A database-based 3D printing factory data processing method and system

Abstract

The application relates to the technical field of 3D printing data processing, and discloses a 3D printing factory data processing method and system based on a database, which comprises the following steps: S101, a 3D printing model is intelligently segmented into multiple model data blocks according to preset geometric characteristics, and a space index is constructed; S102, data blocks are stored in multiple nodes in a distributed manner through a distributed file system, and multiple copies are stored in a redundant manner; S103, printing parameters of a printing task are collected and written into a distributed database; S104, product detection data corresponding to the printing task are collected, and a mapping relationship is established with the printing parameters; S105, clustering analysis is carried out based on the mapping relationship between the printing parameters and the product detection data, a parameter configuration template corresponding to a printing scene is generated, and the parameter configuration template is stored in association with the model data blocks. The application realizes efficient data storage, intelligent resource management and accurate printing task configuration.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of 3D printing data processing, and particularly relates to a 3D printing factory data processing method and system based on a database. BACKGROUND

[0002] With the rapid development of 3D printing technology, more and more 3D printing factories have begun to carry out large-scale production, generating a large amount of complex production data, including 3D printing models, equipment states, process parameters, sensor data, etc. These data are crucial for ensuring production efficiency, product quality and equipment maintenance. However, as the production scale expands, how to efficiently manage and process these diversified massive data has become a major challenge faced by 3D printing factories.

[0003] Currently, many 3D printing factories use distributed databases and file systems to store and manage data. Distributed database technology solves the performance bottleneck of single-node databases through data partitioning and distributed storage, ensuring high availability and fault tolerance of data. Distributed file systems, such as HDFS and Ceph, are usually used to store large 3D model files and production logs. However, these existing technologies have some limitations in processing the data of 3D printing factories.

[0004] Firstly, traditional distributed storage solutions are mainly used for structured or semi-structured data, and the storage and retrieval efficiency of 3D model files and real-time production data is low, making it difficult to meet the needs of fast retrieval and modification. Since 3D model files are often large and complex, traditional file systems have performance bottlenecks when processing these large files, making it difficult to meet the growing data processing needs of factories. Secondly, existing systems have not fully optimized the storage structure, and cannot flexibly configure parameters and optimize storage according to the different needs of printing tasks. This leads to waste of storage and computing resources when processing different types of printing tasks, thereby affecting production efficiency. Finally, although distributed systems can provide redundant storage of data, in high concurrency situations, the existing redundancy mechanism cannot efficiently update and manage massive data, and there are certain risks in the consistency and data access performance of the system. SUMMARY

[0005] The application provides a 3D printing factory data processing method and system based on a database, which solves the technical problems in the related art that large-scale 3D models and real-time production data cannot be efficiently processed, the storage and retrieval efficiency is low, and flexible optimization cannot be performed according to the diverse needs of printing tasks.

[0006] The application provides a 3D printing factory data processing method and system based on a database, which includes the following steps:

[0007] S101, intelligently segmenting a large-size 3D printing model according to preset geometric features, generating a plurality of model data blocks, and constructing a spatial index for each model data block, the spatial index containing position information and data block feature type of each model data block;

[0008] S102, dispersing and storing the segmented model data blocks to a plurality of nodes through a distributed file system, and storing multiple copies in a redundant manner;

[0009] S103, for each 3D printing task, collecting printing parameters of the 3D printing equipment during the printing process, and writing the printing parameters into a distributed database;

[0010] The printing parameters include temperature, speed, material consumption and printing scene.

[0011] S104, collecting product detection data corresponding to the printing task, and establishing a mapping relationship between the product detection data and the printing parameters;

[0012] The product detection data includes size, accuracy and strength of the printed product.

[0013] S105, based on the mapping relationship between the printing parameters and the product detection data, performing cluster analysis on the printing parameters, generating a parameter configuration template corresponding to the printing scene, and storing the parameter configuration template and the corresponding model data block in association.

