Intelligent traceability management system and method for electronic resumes of products

Abstract

The invention discloses a product electronic resume intelligent traceability management system and method, belongs to the technical field of product traceability, and solves the problem of insufficient anti-counterfeiting verification function of a product traceability mode. The system comprises a user traceability management client which obtains a product number and quality inspection data of a to-be-traced product; the MES batch management server comprises a storage module and a retrieval module; the storage module is used for storing a product electronic resume of the product, and the product electronic resume comprises a product number, a batch fingerprint feature of a current production batch and an AI model matched with the batch fingerprint feature; the retrieval module retrieves the electronic resumes of the products with the same product numbers according to the product numbers of the to-be-traced products; the traceability matching module is used for processing the quality inspection data of the to-be-traced product by utilizing the retrieved AI model if the retrieval is successful, so as to obtain the batch fingerprint characteristics of the to-be-traced product; if the batch fingerprint features of the to-be-traced product are matched with the retrieved batch fingerprint features, the to-be-traced product is traced successfully; otherwise, the traceability of the to-be-traced product fails.

Description

Technical Field

[0001] The present invention relates to the technical field of product traceability, and in particular to a product electronic history intelligent traceability management system and method. Background Art

[0002] During the batch management process, the MES system can automatically generate batch information for each production batch, and record and query the batch information of each production batch afterwards. If there is a problem with the product during use, the MES system can quickly locate the problem batch through the traceability function, so as to deal with the problem in time and ensure the quality and safety of the product. However, the current production batch management method still has many defects, especially in the anti-counterfeiting verification function, and there are still the following deficiencies: (1) Batch management relies on manual records and post-inquiry: The existing production batch management method is mainly responsible for recording and post-inquiry of the production process, and lacks the ability of real-time monitoring and dynamic management. This method that relies on manual records is susceptible to human errors and fraud. Once someone tampers with records or forges item information during the production process, the entire production batch management and traceability management will become invalid.

[0003] (2) Lack of anti-counterfeiting verification function: Many MES systems lack effective anti-counterfeiting verification functions. If the information in the product label is copied by malicious counterfeiters, the counterfeit products produced can be traced through the label, which makes the traceability system lose its due meaning and function.

[0004] (3) Vulnerability to tampering and counterfeiting: Due to the lack of adequate security measures, information in the production process can be easily tampered with. For example, if someone deliberately changes production data or introduces counterfeit products into the supply chain, existing traceability methods may not be able to detect these changes, resulting in counterfeit products entering the market.

[0005] (4) Lack of digital management of the entire process: Anti-counterfeiting traceability methods require recording the entire process of products from raw material procurement to finished products. However, existing batch management solutions often only focus on the production process and ignore the digital management of other links such as warehousing, distribution, and logistics. This limits the comprehensiveness and accuracy of traceability.

[0006] (5) Singleness of technical means: Existing anti-counterfeiting and traceability technical means are relatively single, such as relying solely on QR codes or barcodes, which are easy to copy and forge.

[0007] (6) Limitations of data analysis and application: The existing traceability system has limitations in data analysis and application, and cannot fully utilize traceability data for in-depth analysis to support corporate decision-making. More advanced data analysis technology is needed to extract valuable information to optimize production processes and improve product quality.

[0008] In summary, the existing production batch management methods have obvious deficiencies in anti-counterfeiting verification, real-time monitoring, full-process management, and data analysis. How to improve the anti-counterfeiting verification function of products and optimize the traceability management method of product electronic records is a technical problem that urgently needs to be solved. Summary of the invention

[0009] In view of the above analysis, the embodiments of the present invention aim to provide a product electronic history intelligent traceability management system and method to solve the problem that the existing product electronic history traceability method has insufficient anti-counterfeiting verification function.

[0010] First, the present invention discloses a product electronic resume intelligent traceability management system, the system includes a user traceability management client, an MES batch management server and a traceability matching module; wherein, User traceability management client to obtain product number and quality inspection data of the product to be traced; The MES batch management server includes a storage module and a retrieval module; wherein, A storage module for storing the electronic product history of the product; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; The search module searches for the electronic history of products with the same product number according to the product number of the product to be traced. If the search fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; The traceability matching module, if the retrieval is successful, uses the retrieved AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails.

