A production batch intelligent management method and system based on an MES system

Abstract

The application relates to a production batch intelligent management method and system based on an MES system, and belongs to the technical field of production batch intelligent management, and solves the problems that the existing production batch management is not highly intelligent and is easy to be counterfeited. The method comprises the following steps: a DNC server formulates a production scheduling plan according to a production plan issued by an MES system; the production scheduling plan is divided into a plurality of production batches, and each production batch corresponds to one production task; the DNC server issues the production tasks of the corresponding production batches to each numerical control machine tool, each numerical control machine tool selects an appropriate tool, and products are produced by executing a corresponding version of a production program; the products are subjected to quality inspection to obtain quality inspection data; all the quality inspection data of the same production batch are used to train an AI model to obtain batch fingerprint data of the corresponding production batch and the matched AI model; and the MES system intelligently manages production process data, batch fingerprint data and the matched AI model of each production batch.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of production batches, and in particular to a method and system for intelligent management of production batches based on an MES system. Background Art

[0002] As key functions within the MES (Manufacturing Execution System) system, material tracking and batch management play a crucial role in achieving production traceability. Through material tracking and batch management, companies can fully understand the production process, identify problems promptly, and ensure product quality and safety. Therefore, companies should strengthen their application of these functions to improve production efficiency and traceability, thereby better meeting market demand.

[0003] Smart manufacturing batch management refers to the management of batches during the production process, including batch generation, recording, querying, and processing. Batch management can help companies understand the status of each batch during the production process, identify problems promptly, and prevent substandard products from entering the market, thereby ensuring product quality and safety.

[0004] Batch management can be achieved through an MES system, which automatically generates batch numbers and records and queries each batch. During the production process, the MES system automatically records the production status of each batch, including production time, process parameters, production personnel, and other information. If problems arise, the MES system can quickly locate the problematic batch through traceability, allowing timely resolution and ensuring product quality and safety.

[0005] Current batch management solutions only enable production process records and post-production inquiries, but lack anti-counterfeiting capabilities. Furthermore, post-production traceability relies on a complete lack of falsification throughout the entire production process. If anyone tampers with records or falsifies item information during the production process, batch and traceability management for the entire production process becomes ineffective. For example, if a malicious counterfeiter copies the information on a product label and uses it to produce a batch of counterfeit products, traceability based on the product label becomes meaningless. Summary of the Invention

[0006] In view of the above analysis, the embodiments of the present invention aim to provide a production batch intelligent management method and system based on the MES system, so as to solve the problem that the existing production batch management is not intelligent enough and is easy to be forged.

[0007] In one aspect, the present invention discloses a production batch intelligent management method based on an MES system, the method comprising:

[0008] The DNC server formulates a production schedule based on the production plan issued by the MES system; the production schedule is divided into several production batches, each of which corresponds to a production task; the production task includes the CNC machine tool number, the production program version, and the production quantity of the product;

[0009] The DNC server issues production tasks for the corresponding production batches to each CNC machine tool. Each CNC machine tool selects the appropriate tool and produces products by executing the corresponding version of the production program. Quality inspection is performed on the produced products to obtain quality inspection data for each product.

[0010] Use all quality inspection data from the same production batch to train the AI ​​model and obtain the batch fingerprint data of the corresponding production batch and its matching AI model;

[0011] The MES system intelligently manages the production process data, batch fingerprint data and the matching AI model of each production batch.

[0012] On the basis of the above solution, the present invention also makes the following improvements:

[0013] Furthermore, the AI ​​model is used to train all quality inspection data of the same production batch and perform:

[0014] Perform feature extraction on all quality inspection data of the same production batch to obtain the feature vector of the corresponding quality inspection data;

[0015] Use the feature vectors of all quality inspection data from the same production batch as positive sample data to train the AI ​​model to learn the common features in the positive sample data;

[0016] After the AI ​​model training is completed, the common features in the positive sample data extracted by the AI ​​model are used as the batch fingerprint data of the corresponding production batch, and the trained AI model is used as the AI ​​model that matches the batch fingerprint data of the corresponding production batch.

