An electronic component authentication method and system

Abstract

The application discloses an electronic component authentication method and system, which is characterized in that: through a code scanning, camera shooting and RFID reading device, multi-dimensional collection of graphic codes, images and RFID data of target batch electronic components is carried out within an authentication period; according to an authentication result, the number N of batch authentication components is dynamically adjusted, and multi-dimensional authentication feature data and record data are generated; feature sets are formed by collecting feature data of multiple authentication periods; an authentication classification model is constructed by using a decision tree; and efficient classification and query of batch authentication records of electronic components are realized through the classification model; and through the application, the retrieval efficiency of product authentication information can be effectively improved, and the authentication data management efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of electronic component information authentication, and more specifically, to an electronic component authentication method and system. Background Technology

[0002] With the rapid development of electronic technology, the types and quantities of electronic components have increased dramatically. Ensuring the quality, security, and compliance of these components during production and encryption authentication processes has become a pressing issue. Traditional electronic component authentication methods largely rely on manual inspection and auditing, which is not only time-consuming and labor-intensive but also prone to errors. Furthermore, existing electronic component authentication processes are inefficient, often involving individual authentication and storing authentication data record by record, lacking efficient batch authentication storage methods. The single storage method for authentication records hinders subsequent record maintenance and results in low query efficiency. Simultaneously, data security cannot be guaranteed. Therefore, an efficient electronic component authentication method is needed to meet the large-scale authentication and query needs of electronic components and related products. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and proposes a method and system for authenticating electronic components.

[0004] The first aspect of this invention provides a method for certifying electronic components, comprising: Obtain the testing and certification plan for the target batch of electronic components, and set the initial batch certification quantity N based on the certification plan; During an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, camera, and RFID reader, resulting in graphic code data, image data, and RFID identification data. Multi-mode data authentication and recognition are performed on graphic code data, image data, and RFID identification data. The authentication result is judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated for graphic code data, image data, and RFID identification data based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components. Authentication feature data corresponding to multiple authentication time periods are collected to form an authentication feature set. Based on the decision tree model, feature selection and feature construction are performed on the feature data in the authentication feature set to construct multiple condition nodes. An authentication classification model based on decision tree is then constructed through these multiple condition nodes. The system associates authentication feature data with corresponding authentication record data and stores them in the system database. The system obtains authentication query tags for electronic components through user terminals, generates component authentication features based on the authentication query tags, imports the component authentication features into the authentication classification model for authentication classification, identifies the authentication classification results, and filters user authentication record data from the system database based on the authentication classification results.

[0005] In this solution, the step of obtaining the testing and certification plan for the target batch of electronic components, and setting the initial batch certification quantity N according to the certification plan, specifically involves: Obtain the testing and certification plan for the target batch of electronic components, wherein the testing and certification plan includes information on the number of electronic components to be certified, the testing and certification frequency, and the testing and certification mode. By conducting a testing and certification plan, a feasibility analysis is performed on the certification requirements and certification equipment, and the initial batch certification quantity N of components is set.

[0006] In this solution, within an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, a camera, and an RFID reader, yielding graphic code data, image data, and RFID identification data, specifically: Multiple certification periods are set based on the testing and certification plan; During an authentication period, N electronic components are scanned and data is collected using a barcode scanner, a camera, and an RFID reader, resulting in graphic code data, image data, and RFID identification data. Image denoising, enhancement, and standardization preprocessing are performed on graphic code data and image data.

[0007] In this scheme, multi-mode data authentication and recognition are performed on graphic code data, image data, and RFID identification data. The authentication result is then judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components. Specifically: Based on the graphic code data, graphic code feature information is extracted to obtain graphic code feature data; Contour recognition of electronic components is performed on image data. The grayscale difference of image pixels is analyzed, and contour features and texture features are extracted by the Prewitt operator. The contour features and texture features are integrated as the appearance feature data of electronic components. RFID identification data is analyzed to form RFID identification tag data; Multi-mode identification and authentication of electronic components is performed based on graphic code feature data, appearance feature data, and RFID identification tag data, generating authentication results in three dimensions. If the authentication result meets the expected result, the value N will be dynamically increased based on the preset step size. Feature analysis is performed on graphic code feature data, appearance feature data and RFID identification tag data. The main features are extracted using PCA analysis. Based on the feature splicing method, the extracted main feature data are spliced ​​to form multi-dimensional authentication feature data. The graphic code data, image data, RFID identification data, and authentication results are integrated into authentication record data.

