Production batch intelligent management method and system based on MES system

Abstract

The invention relates to a production batch intelligent management method and system based on an MES system, belongs to the technical field of production batch intelligent management, and solves the problems that existing production batch management is not high in intelligent degree and is easy to forge. 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 a production task of a corresponding production batch to each numerical control machine tool, each numerical control machine tool selects an adaptive cutter, and a product is produced by executing a production program of a corresponding version; performing quality inspection on the produced product to obtain quality inspection data; training an AI model by using all quality inspection data of the same production batch to obtain batch fingerprint data of the corresponding production batch and the matched AI model; and the MES system performs intelligent management on the production process data, the 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 production batch intelligent management, and in particular to a production batch intelligent management method and system based on a MES system. Background Art

[0002] As one of the important functions in the MES system (manufacturing execution system), material tracking and batch management play a vital role in achieving production traceability. Through material tracking and batch management, enterprises can fully grasp the situation in the production process, find problems in time, and ensure product quality and safety. Therefore, enterprises should strengthen the application of functions such as material tracking and batch management to improve production efficiency and traceability, so as to better meet market demand.

[0003] Smart manufacturing batch management refers to the management of batches in the production process, including batch generation, recording, query and processing. Batch management can help companies understand the situation of each batch in the production process, find problems in time, and prevent unqualified products from entering the market, thereby ensuring product quality and safety.

[0004] Batch management can be achieved through the MES system, which can automatically generate batch numbers and record and query each batch. During the production process, the MES system will automatically record the production status of each batch, including production time, process parameters, production personnel and other information. If a problem occurs, the MES system can quickly locate the problem batch through the traceability function, so as to deal with the problem in a timely manner and ensure the quality and safety of the product.

[0005] The current batch management solution can only realize the production process record and post-inquiry, and does not have anti-counterfeiting function. At the same time, the post-investigation traceability also depends on the fact that no one falsifies the entire production process. Once someone tampers with the records or falsifies the item information during the production process, the batch management and traceability management of the entire production process will be completely invalid. For example: if the information in the product label is copied by a malicious counterfeiter, and a batch of counterfeit products are produced using the information in the copied product label, it will make the traceability through the product label 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] On the one hand, 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 according to 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 number of the CNC machine tool, the version of the production program and the production quantity of the product;

[0009] The DNC server sends the production tasks of the corresponding production batches to each CNC machine tool, and each CNC machine tool selects the appropriate tool and produces products by executing the corresponding version of the production program; the produced products are quality inspected to obtain the quality inspection data of each product;

[0010] Use all quality inspection data of 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 scheme, 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 execute:

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

[0015] 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;

[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] Arrange multiple dimensions involved in the quality inspection standards of products of 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 subroutines act directly 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 produced products 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 in multiple dimensions are set, and quality inspections in multiple dimensions are performed on products to obtain quality inspection data of corresponding products.

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

[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 DNC server of a 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 with 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,

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

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

[0032] 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 scheme, 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 numerical control 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 output by the AI ​​intelligent batch management module and its matching AI model.

[0040] Furthermore, the production process data of each production batch includes: production batch number, factory number, version number of the production program, CNC machine 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 intelligent management method for production batches based on the MES system provided by the present invention formulates a production scheduling plan through a DNC server to obtain the production tasks of each production batch; after the DNC server executes the production tasks, the produced products can be quality inspected to obtain the quality inspection data of each product. The present invention uses all the quality inspection data of the same production batch to train the AI ​​model, extracts the common features of all the quality inspection data of the same production batch, and thus obtains 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 its matching AI model of each production batch.

[0043] In the present invention, since the batch fingerprint data is directly related to the machine tool and tool information used in the current production batch, other counterfeit products will fail as long as they are matched with the batch fingerprint data through the AI ​​model, even if the product number is the same as that of the current production batch, so that the above method can achieve effective management of production batches and avoid counterfeit products from misappropriating product number information. It effectively solves the problem that the existing production batch management is not intelligent enough and is easy to be forged.

[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 also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the 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. In the entire drawings, the same reference symbols represent the same components;

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

[0048] Figure 2 A schematic diagram of the structure 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, but 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, and the flowchart of the method is as follows: Figure 1 The specific instructions are as follows.

[0051] Step S1: The DNC server formulates a production schedule according to 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 (performing the current production task), the version of the production program (performing 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 of 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 sends the production tasks of the corresponding production batches to each CNC machine tool, and 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 the quality inspection data of each product.

[0055] During the specific implementation process, the DNC server sends the production tasks of the corresponding production batches to each CNC machine tool, and also sends the production tasks of each production batch to the MES batch management server in the MES system. In addition, after each CNC machine tool selects the appropriate tool, it also sends the number of the selected tool to the MES batch management server via the DNC 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 produced products 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, the product can be inspected according to the pre-set quality inspection standards to ensure that the product meets the quality inspection requirements. Generally, multiple dimensions of quality inspection standards can be set, and the product can be inspected in multiple dimensions to obtain the quality inspection data of the corresponding product. For example, the quality inspection standards of the product can be designed from 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 the surface roughness parameter.