[0017] Furthermore, the feature vector of the quality inspection data is obtained by performing the following operations:

[0018] Sort multiple dimensions involved in the quality inspection standards of products from the same production batch in a fixed manner to form a feature extraction template;

[0019] Feature extraction is performed on each quality inspection data in the same production batch according to the feature extraction template to obtain the feature vector of the corresponding quality inspection data.

[0020] Furthermore, the production tasks of the same production batch are completed based on the same CNC machine tools and cutting tools.

[0021] Furthermore, the production procedure includes a product production sub-procedure, a number printing sub-procedure, and a quality inspection sub-procedure; wherein,

[0022] Product production subroutine directly acts on data machine tools and cutting tools to control the production of products;

[0023] The number printing subroutine is used to print the product number on the produced products;

[0024] The quality inspection subroutine is used to inspect the products produced according to the quality inspection standards and obtain the quality inspection data of each product.

[0025] Furthermore, in the quality inspection subroutine, quality inspection standards of multiple dimensions are set, and quality inspection of products in multiple dimensions is performed to obtain quality inspection data of corresponding products.

[0026] Furthermore, the production procedure includes a subroutine for uploading production batch information;

[0027] The production batch information upload subroutine is used to upload the batch number of the current production batch, as well as the product number, product photo, and quality inspection data of each product produced in the current production batch to the MES batch management server via the DNC server.

[0028] On the other hand, the present invention also provides a production batch intelligent management system based on the MES system, the intelligent management system includes: a factory DNC server, several CNC machine tools, a batch product processing module, an AI intelligent batch management module and an MES batch management server; the factory DNC server is bidirectionally connected to the several CNC machine tools in the factory, the output end of the batch product processing module is connected to the input end of the AI ​​intelligent batch management module, and the output ends of the factory DNC server, batch product processing module and AI intelligent batch management module are all connected to the input end of the MES batch management server; wherein,

[0029] The factory's DNC server is used to formulate a production schedule based on the production plan issued by the MES system. The production schedule is divided into several production batches, each of which corresponds to a production task. The production task includes the CNC machine tool number, the production program version, and the product production quantity. The server is also used to issue the production task corresponding to the production batch to each CNC machine tool.

[0030] Each CNC machine tool is used to select an appropriate tool and produce products by executing the corresponding version of the production program;

[0031] Batch product processing module, used to add product codes to produced products; and used to perform quality inspection on produced products and obtain quality inspection data of each product;

[0032] The AI ​​intelligent batch management module is used to obtain all quality inspection data of the same production batch and train it using the AI ​​model to obtain the batch fingerprint data of the corresponding production batch and its matching AI model;

[0033] The MES batch management server is built into the MES system and is used to intelligently manage the production process data, batch fingerprint data and the matching AI model of each production batch.

[0034] On the basis of the above solution, the present invention also makes the following improvements:

[0035] Furthermore, the MES batch management server,

[0036] It is also used to receive the production schedule sent by the factory's DNC server;

[0037] Also used for receiving a selected tool sent by the CNC machine tool via the DNC server;

[0038] It is also used to receive the product code and quality inspection data of each product output by the batch product processing module;

[0039] It is also used to receive the batch fingerprint data of each production batch and its matching AI model output by the AI ​​intelligent batch management module.

[0040] Furthermore, the production process data of each production batch includes: production batch number, factory number, production program version number, CNC machine tool number, tool number, product number, quality inspection data, batch fingerprint data of the current production batch and its matching AI model.

[0041] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0042] The present invention provides an intelligent production batch management method based on an MES system. A DNC server is used to formulate a production schedule and obtain production tasks for each production batch. After the DNC server executes the production tasks, it performs quality inspections on the products produced, obtaining quality inspection data for each product. The present invention uses all quality inspection data from the same production batch to train an AI model, extracting common features from all quality inspection data from the same production batch, thereby obtaining batch fingerprint data for the corresponding production batch and its corresponding AI model. The MES system intelligently manages the production process data, batch fingerprint data, and its corresponding AI model for each production batch.

[0043] In this invention, because batch fingerprint data is directly related to information such as the machine tools and cutting tools used in this production batch, other counterfeit products, even if they have the same product number as the current production batch, will fail to match the batch fingerprint data through the AI ​​model. This enables the above method to effectively manage production batches and prevent counterfeit products from misappropriating product numbers. This effectively solves the problem of low intelligence in existing production batch management and the susceptibility to counterfeiting.