[0011] On the basis of the above scheme, the present invention also makes the following improvements: Furthermore, the product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number; The user traceability management client is also used to obtain product-related information of the product to be traced; The traceability matching module, if the retrieval is successful, first determines whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic resume. If they are consistent, the retrieved AI model is used to process the quality inspection data of the product to be traced; otherwise, the traceability of the product to be traced fails.

[0012] Furthermore, in the traceability matching module, a similarity measurement is used to determine whether the batch fingerprint feature of the product to be traced matches the retrieved batch fingerprint feature.

[0013] Furthermore, the traceability matching module is independently set, or is set in the user traceability management client, or is set in the MES batch management server; If the traceability matching module is set up independently, or is set up in the MES batch management server, the traceability results will also be fed back to the user's traceability management client.

[0014] Furthermore, in the MES batch management server, all quality inspection data of the same production batch are used in advance to train the AI ​​model as follows to obtain the batch fingerprint features of the corresponding production batch and its matching AI model: 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 features of the corresponding production batch, and the trained AI model is used as the AI ​​model for matching the batch fingerprint features of the corresponding production batch.

[0015] Furthermore, in the process of pre-training the AI ​​model using all the quality inspection data of the same production batch, the feature vector of the quality inspection data is obtained by performing the following operations: 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; 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.

[0016] Second, the present invention also discloses a method for intelligent traceability management of product electronic records, the method comprising: Start the user traceability management client, obtain the product code of the product to be traced, and send it to the MES batch management server; The MES batch management server retrieves the electronic product history of each product according to the product code of the product to be traced; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; if the retrieval is successful, the retrieved electronic product history with the same product number is sent to the user traceability management client; otherwise, if the retrieval fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; If the search is successful, the user traceability management client starts the quality inspection program to conduct quality inspection on the product to be traced and obtains the quality inspection data of the product to be traced; The user traceability management client uses the received AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the received batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails.

[0017] On the basis of the above scheme, the present invention also makes the following improvements: Furthermore, the product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number; The method further comprises: After starting the user traceability management client, the product association information of the product to be traced is also obtained; After receiving the electronic resume of the product with the same product number, the user traceability management client first determines whether the product association information of the product to be traced corresponds to the product association information in the received electronic resume of the product. If they correspond, it jumps to the step of starting the quality inspection procedure of the user traceability management client. Otherwise, the traceability of the product to be traced fails.

[0018] Third, the present invention also discloses a method for intelligent traceability management of product electronic records, the method comprising: Start the user traceability management client to obtain the product code of the product to be traced; the user traceability management client starts the quality inspection program to conduct quality inspection on the product to be traced and obtain the quality inspection data of the product to be traced; send the product code and quality inspection data of the product to be traced to the MES batch management server; The MES batch management server retrieves the electronic product history of each product according to the product code of the product to be traced; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; if the retrieval fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; If the retrieval is successful, the MES batch management server directly uses the retrieved AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails; and the traceability results are fed back to the user traceability management client.

[0019] On the basis of the above scheme, the present invention also makes the following improvements: Furthermore, the product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number; The method further comprises: After starting the user traceability management client, the product association information of the product to be traced is also obtained; the product code, product association information and quality inspection data of the product to be traced are sent to the MES batch management server together; The MES batch management server retrieves the product electronic history of each product based on the product code of the product to be traced. If the retrieval is successful, it first determines whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic history. If they correspond, it jumps to the step in which the MES batch management server directly uses the retrieved AI model to process the quality inspection data of the product to be traced; otherwise, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client.