[0008] In this solution, the determination of the authentication result also includes: If the authentication result does not meet expectations, the value N will be dynamically reduced based on a preset step size.

[0009] In this scheme, authentication feature data corresponding to multiple authentication time periods are collected to form an authentication feature set. Based on a decision tree model, feature selection and feature construction are performed on the feature data in the authentication feature set to construct multiple condition nodes. A decision tree-based authentication classification model is then constructed using these multiple condition nodes. Specifically: Collect authentication feature data corresponding to multiple authentication time periods to form an authentication feature set; For each authentication feature set as a subsample set, features with a preset amount of data are randomly selected from each subsample set to transform the feature judgment conditions and generate multiple judgment nodes. Based on the decision nodes, the root node and internal nodes are determined using the CART algorithm, and leaf nodes are generated step by step to obtain an authentication classification model based on a decision tree. The authentication classification model is tested and trained using authentication feature data corresponding to K preset authentication time periods as test data.

[0010] In this solution, the authentication feature data is associated with the corresponding authentication record data and stored in the system database. The authentication query tag of the electronic component is obtained through the user terminal. The component authentication feature is generated based on the authentication query tag. The component authentication feature is imported into the authentication classification model for authentication classification, and the authentication classification result is identified. Based on the authentication classification result, user authentication record data is filtered from the system database. Specifically: The authentication feature data is associated with the corresponding authentication record data and stored in the system database; Users input authentication query information through their user terminals, and authentication query tags are generated based on this information. Component authentication features are generated by using authentication query tags. These features are then imported into an authentication classification model to perform initial classification of authentication information, resulting in authentication classification results. Based on the authentication classification results, the corresponding authentication record dataset is selected from the system database; A secondary retrieval of records is performed from the authentication record dataset, and user authentication record data is further retrieved.

[0011] A second aspect of the present invention also provides an electronic component authentication system, the system comprising: a memory and a processor, wherein the memory includes an electronic component authentication program, and the electronic component authentication program, when executed by the processor, performs the following steps: Obtain the testing and certification plan for the target batch of electronic components, and set the initial batch certification quantity N based on the certification plan; During an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, camera, and RFID reader, resulting in graphic code data, image data, and RFID identification data. Multi-mode data authentication and recognition are performed on graphic code data, image data, and RFID identification data. The authentication result is judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated for graphic code data, image data, and RFID identification data based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components. Authentication feature data corresponding to multiple authentication time periods are collected to form an authentication feature set. Based on the decision tree model, feature selection and feature construction are performed on the feature data in the authentication feature set to construct multiple condition nodes. An authentication classification model based on decision tree is then constructed through these multiple condition nodes. The system associates authentication feature data with corresponding authentication record data and stores them in the system database. The system obtains authentication query tags for electronic components through user terminals, generates component authentication features based on the authentication query tags, imports the component authentication features into the authentication classification model for authentication classification, identifies the authentication classification results, and filters user authentication record data from the system database based on the authentication classification results.

[0012] A third aspect of the present invention also provides a computer-readable storage medium including an electronic component authentication program, which, when executed by a processor, implements the steps of the electronic component authentication method as described in any of the preceding claims.

[0013] This invention discloses an electronic component authentication method and system. It utilizes barcode scanning, imaging, and RFID reading devices to collect multi-dimensional data (graphic codes, images, and RFID data) of target batches of electronic components during the authentication period. The number of components (N) to be authenticated in a batch is dynamically adjusted based on the authentication results, and multi-dimensional authentication feature data and record data are generated. Feature data from multiple authentication periods are collected to form a feature set. A authentication classification model is constructed using a decision tree. This model enables efficient classification and querying of batch authentication records for electronic components. This invention effectively improves the retrieval efficiency of product authentication information and enhances the efficiency of authentication data management. Attached Figure Description

[0014] Figure 1 A flowchart of an electronic component authentication method according to the present invention is shown; Figure 2 A block diagram of an electronic component authentication system according to the present invention is shown. Detailed Implementation

[0015] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0016] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0017] Figure 1 A flowchart of an electronic component authentication method according to the present invention is shown.

