A multi-station data processing method and related equipment for customized door and window production lines

Abstract

The present invention provides a multi-station data processing method and related equipment for a door and window customization production line, and relates to the technical field of data processing for door and window customization production lines. The method comprises the steps of: acquiring CAD drawing data and process parameter data; determining the current resource occupancy status of an edge server; generating a preprocessing strategy for the current station; preprocessing the CAD drawing data according to the preprocessing strategy to obtain preprocessed CAD drawing data; obtaining optimized loaded process parameter data according to the processing requirements of the current station; and sending the preprocessed CAD drawing data and the optimized loaded process parameter data to the control system of the current station for processing control. The method of the present invention proposes a solution to the contradiction between the complex structure and large amount of order data and the limited resources of the edge server, thereby significantly reducing data processing delays and improving the response speed and production efficiency of the production line.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing technology for customized door and window production lines, and in particular to a multi-station data processing method and related equipment for customized door and window production lines. Background Art

[0002] With the growing demand for customized doors and windows, production orders are characterized by diverse parameters and massive amounts of data. To meet market demands for rapid delivery and personalized products, door and window production lines generally adopt a multi-station collaborative processing model. To optimize production processes, reduce data transmission latency, and improve data processing efficiency, modern door and window production lines are beginning to incorporate edge computing architectures. In this architecture, edge servers are deployed at the production site, closer to production equipment, responsible for real-time processing of production data from each workstation, including critical information such as CAD drawings and process parameters.

[0003] In custom door and window production, each order may correspond to a unique design. CAD drawings contain detailed geometric information, such as precise dimensions, complex contours, and various detailed features. These CAD drawings are data-intensive and complex. Furthermore, custom orders often include numerous process parameters, such as cutting speeds for different materials, drilling depths, and assembly sequences.

[0004] However, compared to data centers, edge servers have relatively limited computing resources and storage space. When edge servers receive order data containing complete CAD drawings and all process parameters, lacking targeted data processing methods often places enormous pressure on edge-side data processing, leading to increased data processing latency and severely impacting production line response speed and efficiency. This problem is particularly prominent in customized production scenarios that require frequent order switching.

[0005] Therefore, how to efficiently process massive and complex customized order data in an environment with limited edge computing resources has become a key bottleneck restricting the efficiency improvement of customized door and window production lines. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-station data processing method and related equipment for a door and window customization production line, and to propose a solution to the contradiction between the complex order data structure, large data volume and limited edge server resources, so as to significantly reduce data processing delays and improve production line response speed and production efficiency.

[0007] In a first aspect, the present invention provides a multi-station data processing method for a door and window customization production line, which is applied to an edge server and includes the following steps:

[0008] S1. Analyze the customized data of doors and windows to obtain CAD drawing data and process parameter data;

[0009] S2. Analyze the processing requirements of the current workstation to determine the CAD drawing information and accuracy requirements required for the current workstation;

[0010] S3. Real-time monitoring of edge servers to determine the current resource usage of edge servers;

[0011] S4. Generate a preprocessing strategy for the current workstation based on the CAD drawing information, the accuracy requirements, and the resource occupancy status; the preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization, and whether to extract size parameters;

[0012] S5. According to the preprocessing strategy, the CAD drawing data is preprocessed to obtain preprocessed CAD drawing data;

[0013] S6. According to the processing requirements of the current station, the process parameters required for the current station are filtered and loaded from the process parameter data to obtain the optimized loaded process parameter data;

[0014] S7. Send the pre-processed CAD drawing data and the optimized loaded process parameter data to the control system of the current workstation for processing control.

[0015] The multi-station data processing method for the door and window customization production line provided by the present invention realizes adaptive simplified preprocessing of CAD drawing data under the premise of limited computing resources of the edge server, and optimizes the loading of process parameters in combination with the station requirements, thereby ensuring low-latency analysis and calculation of massive complex customized order data on the edge side, which is conducive to meeting the customized production needs of efficient operation and rapid response of the production line.

[0016] Furthermore, the specific steps in step S1 include:

[0017] S1A1 determines the format type of the CAD drawing file contained in the custom doors and windows data, and determines whether the format type is a format type supported by the edge server;

[0018] S1A2 If the format type is not a format type supported by the edge server, the CAD drawing file is converted to a format type supported by the edge server to obtain a CAD drawing file format compatible;

[0019] S1A3. After establishing an edge server data parsing rule library including a mapping relationship between multiple format types and corresponding parsing rules, select a corresponding parsing rule from the data parsing rule library according to the format type of the format-compatible CAD drawing file, and parse the format-compatible CAD drawing file according to the selected parsing rule to obtain the CAD drawing data.

[0020] Through the above steps, even if the original CAD drawing file formats are diverse, the edge server can ensure that the CAD drawing data is accurately and efficiently parsed through format compatibility processing and regular parsing, laying the foundation for data processing and processing control of subsequent workstations.

[0021] Furthermore, the specific steps in step S1A3 include:

[0022] S1A31. After establishing the edge server data parsing rule library and constructing dedicated line parsing optimizers and arc parsing optimizers for the line and arc primitive objects frequently used in the custom door and window production line, the corresponding parsing rule is selected from the data parsing rule library based on the format type of the compatible CAD drawing file. If the CAD drawing file contains line or arc primitive objects, the corresponding line parsing optimizer or arc parsing optimizer is further selected;

[0023] S1A32. According to the selected parsing rules and in combination with the straight line parsing optimizer or the arc parsing optimizer, the CAD drawing file compatible with the format is parsed; wherein, the straight line primitive object is parsed by the straight line parsing optimizer, the arc primitive object is parsed by the arc parsing optimizer, and the remaining primitive objects are parsed using the selected parsing rules to obtain the CAD drawing data.

[0024] By combining this general parsing rule with a dedicated parsing optimizer, we can fully utilize the advantages of various parsing methods and effectively improve the parsing efficiency while ensuring the parsing integrity of CAD drawing data, thus laying the foundation for subsequent data processing and processing control links.

[0025] Furthermore, the specific steps in step S1 include:

[0026] S1B1. Based on the process parameters contained in the door and window customization data, a key-value pair matching algorithm is used to extract the values ​​corresponding to all process parameters, and the extracted values ​​are stored in the database as the process parameter data according to a preset storage structure.

[0027] Furthermore, the specific steps in step S4 include:

[0028] S41. Monitor the edge server network status to determine whether there is a risk of network interruption;

[0029] S42. When it is determined that there is a risk of network interruption, evaluate whether the amount of locally cached CAD drawing data exceeds a preset threshold;

[0030] S43. If the amount of CAD drawing data exceeds the preset threshold, an emergency preprocessing strategy is generated as a preprocessing strategy for the current workstation. The emergency preprocessing strategy includes: performing maximum simplification of the graphic outline and abandoning the integrity of the graphic outline, setting the resolution after rasterization to the lowest possible level, and extracting only necessary dimensional parameters;

[0031] S44. If the amount of CAD drawing data does not exceed the preset threshold, a conventional preprocessing strategy is generated as the preprocessing strategy for the current workstation based on the CAD drawing information, the accuracy requirements and the resource occupancy status. The conventional preprocessing strategy includes: simplifying the graphic outline within a preset degree range, adjusting the resolution after rasterization within a preset resolution range, and extracting necessary and unnecessary size parameters as needed.

[0032] By following these steps, you can quickly switch to emergency response mode when the network status is unstable or there is a risk of interruption, prioritizing the stable operation of the production line and avoiding stagnation of the entire production line due to network problems. This will improve the adaptability and production efficiency of the customized door and window production line in complex network environments.

[0033] Furthermore, the specific steps in step S6 include:

[0034] S61. After establishing a station requirement parameter library including a mapping relationship between station identification and process list, according to the station identification of the current station, the process list of the current station is obtained from the station requirement parameter library; the process list includes all processes required to be completed at the current station;

[0035] S62. By parsing the process parameter data, a plurality of process parameters and parameter values ​​corresponding to each of the process parameters are obtained;

[0036] S63. According to the process list, the process parameters required for the current workstation are screened out from the multiple process parameters, and after loading the corresponding parameter values, the process parameters are packaged according to the preset data structure to obtain optimized loaded process parameter data; the data structure includes parameter identification, parameter value and data verification code.

[0037] Furthermore, the specific steps in step S63 include:

[0038] S631. For the current station, determine the priority of each of the process parameters according to the preset parameter priority strategy; the parameter priority strategy comprehensively considers the influence of the process parameters on the processing accuracy, the real-time requirements of the process parameters during the processing, and the historical frequency of use of the process parameters;

[0039] S632. According to the process list, filter out the process parameters required for the current station from the multiple process parameters, and sort the filtered process parameters according to the parameter priority strategy to form a sorted process parameter list;

[0040] S633. According to the order of the sorted process parameter list, the parameter values ​​corresponding to each process parameter are loaded in sequence, and a segmented data encapsulation method is adopted to encapsulate each process parameter of the loaded parameter value according to the preset data structure to obtain the optimized loaded process parameter data; the data structure includes parameter identification, parameter value, data check code and priority identification; wherein, the process parameters with high priority are encapsulated in the header of the data packet, and the process parameters with low priority are encapsulated in the tail of the data packet, so that the workstation control system can give priority to parsing and using the process parameters with high priority.