[0020] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: The product electronic resume intelligent traceability management system disclosed in the present invention can use the product number of the product to be traced to retrieve the product electronic resume of the same product number, and use the batch fingerprint characteristics in the product electronic resume and the particularity of the matching AI model to determine whether the product to be traced matches the pre-retained product information, thereby realizing anti-counterfeiting verification, and effectively solving the problem of insufficient anti-counterfeiting verification function of the product electronic resume traceability method in the prior art. In the specific implementation process, by limiting the specific functions and mutual coordination relationships of each component, it is possible to well realize the intelligent traceability management of the product electronic resume, and meet the system configuration requirements under different hardware conditions.

[0021] The product electronic resume intelligent traceability management method disclosed in the present invention can also use the batch fingerprint features in the product electronic resume and the particularity of the matching AI model to determine whether the product to be traced matches the pre-retained product information, thereby realizing anti-counterfeiting verification, and effectively solving the problem of insufficient anti-counterfeiting verification function of the product electronic resume traceability method in the prior art. At the same time, by limiting the process steps of two different implementation methods, it is convenient for relevant technical personnel to select the optimal process implementation steps according to actual research and development conditions, which has strong applicability and can meet the needs of product electronic resume intelligent traceability management in different application scenarios.

[0022] 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

[0023] 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; Figure 1 A schematic diagram of the structure of the product electronic resume intelligent traceability management system provided in Example 1 of the present invention; Figure 2 A flowchart of a method for intelligent traceability management of product electronic records provided in Example 2 of the present invention; Figure 3 A flowchart of another method for intelligent traceability management of product electronic records provided in Example 2 of the present invention; Figure 4 This is a flow chart of the product electronic history intelligent traceability management method provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present invention are 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.

[0025] After a user purchases a product, the product number should be printed on it under normal circumstances, and other information such as the declared production factory, CNC machine tool number, etc. may also be printed on it. However, this information may also be a product with the same number forged by a malicious counterfeiter. Therefore, it is necessary to trace the authenticity of the product in the following way. The following embodiment is a product electronic resume intelligent traceability management solution provided to solve this technical problem, which is described in detail as follows.

[0026] A specific embodiment 1 of the present invention discloses a product electronic resume intelligent traceability management system, the structural diagram of which is shown in FIG. Figure 1 As shown, the system includes a user traceability management client, an MES batch management server and a traceability matching module; wherein, User traceability management client to obtain product number and quality inspection data of the product to be traced; The MES batch management server includes a storage module and a retrieval module; wherein, A storage module for storing the electronic product history of the product; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; The search module searches for the electronic history of products with the same product number according to the product number of the product to be traced. If the search fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; The traceability matching module, if the retrieval is successful, uses the retrieved AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails.

[0027] Compared with the prior art, this embodiment uses the product number of the product to be traced to retrieve the electronic history of the product with the same product number, and uses the batch fingerprint features in the product electronic history and the particularity of the AI ​​model it matches to determine whether the product to be traced matches the pre-stored product information, thereby achieving anti-counterfeiting verification and effectively solving the problem of insufficient anti-counterfeiting verification function of the product electronic history traceability method in the prior art.

[0028] Preferably, in this embodiment, the product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number. In this case: The user traceability management client is also used to obtain product-related information of the product to be traced; the product-related information includes at least one of a factory number and a data machine tool number; The traceability matching module, if the retrieval is successful, first determines whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic resume. If they are consistent, the retrieved AI model is used to process the quality inspection data of the product to be traced; otherwise, the traceability of the product to be traced fails.

[0029] In this process, by judging whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic resume, counterfeit products with obviously inconsistent information are filtered out from a relatively simple level, which can effectively improve the traceability efficiency, shorten the traceability time, and reduce the workload of AI model prediction and subsequent batch fingerprint feature matching. Preferably, in the traceability matching module, the batch fingerprint feature of the product to be traced is judged by similarity measurement to determine whether it matches the retrieved batch fingerprint feature.