[0014] Further, the preset geometric features include at least one of:

[0015] A curved surface region with a curvature greater than a first preset threshold;

[0016] A cantilever structure or support structure in the 3D printing model;

[0017] A thin-walled region with a wall thickness less than a second preset threshold;

[0018] A high-precision feature region manually labeled by a user.

[0019] Further, the intelligent segmentation includes:

[0020] Performing stress analysis on the 3D printing model, and taking the stress concentration region as an independent model data block;

[0021] According to the thermal deformation coefficient of the material used in the 3D printing model, increasing the segmentation density in the expected deformation region;

[0022] For the region in the 3D printing model that needs to be polished later, do not segment.

[0023] Further, the data block feature types include: curved surface area, overhanging structure, support structure, thin-walled area, and filling area.

[0024] The filling area is defined as a non-critical area, and the data block feature types other than the filling area are defined as critical areas.

[0025] The multiple-copy storage includes storing three copies of the model data block of the critical area and one copy of the model data block of the non-critical area.

[0026] The storage strategy of the model data block includes: in a high-precision printing scenario, preferentially storing the model data block to the SSD node; in a batch production printing scenario, preferentially storing the model data block to the HDD node; and when detecting that the CPU usage rate of a node exceeds a third preset threshold, migrating the corresponding model data block to a low-load node.

[0027] Further, the mapping relationship between the printing parameters and the product detection data is stored through a distributed database and is optimized based on historical data of the printing task, and the optimization processing content includes:

[0028] For the same type of printing task, a parameter configuration template is automatically generated based on the historical mapping relationship, and template parameters of the parameter configuration template include printing speed, layer height, and filling rate.

[0029] Intelligently classifying the printing parameters and the product detection data of each printing task to generate data labels.

[0030] Based on whether the product detection data corresponding to each printing task meets a preset quality standard, the printing parameters and the product detection data are marked as high-quality or low-quality data types and are respectively stored to the SSD node and the HDD node.

[0031] Further, when the 3D printing model or the printing parameters associated therewith changes, the affected model data block or printing parameter is located through the spatial index and the mapping relationship between the printing parameters and the product detection data, and a local update operation is performed.

[0032] Further, the generation step of the parameter configuration template includes:

[0033] S201, based on the data block feature types of the model data block corresponding to the printing task, the printing parameters, and the product detection data, a multi-dimensional mapping sample set is constructed.

[0034] S202, the multi-dimensional mapping sample set is analyzed through a clustering algorithm.

[0035] S203, generating a parameter configuration sub-template for each cluster according to the clustering analysis result, the parameter configuration sub-template including template parameters suitable for model data blocks of different structural regions;

[0036] S204, associating the parameter configuration sub-template with the model data blocks of the corresponding structural region.

[0037] Further, the manner of associating the parameter configuration sub-template with the model data blocks of the corresponding structural region includes:

[0038] matching the parameter configuration sub-template based on the spatial index of the model data block;

[0039] matching based on a correspondence between the data block feature type and a preset label of the parameter configuration sub-template.

[0040] The present application provides a kind of 3D printing factory data processing system based on database, comprising:

[0041] Intelligent segmentation module, for the large size 3D printing model is according to preset geometric feature Intelligent segmentation, generate multiple model data blocks, and construct spatial index for each model data block, the spatial index includes the location information and data block feature type of each model data block;

[0042] Distributed storage module, for the model data block after segmentation is dispersed to multiple nodes by distributed file system and is stored in multiple copies using redundancy mode;

[0043] Printing parameter acquisition module, for each 3D printing task, 3D printing equipment in printing process is collected printing parameter, and printing parameter is written into distributed database;

[0044] Wherein, printing parameter includes: temperature, speed, material consumption and printing scene;

[0045] Product quality mapping module, for collecting the product detection data corresponding to printing task, and the product detection data and printing parameter establish mapping relationship;

[0046] Wherein, product detection data includes: the size, accuracy and strength of printing finished product;

[0047] Configuration module generation module, for the mapping relationship between printing parameter and product detection data, the printing parameter is clustered and analyzed, and the parameter configuration template corresponding to printing scene is generated, and parameter configuration template and corresponding model data block are associated and stored.

