Grouping and Assembly Optimization Method and System Based on Industrial Internet Technology

Abstract

This invention belongs to the field of intelligent manufacturing technology, and particularly relates to a group assembly optimization method and system based on Industrial Internet technology. The method includes the following steps: S1, collecting inspection and assembly dimension data of batch parts using IoT technology; S2, analyzing the data distribution of the inspection and assembly dimension data of the parts to obtain the normal distribution curve of the actual mating dimensions of the parts; S3, comparing the normal distribution curve of the actual mating dimensions of the parts with the normal distribution curve of the theoretical mating dimensions to obtain the mean skewness error; S4, using the mean skewness error obtained in S3 to dynamically correct and compensate the process of the edge processing equipment, so that the corrected mean skewness error is less than the preset accuracy deviation. This invention can reduce the mismatch error of the number of groups, improve the assembly adaptability of parts, reduce the management and processing costs of enterprises, and improve enterprise production efficiency.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology, and in particular relates to a group assembly optimization method and system based on industrial internet technology. Background Technology

[0002] Mechanical products are generally composed of many parts and components. The process of fitting and connecting these parts and components according to specified technical requirements to create semi-finished or finished products is called assembly.

[0003] The degree to which the actual geometric parameters of an assembled product or component match the ideal parameters is called assembly accuracy. When assembling mechanical products, reducing component processing costs and improving economic efficiency while ensuring assembly accuracy are crucial considerations. However, higher assembly accuracy requires higher manufacturing precision for related parts, making machining uneconomical and sometimes impossible. Therefore, it is essential to adopt appropriate assembly processes to ensure assembly accuracy without increasing the manufacturing precision of related parts, given specific production conditions. Group assembly technology in industrial production can meet these assembly requirements.

[0004] Group assembly technology is a method that sets tolerances for the mating dimensions of parts according to economical machining accuracy, inspects, groups, and marks the group numbers after completion, and then assembles parts with the same group number. Group assembly technology can improve the machinability and economy of the mating dimensions of parts, and is a non-completely interchangeable method that meets high mating accuracy requirements at an economical machining cost. One of the most prominent problems with this group assembly method based on intra-group complete interchangeability is that the actual dimensional distribution center of a batch of parts does not match the design tolerance center of the parts. This results in an imbalance in the number of parts in each group when the batch of mating parts is equal, affecting the fit rate of the group assembly and thus increasing production costs.

[0005] In actual production, although the company's total quality management department knew the cause through product quality data analysis, the only solution they took was to notify the parts processing department to improve the process via quality rectification notices. However, this approach failed to make timely corrections and effectively solve the problem. Ultimately, the only solution was to process a batch of parts specifically to match the remaining parts in each group, thereby increasing management and processing costs and reducing timeliness.

[0006] Therefore, how to reduce the mismatch error between the quantities of each group, improve the assembly compatibility rate of parts, reduce the management and processing costs of enterprises, and improve the production efficiency of enterprises has become an urgent problem to be solved. Summary of the Invention

[0007] To address the shortcomings of the existing technologies, this invention provides a group assembly optimization method based on industrial internet technology, which can reduce the mismatch error between the number of groups, improve the component assembly adaptability, reduce enterprise management and processing costs, and improve enterprise production efficiency.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] The group assembly optimization method based on industrial internet technology includes the following steps:

[0010] S1. Collect inspection and assembly dimension data of batch parts using IoT technology;

[0011] S2. Analyze the data distribution of the inspection and assembly dimensions of the parts to obtain the normal distribution curve of the actual mating dimensions of the parts;

[0012] S3. Compare the normal distribution curve of the actual mating dimensions of the parts with the normal distribution curve of the theoretical mating dimensions to obtain the mean skewness error;

[0013] S4. Using the mean skewness error obtained in S3, dynamically correct and compensate the process of the edge processing equipment so that the corrected mean skewness error is less than the preset accuracy deviation.

[0014] Preferably, in S3, the mean skewness error is the deviation between the actual center and the design center; wherein, the actual center is the center of the normal distribution curve of the actual mating dimensions of the part, and the design center is the center of the normal distribution curve of the theoretical mating dimensions.

[0015] Preferably, in S4, when dynamically correcting and compensating the process of the edge processing equipment, the center position of the part size distribution range is changed.

[0016] Preferably, after S4, S5 is also included, in which, after receiving the analysis request signal from the management terminal, the process data of dynamically correcting and compensating the process of the processing equipment is sent to the management terminal.

