information irradiated to the target area in real time to obtain regional standard detection light data.

[0203] Step S412: acquiring a functional regional spectrum diagram of the regional standard detection light to obtain a functional regional spectrum diagram;

[0204] In the embodiment of the present invention, by keeping the illumination condition of the standard light source unchanged, the spectral information of each functional area is collected by using a spectrometer to obtain a functional area spectrum diagram containing the reflected light and scattered light information of the target area.

[0205] Step S413: performing production dust reflection analysis on the functional area spectrum to obtain production dust reflection spectrum data;

[0206] In an embodiment of the present invention, by comparing the regional standard detection light data and the functional area spectrum, the spectral signal generated by the production dust reflection is identified and separated. This can be achieved by analyzing the spectral characteristic peaks and absorption valley information, and finally the production dust reflection spectrum data is obtained.

[0207] Step S414: performing regional production dust content analysis on the production dust reflectance spectrum data to obtain the regional production dust content;

[0208] In an embodiment of the present invention, according to the Lambert-Beer law, the absorption intensity of light by a substance is proportional to its concentration. The production dust content in each functional area can be quantitatively calculated by analyzing the intensity of the production dust reflection spectrum data in combination with a pre-established dust concentration-spectral intensity calibration curve.

[0209] Step S415: Perform dust distribution analysis on regional production dust content based on the three-dimensional model of the basic layout of the production line to obtain production line dust distribution data.

[0210] In an embodiment of the present invention, by mapping the regional production dust content data into the three-dimensional model of the basic layout of the production line, a spatial interpolation algorithm, such as the inverse distance weighted method and the Kriging interpolation method, is used to convert the discrete regional dust content data into a continuous dust concentration distribution map, and finally the production line dust distribution data is obtained.

[0211] The present invention can establish an initial and unified detection basis by injecting standard detection light into the functional area of ​​the production line. This step can ensure that the intensity and wavelength factors of the detected light are unified, providing a reliable basis for subsequent analysis, which can reduce the randomness in the process of obtaining the spectrum and make the detection result more accurate; by obtaining the functional area spectrum, the components of the light in the area can be analyzed to provide a data basis for identifying and separating the dust reflection spectrum, thereby realizing accurate detection of production dust; by analyzing the functional area spectrum, the reflection spectrum data of the production dust can be identified and separated, the interference of other substances on dust detection can be eliminated, and the accuracy of dust content analysis can be improved; according to the intensity and characteristics of the production dust reflection spectrum data, the regional production dust content is analyzed, the concentration changes of dust in the production environment can be monitored in real time, and data support can be provided for timely taking dust reduction measures; combined with the basic layout three-dimensional model of the production line, the spatial distribution analysis of the regional production dust content can be carried out, which can intuitively present the distribution of production line dust, help identify dust pollution sources and diffusion paths, and provide a scientific basis for optimizing the production environment.

[0212] Preferably, step S44 includes the following steps:

[0213] Step S441: acquiring a transmission signal from a data transmission line to obtain line transmission signal data;

[0214] In an embodiment of the present invention, a signal generator and a signal receiver are respectively connected at both ends of a data transmission line, the signal generator generates a signal of a specific frequency and amplitude, such as a sine wave signal, and loads it onto the data transmission line, the signal receiver collects the signal on the transmission line in real time, and converts the analog signal into a digital signal, which is stored as time series data, i.e., transmission signal data.

[0215] Step S442: performing spectrum conversion on the line transmission signal data to obtain a transmission signal spectrum;

[0216] In the embodiment of the present invention, the spectrum of the collected transmission signal data is converted by using a fast Fourier transform algorithm, and the signal data in the time domain is converted to the frequency domain to obtain the amplitude and phase information of the signal at different frequencies.

[0217] Step S443: performing signal phase change rate analysis on the transmission signal spectrum to obtain the signal spectrum phase change rate;

[0218] In an embodiment of the present invention, by selecting the main frequency component in the transmission signal spectrum, such as the signal frequency generated by a signal generator, the phase information corresponding to the frequency is extracted, and its change over time is analyzed, and the instantaneous change rate of the phase at each time point is calculated, that is, the signal spectrum phase change rate.