[0048] The present application has the beneficial effect that the present application generates a multi-dimensional mapping sample set and performs clustering analysis by combining model data block features, printing parameters, and product detection data, thereby optimizing printing parameter configuration. This method can generate specialized parameter configuration templates according to different structural regions, ensuring that each region uses the most suitable printing settings, improving printing precision and efficiency. By automatically generating and applying these templates, manual intervention is reduced, and operational errors are reduced. At the same time, based on printing scenarios and data block features, the system can intelligently adjust storage strategies and the number of copies, improving storage efficiency and optimizing resource usage. Clustering analysis and multi-dimensional data mapping enhance the adaptability and flexibility of the system, enabling it to efficiently handle different printing task requirements, providing an efficient and reliable data processing solution. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of a database-based 3D printing factory data processing method of the present application. DETAILED DESCRIPTION

[0050] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is merely meant to provide a better understanding of the subject matter described herein and can include changes, modifications, or additions of elements to the functions and arrangements of the elements discussed without departing from the scope of the present description. Various examples can omit, substitute, or add various procedures or components as appropriate, or in appropriate combination. Also, features described with respect to some examples can be combined in other examples.

[0051] It should be noted that the technical terms or scientific terms used in one or more embodiments of the present application should be understood as the usual meaning understood by those skilled in the art to which the present application belongs, unless otherwise defined. The terms "first", "second", and similar words used in one or more embodiments of the present application do not represent any order, number, or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar words mean that the elements or objects appearing before the words include the elements or objects listed after the words and their equivalents, and do not exclude other elements or objects. The terms "connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right", and the like are only used to represent relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0052] As shown in Figure 1 , a database-based 3D printing factory data processing method and system includes:

[0053] S101, intelligently segmenting a large-size 3D printing model according to preset geometric features, generating a plurality of model data blocks, and constructing a spatial index for each model data block, the spatial index containing position information and data block feature type of each model data block;

[0054] S102, dispersing and storing the segmented model data blocks to a plurality of nodes through a distributed file system, and storing multiple copies in a redundant manner;

[0055] S103, for each 3D printing task, collecting printing parameters of the 3D printing equipment in the printing process, and writing the printing parameters into a distributed database;

[0056] Wherein, the printing parameters include: temperature, speed, material consumption and printing scene;

[0057] S104, collecting product detection data corresponding to the printing task, and establishing a mapping relationship between the product detection data and the printing parameters;

[0058] Wherein, the product detection data includes: size, accuracy and strength of the printed product;

[0059] S105, based on the mapping relationship between the printing parameters and the product detection data, performing cluster analysis on the printing parameters, generating a parameter configuration template corresponding to the printing scene, and storing the parameter configuration template and the corresponding model data block in association.

[0060] In an embodiment of the present application, a temperature sensor is used to collect the temperature of the print head component in real time; a speed sensor is used to collect the moving speed of the print head; a material flow sensor is used to collect the consumption of materials during printing; and the printing scene is automatically identified according to the printing, heating and cooling stages of the printing task.

[0061] In an embodiment of the present application, the geometric features include a plurality of key elements related to the model structure, and these features are selected based on the printing process requirements and printing quality requirements. The preset geometric features include at least one of the following:

[0062] Curved surface area with curvature greater than a first preset threshold: such areas are usually highly curved or complex geometric structures, which may affect the printing accuracy and require higher accuracy and finer segmentation to ensure printing quality;

[0063] Overhanging structure or support structure in the 3D printing model: overhanging structure refers to the part that needs additional support, and the structural stability of this area must be considered during segmentation to ensure the stability of the support and the success rate of printing;

[0064] Thin-walled regions with a wall thickness less than a second preset threshold: for thin-walled regions, too fine a segmentation can lead to printing failure, so a larger segmentation size can be selected in these regions to reduce material waste and support requirements during printing;

[0065] High-precision feature regions manually annotated by the user: these regions are key parts specially marked by the user, and usually require the highest printing precision, so these regions need to be segmented more finely according to the printing task.