[0017] Preferably, the management terminal is a PC with the corresponding application installed. After receiving the process data for a preset time, the management terminal determines whether the process data is in a read state. If not, the management terminal stores the process data in the business storage unit and issues a processing reminder when a preset reminder state appears on the management terminal. The processing reminder includes a text reminder. The reminder state includes no operation on the business terminal within the preset reminder duration, or the management terminal being turned off and then turned on or restarted.

[0018] This invention also provides a group assembly optimization system based on industrial internet technology, used in the aforementioned group assembly optimization method based on industrial internet technology, comprising an edge terminal and a platform terminal that communicate with each other; the edge terminal includes a data acquisition unit and a device control unit; the platform terminal includes a processing unit.

[0019] The acquisition unit is used to collect the inspection and assembly dimension data of the parts and send it to the platform. The processing unit is used to analyze the data distribution of the inspection and assembly dimension data to obtain the normal distribution curve of the actual mating dimensions of the parts. The processing unit is also used to compare the normal distribution curve of the actual mating dimensions of the parts with the normal distribution curve of the design theoretical mating dimensions to obtain the mean skewness error and send it to the edge end.

[0020] The equipment control unit is used to dynamically correct and compensate the process of the processing equipment based on the mean skewness error, so that the corrected mean skewness error is less than the preset accuracy deviation.

[0021] Preferably, the acquisition unit is also used to preprocess the acquired data.

[0022] Preferably, the processing unit analyzes and obtains the normal distribution curve of the actual mating dimensions of the parts through a preset group assembly mechanism analysis model.

[0023] Preferably, the processing unit obtains the theoretical design and processing data standard of the part through the part attributes, thereby obtaining the normal distribution curve of the design theoretical fit dimensions.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention utilizes IoT (Internet of Things) and Industrial Internet technologies to collect actual mating dimension data of parts during batch processing using a data acquisition unit. This data is then transmitted to the big data processing center of the Industrial Internet platform. Statistical analysis of the actual mating dimension data yields a normal distribution curve of the actual mating dimensions of the parts. This curve is then compared with the normal distribution curve of the theoretical mating dimensions to obtain the mean skewness error of the batch of parts, i.e., the deviation between the actual center and the design center.

[0026] Then, through the remote control technology of the Industrial Internet, the mean skewness error of this batch of parts is used to dynamically correct and compensate the process of the edge processing equipment. That is, the mean skewness error is used as a correction signal for the machining dimensions of the equipment's NC program, and the equipment is dynamically modified to change the distribution range of the actual machined dimensions of the parts. This makes the distribution center of the actual machined mating dimensions of this batch of parts closer to the distribution center of the theoretical normal dimensions, reducing the mismatch error between the quantities of each group. This greatly improves the assembly fit rate of this batch of parts, reduces management costs, and increases production efficiency.

[0027] In summary, this method can reduce the mismatch error between the quantities of each group, improve the assembly fit rate of parts, reduce the management and processing costs of enterprises, and improve the production efficiency of enterprises.

[0028] 2. This invention realizes the application of SPC (Statistical Process Control) industrial internet platform, which is faster and more efficient than traditional SPC systems; moreover, it reduces the software and hardware investment of SPC quality management systems for small and medium-sized enterprises and improves work efficiency.

[0029] 3. When managers need to use process data for dynamic correction and compensation of the processing equipment to handle matters, this invention can remind them of the matters without affecting their current important work, ensuring that they complete the relevant reminders. Attached Figure Description

[0030] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0031] Figure 1 This is a schematic diagram of the normal distribution curve for the dimensional data in the batch machining of parts as explained in Example 1.

[0032] Figure 2 This is a schematic diagram of the grouping quantity under the theoretical design state described in Example 1;

[0033] Figure 3 This is a schematic diagram illustrating the number of groups under the actual conditions described in Example 1.

[0034] Figure 4 This is a flowchart of the present invention;

[0035] Figure 5 This is a diagram illustrating the piston pin grouping in a specific example of Embodiment 1;

[0036] Figure 6 This is a schematic diagram of the system functional structure in Example 2;

[0037] Figure 7 This is a schematic diagram of the working process of the testing equipment in Example 2;

[0038] Figure 8 This is a schematic diagram of the equipment process optimization process in Example 2;

[0039] Figure 9 This is a schematic diagram of the working process of the industrial internet platform in Example 2. Detailed Implementation

[0040] The following detailed explanation illustrates the specific implementation methods:

[0041] Example 1

[0042] To facilitate a better understanding of the present invention by those skilled in the art, the principles of the present invention will first be explained as follows.