[0219] Step S444: plotting a change rate curve of the signal spectrum phase change rate to obtain a change rate curve;

[0220] In the embodiment of the present invention, the signal spectrum phase change rates calculated at different time points are plotted into a curve graph, where the abscissa represents time and the ordinate represents the phase change rate.

[0221] Step S445: Calculate the slope of the change rate curve to obtain the slope of the curve;

[0222] In an embodiment of the present invention, by selecting an interval with significant change characteristics in the change rate curve, such as an interval with a larger curve slope, a linear regression method is used to fit the curve of the interval, and the slope of the fitting line is calculated. The slope of the curve can quantitatively describe the speed of change of the phase change rate.

[0223] Step S446: performing response time analysis on the slope of the curve to obtain response time data;

[0224] In an embodiment of the present invention, when the dust content in the data transmission line changes, the slope of the curve will change accordingly and eventually tend to be stable. The response time refers to the time required for the slope of the curve to change from an initial state to a stable state. The arrival of the stable state can be determined by setting a threshold or other algorithms.

[0225] Step S447: Plotting a dust impact response time curve for the response time data based on the dust content in the line area to obtain a dust impact response time curve.

[0226] In an embodiment of the present invention, by controlling the experimental environment, setting different dust contents, and repeating the steps, response time data corresponding to different dust contents are obtained, and these data are plotted into a curve graph, with the horizontal axis representing the dust content and the vertical axis representing the response time.

[0227] The present invention can monitor the operation status of the transmission line in real time by acquiring the sensor signal of the data transmission line. By collecting and analyzing the data of the sensor signal, any abnormal situation in the transmission line, such as signal interference, data loss or transmission delay, can be detected, which helps to timely discover and solve potential problems in the data transmission process and ensure the integrity and accuracy of the data; the spectrum conversion of the transmitted sensor signal data can observe the signal characteristics more clearly from the frequency domain, and provide data for analyzing the impact of dust on signals of different frequencies; the phase change rate of the transmission signal spectrum can capture the subtle delay and distortion caused by the influence of dust in the transmission process of the signal, and then quantify the impact of dust on signal transmission; the signal spectrum phase The change rate curve can intuitively show the trend of the impact of dust on signal transmission, providing a visual basis for subsequent analysis; calculating the slope of the change rate curve can quantitatively describe the speed of change of the impact of dust on signal transmission, providing a more accurate indicator for evaluating the degree of dust impact; by analyzing the slope of the curve, the change in signal response time can be inferred, reflecting the impact of dust on signal transmission delay, providing a basis for evaluating the real-time and stability of data transmission; combined with the dust content data of the transmission line, a response time and dust content relationship curve is drawn, which can intuitively show the influence of dust concentration on signal transmission response time, provide a scientific basis for predicting and warning the impact of dust on data transmission, and provide data support for formulating corresponding dust control measures.

[0228] Preferably, step S45 includes the following steps:

[0229] Step S451: Performing capacitive sensing processing on the processing surface of the functional area of ​​the production line to obtain capacitive sensing data of the processing surface;

[0230] In an embodiment of the present invention, a capacitive sensor array is installed on the processing surface of the functional area of ​​the production line. Each sensor unit is composed of two parallel electrode plates and maintains a small distance from the measured processing surface. When the processing surface is covered with dust, the dielectric constant between the two electrode plates of the sensor will change, thereby causing a change in capacitance. By measuring the capacitance value of each unit in the sensor array and performing data acquisition and processing, the capacitive sensing data of the processing surface can be obtained.

[0231] Step S452: performing dust coverage ratio analysis on the processing surface capacitance sensing data based on the dust content in the line area to obtain the processing surface dust coverage ratio;

[0232] In the embodiment of the present invention, the dust coverage ratio of the processing surface can be obtained by converting the capacitance value of each sensor unit into the corresponding dust coverage area, adding up the dust coverage areas of all sensor units and dividing the sum by the total area of ​​the processing surface.