[0041] In a second aspect, the present invention provides a multi-station data processing device for a door and window customization production line, which is applied to an edge server and includes:

[0042] Parsing module, used to parse door and window customization data to obtain CAD drawing data and process parameter data;

[0043] Analysis module, used to analyze the processing requirements of the current workstation to determine the CAD drawing information and accuracy requirements required by the current workstation;

[0044] A monitoring module is used to monitor the edge server in real time to determine the current resource usage status of the edge server;

[0045] A generation module is used to generate a preprocessing strategy for the current workstation based on the CAD drawing information, the accuracy requirements, and the resource occupancy status; the preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization, and whether to extract size parameters;

[0046] A preprocessing module, configured to preprocess the CAD drawing data according to the preprocessing strategy to obtain preprocessed CAD drawing data;

[0047] The optimization module is used to filter and load the process parameters required for the current station from the process parameter data according to the processing requirements of the current station, and obtain the optimized loaded process parameter data;

[0048] The sending module is used to send the pre-processed CAD drawing data and the optimized loaded process parameter data to the control system of the current workstation for processing control.

[0049] The multi-station data processing device for the customized door and window production line provided by the present invention can efficiently process the multi-station data of the customized door and window production line in a resource-constrained environment, reduce data processing delays, improve data processing efficiency, and ultimately improve the production efficiency of the customized door and window production line.

[0050] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps of the multi-station data processing method for the door and window customization production line provided in the first aspect are executed.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps of the multi-station data processing method for the door and window customization production line provided in the first aspect above.

[0052] As can be seen from the above, the multi-station data processing method for customized door and window production lines provided by this invention enables edge servers to process data in a targeted manner through adaptive simplification of CAD drawing data and on-demand loading of process parameters, thereby significantly reducing the amount of data that edge servers need to process and transmit, thereby significantly reducing data processing latency and improving production line response speed. In addition, low-latency data processing and efficient resource utilization enable the customized door and window production line to quickly respond to diverse customized order requirements, achieving efficient and flexible customized production and improving the overall production efficiency of the production line.

[0053] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flowchart of a multi-station data processing method for a door and window customization production line provided by an embodiment of the present invention.

[0055] Figure 2 A structural schematic diagram of a multi-station data processing device for a door and window customization production line provided by an embodiment of the present invention.

[0056] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0057] Description of labels:

[0058] 100. Parsing module; 200. Analysis module; 300. Monitoring module; 400. Generation module; 500. Preprocessing module; 600. Optimization module; 700. Sending module; 13. Electronic device; 1301. Processor; 1302. Memory; 1303. Communication bus. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0060] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0061] Reference Attachment Figure 1 The present invention provides a multi-station data processing method for a door and window customization production line, which is applied to an edge server and includes the following steps:

[0062] S1. Analyze the customized data of doors and windows to obtain CAD drawing data and process parameter data;

[0063] S2. Analyze the processing requirements of the current workstation to determine the CAD drawing information and accuracy requirements required for the current workstation;

[0064] S3. Real-time monitoring of edge servers to determine the current resource usage of edge servers;

[0065] S4. Generate a preprocessing strategy for the current workstation based on CAD drawing information, accuracy requirements, and resource usage. The preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization, and whether to extract dimensional parameters.

[0066] S5. Preprocess the CAD drawing data according to the preprocessing strategy to obtain the preprocessed CAD drawing data;

[0067] S6. According to the processing requirements of the current station, the process parameters required for the current station are filtered and loaded from the process parameter data to obtain the optimized loaded process parameter data;

[0068] S7. Send the pre-processed CAD drawing data and the optimized loaded process parameter data to the control system of the current workstation for processing control.

[0069] In step S1, the door and window customization data is parsed to extract CAD drawing data and process parameter data. CAD drawing data can be extracted from various drawing file formats, such as DXF and DWG. The parsing process utilizes format compatibility conversion and rule library matching to ensure data accuracy and usability. Process parameter data is extracted from the order data using a key-value pair matching algorithm and stored in a database, enabling structured storage and rapid retrieval.

[0070] In step S2, the processing requirements of the current workstation are analyzed to determine the required CAD drawing information and precision requirements. This processing requirements analysis can be based on factors such as the workstation type, the object being processed, and the process flow. For example, a cutting station may require detailed contour information and dimensional parameters, while an assembly station may only require critical positioning information. Precision requirements are determined based on product quality standards and process requirements.

[0071] In step S3, the resource usage of the edge server is monitored in real time, including CPU usage, memory usage, network bandwidth, and storage space. This resource monitoring data provides a basis for the subsequent preprocessing strategy generation, ensuring that the preprocessing process does not overload the edge server.

[0072] In step S4, a preprocessing strategy is generated based on the CAD drawing information, accuracy requirements, and resource occupancy status. The preprocessing strategy is customized for the current workstation and includes the degree of graphic outline simplification, rasterization resolution, and dimension parameter extraction scheme. For example, when server resources are tight or network conditions are poor, a maximum degree of graphic simplification and low-resolution rasterization strategy can be adopted, and only necessary dimension parameters can be extracted to reduce data processing and transmission volume. When server resources are sufficient and the network is stable, a lower degree of graphic simplification and high-resolution rasterization strategy can be adopted, and dimension parameters can be extracted on demand to ensure processing accuracy and product quality.

[0073] In step S5, the CAD drawing data is preprocessed according to a preprocessing strategy. Preprocessing operations include graphic simplification, rasterization, and dimensional parameter extraction. Graphic simplification can use the Douglas-Peucker algorithm or a similar algorithm to reduce the number of points on the graphic outline and reduce data complexity. Rasterization converts vector graphics into raster images, and the resolution can be adjusted according to the preprocessing strategy. Dimension parameter extraction extracts key dimensional information from the CAD drawing data, such as length, width, and aperture.

[0074] In step S6, the process parameter data is optimized and loaded according to the processing requirements of the current workstation. The optimization loading process includes process parameter screening and data encapsulation. Process parameter screening is to screen out the parameters required for the current workstation from the process parameter database based on the process list of the current workstation. Data encapsulation is to encapsulate the screened process parameters according to a preset data structure. The data structure may include parameter identification, parameter value, data check code and priority identification, etc. Among them, the parameter identification is the unique identification corresponding to the process parameter, the parameter value is the corresponding numerical value, the data check code ensures data integrity and accuracy, and the priority identification can be determined based on factors such as the degree of influence of the parameter on the processing accuracy, real-time requirements and historical usage frequency, to ensure that key parameters are loaded and processed first.

[0075] In step S7, the preprocessed CAD drawing data and optimized, loaded process parameter data are sent to the control system of the current workstation for processing control. Data transmission can utilize industrial communication protocols such as Industrial Ethernet and PROFINET to ensure real-time and reliable data transmission. After receiving the data, the control system analyzes it and controls the workstation equipment to perform the corresponding processing operations.

[0076] Specifically, the multi-station data processing method for customized doors and windows production lines proposed in this application works in that after the edge server receives the customized doors and windows data, it first parses the CAD drawing data and process parameter data. Then, the processing requirements of the current station and the resource occupancy status of the edge server are analyzed, and a preprocessing strategy for the current station is generated based on the analysis results. The preprocessing strategy guides the preprocessing of CAD drawing data and the optimized loading of process parameter data. The preprocessed CAD drawing data and the optimized loaded process parameter data are sent to the control system of the current station for controlling the processing equipment for production. In this way, the edge server can efficiently process the multi-station data of the customized doors and windows production line in a resource-constrained environment, reduce data processing delays, improve data processing efficiency, and ultimately improve the production efficiency of the customized doors and windows production line.

[0077] In some specific embodiments, for a door and window cutting station, an edge server receives order data containing CAD drawings and process parameters for the doors and windows. The edge server first parses the CAD drawings, identifies them as DXF, and applies DXF parsing rules to extract vector data for the door and window outlines. Simultaneously, a key-value pair matching algorithm is used to extract process parameters such as cutting speed and cutting depth. The processing requirements of the cutting station are then analyzed to determine the detailed geometric information and high-precision requirements required for the door and window outlines. The edge server's CPU utilization is monitored in real time at 60% and its memory usage is 70%. Based on the processing requirements and resource status, a preprocessing strategy is generated: a medium level of outline simplification, a rasterization resolution of 200 DPI, and the extraction of all dimensional parameters. Based on this preprocessing strategy, the Douglas-Peucker algorithm is used to simplify the door and window outlines, rasterize them into a 200 DPI image, and extract dimensional parameters such as length and width. Simultaneously, the cutting speed and cutting depth parameters required for the cutting station are retrieved from the process parameter database and packaged into a data structure containing parameter identifiers, parameter values, and a data checksum. Finally, the pre-processed CAD drawing data and the optimized loaded process parameter data are sent to the control system of the cutting station via the PROFINET protocol, controlling the cutting equipment to perform cutting processing according to the optimized data.

[0078] It should be noted that the method flow of this embodiment is applicable to all workstations. In actual application, the edge server will distribute the processed CAD drawing data and process parameter data to the control system of the corresponding workstation according to the workstation identification.

[0079] In some embodiments, the specific steps in step S1 include:

[0080] S1A1 determine the format type of the CAD drawing file contained in the custom doors and windows data, and determine whether the format type is a format type supported by the edge server;

[0081] S1A2 If the format type is not a format type supported by the edge server, the CAD drawing file is converted to a format type supported by the edge server to obtain a CAD drawing file format compatible;

[0082] S1A3. After establishing an edge server data parsing rule library that includes mapping relationships between multiple format types and corresponding parsing rules, select corresponding parsing rules from the data parsing rule library according to the format type of the format-compatible CAD drawing file, and parse the format-compatible CAD drawing file according to the selected parsing rule to obtain CAD drawing data.