[0018] like Figure 1 As shown, the first aspect of the present invention provides an electronic component authentication method, comprising: S102, Obtain the testing and certification plan for the target batch of electronic components, and set the initial batch certification quantity N according to the certification plan; S104, within an authentication period, uses a barcode scanner, a camera, and an RFID reader to collect multi-dimensional authentication data from N electronic components, obtaining graphic code data, image data, and RFID identification data respectively. S106, perform multi-mode data authentication and recognition on graphic code data, image data, and RFID identification data, judge the authentication result, and if the authentication result meets the expected result, generate multi-dimensional authentication feature data and corresponding authentication record data on graphic code data, image data, and RFID identification data according to the authentication result, and dynamically increase the value N. In the next authentication period, data collection and authentication are performed on N electronic components. S108: Collect authentication feature data corresponding to multiple authentication time periods to form an authentication feature set. Based on the decision tree model, perform feature selection and feature construction on the feature data in the authentication feature set to build multiple condition nodes. Construct an authentication classification model based on decision tree through multiple condition nodes. S110: Associate the authentication feature data with the corresponding authentication record data and store them in the system database. Obtain the authentication query tag of the electronic component through the user terminal, generate the component authentication feature through the authentication query tag, import the component authentication feature into the authentication classification model for authentication classification, and identify the authentication classification result. Based on the authentication classification result, filter the user authentication record data from the system database.

[0019] It should be noted that during the production and warehousing of electronic components, multi-mode certification and testing are often required to certify the production of the components and ensure their quality, safety, and compliance.

[0020] The graphic code data, image data, and RFID identification data are all authentication data and possess a certain degree of confidentiality. Within the device-side software platform, multi-CAN port communication technology is used to generate corresponding data encryption schemes based on different component products. Data generated by different components is categorized and managed. Based on different encryption schemes, relevant keys are generated and recorded within the platform. The platform distributes keys to users with different permissions. Keys are transmitted and distributed via secure transmission protocols (such as TLS / SSL). The transmission employs a two-way authentication mechanism using national standard symmetric encryption to ensure the security of key transmission. The device-side software platform has functions such as key generation, key storage, key distribution, key update, and key destruction. The platform has remote monitoring capabilities, enabling the tracing of the source of abnormal components and the preservation of authentication records for components that fail authentication, thus identifying the responsible party.

[0021] According to an embodiment of the present invention, the step of obtaining the testing and certification plan for the target batch of electronic components, and setting the initial batch certification quantity N according to the certification plan, specifically involves: Obtain the testing and certification plan for the target batch of electronic components, wherein the testing and certification plan includes information on the number of electronic components to be certified, the testing and certification frequency, and the testing and certification mode. By conducting a testing and certification plan, a feasibility analysis is performed on the certification requirements and certification equipment, and the initial batch certification quantity N of components is set.

[0022] It should be noted that the initial batch certification quantity N is a low value used to set the initial one-time identification quantity of the certification device. The certification device includes a barcode scanner, a camera, and an RFID reader. For different identification modes, it has the capability to collect and identify data from multiple electronic components at once. For example, for the RFID reader, it can simultaneously read and identify the electronic tags of multiple components. By dynamically setting the quantity N, batch certification of components can be effectively achieved and certification efficiency can be maximized.

[0023] Within each certification period, a batch identification process is performed. By dynamically setting the value N, the batch identification is maximized by utilizing the detection device. Subsequently, a classification model is constructed based on the corresponding certification feature data collected in each certification period, which effectively enables the classification and efficient querying of certification data in the system database, thereby achieving a highly automated certification process for electronic component systems.

[0024] According to an embodiment of the present invention, within an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, a camera, and an RFID reader to obtain graphic code data, image data, and RFID identification data, respectively. Specifically: Multiple certification periods are set based on the testing and certification plan; During an authentication period, N electronic components are scanned and data is collected using a barcode scanner, a camera, and an RFID reader, resulting in graphic code data, image data, and RFID identification data. Image denoising, enhancement, and standardization preprocessing are performed on graphic code data and image data.