[0043] This invention utilizes the principles of data statistics and the big data processing capabilities of the Industrial Internet to optimize the machining process of mechanical parts and improve product quality by analyzing the distribution of dimensional data of machined parts.

[0044] Distribution of the number of parts in design theory

[0045] Under normal circumstances, according to statistical theory, the dimensional data in the machining of batches of parts follows a normal distribution curve, which is also the most ideal dimensional design distribution for the machined parts. Figure 1 As shown:

[0046] The figure shows the distribution curve of the dimensional data of a batch of parts after machining. This curve is a typical normal distribution curve. The vertical axis f(x) represents the frequency function of the batch of parts in a certain size range, and the horizontal axis X represents the size of the parts after machining.

[0047] μ is the arithmetic mean of all dimensions of a batch of parts. The μ value depends on the sum of the machine tool process adjustment dimensions and systematic errors. The machine tool process adjustment dimensions are the tolerance specification center. Systematic errors refer to the errors whose magnitude and direction remain unchanged or change according to a certain pattern in the continuous processing of a batch of workpieces.

[0048] The mean μ can affect the left and right horizontal positions of the curve on the X-axis, but does not affect the shape of the curve. In the figure, x = μ is the axis of symmetry of the distribution curve, and also the position of the tolerance specification center U.

[0049] The distribution of the number of parts in the figure is symmetrical around the mean center line x = μ. The closer to the center, the more parts there are; the farther away from the center, the fewer parts there are. In the figure, USL and LSL are the upper and lower limits of the tolerance, respectively. The tolerance T = USL - LSL, and the tolerance specification center U = (USL + LSL) / 2.

[0050] UCL and LCL are the upper and lower limits of quality control, and the quality control midline CL = (UCL - LCL) / 2.

[0051] As can be seen from the figure, the center of the distribution curve coincides with the tolerance specification center U and the quality control center line CL. As long as the final size of the part is within the upper and lower limits of the tolerance, the part is considered a qualified product.

[0052] Grouping under theoretical design conditions

[0053] To ensure assembly accuracy without improving the manufacturing precision of the relevant parts, a group assembly process is adopted to meet higher assembly precision requirements, such as... Figure 2 As shown.

[0054] The grouping method involves grouping batches of parts within a certain tolerance range while ensuring assembly accuracy. In the above figure, the width of each rectangle in the x-direction is a grouping tolerance range. The size of this grouping tolerance range can guarantee the assembly accuracy of the parts in the group, and each rectangle has the same width.

[0055] Assemble the parts according to their groups to ensure the required assembly accuracy. In the diagram above, the batch of parts is divided into 7 groups (ag) based on their machining dimensions. These 7 groups are assembled according to a specific assembly process, ensuring the required assembly accuracy.

[0056] The figure shows the distribution of part assembly dimensions under theoretical design conditions. The part quantity distribution density is highest with μ as the center of symmetry, decreasing with left and right symmetry. Group d0 is exactly at the center of symmetry, and its distribution density is the highest. The relationship between the number of parts in each group satisfies the following mathematical relationship:

[0057] d0>c0>b0>a0 and d0>e0>f0>g0

[0058] Number of groups in actual condition

[0059] In actual machining, the actual dimensional error of a part is affected by the combined effect of many independent random errors. In reality, the actual dimensional distribution center of a batch of parts does not match the design normal distribution center of the parts. The actual measured dimensional distribution center of the parts deviates by M and also deviates from the design tolerance specification center.

[0060] Deviation refers to the difference between the design average and the actual value of data. It can be divided into positive and negative deviations. Positive deviation indicates that the average value of the data is too high, while negative deviation indicates that the average value of the data is too low. Both situations will cause changes in the number of each group of parts that are assembled and mated together.

[0061] like Figure 3 As shown in the figure, the actual center of symmetry of the curve has shifted to the left, deviating from the theoretical design center of the dimensional distribution. The deviation is:

[0062] M = X1 - X0 = μ - σ - μ = -σ;

[0063] When the curve deviates, the actual part size distribution center does not coincide with the design normal distribution center.