[0066] In an embodiment of the present application, the intelligent segmentation not only considers traditional geometric shapes, but also combines physical properties, and the intelligent segmentation includes:

[0067] Stress analysis of the 3D printing model: by analyzing the stress distribution in different regions of the printing model, the system can divide regions with concentrated stress into independent model data blocks, which usually need to be printed separately to ensure that they can withstand higher loads during printing and avoid printing failure;

[0068] Increasing the segmentation density in the expected deformation region according to the thermal deformation coefficient of the material used in the 3D printing model: for high-temperature deformation sensitive regions, by calculating the thermal deformation coefficient of each part, the segmentation density can be increased in these regions to ensure that these regions can withstand temperature fluctuations and avoid deformation due to thermal expansion;

[0069] Processing of polishing regions: for regions in the 3D printing model that need to be polished later, no segmentation is performed during the segmentation process to maintain geometric integrity, ensure polishing quality, and avoid unnecessary structural changes caused by segmentation.

[0070] In an embodiment of the present application, after completing the intelligent segmentation of the model, in order to improve the efficiency of data access, a spatial index is created for each segmented model data block, the spatial index adopts an octree structure, and the index node of each model data block contains the position information, geometric features and printing scene parameters of the model data block. Through the spatial index, the system can quickly locate and retrieve each model data block, especially when processing large-scale 3D models, which can significantly improve the data retrieval speed.

[0071] In an embodiment of the present application, the data block feature types include: curved surface regions, overhanging structures, support structures, thin-walled regions, and filling regions;

[0072] Defining the filling region as a non-critical region and defining the data block feature types other than the filling region as a critical region;

[0073] The redundancy mode refers to generating two or more copies of the same model data block in the distributed file system, and storing the copies on different nodes respectively, wherein the copy refers to multiple copies of the same data on different storage nodes, so as to ensure that when a storage node fails, other copies can continue to provide services and avoid data loss.

[0074] The multi-copy storage includes storing three copies of the model data block of the key area and storing one copy of the model data block of the non-key area.

[0075] The storage strategy of the model data block includes: in the high-precision printing scene, the model data block is preferentially stored to the SSD node; in the batch production printing scene, the model data block is preferentially stored to the HDD node; and when it is detected that the CPU usage rate of the node exceeds a third preset threshold, the corresponding model data block is migrated to a low-load node. The printing scene includes but is not limited to the high-precision printing scene, the batch production printing scene, the rapid prototyping scene, etc.; the SSD node refers to a node with a solid state disk as the main storage medium, which has the characteristics of fast reading; and the HDD node refers to a node with a mechanical hard disk as the main storage medium, which has the characteristics of large capacity.

[0076] In an embodiment of the present application, the present application continuously monitors the running load of each distributed node, including but not limited to CPU usage, memory occupation, network delay and other indicators. When it is detected that the CPU usage of a certain node continuously exceeds 80%, the system will identify the hot model data block stored on the node and migrate it to a low-load node currently in an idle state. The data migration adopts an asynchronous mode, and the location of the data block is automatically updated in combination with the spatial index information, so as to ensure that the access path is correct and does not affect the execution of the current task, thereby improving the overall system throughput and load balancing capability.

[0077] Compared with the existing fixed-copy distributed storage technology, the present application dynamically adjusts the number of copies and the node storage mode according to the data block feature type and the printing scene, significantly reduces the storage redundancy rate, and improves the data access efficiency; by combining the real-time load monitoring mechanism to realize hot spot migration, the stability and expansion capability of the system are further improved.