[0025] It should be noted that the graphic code data is used for QR code and barcode authentication, the image data is used to analyze the product's appearance features, and the RFID identification data is used to analyze RFID tag data. All three types of data are used for product authentication.

[0026] According to an embodiment of the present invention, the multi-mode data authentication and recognition of graphic code data, image data, and RFID identification data is performed, and the authentication result is judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated for the graphic code data, image data, and RFID identification data based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components, specifically as follows: Based on the graphic code data, graphic code feature information is extracted to obtain graphic code feature data; Contour recognition of electronic components is performed on image data, grayscale differences of image pixels are analyzed, contour features and texture features are extracted using the Prewitt operator, and the contour features and texture features are integrated as appearance feature data of electronic components. RFID identification data is analyzed to form RFID identification tag data; Multi-mode identification and authentication of electronic components is performed based on graphic code feature data, appearance feature data, and RFID identification tag data, generating authentication results in three dimensions. If the authentication result meets the expected result, the value N will be dynamically increased based on the preset step size. Feature analysis is performed on graphic code feature data, appearance feature data and RFID identification tag data. The main features are extracted using PCA analysis. Based on the feature splicing method, the extracted main feature data are spliced ​​to form multi-dimensional authentication feature data. The graphic code data, image data, RFID identification data, and authentication results are integrated into authentication record data.

[0027] It should be noted that the multi-mode identification authentication of electronic components based on graphic code feature data, appearance feature data, and RFID identification tag data generates three-dimensional authentication results. Different authentication modes share certain identifiers. For example, graphic code feature data can identify the electronic component ID, specifications, manufacturer name, and production date; appearance feature data can identify specifications and appearance; and RFID identification tag data can identify the electronic component ID, logistics history, and batch information. The corresponding authentication results include authentication results for all three modes (i.e., three dimensions). If all authentications are successful, the result meets expectations. After dynamically increasing the value N, in the next authentication period, data collection and authentication are performed on N electronic components based on the increased N. Authentication record data also includes authentication time, number of authentications, and the authentication mode set. In the multi-dimensional authentication feature data, each dimension corresponds to one type of authentication data.

[0028] Through the process of this invention, electronic components can be quickly queried, filtered, and classified in multiple batches or large-scale certification data, and the corresponding certification records can be located and retrieved. This improves the efficiency of product information retrieval and certification data management. Compared with the traditional certification records and queries of each electronic component individually, this invention can greatly reduce the amount of data processing and is conducive to data storage for security platforms such as blockchain.

[0029] According to an embodiment of the present invention, the determination of the authentication result further includes: If the authentication result does not meet expectations, the value N will be dynamically reduced based on a preset step size.

[0030] According to an embodiment of the present invention, the step of collecting authentication feature data corresponding to multiple authentication time periods to form an authentication feature set, and based on a decision tree model, performing feature selection and feature construction on the feature data in the authentication feature set to construct multiple condition nodes, and constructing an authentication classification model based on a decision tree through the multiple condition nodes, specifically: Collect authentication feature data corresponding to multiple authentication time periods to form an authentication feature set; For each authentication feature set as a subsample set, features with a preset amount of data are randomly selected from each subsample set to transform the feature judgment conditions and generate multiple judgment nodes. Based on the decision nodes, the root node and internal nodes are determined using the CART algorithm, and leaf nodes are generated step by step to obtain an authentication classification model based on a decision tree. The authentication classification model is tested and trained using authentication feature data corresponding to K preset authentication time periods as test data.

[0031] It should be noted that the model training includes node selection optimization and branch point redetering.

[0032] According to an embodiment of the present invention, the steps of associating authentication feature data with corresponding authentication record data and storing it in the system database, obtaining authentication query tags for electronic components through user terminals, generating component authentication features through authentication query tags, importing component authentication features into an authentication classification model for authentication classification, identifying the authentication classification results, and filtering user authentication record data from the system database based on the authentication classification results are as follows: The authentication feature data is associated with the corresponding authentication record data and stored in the system database; Users input authentication query information through their user terminals, and authentication query tags are generated based on this information. Component authentication features are generated by using authentication query tags. These features are then imported into an authentication classification model to perform initial classification of authentication information, resulting in authentication classification results. Based on the authentication classification results, the corresponding authentication record dataset is selected from the system database; A secondary retrieval of records is performed from the authentication record dataset, and user authentication record data is further retrieved.