[0064] Since the grouping is symmetrical about the design center, under normal circumstances, the groups closer to the design center have the largest number of groups, and the groups farther away from the design center have the smaller number of groups. The relationship between the number of groups is as follows:

[0065] d0 > c0 > b0 > a0;

[0066] d0 > e0 > f0 > g0;

[0067] In the formula, d0 has the largest quantity, and it is closest to the center of symmetry of the theoretical design.

[0068] Due to the offset of the actual part machining dimension distribution center, the quantities of each group have also changed, and the quantitative relationship is as follows:

[0069] c1 > b1 > a1;

[0070] c1 > d1 > e1 > f1 > g1;

[0071] Since c1 is now located at the center of symmetry of the normal distribution of the actual machining dimensions, the quantity of c1 is at its maximum.

[0072] Therefore, the actual size distribution center of the batch of parts deviates from the normal distribution center and tolerance specification center of the part design, which will cause abnormal grouping quantity, seriously affecting the matching of the assembly quantity of parts in the same group, resulting in an unbalanced matching of the number of parts in each group under the condition that the mating parts are put into equal batches, and affecting the fit rate of group assembly.

[0073] Fit rate refers to a company's ability to effectively assemble various components during the production process, thereby producing high-quality products. Fit rate is one of the important standards for evaluating a company's production capacity; the higher the fit rate, the higher the company's production efficiency and the better the product quality. The formula for fit rate is:

[0074] Fit rate = Number of parts assembled ÷ Total number of parts × 100%;

[0075] In the formula, the number of parts that can be assembled is the sum of the number of parts that can be assembled in each group. The number of parts that can be assembled in each group is related to the number of matching parts in the same group, and the number of matching parts in the same group determines the number of parts that can be assembled in this group. The total number of parts is the total number of parts produced in the batch. Therefore, a decrease in the number of matching parts in the same group will seriously affect the fit rate, thereby affecting production efficiency and production quality, and increasing production costs.

[0076] Actual deviation process optimization

[0077] To ensure optimal assembly fit in group assemblies, we can collect batch assembly dimension data using IoT technology. Then, we can analyze and process this data using an industrial internet platform data processing center to determine the deviation M between the actual assembly dimension distribution center and the design center. Through remote internet control technology, we can optimize the process parameters of the edge application's part processing equipment, artificially altering the center position of the part dimension distribution range. This aims to approximate the normal distribution curve of the actual processed dimensions with the theoretical design curve, reducing the value of deviation M and improving the matching of each group's quantity.

[0078] like Figure 4 As shown, this embodiment discloses a group assembly optimization method based on industrial internet technology, including the following steps:

[0079] S1. Collect inspection and assembly dimension data of batch parts using IoT technology;

[0080] S2. Analyze the data distribution of the inspection and assembly dimensions of the parts to obtain the normal distribution curve of the actual mating dimensions of the parts;

[0081] S3. Compare the normal distribution curve of the actual mating dimensions of the part with the normal distribution curve of the theoretical mating dimensions to obtain the mean skewness error; the mean skewness error is the deviation between the actual center and the design center; wherein, the actual center is the center of the normal distribution curve of the actual mating dimensions of the part, and the design center is the center of the normal distribution curve of the theoretical mating dimensions.

[0082] S4. Using the mean skewness error obtained in S3, dynamically correct and compensate the process of the edge processing equipment to make the corrected mean skewness error less than the preset accuracy deviation. Specifically, when dynamically correcting and compensating the process of the edge processing equipment, the center position of the part size distribution range is changed.

[0083] This invention also provides a group assembly optimization system based on industrial internet technology, used in the aforementioned group assembly optimization method based on industrial internet technology, comprising an edge terminal and a platform terminal that communicate with each other; the edge terminal includes a data acquisition unit and a device control unit; the platform terminal includes a processing unit.

[0084] The data acquisition unit is used to collect the inspection and assembly dimension data of the parts and send it to the platform. In practice, the data acquisition unit is also used to preprocess the collected data.

[0085] The processing unit analyzes the data distribution of the inspection assembly dimensions to obtain the normal distribution curve of the actual mating dimensions of the parts. It also compares the normal distribution curve of the actual mating dimensions with the normal distribution curve of the theoretical mating dimensions to obtain the mean skewness error, which is then sent to the edge. Specifically, the processing unit uses a preset group assembly mechanism analysis model to analyze and obtain the normal distribution curve of the actual mating dimensions of the parts. Furthermore, the processing unit obtains the theoretical design and processing data standards of the parts through their attributes, thereby obtaining the normal distribution curve of the theoretical mating dimensions.