[0030] Preferably, the traceability matching module is independently set, or is set in the user traceability management client, or is set in the MES batch management server. Since the user traceability management client is designed directly for users, if the traceability matching module is independently set, or is set in the MES batch management server, the traceability result will also be fed back to the user traceability management client. The setting position of the traceability matching module can be set according to the actual situation of the system. When the memory of the user traceability management client and the MES batch management server is relatively limited, the AI ​​model that occupies more memory can be run through the independently set traceability matching module to improve the overall operation efficiency of the system. When the memory of the user traceability management client is large, it can be considered to set the traceability matching module in the user traceability management client, and the traceability result can be directly output. In addition, when the memory of the MES batch management server is large, the traceability matching module can also be set in the MES batch management server, saving the transmission time and transmission cost of the product electronic resume, and avoiding the batch fingerprint characteristics of the production batch and the AI ​​model matching it from being illegally intercepted and obtained.

[0031] It should be noted that, in the MES batch management server of this embodiment, the AI ​​model is trained in advance using all quality inspection data of the same production batch to obtain the batch fingerprint features of the corresponding production batch and its matching AI model.

[0032] (1) Perform feature extraction on all quality inspection data of the same production batch to obtain the feature vector of the corresponding quality inspection data.

[0033] During the specific implementation process, the feature vector of the quality inspection data can be obtained by performing the following operations: sorting the multiple dimensions involved in the quality inspection standards of products in the same production batch in a fixed manner to form a feature extraction template; performing feature extraction 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.

[0034] (2) Use the feature vectors of all quality inspection data of the same production batch as positive sample data and train the AI ​​model to learn the common features in the positive sample data.

[0035] During the specific implementation process, we can select a suitable AI model based on actual experience or through 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 from the same production batch.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] (3) 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 features of the corresponding production batch, and the trained AI model is used as the AI ​​model for matching the batch fingerprint features of the corresponding production batch.

[0040] The AI ​​model that matches the batch fingerprint feature of the current production batch can make the quality inspection data of all products in the current production batch meet the batch fingerprint feature of the current production batch (that is, the batch fingerprint feature 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 feature of the current production batch), while the quality inspection data of products from other production batches do not meet the batch fingerprint feature of the current production batch (that is, the batch fingerprint feature 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 feature of the current production batch). This makes it possible to use the trained AI model and batch fingerprint feature of the corresponding batch to trace the product.

[0041] In addition, the product electronic history can also include product-related information such as production batch number, production program version number, tool number, and quality inspection data. In the specific implementation process, the product-related information that can be obtained from the product to be traced can be matched with the product-related information in the product electronic history to further optimize the traceability management process.

[0042] Specific embodiment 2 of the present invention also provides a product electronic resume intelligent traceability management method, the flow chart is as follows Figure 2 and Figure 3 The method comprises the following steps.

[0043] Step S1: Start the user traceability management client, obtain the product code of the product to be traced, and send it to the MES batch management server.

[0044] In this step, the product number of the product to be traced can be obtained by scanning the code of the product to be traced or directly reading it.

[0045] Step S2: The MES batch management server retrieves the product electronic resume of each product according to the product code of the product to be traced; the product electronic resume includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; if the retrieval is successful, the retrieved product electronic resume with the same product number is sent to the user traceability management client; otherwise, if the retrieval fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client.

[0046] Step S3: If the search is successful, the user traceability management client starts the quality inspection program to perform quality inspection on the product to be traced and obtain the quality inspection data of the product to be traced.

[0047] It should be emphasized that the quality inspection procedure initiated by the user traceability management client should be consistent with the quality inspection procedure of the genuine product with the same product number to ensure that it is carefully inspected according to the same quality inspection standards. 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: (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.

[0048] (2) Surface roughness of the product: Evaluate the smoothness of the product surface, which is usually measured by the surface roughness parameter.

[0049] (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.

[0050] (4) Casting and forging defects of products: For castings and forgings, it is necessary to check whether they have casting defects or forging defects, such as shrinkage holes, looseness, overburning, etc.

[0051] In the actual product quality inspection process, other specific requirements may also be involved, which depend on the specific application scenarios and industry standards.