[0033] It should be noted that authentication query tags can include various types of data, such as QR codes, appearance features, RFID electronic tags, specifications, and electronic component IDs. This depends on the type of data the user needs for authenticating the electronic component. Based on this tag information, corresponding feature data can be converted. The user authentication record data is the final search result for the authenticated electronic component queried by the user.

[0034] According to an embodiment of the present invention, the step of associating authentication feature data with corresponding authentication record data and storing it in the system database further includes: In M preset authentication time periods, for each authentication time period, the authentication feature data is associated with the corresponding authentication record data and a data block is formed; Through the blockchain network, based on the asymmetric encryption algorithm, M data blocks are encrypted sequentially and uploaded to the certification center platform; Before the upload process, the M authentication feature data corresponding to the M data blocks are marked, the M authentication feature data are imported into the authentication classification model for data classification, and real-time classification results are generated. After verifying the data process, the authentication center platform broadcasts the upload information of M data blocks to the remaining terminal network nodes and completes the data block upload for the remaining terminal network nodes. In the remaining terminal network nodes, the M authentication feature data are obtained through the blockchain network, and a second classification result is generated based on the authentication classification model; By comparing the real-time classification results with the second classification results, the consistency of the data blocks stored in the blockchain network of the remaining terminal network nodes and the certification center platform can be determined.

[0035] It should be noted that, in addition to storing authentication record data in a general database, this invention also includes storing it on a blockchain network for security reasons.

[0036] It's worth noting that maintaining data consistency across network nodes is crucial for blockchain storage. However, data consistency verification in authentication data platforms is often inefficient, lacking a suitable and efficient data block consistency assessment method for batch authentication data. This leads to low data processing efficiency when combining the authentication center platform with the blockchain network, compromising data security. Therefore, this invention addresses this issue by importing authentication feature data into an authentication classification model during the storage process, considering multiple authentication time periods for real-time analysis. The classification results include multiple sets of data, each corresponding to multiple data blocks or multiple authentication feature data, with each data block representing one authentication feature data. Furthermore, by assessing the differences in the classification results, the consistency of data blocks across different network nodes is evaluated. This achieves an efficient consistency assessment process for batch authentication data stored across multiple network nodes, improving blockchain storage efficiency and demonstrating significant practicality for authentication platforms.

[0037] The analysis process for the real-time classification results is consistent with that for the second classification results. In the blockchain network, all other terminal network nodes include corresponding data blocks, and these data blocks must be consistent with the authentication center platform.

[0038] Figure 2A block diagram of an electronic component authentication system according to the present invention is shown.

[0039] A second aspect of the present invention also provides an electronic component authentication system 2, the system comprising: a memory 21 and a processor 22, wherein the memory 21 includes an electronic component authentication program, and the electronic component authentication program, when executed by the processor 22, performs the following steps: Obtain the testing and certification plan for the target batch of electronic components, and set the initial batch certification quantity N based on the certification plan; During an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, camera, and RFID reader, resulting in graphic code data, image data, and RFID identification data. Multi-mode data authentication and recognition are performed on graphic code data, image data, and RFID identification data. The authentication result is judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated for graphic code data, image data, and RFID identification data based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components. Authentication feature data corresponding to multiple authentication time periods are collected to form an authentication feature set. Based on the decision tree model, feature selection and feature construction are performed on the feature data in the authentication feature set to construct multiple condition nodes. An authentication classification model based on decision tree is then constructed through these multiple condition nodes. The system associates authentication feature data with corresponding authentication record data and stores them in the system database. The system obtains authentication query tags for electronic components through user terminals, generates component authentication features based on the authentication query tags, imports the component authentication features into the authentication classification model for authentication classification, identifies the authentication classification results, and filters user authentication record data from the system database based on the authentication classification results.

[0040] It should be noted that during the production and warehousing of electronic components, multi-mode certification and testing are often required to certify the production of the components and ensure their quality, safety, and compliance.