[0083] In step S1A1, determining the format type of the CAD drawing file can be implemented by reading the file header information of the CAD drawing file or analyzing the file extension to identify the format used by the CAD drawing file, such as DXF, DWG, or STEP. Determining whether the format type is a format type supported by the edge server can be implemented by comparing the identified format type with a list of supported formats pre-set by the edge server. If a match is found in the supported format list, the format type is determined to be supported; otherwise, the format type is determined to be unsupported.

[0084] In step S1A2, the format conversion process can be implemented using a format conversion tool or software development kit. For example, when it is detected that the CAD drawing file is in an unsupported DXF format, the system can automatically call the format conversion module to convert the DXF format file into a DWG format file supported by the edge server.

[0085] In step S1A3, a data parsing rule library can be constructed in the form of a database or configuration file. The rule library stores a variety of CAD drawing format types and corresponding parsing rules. The parsing rules define how to extract CAD drawing data such as metadata and attribute information from CAD drawing files of a specific format. Selecting a parsing rule can be implemented by searching the data parsing rule library for the parsing rule corresponding to the CAD drawing file format type determined in step S1A1. Parsing a format-compatible CAD drawing file can be implemented by calling a parser that matches the selected parsing rule. The parser extracts CAD drawing data for use in subsequent steps from the format-compatible CAD drawing file according to the parsing logic preset in the rule library.

[0086] Specifically, for the door and window customization data parsing process, step S1A1 is first executed to determine the format of the received CAD drawing file. For example, if the extension of the received order CAD drawing file is ".dxf", it is determined that the file format type is DXF format. The system then determines whether the DXF format is in the list of formats supported by the edge server. Assume that the edge server natively supports the DWG format but does not directly support the DXF format. At this time, step S1A2 is executed, and the system automatically calls the format conversion tool to convert the CAD drawing file in DXF format into a CAD drawing file in DWG format to obtain a format-compatible CAD drawing file. Then, step S1A3 is executed, and the edge server searches for the parsing rule corresponding to the DWG format in the data parsing rule library and selects the parsing rule. The edge server parses the converted DWG format CAD drawing file according to the selected DWG parsing rule, extracts the geometric information and attribute data of graphic objects such as straight lines, arcs, and dimensioning, and finally obtains CAD drawing data that can be used by subsequent processes. Through the above steps, even if the original CAD drawing file formats are diverse, the edge server can ensure that the CAD drawing data is accurately and efficiently parsed through format compatibility processing and regular parsing, laying the foundation for data processing and processing control of subsequent workstations.

[0087] In some specific embodiments, the data parsing rule library can be pre-configured in the edge server, and the rule library contains parsing rules for various commonly used CAD drawing formats such as DWG, DXF, and STEP. When step S1A1 determines that the CAD drawing file is in IGES format, and the edge server only supports DWG and DXF formats, step S1A2 converts the CAD drawing file in IGES format into the DWG format supported by the edge server using the file format conversion interface. Step S1A3 selects the corresponding DWG parsing rules from the data parsing rule library according to the DWG format, and uses the rules to parse the DWG file, thereby obtaining standardized CAD drawing data for subsequent preprocessing and processing control. By judging the format type, not supporting format conversion, and parsing the rule library, it is ensured that the edge server can handle various CAD drawing formats, thereby improving system compatibility and data processing efficiency.

[0088] In some embodiments, the specific steps in step S1A3 include:

[0089] S1A31. After establishing a data parsing rule library for the edge server and building dedicated line and arc parsing optimizers for the line and arc primitive objects frequently used in the custom door and window production line, the corresponding parsing rule is selected from the data parsing rule library based on the format type of compatible CAD drawing files. If the CAD drawing file contains line or arc primitive objects, the corresponding line parsing optimizer or arc parsing optimizer is further selected.

[0090] S1A32. According to the selected parsing rules and in combination with the line parsing optimizer or the arc parsing optimizer, the format-compatible CAD drawing file is parsed; wherein, the line primitive objects are parsed using the line parsing optimizer, and the arc primitive objects are parsed using the arc parsing optimizer, and the remaining primitive objects are parsed using the selected parsing rules to obtain CAD drawing data.

[0091] In this embodiment, a data parsing rule library is pre-established, and the library stores a mapping relationship between multiple format types and corresponding parsing rules for format identification and parsing rule selection of CAD drawing files. For the straight line and circular arc primitive objects that frequently appear in the CAD drawing files in the door and window customization production line, exclusive straight line parsing optimizers and circular arc parsing optimizers are constructed respectively. When parsing CAD drawing data, first, according to the format type of the format-compatible CAD drawing file, the corresponding general parsing rule is selected from the data parsing rule library. Then, the system will detect whether the CAD drawing file contains straight line or circular arc primitive objects. If so, the corresponding straight line parsing optimizer or circular arc parsing optimizer is further selected. During the parsing process, straight line primitive objects are parsed by the straight line parsing optimizer, circular arc primitive objects are parsed by the circular arc parsing optimizer, and other types of primitive objects are parsed using the previously selected general parsing rules. As a result, the parsing efficiency of straight line and circular arc primitive objects is improved, and the overall parsing efficiency of CAD drawing data is also improved.

[0092] Specifically, after receiving a CAD drawing file with a compatible format, the edge server first identifies the file format and searches the data parsing rule library for a universal parsing rule that matches that format. For example, if the CAD drawing file is in DXF format, the universal parsing rule corresponding to the DXF format is selected. Furthermore, the parser scans the CAD drawing file content and identifies the types of primitive objects contained in the file. When a line primitive object is detected, the system invokes the line parsing optimizer, which is designed to process line data with a more efficient algorithm. For example, it uses a pre-set line parameter extraction template to quickly extract key information such as the line's start and end coordinates. Similarly, when an arc primitive object is detected, the system invokes the arc parsing optimizer, which optimizes the arc's geometric characteristics. For example, it uses parametric representations such as the arc's center, radius, start angle, and end angle to achieve rapid parsing. For other primitive objects in the CAD drawing file that are not lines or arcs, such as splines and polygons, the system still uses the previously selected universal parsing rule for processing. By combining this general parsing rule with a dedicated parsing optimizer, we can fully utilize the advantages of various parsing methods and effectively improve the parsing efficiency while ensuring the parsing integrity of CAD drawing data, thus laying the foundation for subsequent data processing and processing control links.

[0093] In some specific embodiments, the edge server's data parsing rule library can utilize a key-value pair storage structure, where the key represents the format type of the CAD drawing file, such as "DXF," "DWG," or "IGES," and the value represents an index or pointer to the corresponding universal parsing rule. The line parsing optimizer and arc parsing optimizer can be independent software modules or function libraries, each containing parsing algorithms optimized for line and arc primitive objects. For example, the line parsing optimizer can utilize a vectorization algorithm to directly extract line vector information from the CAD drawing data, avoiding complex geometric calculations. The arc parsing optimizer can utilize a variant of the Bresenham algorithm or the midpoint circle algorithm to quickly generate the coordinates of discrete points in the arc. When parsing a CAD drawing file, the data parsing rule library is first queried to obtain universal parsing rules. Then, the primitive objects in the CAD drawing file are sequentially read. For each primitive object, its type is determined. If it is a line, the line parsing optimizer is invoked for parsing; if it is an arc, the arc parsing optimizer is invoked for parsing; if it is any other type, the universal parsing rules are invoked for parsing. The parsed results are integrated into a unified CAD drawing data format, such as JSON or XML, for use in subsequent processes. This enables efficient and accurate data parsing of CAD drawing files commonly used in custom door and window production lines.

[0094] In some embodiments, the specific steps in step S1 include:

[0095] S1B1. Based on the process parameters contained in the door and window customization data, a key-value pair matching algorithm is used to extract the values ​​corresponding to all process parameters, and the extracted values ​​are stored in the database as process parameter data according to a preset storage structure.

[0096] To address the inefficiency of process parameter data parsing, this solution takes specific steps. First, the process parameters contained in the door and window customization data are analyzed. Then, a key-value pair matching algorithm is used to extract the corresponding values ​​for all process parameters. After extraction, these values ​​are stored in a database according to a pre-defined storage structure, and the data in the database is used as the process parameter data.

[0097] A key-value pair matching algorithm is used to extract numerical values, effectively avoiding global scanning and complex parsing of process parameter data, and only extracting numerical information, thereby improving data extraction efficiency and accuracy.

[0098] The extracted values ​​are stored in the database in a preset storage structure, realizing structured storage of process parameter data, facilitating rapid retrieval and calling of process parameter data in subsequent steps, thereby improving data processing efficiency.

[0099] Specifically, after receiving the custom door and window data, the system first performs data parsing. Regarding process parameter parsing, the system does not directly parse all process parameter information using traditional methods. Instead, it first identifies the process parameter fields within the custom door and window data. For example, the custom door and window data may contain process parameter descriptions such as "cutting speed: 3000mm / min" or "drilling diameter: 5mm."

[0100] After identifying these process parameter fields, a key-value pair matching algorithm is used to extract the parameter values. For example, in the example above, "cutting speed" is the key, "3000 mm / min" is the value, and "drill diameter" is the key, "5 mm" is the value. The algorithm focuses on extracting the numerical value after the ":" and ignores non-numeric information such as units, thereby achieving accurate numerical extraction.