[0044] The production batch intelligent management system based on the MES system provided by the present invention is implemented based on the same principle as the above method and has the same technical effect, which will not be repeated here.

[0045] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. Like reference symbols denote like components throughout the accompanying drawings.

[0047] Figure 1 Flowchart of the production batch intelligent management method based on the MES system provided in Example 1 of the present invention;

[0048] Figure 2 This is a structural diagram of the production batch intelligent management system based on the MES system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0050] Specific embodiment 1 of the present invention discloses a production batch intelligent management method based on the MES system, the flow chart of the method is as follows Figure 1 The specific instructions are as follows.

[0051] Step S1: The DNC server formulates a production schedule based on the production plan issued by the MES system.

[0052] During the specific implementation process, the MES system formulates detailed production plans and scheduling arrangements based on factors such as order requirements, equipment capabilities and material inventory to ensure production efficiency and delivery time.

[0053] After receiving the production plan issued by the MES system, the factory's DNC (Distributed Numerical Control) server can formulate a corresponding production schedule based on the overall production plan. During the specific implementation process, the factory's DNC server can divide the production schedule into multiple production batches for easy management and scheduling. Each production batch corresponds to a production task. In each production batch, specific production tasks are listed in detail, including: the number of the CNC machine tool (that executes the current production task), the version of the production program (that executes the current production task), and the production quantity of the product. During the specific implementation process, the production task may also include the type and quantity of raw materials required to complete the current production task, the deadline requirements for the production task, etc. The DNC server ensures that each CNC machine tool can use the correct production program to process the product by specifying the version of the production program in the production task of each production batch.

[0054] Step S2: The DNC server issues production tasks corresponding to production batches to each CNC machine tool. Each CNC machine tool selects an appropriate tool and produces products by executing the corresponding version of the production program. The produced products are quality inspected to obtain quality inspection data for each product.

[0055] During implementation, the DNC server not only issues production tasks for each CNC machine tool, but also sends these tasks to the MES batch management server within the MES system. Furthermore, after each CNC machine tool selects its appropriate tool, the DNC server also sends the tool number to the MES batch management server.

[0056] Preferably, in this embodiment, the production procedure includes a product production subroutine, a number printing subroutine, a quality inspection subroutine, and a production batch process information uploading subroutine, which are specifically described as follows.

[0057] The product production subroutine directly acts on CNC machine tools and cutting tools to control the production of products.

[0058] The number printing subroutine is used to print the product number on the produced products; anyone can see the product number from the product.

[0059] The quality inspection subroutine is used to inspect the products produced according to the quality inspection standards and obtain the quality inspection data of each product.

[0060] The production batch process information upload subroutine is used to upload the batch number of the current production batch, as well as the product number, product photo, quality inspection data and other information of each product produced in the current production batch to the MES batch management server via the DNC server.

[0061] It should be noted that after the product is produced, it can be inspected according to the pre-set quality inspection standards to ensure that the products meet the quality inspection requirements. Generally, multiple dimensions of quality inspection standards can be set, and the products can be inspected in multiple dimensions to obtain the quality inspection data of the corresponding products. For example, the quality inspection standards of the products can be designed based on the following dimensions:

[0062] (1) Product dimensional accuracy: Ensure that the product dimensions meet the design requirements, which usually involves the accuracy of dimensions such as length, diameter, angle, shape and position.

[0063] (2) Surface roughness of the product: Evaluate the smoothness of the product surface, which is usually measured by surface roughness parameters.

[0064] (3) Material defects of the product: Check whether there are defects in the material, such as cracks, pores, inclusions, etc. These defects may affect the strength and durability of the parts.

[0065] (4) Casting and forging defects of products: For castings and forgings, it is necessary to check whether there are casting defects or forging defects, such as shrinkage holes, porosity, overburning, etc.

[0066] In the actual product quality inspection process, other specific requirements may also be involved. These requirements depend on the specific application scenarios and industry standards.