[0052] In the above-mentioned 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 embodied in the form of 0 / 1 data, "0" corresponds to "no" and "1" corresponds to "yes". 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 products produced in the same production batch, and there are common characteristics between the quality inspection data of 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, and can also objectively reflect the differences between products of different production batches due to the use of different CNC machine tools and tools. Based on this technical concept, 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. In Example 1, a specific process of training the AI ​​model using all quality inspection data of the same production batch and obtaining the batch fingerprint features of the corresponding production batch and its matching AI model is given, which will not be repeated here.

[0053] Step S4: The user traceability management client uses the received AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the received batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails.

[0054] This method is suitable for situations where the user traceability management client has a large memory, and can effectively ensure that the AI ​​model can quickly process the quality inspection data of the products to be traced and obtain the batch fingerprint characteristics of the products to be traced.

[0055] Preferably, the product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number. In this case, the method further includes: In the process of executing step S1, after starting the user traceability management client, the product code of the product to be traced is obtained, and the product association information of the product to be traced is also obtained.

[0056] In the process of executing step S4, after receiving the electronic resume of the product with the same product number, the user traceability management client first determines whether the product association information of the product to be traced corresponds to the product association information in the received electronic resume of the product. If they correspond, it jumps to the step of starting the quality inspection procedure of the user traceability management client. Otherwise, the traceability of the product to be traced fails.

[0057] Specific embodiment 3 of the present invention also provides another product electronic resume intelligent traceability management method, the flow chart is as follows Figure 4 As shown, the method includes the following steps.

[0058] Step S1: Start the user traceability management client to obtain the product code of the product to be traced; the user traceability management client starts the quality inspection program to perform quality inspection on the product to be traced and obtain the quality inspection data of the product to be traced; the product code and quality inspection data of the product to be traced are sent to the MES batch management server.

[0059] Step S2: The MES batch management server retrieves the electronic product history of each product according to the product code of the product to be traced; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; if the retrieval fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client.

[0060] Step S3: If the retrieval is successful, the MES batch management server directly uses the retrieved AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails; and the traceability results are fed back to the user traceability management client.

[0061] Preferably, the product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number. At this time, the method further performs the following operations.

[0062] During the execution of step S1, after starting the user traceability management client, while obtaining the product code of the product to be traced, the product-related information of the product to be traced is also obtained; the product code, product-related information and quality inspection data of the product to be traced are sent together to the MES batch management server.

[0063] During the execution of step S4, the MES batch management server retrieves the product electronic history of each product according to the product code of the product to be traced. If the retrieval is successful, it first determines whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic history. If they correspond, it jumps to the step in which the MES batch management server directly uses the retrieved AI model to process the quality inspection data of the product to be traced; otherwise, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client.

[0064] This method is suitable for situations where the MES batch management server has a large memory, and can effectively ensure that the AI ​​model can quickly process the quality inspection data of the products to be traced and obtain the batch fingerprint characteristics of the products to be traced.

[0065] The specific implementation process of Embodiment 2 and Embodiment 3 of the present invention may refer to the above-mentioned system Embodiment 1, and Embodiment 2 and Embodiment 3 will not be described in detail here.

[0066] Since the principles of the present embodiments 2 and 3 are the same as those of the above-mentioned system embodiment 1, the present method embodiments 2 and 3 also have the corresponding technical effects of the above-mentioned method embodiments.

[0067] 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.

[0068] 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 product electronic resume intelligent traceability management system, characterized in that: The system includes a user traceability management client, an MES batch management server and a traceability matching module; wherein, User traceability management client to obtain product number and quality inspection data of the product to be traced; The MES batch management server includes a storage module and a retrieval module; wherein, A storage module for storing the electronic product history of the product; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; The search module searches for the electronic history of products with the same product number according to the product number of the product to be traced. If the search fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; The traceability matching module, if the retrieval is successful, uses the retrieved AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails.

2. The product electronic history intelligent traceability management system according to claim 1 is characterized in that: The product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number; The user traceability management client is also used to obtain product-related information of the product to be traced; The traceability matching module, if the retrieval is successful, first determines whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic resume. If they are consistent, the retrieved AI model is used to process the quality inspection data of the product to be traced; otherwise, the traceability of the product to be traced fails.