[0101] The extracted values ​​are then stored in the database according to a preset storage structure. The preset storage structure can be an optimized data table structure. For example, a data table called "Process Parameter Table" can be created, which contains two fields: "Parameter Name" and "Parameter Value." Parameter names such as "Cutting Speed" and "Drill Diameter" are stored in the "Parameter Name" field, and the extracted values ​​such as "3000" and "5" are stored in the corresponding "Parameter Value" field. Through this structured storage, the process parameter data is organized, facilitating subsequent data query and call.

[0102] Through the application of the above key-value pair matching algorithm and the preset storage structure, the parsing and storage efficiency of process parameter data have been effectively improved, laying a foundation for subsequent data processing of multi-station collaborative machining.

[0103] In some specific embodiments, the customized window and door data are received in the form of XML format files. In the XML file, the process parameter information is organized under specific tags, for example:

[0104] <Process Parameters>

[0105] <Cutting Speed>3000mm / min< / Cutting Speed>

[0106] <Drilling Diameter>5mm< / Drilling Diameter>

[0107] <Material>Aluminum Alloy< / Material>

[0108] < / Process Parameters>

[0109] The parsing module first identifies the "<Process Parameters>" tag in the XML file to determine the process parameter data area. Then, the key-value pair matching algorithm is applied to parse each sub-tag in this area. For the tag "<Cutting Speed>3000mm / min< / Cutting Speed>", the algorithm extracts "Cutting Speed" as the key and "3000" as the value (the unit "mm / min" can be ignored or processed separately). For the tag "<Drilling Diameter>5mm< / Drilling Diameter>", the algorithm extracts "Drilling Diameter" as the key and "5" as the value.

[0110] The extracted key-value pairs are then stored in a pre-set database table. For example, in a MySQL database, a "Process Parameter Table" is created, which contains three fields: "Parameter ID" (auto-incrementing primary key), "Parameter Name" (VARCHAR type), and "Parameter Value" (VARCHAR type). The parsed key-value pairs are inserted into this table one by one to achieve the structured storage and management of process parameter data. Thus, subsequent workstations can quickly retrieve and call the required process parameters from the database, improving data processing efficiency.

[0111] In certain embodiments, the specific steps in step S4 include:

[0112] S41. Monitor the network status of the edge server and determine whether there is a risk of network interruption;

[0113] S42. When it is determined that there is a risk of network interruption, evaluate whether the amount of CAD drawing data cached locally exceeds a preset threshold; <S43. If the amount of CAD drawing data exceeds a preset threshold, an emergency preprocessing strategy is generated as a preprocessing strategy for the current workstation. The emergency preprocessing strategy includes: performing maximum simplification of the graphic outline and abandoning the integrity of the graphic outline, setting the resolution after rasterization to the lowest possible level, and extracting only necessary dimensional parameters;

[0115] S44. If the amount of CAD drawing data does not exceed the preset threshold, a conventional preprocessing strategy is generated as the preprocessing strategy for the current workstation based on the CAD drawing information, accuracy requirements and resource occupancy status. The conventional preprocessing strategy includes: simplifying the graphic outline within a preset degree range, adjusting the resolution after rasterization within a preset resolution range, and extracting necessary and unnecessary size parameters as needed.

[0116] In step S41, network status monitoring can be achieved by periodically sending detection packets to specific nodes in the network via the edge server and analyzing the response of the detection packets. For example, if no response is received within a preset time or the response time exceeds a threshold, it is determined that there is a risk of network interruption.

[0117] In step S42, the amount of locally cached CAD drawing data can be evaluated by real-time detection of the storage space occupancy rate for caching CAD drawing data in the edge server, and the occupancy rate is compared with a preset threshold to determine whether the data amount exceeds the threshold.

[0118] In step S43, the emergency preprocessing strategy sets the maximum level of graphic outline simplification. For example, a complex curved outline can be simplified to an outline approximated by a small number of straight line segments. Abandoning the integrity of the graphic outline means that in an emergency, some graphic detail information may be sacrificed in order to maximize data compression. The resolution after rasterization is set to the lowest possible level; for example, the resolution can be set to 72 DPI. Extracting only necessary dimensional parameters means extracting only the dimensional information necessary for the current processing station. For example, a cutting station only needs to extract the cutting dimensions, without extracting the location dimensions of the mounting holes.

[0119] In step S44, the degree of graphic outline simplification of the conventional preprocessing strategy is within a preset range. For example, a moderate degree of outline simplification can be selected based on the accuracy requirements of the current workstation and the resource usage status of the edge server. After rasterization, the resolution is adjusted within a preset resolution range. For example, the resolution can be adjusted within the range of 150DPI to 300DPI based on the accuracy requirements. On-demand extraction of necessary and non-essential size parameters means selectively extracting non-essential size parameters while ensuring that necessary size parameters are extracted based on the needs of the current workstation and the resource status of the edge server to provide more comprehensive graphic information.

[0120] Specifically, in the multi-station data processing method for the customized door and window production line, step S41 is first executed, and the edge server monitors the network status in real time to determine whether there is a risk of network interruption. In this way, changes in the network environment can be predicted in advance. When step S42 determines that there is a risk of network interruption, it further evaluates whether the amount of CAD drawing data cached locally exceeds the preset threshold to evaluate the degree of data backlog. If step S43 determines that the amount of CAD drawing data exceeds the preset threshold, an emergency preprocessing strategy is generated. The emergency preprocessing strategy achieves rapid compression of the data volume by simplifying the graphic outline to the greatest extent, reducing the rasterization resolution, and extracting only the necessary size parameters, thereby avoiding data backlog and processing delays and ensuring basic production operations. On the contrary, if step S44 determines that the amount of CAD drawing data does not exceed the preset threshold, a conventional preprocessing strategy is adopted. The conventional preprocessing strategy simplifies the graphic outline within a preset range, adjusts the rasterization resolution, and extracts size parameters as needed, thereby taking into account data processing efficiency while ensuring data integrity and processing accuracy. By following these steps, you can quickly switch to emergency response mode when the network status is unstable or there is a risk of interruption, prioritizing the stable operation of the production line and avoiding stagnation of the entire production line due to network problems. This will improve the adaptability and production efficiency of the customized door and window production line in complex network environments.

[0121] In some specific embodiments, the edge server of the door and window customization production line is configured to detect the network status every 1 second. The network status detection method is: the edge server sends an ICMP EchoRequest message to the preset central server IP address and waits for a response. If the ICMP Echo Reply message from the central server is not received within 2 seconds, or no response is received after three consecutive detections, it is determined that the current network is at risk of interruption. The preset CAD drawing data volume threshold is set to 500MB. When it is determined that there is a risk of network interruption and the amount of locally cached CAD drawing data exceeds 500MB, the edge server generates an emergency preprocessing strategy. The emergency preprocessing strategy sets the degree of simplification of the graphic outline of the CAD drawing to the maximum. The specific simplification algorithm is to approximate all arcs and spline curve primitives in the CAD drawing with straight line segments, and the approximation error does not exceed 0.1mm; the rasterization resolution is set to the lowest, that is, 72DPI; the size parameters only extract the door and window frame size, profile cutting size, etc. as the necessary size parameters for the current work station. When it is determined that the network status is good or the amount of locally cached CAD drawing data does not exceed 500MB, the edge server generates a conventional preprocessing strategy. The conventional preprocessing strategy sets the degree of simplification of the graphic outline of the CAD drawing to medium. The specific simplification algorithm is to reduce the number of control points while ensuring the curvature accuracy of arcs and spline curves; the rasterization resolution is adaptively adjusted within the range of 150DPI to 300DPI according to the accuracy requirements of the current workstation; the dimensional parameters are extracted on demand. In addition to the necessary dimensional parameters, non-essential dimensional parameters such as the mounting hole position dimensions and hardware installation dimensions are also extracted.

[0122] In some embodiments, the specific steps in step S6 include:

[0123] S61. After establishing a station requirement parameter library including a mapping relationship between the station identification and the process list, according to the station identification of the current station, the process list of the current station is obtained from the station requirement parameter library; the process list includes all processes required to be completed at the current station;

[0124] S62. By parsing the process parameter data, multiple process parameters and parameter values ​​corresponding to each process parameter are obtained;

[0125] S63. According to the process list, the process parameters required for the current workstation are screened out from multiple process parameters, and after loading the corresponding parameter values, the process parameters are packaged according to the preset data structure to obtain optimized loaded process parameter data; the data structure includes parameter identification, parameter value and data verification code.

[0126] For step S61, the establishment of the workstation requirement parameter library is to realize the mapping between the workstation identification and the process list. As a possible implementation method, the workstation requirement parameter library is constructed as a data table or a key-value pair storage structure, in which the workstation identification serves as an index or key, and the process list corresponding to the workstation serves as a data value. The process list lists in detail all the process steps that need to be performed at each workstation, such as cutting, drilling, milling, etc. In actual applications, the workstation identification can be the name, number or other unique mark of the workstation. The process list can be pre-configured and maintained as the production line is adjusted or the process is updated. Therefore, by querying the workstation requirement parameter library, the system can quickly determine the range of process parameters that need to be loaded at the current workstation, laying the foundation for subsequent process parameter screening.