[0067] In the aforementioned quality inspection standards, the quality inspection results for product dimensional accuracy and surface roughness can be presented as numerical values. The quality inspection results for product material defects and casting and forging defects can be presented as logical data. Logical data can be represented as 0 / 1 data, with "0" corresponding to "no" and "1" corresponding to "yes."

[0068] It should be noted that, in this embodiment, since each product is naturally generated based on the inherent characteristics of the data machine tools and cutting tools, it is not easy to forge even if it is made public, and the production tasks of the same production batch are completed based on the same CNC machine tools and cutting tools. Therefore, there are commonalities between the products produced in the same production batch, and there are also common characteristics between the quality inspection data of the products produced in the same production batch. The above quantifiable quality inspection data can objectively reflect the commonalities between all products of the same production batch due to the use of the same CNC machine tools and cutting tools for production, and can also objectively reflect the differences between products of different production batches due to the use of different CNC machine tools and cutting tools for production. Based on this technical concept, in the subsequent step S3, this embodiment extracts the common characteristics of the quality inspection data of the same production batch by means of an AI model to realize the intelligent management of product information of the same production batch.

[0069] Step S3: Use all quality inspection data of the same production batch to train the AI ​​model to obtain the batch fingerprint data of the corresponding production batch and its matching AI model.

[0070] In step S3, the AI ​​model is used to train and extract the common features of the quality inspection data of the same production batch as batch fingerprint data, which usually involves the following steps.

[0071] Step S31: performing feature extraction on all quality inspection data of the same production batch to obtain feature vectors of the corresponding quality inspection data.

[0072] During the specific implementation process, the multiple dimensions involved in the quality inspection standards of products in the same production batch can be sorted in a fixed manner to form a feature extraction template; feature extraction is performed on each quality inspection data in the same production batch according to the feature extraction template to obtain the feature vector of the corresponding quality inspection data.

[0073] Step S32: Use the feature vectors of all quality inspection data of the same production batch as positive sample data to train the AI ​​model to learn the common features in the positive sample data.

[0074] During the specific implementation process, we can select a suitable AI model based on actual experience or conduct a large number of experiments, such as a deep learning model (using DNN, CNN, Transformer, etc.), use the feature vectors of all quality inspection data from the same batch as positive sample data, and train the AI ​​model to learn the common features in the positive sample data. These common features can characterize the common properties of all products produced in the same production batch.

[0075] During the specific implementation process, unsupervised learning methods such as autoencoders can be used to automatically learn useful feature representations from the feature vectors of all quality inspection data of the same production batch.

[0076] During AI model training, appropriate evaluation metrics, such as precision, recall, and F1 score, can be used to assess the model's performance. This ensures that the model can effectively extract common features from positive sample data and has good generalization capabilities. Based on the evaluation results, the AI ​​model can be adjusted and optimized to improve its accuracy and practicality.

[0077] In practical applications, both feature extraction and model training for AI models require consideration of specific production requirements and data characteristics, selecting the appropriate model architecture and training strategy. Furthermore, AI model evaluation and optimization is an ongoing process, requiring continuous adjustments based on training feedback. In this way, AI models can help companies more effectively conduct quality control and make product counterfeiting more difficult.

[0078] Step S33: After the AI ​​model training is completed, the common features in the positive sample data extracted by the AI ​​model are used as the batch fingerprint data of the corresponding production batch, and the trained AI model is used as the AI ​​model matching the batch fingerprint data of the corresponding production batch.

[0079] The AI ​​model that matches the batch fingerprint data of the current production batch can ensure that the quality inspection data of all products in the current production batch conform to the batch fingerprint data of the current production batch (that is, the batch fingerprint data obtained by inputting the feature vectors of each quality inspection data of the same production batch into the corresponding AI model matches the batch fingerprint data of the current production batch), while the quality inspection data of products from other production batches do not conform to the batch fingerprint data of the current production batch (that is, the batch fingerprint data obtained by inputting the feature vectors of the quality inspection data of other production batches into the corresponding AI model does not match the batch fingerprint data of the current production batch). This makes it possible to use the trained AI model and batch fingerprint data of the corresponding batch for anomaly detection.

[0080] After training is completed, the batch fingerprint data of the corresponding production batch and its matching AI model can also be sent to the MES batch management server.