[0086] The equipment control unit is used to dynamically correct and compensate the process of the processing equipment based on the mean skewness error, so that the corrected mean skewness error is less than the preset accuracy deviation.

[0087] This invention utilizes IoT (Internet of Things) and Industrial Internet technologies. A data acquisition unit collects actual mating dimension data of parts during batch processing and transmits it to the Industrial Internet platform's big data processing center. Statistical analysis of the actual mating dimension data yields a normal distribution curve of the actual mating dimensions. This curve is then compared with the normal distribution curve of the theoretically designed mating dimensions to determine the mean skewness error of the batch of parts—the deviation between the actual center and the design center. Furthermore, through remote control technology of the Industrial Internet, this mean skewness error is used to dynamically correct and compensate the process of the edge-end processing equipment. Specifically, the mean skewness error is used as a correction signal for the NC program's machining dimensions, dynamically adjusting the equipment's process to change the distribution range of the actual machined dimensions of the parts. This brings the distribution center of the actual machined mating dimensions of the batch of parts closer to the center of the theoretically designed normal dimension distribution, reducing mismatch errors between different groups. This significantly improves the assembly fit rate of the batch of parts, reduces management costs, and increases production efficiency. In addition, this invention realizes the application of SPC (Statistical Process Control) industrial internet platform, which is faster and more efficient than traditional SPC systems; moreover, it reduces the software and hardware investment of SPC quality management systems for small and medium-sized enterprises and improves work efficiency.

[0088] To facilitate a better understanding of the present invention, a specific example will be used for illustration.

[0089] In the assembly of pistons and piston pins in large internal combustion engines, the nominal size of the piston pin is... Based on the assembly precision requirements, the assembly tolerance of the piston pins must be within 0.008mm. A batch of piston pin parts is grouped into 5 groups with a group tolerance of 0.008mm, based on nominal dimensions. As the grouping center, its grouping situation is as follows: Figure 5 As shown.

[0090] Based on the characteristics of the normal distribution curve, design the nominal size. With the mean center, most parts are centered around the nominal size. Nearby. Group C is the central group for dimensional grouping, and the tolerance zone for Group C is... Theoretically, group C produces the most parts.

[0091] When analyzing a batch of 1000 parts, due to manufacturing deviations, the actual average size is within... Between, the mean center is assumed to be Deviation at this time:

[0092]

[0093] In order to artificially adjust the actual size distribution center of the part to the design mean center The system provides a deviation signal M = -0.008 mm. Through the remote control technology of the Industrial Internet and the equipment NC parameter optimization program, the deviation signal M is superimposed to dynamically correct and compensate, thereby completing the optimization of the part processing technology.

[0094] Example 2

[0095] To facilitate the implementation of this invention by those skilled in the art, the system architecture and usage process used in the specific implementation of this invention are described as follows.

[0096] System Functional Structure

[0097] The system consists of two parts: processing and inspection equipment at the factory edge and an industrial internet platform-based intelligent optimization management system for equipment processes. The system's functional structure is as follows: Figure 6 As shown.

[0098] Factory edge application end inspection equipment

[0099] The inspection equipment at the factory application end is equipped with a data acquisition device, which is responsible for collecting the dimensional data of the parts through various sensors, such as CCD industrial cameras, laser displacement sensors, laser rangefinders, laser position sensors, linear displacement sensors, magnetostrictive sensors, angle sensors, rotary encoders, and grating rulers.

[0100] The data acquisition device can preprocess the data and transmit it to the industrial internet platform via Ethernet communication technology or modern 4 / 5G wireless communication technology.

[0101] Factory edge application end processing equipment

[0102] The factory's processing equipment is precision CNC machining equipment with automatic machining compensation functions, capable of interpolation compensation machining. It connects to an industrial internet platform via Ethernet or modern 4 / 5G wireless communication technology, receiving remote optimization control from the platform's intelligent equipment process optimization management system.

[0103] General-purpose testing equipment is usually equipped with a standard communication interface, which can be easily connected to Ethernet to realize the device networking function.

[0104] Industrial Internet Platform

[0105] The platform equipment process intelligent optimization management system is responsible for the daily management of this system, such as the access management of application terminal equipment, data communication and interaction, data analysis and processing, dynamic process optimization, and remote optimization control of equipment.