[0078] In an embodiment of the present application, the mapping relationship between the printing parameters and the product detection data is stored through a distributed database, and is optimized based on the historical data of the printing task, wherein the mapping relationship is used to record the association between the printing input and the finished product quality, each mapping record corresponds to a printing task, records the parameter combination of the task and the corresponding detection result, and can be indexed and queried through the task number, and the optimization processing content includes:

[0079] For the same type of printing task, a parameter configuration template is automatically generated based on a historical mapping relationship, template parameters of the parameter configuration template include printing speed, layer height and filling rate, wherein the printing task type can be classified based on material type, printing accuracy, model complexity, yield requirement and the like, and the task type is automatically labeled through related parameter setting when the task is submitted, for subsequent data classification and template generation;

[0080] The printing parameters and product detection data of each printing task are intelligently classified, and data labels are generated for subsequent query and analysis, specifically, the label content includes material type, error level, strength level, task type, etc., and is stored in a key-value pair structure, for fast retrieval, abnormal screening and other applications in the database;

[0081] Based on whether the product detection data corresponding to each printing task meets the preset quality standard, the printing parameters and product detection data are marked as high-quality or low-quality data types, and are respectively stored in the SSD node and the HDD node, the preset quality standard includes but is not limited to whether the product size error is within a first preset error range, whether the strength of the printed part reaches a fourth preset threshold, whether the surface finish is qualified, etc., the above storage strategy can differentially allocate resources according to data value and access frequency, improve the response efficiency of high-quality data, and at the same time save the overall storage resources of the system.

[0082] In an embodiment of the present application, when the stored 3D printing model file or its corresponding printing parameter data is changed due to model modification, process adjustment or the like, the present application does not perform overall reconstruction processing on all model data blocks or all parameter data, but quickly locates the affected data blocks or parameter items through the established spatial index and the mapping relationship between the printing parameters and the product detection data; the present application then performs a local update operation on the part of data.

[0083] The local update operation includes:

[0084] Through the constructed spatial index structure, the spatial position of the model data block that has changed is quickly determined, and through the mapping relationship between the printing parameters and the product detection data, the affected related parameter configuration and its corresponding model data block are further identified, to realize accurate limitation of the update range;

[0085] After determining the update range, the system only performs incremental replacement or adjustment on the identified model data block or printing parameter, and does not operate on other data areas that are not affected, in the incremental processing process, the system uses a version control mechanism to identify and archive the update content, and retains the historical version for traceability or rollback;

[0086] The embodiment performs consistency check on the updated data, ensures cross-node data synchronization and transaction consistency, and writes the updated data into the corresponding node of the distributed storage system after the check is passed, thereby completing the local updating process.

[0087] The local updating operation enables the system to make rapid adjustment to only the minimum necessary data set in the case of changes in the 3D printing model or printing parameters, avoids repeated transmission and full model reconstruction operation, thereby improving data processing efficiency and saving system resources.

[0088] In an embodiment of the present application, the parameter configuration template generation step comprises:

[0089] S201, constructing a multi-dimensional mapping sample set based on the data block feature type of the model data block corresponding to the printing task, the printing parameter and the product detection data;

[0090] S202, performing clustering analysis on the multi-dimensional mapping sample set by using a clustering algorithm, specifically, using a K-means clustering algorithm to analyze and assign printing tasks with similar features to the same class, for example, tasks with similar printing parameters and product quality performance are classified into one class, and tasks with similar model structures are classified into one class;

[0091] S203, generating a parameter configuration sub-template for each class according to the clustering analysis result, the parameter configuration sub-template comprising template parameters suitable for model data blocks of different structure regions, and by generating an independent template for each cluster, the embodiment can optimize the parameter configuration of each printing task targetedly and ensure that each region obtains the most suitable printing setting;

[0092] S204, establishing an association relationship between the parameter configuration sub-template and the model data block of the corresponding structure region.