[0081] Step S4: The MES system intelligently manages the production process data, batch fingerprint data and the matching AI model of each production batch.

[0082] Specifically, in this embodiment, the production process data of each production batch includes but is not limited to: production batch number, factory number, production program version number, CNC machine tool number, tool number, product number, quality inspection data, batch fingerprint data of the current production batch and its matching AI model.

[0083] Since batch fingerprint data is directly related to the machine tools and cutting tools used in this production batch, other counterfeit products will fail to match the batch fingerprint data through the AI ​​model even if the product numbers are the same as those of the current production batch, thereby achieving effective management of production batches and preventing counterfeit products from stealing product number information.

[0084] Specific embodiment 2 of the present invention discloses a production batch intelligent management system based on the MES system, the structural diagram of which is shown in FIG. Figure 2 As shown, the intelligent management system includes: a DNC server of the factory, several CNC machine tools (i.e. Figure 2The DNC server of the factory is connected to several CNC machine tools in the factory in a two-way communication manner. The output end of the batch product processing module is connected to the input end of the AI ​​intelligent batch management module. The output ends of the DNC server, batch product processing module and AI intelligent batch management module of the factory are all connected to the input end of the MES batch management server.

[0085] The factory's DNC server is used to formulate a production schedule based on the production plan issued by the MES system. The production schedule is divided into several production batches, each of which corresponds to a production task. The production task includes the CNC machine tool number, the production program version, and the product production quantity. The server is also used to issue the production task corresponding to the production batch to each CNC machine tool.

[0086] Each CNC machine tool is used to select an appropriate tool and produce products by executing the corresponding version of the production program;

[0087] Batch product processing module, used to add product codes to produced products; and used to perform quality inspection on produced products and obtain quality inspection data of each product;

[0088] The AI ​​intelligent batch management module is used to obtain all quality inspection data of the same production batch and train it using the AI ​​model to obtain the batch fingerprint data of the corresponding production batch and its matching AI model;

[0089] The MES batch management server is built into the MES system and is used to intelligently manage the production process data, batch fingerprint data and the matching AI model of each production batch.

[0090] During the specific implementation process, the MES batch management server is used to uniformly manage the production batch related information. Therefore, in the interaction process of the various components of the above system, the MES batch management server is also used to: receive the production schedule sent by the factory's DNC server; receive the selected tools sent by the CNC machine tool via the DNC server; receive the product code and quality inspection data of each product output by the batch product processing module; receive the batch fingerprint data of each production batch output by the AI ​​intelligent batch management module and its matching AI model to obtain complete production batch related information.

[0091] During specific implementation, the batch product processing module includes a built-in serial number printing subroutine (see Example 1), which allows the addition of product codes to manufactured products. The batch product processing module also includes a built-in quality inspection subroutine, which allows for quality inspection of manufactured products and obtains quality inspection data for each product. The details of the serial number printing subroutine and the quality inspection subroutine are described in Example 1 above and will not be repeated here.

[0092] The specific implementation process of the embodiment of the present invention can be referred to the above method embodiment, which will not be described in detail in this embodiment. Since the principle of this embodiment is the same as that of the above method embodiment, the system also has the corresponding technical effects of the above method embodiment.

[0093] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0094] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A production batch intelligent management method based on MES system, characterized in that: The method comprises: The DNC server formulates a production schedule based on the production plan issued by the MES system; the production schedule is divided into several production batches, each of which corresponds to a production task; the production task includes the CNC machine tool number, the production program version, and the production quantity of the product; The DNC server issues production tasks for the corresponding production batches to each CNC machine tool. Each CNC machine tool selects the appropriate tool and produces products by executing the corresponding version of the production program. Quality inspection is performed on the produced products to obtain quality inspection data for each product. Use all quality inspection data from the same production batch to train the AI ​​model and obtain the batch fingerprint data of the corresponding production batch and its matching AI model; The MES system intelligently manages the production process data, batch fingerprint data, and the matching AI model of each production batch; The AI ​​model is trained using all quality inspection data from the same production batch and performs: Perform feature extraction on all quality inspection data of the same production batch to obtain the feature vector of the corresponding quality inspection data; Use the feature vectors of all quality inspection data from the same production batch as positive sample data to train the AI ​​model to learn the common features in the positive sample data; After the AI ​​model training is completed, the common features in the positive sample data extracted by the AI ​​model are used as the batch fingerprint data of the corresponding production batch, and the trained AI model is used as the AI ​​model for matching the batch fingerprint data of the corresponding production batch; the batch fingerprint data obtained by inputting the feature vectors of the quality inspection data of other production batches into the AI ​​model of the current production batch does not match the batch fingerprint data of the current production batch.