[0127] Specifically, in the data processing flow of the custom door and window production line, step S61 first establishes a workstation requirement parameter library within the edge server, pre-storing the correspondence between each workstation and the required processes. When order data arrives at the edge server and needs to be delivered to a specific workstation, step S61 searches the workstation requirement parameter library based on the current workstation's station identifier, such as "cutting station" or "assembly station," and retrieves a list of processes associated with that station identifier. For example, for a "cutting station," the retrieved process list might include processes such as "contouring" and "chamfering." Next, step S62 parses the received process parameter data, extracting the names and values ​​of all process parameters from the complex process parameter data. Then, step S63 uses the process list obtained in step S61 as a filtering criterion and filters through the multiple process parameters parsed in step S62, retaining only the process parameters required for the current workstation as explicitly specified in the process list and loading the parameter values ​​of these filtered process parameters. Finally, to ensure the accuracy and integrity of data transmission, step S63 also encapsulates the filtered and loaded process parameters according to a preset data structure. The encapsulated data structure contains parameter identifiers, parameter values, and data verification codes for subsequent data parsing and verification by the workstation control system. Thus, through the above steps, on-demand loading of process parameters is achieved, avoiding the edge server from loading and processing process parameters unrelated to the current workstation, reducing the data processing burden and improving data processing efficiency.

[0128] In some specific embodiments, for a cutting station for door and window profiles, the process list stored in the station requirement parameter library may include process parameters such as "cutting speed," "cutting depth," and "tool compensation." When order data arrives at the edge server corresponding to the cutting station, step S61 is first executed. Based on the station identifier of the "cutting station," a process list including "cutting speed," "cutting depth," and "tool compensation" is retrieved from the station requirement parameter library. Next, step S62 is executed to parse all process parameters contained in the order data. For example, these parameters may include "cutting speed = 2000 mm / min," "cutting depth = 10 mm," "tool compensation = 0.5 mm," and "drilling diameter = 5 mm," along with their values. Next, step S63 is executed. Based on the process list obtained in step S61, the three process parameters required for the current station, "cutting speed," "cutting depth," and "tool compensation," are filtered from the process parameters parsed in step S62 and their corresponding values, namely, "2000 mm / min," "10 mm," and "0.5 mm," are loaded. Finally, the filtered and loaded process parameters are packaged according to a preset data structure, such as JSON or XML format. The packaged data packet may be represented as {"parameter identifier":"cutting speed","parameter value":"2000mm / min","data check code":"CRC32 check code 1"}, {"parameter identifier":"cutting depth","parameter value":"10mm","data check code":"CRC32 check code 2"}, {"parameter identifier":"tool compensation","parameter value":"0.5mm","data check code":"CRC32 check code 3"}. The packaged process parameter data is then sent to the cutting station's control system to guide precise processing operations.

[0129] In some embodiments, the specific steps in step S63 include:

[0130] S631. For the current station, determine the priority of each process parameter according to the preset parameter priority strategy; the parameter priority strategy comprehensively considers the influence of the process parameters on the processing accuracy, the real-time requirements of the process parameters during the processing, and the historical frequency of use of the process parameters;

[0131] S632. According to the process list, the process parameters required for the current station are screened from multiple process parameters, and the screened process parameters are sorted according to the parameter priority strategy to form a sorted process parameter list;

[0132] S633. According to the order of the sorted process parameter list, the parameter values ​​corresponding to each process parameter are loaded in sequence, and a segmented data encapsulation method is adopted to encapsulate each process parameter of the loaded parameter value according to the preset data structure to obtain the optimized loaded process parameter data; the data structure includes parameter identification, parameter value, data check code and priority identification; among them, the process parameters with high priority are encapsulated in the header of the data packet, and the process parameters with low priority are encapsulated in the tail of the data packet, so that the work station control system can give priority to parsing and using the process parameters with high priority.

[0133] In this embodiment, the parameter priority strategy can be established using a pre-defined set of rules. The rules can be formulated by comprehensively considering a variety of factors. For example, parameters that significantly impact the processing accuracy of doors and windows, such as cutting dimensional accuracy and angular accuracy, can be assigned a higher priority. Parameters that require real-time adjustment during processing, such as cutting speed and feed rate, and that have high real-time requirements, should also be assigned a higher priority. Furthermore, the historical usage frequency of process parameters can be analyzed; frequently used parameters can be considered more important and given a relatively higher priority. By comprehensively considering these factors, a multi-dimensional, adaptive parameter priority strategy can be established. After determining the priority of each process parameter, the system filters the process parameters required for the current workstation based on the process list. These filtered process parameters are then sorted according to the previously determined parameter priority strategy. The sorting results in a process parameter list, where the order of the parameters in the list represents their priority. During the data packaging phase, the system loads the parameter values ​​for each process parameter in the order listed in the process parameter list. For each loaded process parameter, the system uses a segmented data packaging method, packaging the data according to a pre-defined data structure. In the data structure, the parameter identifier uniquely identifies the parameter, the parameter value is the specific numerical value of the parameter, the data checksum is used to ensure the accuracy of data transmission, and the priority identifier is used to indicate the priority of the parameter. During the packaging process, high-priority process parameters are placed at the head of the data packet, while low-priority parameters are placed at the end of the packet. This packaging method ensures that the workstation control system can prioritize the parsing and use of high-priority process parameters after receiving the data packet, thus ensuring the priority processing of critical process parameters in the event of network instability or resource constraints.

[0134] Specifically, for multi-station data processing in a custom door and window production line, this application proposes a parameter priority processing method to cope with unstable networks or limited edge server resources in an edge computing environment. First, a parameter priority strategy is established. This strategy comprehensively considers the impact of process parameters on machining accuracy, the real-time requirements of process parameters during machining, and the historical frequency of process parameters usage. For example, cutting size parameters directly affect the final dimensional accuracy of doors and windows and are therefore assigned a high priority. Cutting speed parameters require real-time adjustment based on material and tool conditions during machining, and therefore have a high real-time requirement and are also assigned a higher priority. Auxiliary parameters, such as coolant flow rate, have a relatively small impact on machining accuracy and a lower real-time requirement, and can be assigned a lower priority. Second, based on the machining requirements of the current station, the process parameters required for the current station are filtered from the process parameter data. These filtered process parameters are then sorted according to the established parameter priority strategy to form a sorted process parameter list. In the sorted list, high-priority parameters are placed at the front of the list. Parameter values ​​are then loaded in the order of the sorted process parameter list, and encapsulated using a segmented data encapsulation method. After encapsulation, the data for high-priority parameters is encapsulated in the header of the packet, and the data for low-priority parameters is encapsulated in the tail of the packet. Finally, the encapsulated data packet is sent to the control system of the current workstation. Upon receiving the data packet, the workstation control system prioritizes the high-priority parameters in the header, ensuring that critical process parameters are processed and executed first. This ensures timely and reliable transmission of critical process parameters, even under poor network conditions or limited edge server resources, thereby improving production efficiency and product quality on the custom door and window production line.

[0135] In some specific embodiments, taking a door and window cutting station as an example, the control system needs to receive multiple process parameters for precise cutting. These process parameters include cutting size, cutting angle, cutting speed, tool offset, and coolant flow rate. According to the parameter prioritization strategy, cutting size and cutting angle are assigned high priority because they directly affect the dimensional and angular accuracy of doors and windows. Cutting speed and tool offset are assigned medium priority because cutting speed affects processing efficiency, while tool offset affects processing accuracy, but is less critical than size and angle. Coolant flow rate is assigned low priority because it has a relatively small impact on cutting accuracy. During data encapsulation, the cutting size and cutting angle parameters are encapsulated in the packet header, followed by the cutting speed and tool offset parameters, and the coolant flow rate parameter data is encapsulated in the packet trailer. When network fluctuations occur or edge server load is high, the station control system can prioritize parsing and processing the cutting size and cutting angle parameters in the packet header, ensuring that the cutting station receives the most critical processing parameters first and maintaining basic cutting accuracy. Even if there is a delay in the subsequent transmission of cutting speed and coolant flow parameter data, the cutting station can still perform preliminary cutting operations based on the high-priority parameters that have been received, minimizing production stoppages caused by data transmission issues and improving the overall efficiency of the production line.

[0136] In some embodiments, the specific steps in step S631 include:

[0137] S6311 monitors the real-time vibration data of the current station processing process. If the real-time vibration data exceeds the preset threshold, it is determined that the current station is in an unstable state and generates an unstable state signal;

[0138] S6312. In response to the unstable state signal, the parameter priority strategy is dynamically adjusted to increase the priority of the process parameters that have a greater impact on the processing accuracy and reduce the priority of the process parameters that have a smaller impact on the processing accuracy, so as to ensure that when the workstation is in an unstable state, the subsequent steps can prioritize the transmission and processing of the process parameters that have a greater impact on the processing accuracy;

[0139] S6313. Re-determine the priority of each process parameter based on the adjusted parameter priority strategy.

[0140] To monitor real-time vibration data from the machining process at the current workstation, highly sensitive vibration sensors can be installed at key locations, such as near the machining tool or in the workpiece clamping device. These sensors collect real-time vibration data generated by the workstation during machining. This collected vibration data is transmitted to a data processing module, which analyzes it, calculating characteristic parameters such as vibration amplitude and frequency.

[0141] To determine if the current workstation is unstable and generate an unstable state signal, this can be implemented by presetting a vibration threshold. This threshold can be determined based on the door and window processing precision requirements and the performance parameters of the workstation equipment. The data processing module compares the real-time vibration data with the preset threshold. When the real-time vibration data exceeds the preset threshold, the current workstation is determined to be unstable. At this point, the system automatically generates an unstable state signal and sends it to the parameter priority strategy adjustment module.