3. The product electronic history intelligent traceability management system according to claim 1 or 2, characterized in that: In the traceability matching module, the similarity measurement method is used to determine whether the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics.

4. The product electronic history intelligent traceability management system according to claim 1 or 2, characterized in that: The traceability matching module is independently set, or is set in the user traceability management client, or is set in the MES batch management server; If the traceability matching module is set up independently, or is set up in the MES batch management server, the traceability results will also be fed back to the user's traceability management client.

5. The product electronic history intelligent traceability management system according to claim 1 is characterized in that: In the MES batch management server, all quality inspection data of the same production batch are used to train the AI ​​model as follows in advance to obtain the batch fingerprint features of the corresponding production batch and its matching AI model: 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 features of the corresponding production batch, and the trained AI model is used as the AI ​​model for matching the batch fingerprint features of the corresponding production batch.

6. The product electronic history intelligent traceability management system according to claim 5 is characterized in that: In the process of pre-training the AI ​​model using all the quality inspection data of the same production batch, the feature vector of the quality inspection data is obtained by performing the following operations: 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; 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.

7. A method for intelligent traceability management of product electronic records, characterized in that: The method comprises: Start the user traceability management client, obtain the product code of the product to be traced, and send it to the MES batch management server; The MES batch management server retrieves the product electronic resume of each product according to the product code of the product to be traced; the product electronic resume includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; if the retrieval is successful, the retrieved product electronic resume with the same product number is sent to the user traceability management client; otherwise, if the retrieval fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; If the search is successful, the user traceability management client starts the quality inspection program to conduct quality inspection on the product to be traced and obtains the quality inspection data of the product to be traced; The user traceability management client uses the received AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the received batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails.

8. The product electronic history intelligent traceability management method according to claim 7 is characterized in that: The product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number; The method further comprises: After starting the user traceability management client, the product association information of the product to be traced is also obtained; After receiving the electronic resume of the product with the same product number, the user traceability management client first determines whether the product association information of the product to be traced corresponds to the product association information in the received electronic resume of the product. If they correspond, it jumps to the step of starting the quality inspection procedure of the user traceability management client. Otherwise, the traceability of the product to be traced fails.

9. A method for intelligent traceability management of product electronic records, characterized in that: The method comprises: Start the user traceability management client to obtain the product code of the product to be traced; the user traceability management client starts the quality inspection program to conduct quality inspection on the product to be traced and obtain the quality inspection data of the product to be traced; send the product code and quality inspection data of the product to be traced to the MES batch management server; The MES batch management server retrieves the electronic product history of each product according to the product code of the product to be traced; the electronic product history includes: product number, batch fingerprint characteristics of the current production batch and its matching AI model; if the retrieval fails, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client; If the retrieval is successful, the MES batch management server directly uses the retrieved AI model to process the quality inspection data of the product to be traced to obtain the batch fingerprint characteristics of the product to be traced; if the batch fingerprint characteristics of the product to be traced match the retrieved batch fingerprint characteristics, the traceability of the product to be traced is successful; otherwise, the traceability of the product to be traced fails; and the traceability results are fed back to the user traceability management client.

10. A method for intelligent traceability management of product electronic records, characterized in that: The product electronic history also includes product related information; the product related information includes at least one of a factory number and a data machine tool number; The method further comprises: After starting the user traceability management client, the product association information of the product to be traced is also obtained; the product code, product association information and quality inspection data of the product to be traced are sent to the MES batch management server together; The MES batch management server retrieves the product electronic history of each product based on the product code of the product to be traced. If the retrieval is successful, it first determines whether the product-related information of the product to be traced corresponds to the product-related information in the retrieved product electronic history. If they correspond, it jumps to the step in which the MES batch management server directly uses the retrieved AI model to process the quality inspection data of the product to be traced; otherwise, the traceability of the product to be traced fails, and the traceability result is fed back to the user traceability management client.

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