[0106] The platform's data processing center is responsible for data statistical analysis, model data processing, and process optimization signal processing. Through a group assembly mechanism analysis model, combined with the inherent mechanism of assembly group tolerances, the data processing center dynamically analyzes the distribution center of the actual dimensions of the parts.

[0107] By obtaining the theoretical design and machining data standard of the part through part attributes and SIP data, and comparing it with the statistical analysis results of the actual size data of the part, the deviation M of the non-coincidence between the machining assembly size distribution center and the theoretical design center can be obtained.

[0108] Based on the obtained deviation signal, the system communicates with the processing equipment via Internet remote control technology. Based on the dynamic compensation system of the CNC system, the deviation signal is integrated into the CNC system to optimize the process parameters. The center position of the part size distribution range is artificially changed to correct the normal distribution curve of the actual processed size of the part to be as close as possible to the theoretical design curve, thereby reducing the value of deviation M and improving the matching of each group of quantities.

[0109] System Workflow

[0110] Edge application testing equipment

[0111] The main task of the factory inspection equipment is to collect and transmit part inspection dimensional data, and to accept daily management from the equipment process intelligent optimization management system of the industrial internet platform.

[0112] The inspection equipment receives the system's work instructions and selects inspection fixtures equipped with different sensors to collect dimensional data based on the part's structural type and characteristics.

[0113] The inspection equipment is also responsible for marking part attributes, collecting inspection standards, and classifying, preprocessing, and transmitting data.

[0114] The working process of the testing equipment is as follows: Figure 7 As shown.

[0115] The edge detection equipment data preprocessing can perform denoising, enhancement, compensation and other processing on the data signal.

[0116] Utilizing modern communication technologies, data signals are uploaded to the industrial internet platform via Ethernet or 4 / 5G wireless networks to communicate with the equipment process intelligent optimization management system and complete data transmission and interaction.

[0117] Processing equipment end

[0118] The IoT remote control system can be easily integrated into the factory's DNC (Distributed Numerical Control) system. Through the gateway, the field equipment can be networked and controlled, ensuring 24 / 7 access to the industrial internet platform and enabling remote process optimization and parameter tuning.

[0119] The system can also be integrated with the factory's SAP and MES production management systems to achieve the goals of lean production and quality management in a smart factory.

[0120] Equipment process optimization workflow as follows Figure 8 As shown.

[0121] Industrial Internet Platform

[0122] This invention is based on an industrial internet platform and leverages the platform's big data processing capabilities to achieve high-speed statistical processing of part size data.

[0123] The intelligent optimization management system for equipment processes incorporates an assembly process mechanism analysis model and various basic data processing algorithm components to achieve intelligent analysis and processing of part data.

[0124] Based on the deviation signals analyzed and processed by the data analysis center, remote intelligent optimization of the equipment is achieved. The workflow is as follows: Figure 9 As shown.

[0125] Example 3

[0126] Unlike Embodiment 1, the method in this embodiment includes S5 after S4, in which, after receiving the analysis request signal from the management terminal, the process data of dynamically correcting and compensating the process of the processing equipment is sent to the management terminal; the management terminal is a PC with the corresponding application installed.

[0127] After receiving process data for a preset time, the management terminal determines whether the process data is in a read state. If not, the management terminal stores the process data in the business storage unit and issues a processing reminder when a preset reminder state appears on the management terminal. The processing reminder includes text reminders. The reminder state includes no operation on the business terminal within the preset reminder duration, or the management terminal being powered off and then powered on or restarted.

[0128] The specific implementation process is as follows:

[0129] Typically, managers only request process data from staff for dynamic adjustments and compensation of the processing equipment when there are specific usage needs. However, managers have many daily tasks, and if they do not process the received process data within a certain period of time, and the corresponding processing item is not particularly important, they may forget to handle it.

[0130] In this method, after receiving process data for a preset time (e.g., 10 minutes), the management end will determine whether the process data is in a read state. Determining whether process data is in a read state can be done using conventional techniques in this field, and will not be elaborated upon here. If not, it indicates that the aforementioned situation of potentially forgetting to handle the matter has occurred. However, as analyzed above, if a manager might forget to handle the matter, it is usually not of high importance, while the manager has many daily tasks, and the importance and urgency of the current task may be very high. Blindly reminding them would inevitably interrupt their current work. Therefore, if the importance / urgency of their current task is very high, firstly, they will still not handle the process data; secondly, their current work rhythm will be disrupted, affecting their overall work.