[0142] For the dynamic adjustment of parameter priority strategy, the priority of process parameters that have a greater impact on machining accuracy can be increased, and the priority of process parameters that have a smaller impact on machining accuracy can be reduced. This can be achieved by querying the preset parameter priority rule library after the parameter priority strategy adjustment module receives the unstable state signal. The parameter priority rule library stores the priority configuration information of different process parameters, as well as the priority adjustment strategy under the unstable state of the workstation. According to the priority adjustment strategy, the parameter priority strategy adjustment module dynamically adjusts the priority of the process parameters. For example, for process parameters that have a greater impact on machining accuracy, such as cutting depth and contour accuracy, their priority is increased; while for process parameters that have a relatively smaller impact on machining accuracy, such as machining speed and coolant flow, their priority is reduced.

[0143] Re-prioritizing individual process parameters based on the adjusted parameter priority strategy can be achieved by sending the adjusted parameter priority strategy to the process parameter loading module after the parameter priority strategy adjustment module completes the parameter priority adjustment. The process parameter loading module then sorts and packages the process parameters to be loaded subsequently based on the new parameter priority strategy. High-priority process parameters are loaded and processed first, ensuring that machining accuracy is effectively maintained even when the workstation is unstable.

[0144] Specifically, during the multi-station collaborative processing of custom doors and windows on a production line, the edge server needs to send CAD drawing data and process parameter data to the station control system as each station performs its processing task. When a station, such as the cutting station, experiences abnormal vibration during processing due to external interference or the operating status of its own equipment, a vibration sensor installed at that station immediately detects the change and uploads the vibration data to the edge server in real time. Upon receiving the vibration data, the edge server's data processing module rapidly analyzes the vibration amplitude and compares it with a preset vibration threshold. If the vibration amplitude exceeds the threshold, the cutting station is determined to be unstable and generates an unstable state signal. Upon receiving the unstable state signal, the parameter priority strategy adjustment module immediately activates a pre-set emergency priority adjustment strategy. This strategy predetermines the priority adjustment scheme for different process parameters in the event of an unstable state. For example, parameters that directly affect the dimensional and contour accuracy of door and window products, such as cutting depth and cutting path accuracy, are assigned a high priority; while parameters with a relatively small impact on accuracy, such as cutting speed and cooling method, are assigned a low priority. Based on this strategy, the parameter priority strategy adjustment module dynamically increases the priority of parameters such as cutting depth and cutting path accuracy, while decreasing the priority of parameters such as cutting speed and cooling method. The adjusted parameter priority strategy is then sent to the process parameter loading module. During subsequent data loading, the process parameter loading module prioritizes the loading and transmission of high-priority parameters such as cutting depth and cutting path accuracy. This ensures that even when the cutting station is unstable, it can still prioritize the acquisition and execution of process parameters that are critical to machining accuracy. This minimizes the impact of vibration on cutting accuracy and ensures the machining quality of door and window products.

[0145] In some specific embodiments, the vibration sensor can be a piezoelectric acceleration sensor, the preset vibration threshold can be set to 0.5g, and the unstable state signal can be represented by a binary digital signal "1". The parameter priority rule base can be stored in the form of a database or a configuration file, and the parameter priority adjustment strategy can be implemented using a weight adjustment algorithm. For example, the priority weight of the cutting depth parameter is adjusted from 0.8 to 0.95, and the priority weight of the cutting speed parameter is adjusted from 0.6 to 0.4. The process parameter loading module can use a message queue to encapsulate the process parameter data into a message and send it to the workstation control system in accordance with the adjusted parameter priority order. In this way, when an unstable state occurs in the workstation processing process, the system can adaptively adjust the process parameter priority, give priority to the process parameters that have a great impact on the processing accuracy, and ensure the processing accuracy and efficiency of the customized door and window production line in a complex production environment.

[0146] In some embodiments, the specific steps in step S6312 include:

[0147] S63121. In response to the unstable state signal, the cumulative working time of the tool is evaluated, and if the cumulative working time exceeds a preset tool replacement threshold, a tool wear signal is generated;

[0148] S63122. In response to the tool wear signal, dynamically adjust the parameter priority strategy to increase the priority of process parameters for tool compensation value and tool wear amount, and reduce the priority of process parameters with less impact on machining accuracy. This ensures that when the workstation is unstable and the tool is worn, the subsequent steps can prioritize the transmission and processing of tool compensation parameters, thereby adjusting the machining trajectory according to the tool compensation parameters.

[0149] S63123. If the accumulated working time does not exceed the preset tool replacement threshold, the priority of the process parameters that have a greater impact on the machining accuracy is increased, and the priority of the process parameters that have a smaller impact on the machining accuracy is reduced by dynamically adjusting the parameter priority strategy, so as to ensure that when the workstation is unstable, the subsequent steps can give priority to transmitting and processing the process parameters that have a greater impact on the machining accuracy.

[0150] In step S63121, in response to the unstable state signal, an evaluation step is added to evaluate the tool's cumulative operating time. The tool's cumulative operating time is compared with a preset tool replacement threshold to determine whether the tool is worn. The tool replacement threshold is a pre-set indicator of tool life, determined based on factors such as tool material, processing material, and processing conditions. If the cumulative operating time exceeds the tool replacement threshold, the tool is considered likely worn, and a tool wear signal is generated.

[0151] In step S63122, in response to the tool wear signal, the parameter priority strategy is adjusted. Unlike prioritizing only process parameters that have a significant impact on machining accuracy, in the case of tool wear, the priority of process parameters such as tool compensation and tool wear is specifically increased. Tool compensation and tool wear are parameters directly related to the tool state. Prioritizing these parameters allows the control system to promptly adjust the machining trajectory based on tool wear, thereby compensating for machining errors caused by tool wear and ensuring machining accuracy. At the same time, the priority of process parameters that have a minimal impact on machining accuracy is lowered, thereby optimizing data processing and transmission efficiency.

[0152] In step S63123, if the tool is not worn (i.e., the accumulated working time does not exceed the tool replacement threshold), the existing strategy is used: the priority of process parameters with the greatest impact on machining accuracy is increased, while the priority of process parameters with less impact is decreased. This ensures that even if the workstation is unstable but the tool is in good condition, the system can still prioritize important process parameters and maintain machining accuracy.

[0153] Specifically, a multi-station data processing method for a custom door and window production line is applied to an edge server. First, an unstable state signal is determined and generated based on whether the real-time vibration data from the workstation's machining process exceeds a preset threshold. When the real-time vibration data exceeds the preset threshold, the workstation is deemed unstable, and an unstable state signal is generated. Then, in response to the unstable state signal, the edge server evaluates the cumulative operating time of the tool used at the current workstation. This cumulative operating time can be obtained from the tool management system or machining log. The preset tool replacement threshold can be, for example, a tool life parameter provided by the tool manufacturer or a value set based on actual production experience, such as 100 hours. If the estimated cumulative operating time exceeds 100 hours, a tool wear signal is generated, indicating that the tool may need replacement or compensation. Next, in response to the tool wear signal, the edge server dynamically adjusts the parameter priority strategy. This adjustment strategy prioritizes process parameters directly related to tool status, such as tool compensation value and tool wear, higher, while parameters with a relatively small impact on machining accuracy, such as machining speed and coolant flow rate, lower. Parameter priority adjustment can be achieved by modifying the parameter priority list or weight coefficients. For example, tool compensation and tool wear can be prioritized, while machining speed and coolant flow can be prioritized. This prioritizes key parameters such as tool compensation and tool wear during subsequent data processing and transmission, ensuring that the control system can promptly acquire tool status information and adjust the machining trajectory based on tool wear, performing tool compensation to maintain machining accuracy. If the estimated cumulative working time does not exceed the tool change threshold, no tool wear signal is generated. In this case, even if the workstation is unstable, the parameter priority strategy is adjusted only to consider the instability factor. Process parameters with a significant impact on machining accuracy, such as cutting depth and profile dimensions, are prioritized, while process parameters with a lesser impact, such as auxiliary action parameters, are prioritized. This ensures that machining accuracy can be maintained even when the workstation is unstable but the tool is in good condition. Through this refined parameter priority adjustment strategy, key process parameters can be transmitted and processed promptly even when the workstation is unstable and tool wear is likely to occur, effectively improving machining accuracy and efficiency on the custom door and window production line.

[0154] In some specific embodiments, the tool replacement threshold can be set according to different tool materials and processing materials. For example, for carbide tools for processing aluminum alloy doors and windows, the tool replacement threshold can be set to 120 hours; for high-speed steel tools for processing PVC doors and windows, the tool replacement threshold can be set to 80 hours. Furthermore, after the tool wear signal is generated, the system can automatically send a tool replacement warning to the tool management system to remind the operator to replace the tool in time to avoid excessive tool wear leading to a decline in processing quality or damage to the equipment. As a preferred embodiment, the dynamic adjustment of the parameter priority strategy can be implemented in combination with a machine learning algorithm. By collecting and analyzing historical processing data, such as workstation vibration data, cumulative tool working time, processing accuracy data, etc., a machine learning model is trained to predict the degree of influence of each process parameter on the processing accuracy under different working conditions. As a result, the parameter priority strategy can be adaptively adjusted according to the prediction results of the machine learning model, and the intelligence and accuracy of the parameter priority adjustment can be improved.