[0041] According to an embodiment of the present invention, the step of obtaining the testing and certification plan for the target batch of electronic components, and setting the initial batch certification quantity N according to the certification plan, specifically involves: Obtain the testing and certification plan for the target batch of electronic components, wherein the testing and certification plan includes information on the number of electronic components to be certified, the testing and certification frequency, and the testing and certification mode. By conducting a testing and certification plan, a feasibility analysis is performed on the certification requirements and certification equipment, and the initial batch certification quantity N of components is set.

[0042] It should be noted that the initial batch certification quantity N is a low value used to set the initial one-time identification quantity of the certification device. The certification device includes a barcode scanner, a camera, and an RFID reader. For different identification modes, it has the capability to collect and identify data from multiple electronic components at once. For example, for the RFID reader, it can simultaneously read and identify the electronic tags of multiple components. By dynamically setting the quantity N, batch certification of components can be effectively achieved and certification efficiency can be maximized.

[0043] Within each certification period, a batch identification process is performed. By dynamically setting the value N, the batch identification is maximized by utilizing the detection device. Subsequently, a classification model is constructed based on the corresponding certification feature data collected in each certification period, which effectively enables the classification and efficient querying of certification data in the system database, thereby achieving a highly automated certification process for electronic component systems.

[0044] According to an embodiment of the present invention, within an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, a camera, and an RFID reader to obtain graphic code data, image data, and RFID identification data, respectively. Specifically: Multiple certification periods are set based on the testing and certification plan; During an authentication period, N electronic components are scanned and data is collected using a barcode scanner, a camera, and an RFID reader, resulting in graphic code data, image data, and RFID identification data. Image denoising, enhancement, and standardization preprocessing are performed on graphic code data and image data.

[0045] It should be noted that the graphic code data is used for QR code and barcode authentication, the image data is used to analyze the product's appearance features, and the RFID identification data is used to analyze RFID tag data. All three types of data are used for product authentication.

[0046] According to an embodiment of the present invention, the multi-mode data authentication and recognition of graphic code data, image data, and RFID identification data is performed, and the authentication result is judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated for the graphic code data, image data, and RFID identification data based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components, specifically as follows: Based on the graphic code data, graphic code feature information is extracted to obtain graphic code feature data; Contour recognition of electronic components is performed on image data, grayscale differences of image pixels are analyzed, contour features and texture features are extracted using the Prewitt operator, and the contour features and texture features are integrated as appearance feature data of electronic components. RFID identification data is analyzed to form RFID identification tag data; Multi-mode identification and authentication of electronic components is performed based on graphic code feature data, appearance feature data, and RFID identification tag data, generating authentication results in three dimensions. If the authentication result meets the expected result, the value N will be dynamically increased based on the preset step size. Feature analysis is performed on graphic code feature data, appearance feature data and RFID identification tag data. The main features are extracted using PCA analysis. Based on the feature splicing method, the extracted main feature data are spliced ​​to form multi-dimensional authentication feature data. The graphic code data, image data, RFID identification data, and authentication results are integrated into authentication record data.

[0047] It should be noted that the multi-mode identification authentication of electronic components based on graphic code feature data, appearance feature data, and RFID identification tag data generates three-dimensional authentication results. Different authentication modes share certain identifiers. For example, graphic code feature data can identify the electronic component ID, specifications, manufacturer name, and production date; appearance feature data can identify specifications and appearance; and RFID identification tag data can identify the electronic component ID, logistics history, and batch information. The corresponding authentication results include authentication results for all three modes (i.e., three dimensions). If all authentications are successful, the result meets expectations. After dynamically increasing the value N, in the next authentication period, data collection and authentication are performed on N electronic components based on the increased N. Authentication record data also includes authentication time, number of authentications, and the authentication mode set. In the multi-dimensional authentication feature data, each dimension corresponds to one type of authentication data.

[0048] Through the process of this invention, electronic components can be quickly queried, filtered, and classified in multiple batches or large-scale certification data, and the corresponding certification records can be located and retrieved. This improves the efficiency of product information retrieval and certification data management. Compared with the traditional certification records and queries of each electronic component individually, this invention can greatly reduce the amount of data processing and is conducive to data storage for security platforms such as blockchain.