[0233] Step S453: Obtaining the processing surface friction coefficient of the functional area of ​​the production line to obtain the processing surface friction coefficient;

[0234] In an embodiment of the present invention, the friction coefficient of the processing surface of the functional area of ​​the production line is measured by using a friction coefficient measuring instrument, a suitable friction pair material is selected, and a certain normal load is applied to make the friction pair slide relatively on the processing surface, the magnitude of the friction force is measured, and the friction coefficient of the processing surface is calculated according to the definition of the friction coefficient.

[0235] Step S454: analyzing the roughness of the processed surface based on the friction coefficient of the processed surface to obtain the surface roughness;

[0236] In an embodiment of the present invention, the measured friction coefficient of the processed surface is converted into the corresponding surface roughness according to the relationship model between the friction coefficient and the surface roughness, and different roughness parameters can be selected for characterization, such as the arithmetic mean deviation of the profile and the ten-point height of the micro-roughness.

[0237] Step S455: Draw a dust content wear curve for the surface roughness based on the dust coverage ratio of the processed surface to obtain a dust wear curve.

[0238] In an embodiment of the present invention, by controlling the experimental environment, changing the dust content in the functional area of ​​the production line, and repeating the steps, the dust coverage ratio and surface roughness data of the processed surface corresponding to different dust contents are obtained, and these data are plotted into a curve graph, with the horizontal axis representing the dust coverage ratio and the vertical axis representing the surface roughness.

[0239] The present invention can sensitively capture the dust coverage on the processing surface by performing capacitive sensing processing on the functional area of ​​the production line, obtain capacitive sensing data reflecting the degree of dust coverage, and provide a data basis for subsequent analysis of the impact of dust on processing quality; by analyzing the capacitive sensing data of the processing surface, the proportion of the processing surface covered by dust can be calculated. Dust has unique capacitive characteristics. Compared with the surface not covered with dust, the capacitance value of the surface covered with dust will change. By comparing the capacitance sensing data with the capacitance data of the standard surface, the dust coverage ratio of the processed surface in a certain area can be quantitatively analyzed. This step helps to determine the degree of dust contamination on the surface of the functional area of ​​the production line; obtaining the friction coefficient of the processed surface in the functional area of ​​the production line can reflect the smoothness of the processed surface and provide a reference indicator for analyzing the degree of dust wear on the processed surface; by analyzing the friction coefficient of the processed surface, the roughness of the processed surface can be evaluated to provide control data for studying the degree of dust wear; combining the dust coverage ratio of the processed surface and the surface roughness data to draw a dust content wear curve can intuitively show the relationship between the dust coverage and the degree of wear of the processed surface, thereby helping to identify dust wear risks and providing data support for optimizing production processes and formulating dust control strategies.

[0240] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing the layout of a hardware digital production line, characterized in that: The following steps are involved: Step S1: Perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space; The three-dimensional laser data of the production space is densely point clouded to obtain a point cloud set of the production space; based on the point cloud set of the production space, a three-dimensional model of the production space of the hardware production line is constructed to obtain a three-dimensional model of the production space. Step S2: Divide the hardware production line into functional areas to obtain the functional areas of the production line; Performing spatial partition processing on the three-dimensional model of the production space to obtain the three-dimensional partition of the production space; Based on the three-dimensional partitioning of the production space and the functional areas of the production line, the three-dimensional model of the production space is used to generate a basic layout model of the production line, thereby obtaining a three-dimensional model of the basic layout of the production line. Step S3: Perform regional machining tolerance analysis on the functional areas of the production line to obtain machining tolerance data for each area; generate precision requirement sequences for the functional areas of the production line based on the machining tolerance data for each area to obtain precision requirement sequences for each area; perform CNC node optimization positioning on the three-dimensional model of the basic layout of the production line based on the precision requirement sequences for each area to obtain CNC node optimization positioning data. Step S4: performing dust distribution analysis on the functional areas of the production line based on the three-dimensional model of the basic layout of the production line to obtain dust distribution data of the production line; performing data transmission path analysis on the three-dimensional model of the basic layout of the production line based on the CNC node optimization positioning data to obtain an optimized data transmission line; Based on the dust distribution data of the production line, the dust concentration influence model is constructed to optimize the data transmission line and the functional area of ​​the production line, and the dust concentration influence model is obtained; based on the CNC node optimization positioning data, the production line dust distribution data, the dust concentration influence model and the three-dimensional model of the basic layout of the production line, the production line layout of the hardware production line is optimized to obtain the production line layout optimization result.