[0093] In an embodiment of the present application, the way of establishing an association relationship between the parameter configuration sub-template and the model data block of the corresponding structure region comprises:

[0094] Matching the parameter configuration sub-template based on the spatial index of the model data block;

[0095] Matching based on the correspondence relationship between the data block feature type and the preset label of the parameter configuration sub-template, wherein the preset label of the sub-template represents an identifier distinguishing different parameter configuration sub-templates, and each label corresponds to a structure region.

[0096] Embodiments of the present application realize parameter behavior modeling of structural features such as support areas and filling areas in the classification process by introducing data block feature type labels as clustering feature dimensions. The clustering analysis results are used to generate parameter configuration sub-templates at the structural area level, each of which contains specific parameters such as speed, layer thickness, and filling rate applicable to the structural area. The embodiments then associate the corresponding parameter sub-templates with the model data blocks according to their spatial index information, realizing structure-aware parameter recommendation.

[0097] In an embodiment of the present application, through the combination of model data blocks + printing scenarios + template mapping, the system can flexibly automatically select the most suitable printing parameters according to different task requirements, and accurately configure task parameters according to the characteristics of the printing area. This technical solution greatly improves the accuracy and production efficiency of 3D printing tasks, reduces the need for human intervention, and optimizes resource allocation and time consumption while ensuring printing quality.

[0098] Embodiments of the present application also provide a database-based 3D printing factory data processing system, comprising:

[0099] An intelligent segmentation module is configured to intelligently segment a large-size 3D printing model according to preset geometric features, generate a plurality of model data blocks, and construct a spatial index for each model data block, the spatial index containing position information and data block feature types of each model data block.

[0100] A distributed storage module is configured to store the segmented model data blocks in a distributed file system to multiple nodes and store multiple copies in a redundant manner.

[0101] A printing parameter acquisition module is configured to acquire printing parameters of a 3D printing device during a printing process for each 3D printing task and write the printing parameters into a distributed database.

[0102] The printing parameters include temperature, speed, material consumption, and printing scenarios.

[0103] A product quality mapping module is configured to acquire product detection data corresponding to a printing task and establish a mapping relationship between the product detection data and the printing parameters.

[0104] The product detection data includes the size, accuracy, and strength of the printed product.

[0105] A configuration module generation module is configured to perform clustering analysis on the printing parameters based on the mapping relationship between the printing parameters and the product detection data, generate parameter configuration templates corresponding to printing scenarios, and store the parameter configuration templates in association with the corresponding model data blocks.

[0106] The above describes the embodiments of the present application, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are only illustrative but not restrictive, and the person of ordinary skill in the art can make many forms under the inspiration of the present embodiments, which all belong to the protection of the present embodiments.

Claims

1. A database-based 3D printing factory data processing method, characterized by, The method comprises the following steps: S101, intelligently segmenting a large-size 3D printing model according to preset geometric features, generating a plurality of model data blocks, and constructing a spatial index for each model data block, wherein the spatial index comprises position information and data block feature type of each model data block; S102, dispersively storing the segmented model data blocks to a plurality of nodes through a distributed file system, and storing multiple copies in a redundant manner; S103, for each 3D printing task, collecting printing parameters of a 3D printing device in a printing process, and writing the printing parameters into a distributed database; wherein the printing parameters comprise temperature, speed, material consumption and printing scene; S104, collecting product detection data corresponding to the printing task, and establishing a mapping relationship between the product detection data and the printing parameters; wherein the product detection data comprises size, precision and strength of a printed product; S105, based on the mapping relationship between the printing parameters and the product detection data, performing cluster analysis on the printing parameters, generating a parameter configuration template corresponding to the printing scene, and associating and storing the parameter configuration template with the corresponding model data block; The preset geometric features comprise at least one of the following: a curved surface region with a curvature greater than a first preset threshold; a cantilever structure or a support structure in the 3D printing model; a thin-walled region with a wall thickness less than a second preset threshold; a high-precision feature region manually labeled by a user; The generation step of the parameter configuration template comprises: S201, based on the data block feature type of the model data block corresponding to the printing task, the printing parameters and the product detection data, constructing a multi-dimensional mapping sample set; S202, performing cluster analysis on the multi-dimensional mapping sample set through a clustering algorithm; S203, generating a parameter configuration sub-template for each cluster according to the cluster analysis result, wherein the parameter configuration sub-template comprises template parameters suitable for model data blocks of different structure regions; S204, establishing an association relationship between the parameter configuration sub-template and the model data block of the corresponding structure region; The association relationship between the parameter configuration sub-template and the model data block of the corresponding structure region comprises: matching the parameter configuration sub-template based on the spatial index of the model data block; matching based on a preset label corresponding relationship between the data block feature type and the parameter configuration sub-template; The data block feature type comprises: curved surface region, cantilever structure, support structure, thin-walled region and filling region.