[0049] According to an embodiment of the present invention, the determination of the authentication result further includes: If the authentication result does not meet expectations, the value N will be dynamically reduced based on a preset step size.

[0050] According to an embodiment of the present invention, the step of collecting authentication feature data corresponding to multiple authentication time periods to form an authentication feature set, and based on a decision tree model, performing feature selection and feature construction on the feature data in the authentication feature set to construct multiple condition nodes, and constructing an authentication classification model based on a decision tree through the multiple condition nodes, specifically: Collect authentication feature data corresponding to multiple authentication time periods to form an authentication feature set; For each authentication feature set as a subsample set, features with a preset amount of data are randomly selected from each subsample set to transform the feature judgment conditions and generate multiple judgment nodes. Based on the decision nodes, the root node and internal nodes are determined by the CART algorithm, and leaf nodes are generated step by step to obtain an authentication classification model based on decision trees; The authentication classification model is tested and trained using authentication feature data corresponding to K preset authentication time periods as test data.

[0051] It should be noted that the model training includes node selection optimization and branch point redetering.

[0052] According to an embodiment of the present invention, the steps of associating authentication feature data with corresponding authentication record data and storing it in the system database, obtaining authentication query tags for electronic components through user terminals, generating component authentication features through authentication query tags, importing component authentication features into an authentication classification model for authentication classification, identifying the authentication classification results, and filtering user authentication record data from the system database based on the authentication classification results are as follows: The authentication feature data is associated with the corresponding authentication record data and stored in the system database; Users input authentication query information through their user terminals, and authentication query tags are generated based on this information. Component authentication features are generated by using authentication query tags. These features are then imported into an authentication classification model to perform initial classification of authentication information, resulting in authentication classification results. Based on the authentication classification results, the corresponding authentication record dataset is selected from the system database; A secondary retrieval of records is performed from the authentication record dataset, and user authentication record data is further retrieved.

[0053] It should be noted that authentication query tags can include various types of data, such as QR codes, appearance features, RFID electronic tags, specifications, and electronic component IDs. This depends on the type of data the user needs for authenticating the electronic component. Based on this tag information, corresponding feature data can be converted. The user authentication record data is the final search result for the authenticated electronic component queried by the user.

[0054] A third aspect of the present invention also provides a computer-readable storage medium including an electronic component authentication program, which, when executed by a processor, implements the steps of the electronic component authentication method as described in any of the preceding claims.