[0155] Please refer to Figure 2 , Figure 2 In some embodiments of the present invention, a multi-station data processing device for a customized door and window production line is applied to an edge server. The multi-station data processing device for a customized door and window production line is integrated into a back-end control device in the form of a computer program, and includes:

[0156] The parsing module 100 is used to parse the door and window customization data to obtain CAD drawing data and process parameter data;

[0157] Analysis module 200, used to analyze the processing requirements of the current workstation to determine the CAD drawing information and accuracy requirements required by the current workstation;

[0158] The monitoring module 300 is used to monitor the edge server in real time to determine the current resource usage status of the edge server;

[0159] The generation module 400 is used to generate a pre-processing strategy for the current workstation based on the CAD drawing information, accuracy requirements, and resource usage status; the pre-processing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization, and whether to extract size parameters;

[0160] The preprocessing module 500 is used to preprocess the CAD drawing data according to the preprocessing strategy to obtain preprocessed CAD drawing data;

[0161] The optimization module 600 is used to filter and load the process parameters required for the current workstation from the process parameter data according to the processing requirements of the current workstation, and obtain the optimized loaded process parameter data;

[0162] The sending module 700 is used to send the pre-processed CAD drawing data and the optimized loaded process parameter data to the control system of the current workstation for processing control.

[0163] In some embodiments, the parsing module 100 performs the following when parsing the door and window customization data to obtain the CAD drawing data and process parameter data:

[0164] S1A1 determine the format type of the CAD drawing file contained in the custom doors and windows data, and determine whether the format type is a format type supported by the edge server;

[0165] S1A2 If the format type is not a format type supported by the edge server, the CAD drawing file is converted to a format type supported by the edge server to obtain a CAD drawing file format compatible;

[0166] S1A3. After establishing an edge server data parsing rule library that includes mapping relationships between multiple format types and corresponding parsing rules, select corresponding parsing rules from the data parsing rule library according to the format type of the format-compatible CAD drawing file, and parse the format-compatible CAD drawing file according to the selected parsing rule to obtain CAD drawing data.

[0167] In some embodiments, the parsing module 100 selects a corresponding parsing rule from a data parsing rule library according to the format type of the compatible CAD drawing file, and parses the compatible CAD drawing file according to the selected parsing rule to obtain the CAD drawing data, performing the following steps:

[0168] S1A31. After establishing a data parsing rule library for the edge server and building dedicated line and arc parsing optimizers for the line and arc primitive objects frequently used in the custom door and window production line, the corresponding parsing rule is selected from the data parsing rule library based on the format type of compatible CAD drawing files. If the CAD drawing file contains line or arc primitive objects, the corresponding line parsing optimizer or arc parsing optimizer is further selected.

[0169] S1A32. According to the selected parsing rules and in combination with the line parsing optimizer or the arc parsing optimizer, the format-compatible CAD drawing file is parsed; wherein, the line primitive objects are parsed using the line parsing optimizer, and the arc primitive objects are parsed using the arc parsing optimizer, and the remaining primitive objects are parsed using the selected parsing rules to obtain CAD drawing data.

[0170] In some embodiments, the parsing module 100 performs the following when parsing the door and window customization data to obtain the CAD drawing data and process parameter data:

[0171] S1B1. Based on the process parameters contained in the door and window customization data, a key-value pair matching algorithm is used to extract the values ​​corresponding to all process parameters, and the extracted values ​​are stored in the database as process parameter data according to a preset storage structure.

[0172] In some embodiments, the generation module 400 is used to generate a pre-processing strategy for the current workstation based on CAD drawing information, accuracy requirements, and resource occupancy status when executing:

[0173] S41. Monitor the edge server network status to determine whether there is a risk of network interruption;

[0174] S42. When it is determined that there is a risk of network interruption, evaluate whether the amount of locally cached CAD drawing data exceeds a preset threshold;

[0175] S43. If the amount of CAD drawing data exceeds a preset threshold, an emergency preprocessing strategy is generated as a preprocessing strategy for the current workstation. The emergency preprocessing strategy includes: performing maximum simplification of the graphic outline and abandoning the integrity of the graphic outline, setting the resolution after rasterization to the lowest possible level, and extracting only necessary dimensional parameters;

[0176] S44. If the amount of CAD drawing data does not exceed the preset threshold, a conventional preprocessing strategy is generated as the preprocessing strategy for the current workstation based on the CAD drawing information, accuracy requirements and resource occupancy status. The conventional preprocessing strategy includes: simplifying the graphic outline within a preset degree range, adjusting the resolution after rasterization within a preset resolution range, and extracting necessary and unnecessary size parameters as needed.

[0177] In some embodiments, the optimization module 600 is used to filter and load the process parameters required for the current station from the process parameter data according to the processing requirements of the current station, and to obtain the optimized loaded process parameter data when executing the following steps:

[0178] S61. After establishing a station requirement parameter library including a mapping relationship between the station identification and the process list, according to the station identification of the current station, the process list of the current station is obtained from the station requirement parameter library; the process list includes all processes required to be completed at the current station;

[0179] S62. By parsing the process parameter data, multiple process parameters and parameter values ​​corresponding to each process parameter are obtained;

[0180] S63. According to the process list, the process parameters required for the current workstation are screened out from multiple process parameters, and after loading the corresponding parameter values, the process parameters are packaged according to the preset data structure to obtain optimized loaded process parameter data; the data structure includes parameter identification, parameter value and data verification code.

[0181] In some embodiments, the optimization module 600 is configured to filter out the process parameters required for the current station from a plurality of process parameters according to the process list, and after loading the corresponding parameter values, encapsulate the process parameters according to a preset data structure to obtain optimized loaded process parameter data when executing the following steps:

[0182] S631. For the current station, determine the priority of each process parameter according to the preset parameter priority strategy; the parameter priority strategy comprehensively considers the influence of the process parameters on the processing accuracy, the real-time requirements of the process parameters during the processing, and the historical frequency of use of the process parameters;

[0183] S632. According to the process list, the process parameters required for the current station are screened from multiple process parameters, and the screened process parameters are sorted according to the parameter priority strategy to form a sorted process parameter list;

[0184] S633. According to the order of the sorted process parameter list, the parameter values ​​corresponding to each process parameter are loaded in sequence, and a segmented data encapsulation method is adopted to encapsulate each process parameter of the loaded parameter value according to the preset data structure to obtain the optimized loaded process parameter data; the data structure includes parameter identification, parameter value, data check code and priority identification; among them, the process parameters with high priority are encapsulated in the header of the data packet, and the process parameters with low priority are encapsulated in the tail of the data packet, so that the work station control system can give priority to parsing and using the process parameters with high priority.

[0185] In some embodiments, the optimization module 600 is used to determine the priority of each process parameter for the current station according to a preset parameter priority strategy when executing:

[0186] S6311 monitors the real-time vibration data of the current station processing process. If the real-time vibration data exceeds the preset threshold, it is determined that the current station is in an unstable state and generates an unstable state signal;

[0187] S6312. In response to the unstable state signal, the parameter priority strategy is dynamically adjusted to increase the priority of the process parameters that have a greater impact on the processing accuracy and reduce the priority of the process parameters that have a smaller impact on the processing accuracy, so as to ensure that when the workstation is in an unstable state, the subsequent steps can prioritize the transmission and processing of the process parameters that have a greater impact on the processing accuracy;

[0188] S6313. Re-determine the priority of each process parameter based on the adjusted parameter priority strategy.

[0189] In some embodiments, the optimization module 600 is configured to respond to an unstable state signal by dynamically adjusting a parameter priority strategy to increase the priority of process parameters that have a greater impact on machining accuracy and decrease the priority of process parameters that have a smaller impact on machining accuracy, thereby ensuring that, when a workstation is in an unstable state, subsequent steps are preferentially transmitted and processed when the process parameters that have a greater impact on machining accuracy are executed.

[0190] S63121. In response to the unstable state signal, the cumulative working time of the tool is evaluated, and if the cumulative working time exceeds a preset tool replacement threshold, a tool wear signal is generated;

[0191] S63122. In response to the tool wear signal, dynamically adjust the parameter priority strategy to increase the priority of process parameters for tool compensation value and tool wear amount, and reduce the priority of process parameters with less impact on machining accuracy. This ensures that when the workstation is unstable and the tool is worn, the subsequent steps can prioritize the transmission and processing of tool compensation parameters, thereby adjusting the machining trajectory according to the tool compensation parameters.

[0192] S63123. If the accumulated working time does not exceed the preset tool replacement threshold, the priority of the process parameters that have a greater impact on the machining accuracy is increased, and the priority of the process parameters that have a smaller impact on the machining accuracy is reduced by dynamically adjusting the parameter priority strategy, so as to ensure that when the workstation is unstable, the subsequent steps can give priority to transmitting and processing the process parameters that have a greater impact on the machining accuracy.

[0193] Please refer to Figure 3 , Figure 3This is a structural diagram of an electronic device provided in an embodiment of the present invention. The present invention provides an electronic device 13, including: a processor 1301 and a memory 1302. The processor 1301 and the memory 1302 are interconnected and communicate with each other via a communication bus 1303 and / or other forms of connection mechanisms (not shown). The memory 1302 stores computer-readable instructions executable by the processor 1301. When the electronic device is running, the processor 1301 executes the computer-readable instructions to execute the multi-station data processing method for the door and window customization production line in any optional implementation of the above embodiment to achieve the following functions: parsing the door and window customization data to obtain CAD drawing data and process parameter data; analyzing the processing requirements of the current station to determine CAD drawing information and accuracy requirements required for the current workstation; real-time monitoring of the edge server to determine the current resource occupancy status of the edge server; generating a preprocessing strategy for the current workstation based on the CAD drawing information, accuracy requirements and resource occupancy status; the preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization and whether to extract size parameters; according to the preprocessing strategy, preprocess the CAD drawing data to obtain preprocessed CAD drawing data; according to the processing requirements of the current workstation, filter and load the process parameters required for the current workstation from the process parameter data to obtain optimized loaded process parameter data; send the preprocessed CAD drawing data and the optimized loaded process parameter data to the control system of the current workstation for processing control.