2. The production batch intelligent management method based on the MES system according to claim 1 is characterized in that: The feature vector of the quality inspection data is obtained by performing the following operations: Sort multiple dimensions involved in the quality inspection standards of products from the same production batch in a fixed manner to form a feature extraction template; Feature extraction is performed on each quality inspection data in the same production batch according to the feature extraction template to obtain the feature vector of the corresponding quality inspection data.

3. The production batch intelligent management method based on the MES system according to claim 1 or 2, characterized in that: The production tasks of the same production batch are completed based on the same CNC machine tools and cutting tools.

4. The production batch intelligent management method based on the MES system according to claim 3 is characterized in that: The production procedure includes product production sub-procedure, number printing sub-procedure, and quality inspection sub-procedure; wherein, Product production subroutine directly acts on data machine tools and cutting tools to control the production of products; The number printing subroutine is used to print the product number on the produced products; The quality inspection subroutine is used to inspect the products produced according to the quality inspection standards and obtain the quality inspection data of each product.

5. The production batch intelligent management method based on the MES system according to claim 4 is characterized in that: In the quality inspection subroutine, set quality inspection standards in multiple dimensions, conduct quality inspection on products in multiple dimensions, and obtain quality inspection data of the corresponding products.

6. The production batch intelligent management method based on the MES system according to claim 4 is characterized in that: The production procedure includes a production batch information uploading subroutine; The production batch information upload subroutine is used to upload the batch number of the current production batch, as well as the product number, product photo, and quality inspection data of each product produced in the current production batch to the MES batch management server via the DNC server.

7. A production batch intelligent management system based on MES system, characterized by: The intelligent management system is implemented based on the production batch intelligent management method based on the MES system according to any one of claims 1 to 6, and the intelligent management system includes: a DNC server of the factory, several CNC machine tools, a batch product processing module, an AI intelligent batch management module and an MES batch management server; the DNC server of the factory is bidirectionally connected to several CNC machine tools in the factory, the output end of the batch product processing module is connected to the input end of the AI ​​intelligent batch management module, and the output ends of the DNC server, batch product processing module and AI intelligent batch management module of the factory are all connected to the input end of the MES batch management server; wherein, The factory's DNC server is used to formulate a production schedule based on the production plan issued by the MES system. The production schedule is divided into several production batches, each of which corresponds to a production task. The production task includes the CNC machine tool number, the production program version, and the product production quantity. The server is also used to issue the production task corresponding to the production batch to each CNC machine tool. Each CNC machine tool is used to select an appropriate tool and produce products by executing the corresponding version of the production program; Batch product processing module, used to add product codes to produced products; and used to perform quality inspection on produced products and obtain quality inspection data of each product; The AI ​​intelligent batch management module is used to obtain all quality inspection data of the same production batch and train it using the AI ​​model to obtain the batch fingerprint data of the corresponding production batch and its matching AI model; The MES batch management server is built into the MES system and is used to intelligently manage the production process data, batch fingerprint data and the matching AI model of each production batch.

8. The production batch intelligent management system based on the MES system according to claim 7 is characterized in that: MES batch management server, It is also used to receive the production schedule sent by the factory's DNC server; Also used for receiving a selected tool sent by the CNC machine tool via the DNC server; It is also used to receive the product code and quality inspection data of each product output by the batch product processing module; It is also used to receive the batch fingerprint data of each production batch and its matching AI model output by the AI ​​intelligent batch management module.

9. The production batch intelligent management system based on the MES system according to claim 7 or 8, characterized in that: The production process data of each production batch includes: production batch number, factory number, production program version number, CNC machine tool number, tool number, product number, quality inspection data, batch fingerprint data of the current production batch and its matching AI model.

Images