2. The database-based 3D printing factory data processing method of claim 1, wherein, The intelligent segmentation comprises: stress analysis on the 3D printing model, and taking the stress concentration region as an independent model data block; According to the thermal deformation coefficient of the material used in the 3D printing model, the segmentation density is increased in the expected deformation region; For the region in the 3D printing model that needs to be polished later, no segmentation is performed.

3. The database-based 3D printing factory data processing method of claim 1, wherein, The filling region is defined as a non-critical region, and the data block feature types other than the filling region are defined as critical regions; The multiple copy storage comprises: storing three copies of the model data block of the critical region, and storing one copy of the model data block of the non-critical region; The storage strategy of the model data block includes: in a high-precision printing scenario, the model data block is preferentially stored to the SSD node, in a batch production printing scenario, the model data block is preferentially stored to the HDD node, and when it is detected that the CPU usage of the node exceeds a third preset threshold, the corresponding model data block is migrated to a low-load node.

4. The database-based 3D printing factory data processing method of claim 1, wherein, The mapping relationship between the printing parameters and the product detection data is stored through a distributed database, and is optimized based on historical data of the printing task, and the optimization processing content includes: for the same type of printing task, a parameter configuration template is automatically generated based on the historical mapping relationship, template parameters of the parameter configuration template include printing speed, layer height and filling rate; The printing parameters and the product detection data of each printing task are intelligently classified to generate data labels; Based on whether the product detection data corresponding to each printing task meets a preset quality standard, the printing parameters and the product detection data are marked as high-quality or low-quality data types, and are respectively stored to the SSD node and the HDD node.

5. The database-based 3D printing factory data processing method of claim 1, wherein, When the 3D printing model or the printing parameters associated therewith changes, the affected model data block or printing parameter is located through the spatial index and the mapping relationship between the printing parameters and the product detection data, and a local update operation is performed.

6. A database-based 3D printing factory data processing system, characterized by, The method is applied to the database-based 3D printing factory data processing method in any one of claims 1-5, and includes: an intelligent segmentation module for intelligently segmenting a large-size 3D printing model according to a preset geometric feature, generating a plurality of model data blocks, and constructing a spatial index for each model data block, the spatial index containing position information and data block feature type of each model data block; A distributed storage module for dispersing and storing the segmented model data blocks to a plurality of nodes through a distributed file system, and storing multiple copies in a redundant manner; A printing parameter acquisition module for acquiring printing parameters of a 3D printing device in a printing process for each 3D printing task, and writing the printing parameters into a distributed database; The printing parameters include temperature, speed, material consumption and printing scene; A product quality mapping module for acquiring product detection data corresponding to a printing task, and establishing a mapping relationship between the product detection data and the printing parameters; The product detection data includes size, precision and strength of a printed product; A configuration module generation module for clustering analysis of the printing parameters based on the mapping relationship between the printing parameters and the product detection data, generating a parameter configuration template corresponding to a printing scene, and associating and storing the parameter configuration template with a corresponding model data block.

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