[0131] Based on the above reasons, when situations arise where the matter might be forgotten, the present invention will have the management terminal store the process data in the business storage unit and assess its own status. If a reminder status is issued on the management terminal, the administrator will be reminded to handle the relevant matter through a reminder process. Specifically, reminder statuses include no operation on the business terminal within a preset reminder duration (e.g., 5 minutes) or the management terminal being shut down and then restarted. No operation on the business terminal within the preset reminder duration indicates that the administrator has not been using the computer and may have left their work location (e.g., to use the restroom), requiring them to re-enter a working state upon returning to the computer. Similarly, restarting the management terminal requires the administrator to re-enter a working state. Reminding the administrator in such situations will not affect their work status; even if the administrator still does not handle the matter, it will not negatively impact their current work. Furthermore, the reminder will be issued again the next time a reminder status is issued to ensure that the relevant matters related to the process data are processed.

[0132] Using this invention, reminders can be given to managers without affecting their current important work, ensuring that they complete the relevant reminders.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A group assembly optimization method based on Industrial Internet technology, characterized in that, Includes the following steps: S1. Collect the inspection and assembly dimension data of batch parts using IoT technology; wherein, under the condition of meeting the assembly accuracy, the batch parts are grouped according to a certain tolerance dimension range, and the parts to be assembled are assembled according to the group. The grouping is based on the design normal distribution center and is symmetrically grouped left and right. S2. Analyze the data distribution of the inspection and assembly dimensions of the parts to obtain the normal distribution curve of the actual mating dimensions of the parts; S3. Compare the normal distribution curve of the actual mating dimensions of the parts with the normal distribution curve of the theoretical mating dimensions to obtain the mean skewness error; S4. Using the mean skewness error obtained in S3, the process of the edge processing equipment is dynamically corrected and compensated so that the corrected mean skewness error is less than the preset accuracy deviation. In S3, the mean skewness error is the deviation between the actual center and the design center; where the actual center is the center of the normal distribution curve of the actual mating dimensions of the part, and the design center is the center of the normal distribution curve of the theoretical mating dimensions. In S4, when dynamically correcting and compensating the process of the edge processing equipment, the center position of the part size distribution range is changed. Following S4, there is also S5, which, after receiving the analysis request signal from the management terminal, sends the process data of dynamically correcting and compensating the process of the processing equipment to the management terminal.

2. The group assembly optimization method based on industrial internet technology as described in claim 1, characterized in that: The management terminal is a PC with the corresponding application installed. After receiving the process data for a preset time, the management terminal determines whether the process data is in a read state. If not, the management terminal stores the process data in the business storage unit and issues a processing reminder when a preset reminder state appears on the management terminal. The processing reminder includes text reminders. The reminder state includes no operation on the business terminal within the preset reminder duration, or the management terminal being powered off and then powered on or restarted.

3. A group assembly optimization system based on industrial internet technology, used in the group assembly optimization method based on industrial internet technology as described in claim 1, characterized in that: It includes an edge terminal and a platform terminal that communicate with each other; the edge terminal includes a data acquisition unit and a device control unit; the platform terminal includes a processing unit. The acquisition unit is used to collect the inspection and assembly dimension data of the parts and send it to the platform. The processing unit is used to analyze the data distribution of the inspection and assembly dimension data to obtain the normal distribution curve of the actual mating dimensions of the parts. The processing unit is also used to compare the normal distribution curve of the actual mating dimensions of the parts with the normal distribution curve of the design theoretical mating dimensions to obtain the mean skewness error and send it to the edge end. The equipment control unit is used to dynamically correct and compensate the process of the processing equipment based on the mean skewness error, so that the corrected mean skewness error is less than the preset accuracy deviation.

4. The group assembly optimization system based on industrial internet technology as described in claim 3, characterized in that: The acquisition unit is also used to preprocess the acquired data.

5. The group assembly optimization system based on industrial internet technology as described in claim 3, characterized in that: The processing unit analyzes the actual mating dimensions of the parts using a preset group assembly mechanism analysis model to obtain the normal distribution curve.

6. The group assembly optimization system based on Industrial Internet technology as described in claim 5, characterized in that: The processing unit obtains the theoretical design and machining data standard of the part through the part attributes, thereby obtaining the normal distribution curve of the design theoretical fit dimensions.

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