2. The layout optimization method of hardware digital production line according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Perform spatial three-dimensional laser scanning on the hardware production line to obtain three-dimensional laser data of the production space; Step S12: Calculating the laser travel phase difference of the three-dimensional laser data in the production space to obtain laser travel phase difference data; Step S13: acquiring the three-dimensional coordinates of the space surface point based on the laser travel phase difference data to obtain the three-dimensional coordinates of the production space surface point; Step S14: performing a dense point cloud data set on the three-dimensional coordinates of the production space surface points to obtain a production space point cloud set; Step S15: constructing a three-dimensional model of the production space of the hardware production line based on the production space point cloud set to obtain a three-dimensional model of the production space.

3. The layout optimization method of hardware digital production line according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Acquire the functional characteristics of the hardware production line to obtain the functional characteristics of the hardware production line; Step S22: dividing the hardware production line into functional areas based on the functional characteristics of the hardware production line to obtain functional areas of the production line; Step S23: performing spatial partition processing on the three-dimensional model of the production space to obtain three-dimensional partitions of the production space; Step S24: Analyze the basic attribute data of the three-dimensional partitions of the production space to obtain the basic attribute data of the three-dimensional partitions of the production space; Step S25: Based on the basic attribute data of the three-dimensional partition of the production space, the functional area of ​​the production line is positioned to obtain the positioning data of the functional area of ​​the production space; Step S26: Generate a basic layout model of the production line for the three-dimensional model of the production space based on the functional area positioning data of the production space, and obtain a three-dimensional model of the basic layout of the production line.

4. The layout optimization method of hardware digital production line according to claim 3 is characterized in that: Step S24 includes the following steps: Step S241: Calculate the available space volume of the three-dimensional partitions of the production space to obtain available space volume data of the partitions; Step S242: Identify the partition air circulation openings of the three-dimensional partitions of the production space to obtain the partition air circulation opening areas; Step S243: acquiring air pressure remote sensing data of the partitioned air circulation opening area to obtain circulation air pressure remote sensing data; Step S244: analyzing the air circulation area of ​​the partitioned air circulation openings to obtain the partitioned air circulation area; Step S245: Calculate the air circulation degree of the three-dimensional partitions of the production space based on the air pressure remote sensing data and the partition air circulation area to obtain the partition air circulation degree; Step S246: basic attribute data collection is performed on the available spatial volume data of the partition and the air circulation degree of the partition to obtain basic attribute data of the three-dimensional partition of the production space.

5. The layout optimization method of hardware digital production line according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Acquire regional production dimension tolerances for functional areas of the production line to obtain production dimension tolerance data for each area; Step S32: acquiring the product roughness of the functional areas of the production line to obtain product roughness data of each area; Step S33: performing data fitting on the production dimension tolerance data of each region and the product roughness data of each region to obtain the processing tolerance data of each region; Step S34: Calculate the functional area machining accuracy for the machining tolerance data of each area to obtain the machining accuracy data of each area; Step S35: Generate the accuracy requirement sequence for the functional areas of the production line based on the processing accuracy data of each area to obtain the accuracy requirement sequence for each area; Step S36: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the accuracy requirement sequence of each area to obtain CNC node optimization positioning data.