[0194] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for processing multi-station data of a door and window customization production line in any optional implementation of the above-mentioned embodiment is executed to achieve the following functions: parsing door and window customization data to obtain CAD drawing data and process parameter data; analyzing the processing requirements of the current station to determine the CAD drawing information and accuracy requirements required for the current station; monitoring the edge server in real time to determine the current resource occupancy status of the edge server; generating a preprocessing strategy for the current station based on the CAD drawing information, accuracy requirements and resource occupancy status; the preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization and whether to extract size parameters; preprocessing the CAD drawing data according to the preprocessing strategy to obtain preprocessed CAD drawing data; filtering and loading the process parameters required for the current station from the process parameter data according to the processing requirements of the current station to obtain optimized loaded process parameter data; sending the preprocessed CAD drawing data and the optimized loaded process parameter data to the control system of the current station for processing control.

[0195] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0196] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, the indirect coupling or communication connection of the device or unit may be electrical, mechanical or other forms.

[0197] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] Furthermore, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0199] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0200] The foregoing description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A multi-station data processing method for a customized door and window production line, applied to an edge server, characterized in that: The following steps are involved: S1. Analyze the customized data of doors and windows to obtain CAD drawing data and process parameter data; S2. Analyze the processing requirements of the current workstation to determine the CAD drawing information and accuracy requirements required for the current workstation; S3. Real-time monitoring of edge servers to determine the current resource usage of edge servers; S4. Generate a preprocessing strategy for the current workstation based on the CAD drawing information, the accuracy requirements, and the resource occupancy status; the preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization, and whether to extract size parameters; S5. According to the preprocessing strategy, the CAD drawing data is preprocessed to obtain preprocessed CAD drawing data; S6. According to the processing requirements of the current station, the process parameters required for the current station are filtered and loaded from the process parameter data to obtain the optimized loaded process parameter data; S7. Send the pre-processed CAD drawing data and the optimized loaded process parameter data to the control system of the current station for processing control; The specific steps in step S4 include: S41. Monitor the edge server network status to determine whether there is a risk of network interruption; S42. When it is determined that there is a risk of network interruption, evaluate whether the amount of locally cached CAD drawing data exceeds a preset threshold; S43. If the amount of CAD drawing data exceeds the preset threshold, an emergency preprocessing strategy is generated as a preprocessing strategy for the current workstation. The emergency preprocessing strategy includes: performing maximum simplification of the graphic outline and abandoning the integrity of the graphic outline, setting the resolution after rasterization to the lowest possible level, and extracting only necessary dimensional parameters; S44. If the amount of CAD drawing data does not exceed the preset threshold, a conventional preprocessing strategy is generated as the preprocessing strategy for the current workstation based on the CAD drawing information, the accuracy requirements and the resource occupancy status. The conventional preprocessing strategy includes: simplifying the graphic outline within a preset degree range, adjusting the resolution after rasterization within a preset resolution range, and extracting necessary and unnecessary size parameters as needed.

2. The multi-station data processing method for customized door and window production lines according to claim 1 is characterized in that: The specific steps in step S1 include: S1A1 determines the format type of the CAD drawing file contained in the custom doors and windows data, and determines whether the format type is a format type supported by the edge server; S1A2 If the format type is not a format type supported by the edge server, the CAD drawing file is converted to a format type supported by the edge server to obtain a CAD drawing file format compatible; S1A3. After establishing an edge server data parsing rule library including a mapping relationship between multiple format types and corresponding parsing rules, select a corresponding parsing rule from the data parsing rule library according to the format type of the format-compatible CAD drawing file, and parse the format-compatible CAD drawing file according to the selected parsing rule to obtain the CAD drawing data.

3. The multi-station data processing method for customized door and window production lines according to claim 2 is characterized in that: The specific steps in step S1A3 include: S1A31. After establishing the edge server data parsing rule library and constructing dedicated line parsing optimizers and arc parsing optimizers for the line and arc primitive objects frequently used in the custom door and window production line, the corresponding parsing rule is selected from the data parsing rule library based on the format type of the compatible CAD drawing file. If the CAD drawing file contains line or arc primitive objects, the corresponding line parsing optimizer or arc parsing optimizer is further selected; S1A32. According to the selected parsing rules and in combination with the straight line parsing optimizer or the arc parsing optimizer, the CAD drawing file compatible with the format is parsed; wherein, the straight line primitive object is parsed by the straight line parsing optimizer, the arc primitive object is parsed by the arc parsing optimizer, and the remaining primitive objects are parsed using the selected parsing rules to obtain the CAD drawing data.

4. The multi-station data processing method for customized door and window production lines according to claim 1 is characterized in that: The specific steps in step S1 include: S1B1. Based on the process parameters contained in the door and window customization data, a key-value pair matching algorithm is used to extract the values ​​corresponding to all process parameters, and the extracted values ​​are stored in the database as the process parameter data according to a preset storage structure.

5. The multi-station data processing method for customized door and window production line according to claim 1 is characterized in that: The specific steps in step S6 include: S61. After establishing a station requirement parameter library including a mapping relationship between station identification and process list, according to the station identification of the current station, the process list of the current station is obtained from the station requirement parameter library; the process list includes all processes required to be completed at the current station; S62. By parsing the process parameter data, a plurality of process parameters and parameter values ​​corresponding to each of the process parameters are obtained; S63. According to the process list, the process parameters required for the current workstation are screened out from the multiple process parameters, and after loading the corresponding parameter values, the process parameters are packaged according to the preset data structure to obtain optimized loaded process parameter data; the data structure includes parameter identification, parameter value and data verification code.

6. The multi-station data processing method for customized door and window production lines according to claim 5 is characterized in that: The specific steps in step S63 include: S631. For the current station, determine the priority of each of the process parameters according to the preset parameter priority strategy; the parameter priority strategy comprehensively considers the influence of the process parameters on the processing accuracy, the real-time requirements of the process parameters during the processing, and the historical frequency of use of the process parameters; S632. According to the process list, filter out the process parameters required for the current station from the multiple process parameters, and sort the filtered process parameters according to the parameter priority strategy to form a sorted process parameter list; S633. According to the order of the sorted process parameter list, the parameter values ​​corresponding to each process parameter are loaded in sequence, and a segmented data encapsulation method is adopted to encapsulate each process parameter of the loaded parameter value according to the preset data structure to obtain the optimized loaded process parameter data; the data structure includes parameter identification, parameter value, data check code and priority identification; wherein, the process parameters with high priority are encapsulated in the header of the data packet, and the process parameters with low priority are encapsulated in the tail of the data packet, so that the workstation control system can give priority to parsing and using the process parameters with high priority.

7. A multi-station data processing device for a custom door and window production line, applied to an edge server, characterized in that: include: Parsing module, used to parse door and window customization data to obtain CAD drawing data and process parameter data; Analysis module, used to analyze the processing requirements of the current workstation to determine the CAD drawing information and accuracy requirements required by the current workstation; A monitoring module is used to monitor the edge server in real time to determine the current resource usage status of the edge server; A generation module is used to generate a preprocessing strategy for the current workstation based on the CAD drawing information, the accuracy requirements, and the resource occupancy status; the preprocessing strategy includes the degree of simplification of the graphic outline, the resolution after rasterization, and whether to extract size parameters; A preprocessing module, configured to preprocess the CAD drawing data according to the preprocessing strategy to obtain preprocessed CAD drawing data; The optimization module is used to filter and load the process parameters required for the current station from the process parameter data according to the processing requirements of the current station, and obtain the optimized loaded process parameter data; A sending module is used to send the pre-processed CAD drawing data and the optimized loaded process parameter data to the control system of the current station for processing control; The generation module is executed when it is used to generate a preprocessing strategy for the current workstation based on CAD drawing information, accuracy requirements, and resource occupancy status: S41. Monitor the edge server network status to determine whether there is a risk of network interruption; S42. When it is determined that there is a risk of network interruption, evaluate whether the amount of locally cached CAD drawing data exceeds a preset threshold; S43. If the amount of CAD drawing data exceeds a preset threshold, an emergency preprocessing strategy is generated as a preprocessing strategy for the current workstation. The emergency preprocessing strategy includes: performing maximum simplification of the graphic outline and abandoning the integrity of the graphic outline, setting the resolution after rasterization to the lowest, and extracting only necessary size parameters; S44. If the amount of CAD drawing data does not exceed the preset threshold, a conventional preprocessing strategy is generated as the preprocessing strategy for the current workstation based on the CAD drawing information, accuracy requirements, and resource usage status. The conventional preprocessing strategy includes: simplifying the graphic outline within a preset degree range, adjusting the resolution after rasterization within a preset resolution range, and extracting necessary and unnecessary size parameters as needed.

8. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the steps in the multi-station data processing method for the door and window customization production line as described in any one of claims 1 to 6 are executed.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the multi-station data processing method for a customized door and window production line are executed.

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