[0064] (3) Product material defects: 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, looseness, overburning, etc.

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

[0067] In the above quality inspection standards, the quality inspection results of the two dimensions of product dimensional accuracy and product surface roughness can be presented in the form of numerical values. The quality inspection results of the two dimensions of product material defects and product casting and forging defects can be presented in the form of logical data. Logical data can be presented in the form of 0 / 1 data, where "0" corresponds to "no" and "1" corresponds to "yes".

[0068] It should be noted that in this embodiment, since each product is naturally generated according to the inherent characteristics of the data machine tool and the tool, 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 tool and tool. 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 tool and tool 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 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: extracting features from all quality inspection data of the same production batch respectively to obtain feature vectors of the corresponding quality inspection data.

[0072] During the specific implementation process, 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 a 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, such as a deep learning model (using DNN, CNN, Transformer, etc.) based on actual experience or through a large number of experiments. We can 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 from 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] In the process of training AI models, appropriate evaluation indicators, such as accuracy, recall, F1 score, etc., can be used to evaluate the performance of AI models to ensure that the AI ​​models can effectively extract the common features of positive sample data and have good generalization capabilities. Based on the evaluation results, the AI ​​models can be adjusted and optimized to improve their accuracy and practicality.

[0077] In practical applications, both feature extraction and model training of AI models need to be combined with specific production requirements and data characteristics to select appropriate model architecture and training strategies. At the same time, the evaluation and optimization of AI models is also an ongoing process that requires continuous adjustments based on training feedback. In this way, AI models can help companies conduct quality control more effectively and increase the difficulty of product counterfeiting.

[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 that matches 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 make 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 respectively matches the batch fingerprint data of the current production batch), while the quality inspection data of products in 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, version number of the production program, 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 the 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 DNC server of the factory is used to formulate a production schedule according to the production plan issued by the MES system; the production schedule is divided into several production batches, each production batch corresponds to a production task; the production task includes the number of the CNC machine tool, the version of the production program and the production quantity of the product; and is also used to issue the production tasks 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 a corresponding version of the production program;

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

[0088] 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, so as to obtain complete production batch related information.

[0091] In the specific implementation process, the batch product processing module has a built-in number printing subroutine (see Example 1), so as to implement the operation of adding product codes to the products produced. The batch product processing module also has a built-in quality inspection subroutine, so as to implement quality inspection of the products produced and obtain quality inspection data of each product. The relevant contents of the number printing subroutine and the quality inspection subroutine are referred to in the above-mentioned Example 1, which 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, and this embodiment will not be repeated here. 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 processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0094] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope 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 according to 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 number of the CNC machine tool, the version of the production program and the production quantity of the product; The DNC server sends the production tasks of the corresponding production batches to each CNC machine tool, and each CNC machine tool selects the appropriate tool and produces products by executing the corresponding version of the production program; the produced products are quality inspected to obtain the quality inspection data of each product; Use all quality inspection data of 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.

2. The production batch intelligent management method based on the MES system according to claim 1 is characterized in that: The AI ​​model is used to train all quality inspection data of the same production batch and perform: Perform feature extraction on all quality inspection data of the same production batch to obtain feature vectors of corresponding quality inspection data; 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; 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.

3. The production batch intelligent management method based on the MES system according to claim 2 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 of the same production batch in a fixed manner to form a feature extraction template; For each quality inspection data in the same production batch, feature extraction is performed according to the feature extraction template to obtain the feature vector of the corresponding quality inspection data.

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

5. The production batch intelligent management method based on the MES system according to claim 4 is characterized in that: The production procedure includes product production sub-procedure, number printing sub-procedure, and quality inspection sub-procedure; wherein, Product production subroutines act directly 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 produced products according to the quality inspection standards and obtain the quality inspection data of each product.

6. The production batch intelligent management method based on the MES system according to claim 5 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 corresponding products.

7. The production batch intelligent management method based on the MES system according to claim 5 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.

8. A production batch intelligent management system based on MES system, characterized in that: The intelligent management system includes: a DNC server, several CNC machine tools, a batch product processing module, an AI intelligent batch management module and an MES batch management server of the factory; the DNC server of the factory is bidirectionally connected with 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 DNC server of the factory is used to formulate a production schedule according to the production plan issued by the MES system; the production schedule is divided into several production batches, each production batch corresponds to a production task; the production task includes the number of the CNC machine tool, the version of the production program and the production quantity of the product; and is also used to issue the production tasks 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 a corresponding version of the production program; Batch product processing module, used to add product codes to the products produced; and to perform quality inspection on the products produced to obtain quality inspection data of each product; 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.

9. The production batch intelligent management system based on the MES system according to claim 8 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 numerical control 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 output by the AI ​​intelligent batch management module and its matching AI model.

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

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