6. The layout optimization method of hardware digital production line according to claim 5 is characterized in that: Step S36 includes the following steps: Step S361: Perform data transmission line analysis on the three-dimensional model of the basic layout of the production line to obtain data transmission line data; Step S362: performing data transmission response priority sequence analysis on each regional accuracy requirement sequence to obtain a regional data priority response sequence; Step S363: Calculate the priority weight of the regional data priority response sequence to obtain the regional data priority response weight; Step S364: reconstructing the data transmission line data with priority weights based on the regional data priority response weights to obtain a data priority response line; Step S365: Optimize the positioning of the CNC nodes on the three-dimensional model of the basic layout of the production line based on the data priority response line to obtain CNC node optimization positioning data.

7. The method for optimizing the layout of a hardware digital production line according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing dust distribution analysis on the functional areas of the production line based on the three-dimensional model of the basic layout of the production line to obtain dust distribution data of the production line; Step S42: performing data transmission path analysis on the three-dimensional model of the basic layout of the production line based on the numerical control node optimization positioning data to obtain an optimized data transmission line; Step S43: performing dust content analysis on the optimized data transmission line based on the dust distribution data of the production line to obtain the dust content in the line area; Step S44: Drawing a dust impact response time curve for the optimized data transmission line based on the dust content in the line area to obtain a dust impact response time curve; Step S45: Drawing a dust content wear curve for the functional area of ​​the production line based on the dust content of the line area to obtain a dust wear curve; Step S46: constructing a model for the dust impact response time curve and the dust wear curve to obtain a dust concentration impact model; Step S47: Optimizing the layout of the basic layout three-dimensional model of the production line based on the numerical control node optimization positioning data, the production line dust distribution data, and the dust concentration influence model to obtain an optimized production line three-dimensional model; Step S48: Optimizing the layout of the hardware production line based on the optimized production line three-dimensional model to obtain a production line layout optimization result.

8. The method for optimizing the layout of a hardware digital production line according to claim 7, characterized in that: Step S41 includes the following steps: Step S411: injecting standard detection light into the functional area of ​​the production line to obtain regional standard detection light; Step S412: acquiring a functional regional spectrum diagram of the regional standard detection light to obtain a functional regional spectrum diagram; Step S413: performing production dust reflection analysis on the functional area spectrum to obtain production dust reflection spectrum data; Step S414: performing regional production dust content analysis on the production dust reflectance spectrum data to obtain the regional production dust content; Step S415: Perform dust distribution analysis on regional production dust content based on the three-dimensional model of the basic layout of the production line to obtain production line dust distribution data.

9. The method for optimizing the layout of a hardware digital production line according to claim 7, characterized in that: Step S44 includes the following steps: Step S441: acquiring a transmission signal from a data transmission line to obtain line transmission signal data; Step S442: performing spectrum conversion on the line transmission signal data to obtain a transmission signal spectrum; Step S443: performing signal phase change rate analysis on the transmission signal spectrum to obtain the signal spectrum phase change rate; Step S444: plotting a change rate curve for the signal spectrum phase change rate to obtain a change rate curve; Step S445: Calculate the slope of the change rate curve to obtain the slope of the curve; Step S446: performing response time analysis on the slope of the curve to obtain response time data; Step S447: Plotting a dust impact response time curve for the response time data based on the dust content in the line area to obtain a dust impact response time curve.

10. The method for optimizing the layout of a hardware digital production line according to claim 7, characterized in that: Step S45 includes the following steps: Step S451: Performing capacitive sensing processing on the processing surface of the functional area of ​​the production line to obtain capacitive sensing data of the processing surface; Step S452: performing dust coverage ratio analysis on the processing surface capacitance sensing data based on the dust content in the line area to obtain the processing surface dust coverage ratio; Step S453: Obtaining the processing surface friction coefficient of the functional area of ​​the production line to obtain the processing surface friction coefficient; Step S454: analyzing the roughness of the processed surface based on the friction coefficient of the processed surface to obtain the surface roughness; Step S455: Draw a dust content wear curve for the surface roughness based on the dust coverage ratio of the processed surface to obtain a dust wear curve.

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