[0055] This invention discloses an electronic component authentication method and system. It utilizes barcode scanning, imaging, and RFID reading devices to collect multi-dimensional data (graphic codes, images, and RFID data) of target batches of electronic components during the authentication period. The number of components (N) to be authenticated in a batch is dynamically adjusted based on the authentication results, and multi-dimensional authentication feature data and record data are generated. Feature data from multiple authentication periods are collected to form a feature set. A authentication classification model is constructed using a decision tree. This model enables efficient classification and querying of batch authentication records for electronic components. This invention effectively improves the retrieval efficiency of product authentication information and enhances the efficiency of authentication data management.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0057] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0059] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0061] The above description is merely a 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 conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for authenticating electronic components, characterized in that, include: Obtain the testing and certification plan for the target batch of electronic components, and set the initial batch certification quantity N based on the certification plan; During an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, camera, and RFID reader, resulting in graphic code data, image data, and RFID identification data. Multi-mode data authentication and recognition are performed on graphic code data, image data, and RFID identification data. The authentication result is judged. If the authentication result meets the expected result, multi-dimensional authentication feature data and corresponding authentication record data are generated for graphic code data, image data, and RFID identification data based on the authentication result, and the value N is dynamically increased. In the next authentication period, data collection and authentication are performed on N electronic components. Authentication feature data corresponding to multiple authentication time periods are collected to form an authentication feature set. Based on the decision tree model, feature selection and feature construction are performed on the feature data in the authentication feature set to construct multiple condition nodes. An authentication classification model based on decision tree is then constructed through these multiple condition nodes. The system associates the authentication feature data with the corresponding authentication record data and stores them in the system database. The system obtains the authentication query tag of the electronic component through the user terminal, generates the component authentication feature through the authentication query tag, imports the component authentication feature into the authentication classification model for authentication classification, identifies the authentication classification result, and filters the user authentication record data from the system database based on the authentication classification result. Specifically, the process involves performing multi-mode data authentication and recognition on graphic code data, image data, and RFID identification data, determining the authentication result, and if the authentication result meets expectations, generating multi-dimensional authentication feature data and corresponding authentication record data based on the authentication result, and dynamically increasing the value N. In the next authentication period, data collection and authentication are performed on N electronic components. Based on the graphic code data, graphic code feature information is extracted to obtain graphic code feature data; Contour recognition of electronic components is performed on image data. The grayscale difference of image pixels is analyzed, and contour features and texture features are extracted by the Prewitt operator. The contour features and texture features are integrated as the appearance feature data of electronic components. RFID identification data is analyzed to form RFID identification tag data; Multi-mode identification and authentication of electronic components is performed based on graphic code feature data, appearance feature data, and RFID identification tag data, generating authentication results in three dimensions. If the authentication result meets the expected result, the value N will be dynamically increased based on the preset step size. Feature analysis is performed on graphic code feature data, appearance feature data and RFID identification tag data. The main features are extracted using PCA analysis. Based on the feature splicing method, the extracted main feature data are spliced ​​to form multi-dimensional authentication feature data. The graphic code data, image data, RFID identification data, and authentication results are integrated into authentication record data; If the authentication result does not meet expectations, the value N will be dynamically reduced based on a preset step size. Specifically, the process involves collecting authentication feature data corresponding to multiple authentication time periods to form an authentication feature set. Based on a decision tree model, feature selection and feature construction are performed on the feature data in the authentication feature set to construct multiple condition nodes. A decision tree-based authentication classification model is then built using these multiple condition nodes. Collect authentication feature data corresponding to multiple authentication time periods to form an authentication feature set; For each authentication feature set as a subsample set, features with a preset amount of data are randomly selected from each subsample set to transform the feature judgment conditions and generate multiple judgment nodes. Based on the decision nodes, the root node and internal nodes are determined using the CART algorithm, and leaf nodes are generated step by step to obtain an authentication classification model based on a decision tree. The authentication classification model is tested and trained based on the authentication feature data corresponding to K preset authentication time periods. The step of associating authentication feature data with corresponding authentication record data and storing it in the system database also includes: In M preset authentication time periods, for each authentication time period, the authentication feature data is associated with the corresponding authentication record data and a data block is formed; Through the blockchain network, based on the asymmetric encryption algorithm, M data blocks are encrypted sequentially and uploaded to the certification center platform; Before the upload process, the M authentication feature data corresponding to the M data blocks are marked, the M authentication feature data are imported into the authentication classification model for data classification, and real-time classification results are generated. After verifying the data process, the authentication center platform broadcasts the upload information of M data blocks to the remaining terminal network nodes and completes the data block upload for the remaining terminal network nodes. In the remaining terminal network nodes, the M authentication feature data are obtained through the blockchain network, and a second classification result is generated based on the authentication classification model; By comparing the real-time classification results with the second classification results, the consistency of the data blocks stored in the blockchain network of the remaining terminal network nodes and the certification center platform can be determined.

2. The electronic component authentication method according to claim 1, characterized in that, The acquisition of the testing and certification plan for the target batch of electronic components, based on the certification plan, sets the initial batch certification quantity N of components as follows: Obtain the testing and certification plan for the target batch of electronic components, wherein the testing and certification plan includes information on the number of electronic components to be certified, the testing and certification frequency, and the testing and certification mode. By conducting a testing and certification plan, a feasibility analysis is performed on the certification requirements and certification equipment, and the initial batch certification quantity N of components is set.

3. The electronic component authentication method according to claim 1, characterized in that, Within an authentication period, multi-dimensional authentication data is collected from N electronic components using a barcode scanner, a camera, and an RFID reader, yielding graphic code data, image data, and RFID identification data, specifically: Multiple certification periods are set based on the testing and certification plan; During an authentication period, N electronic components are scanned and data is collected using a barcode scanner, a camera, and an RFID reader, resulting in graphic code data, image data, and RFID identification data. Image denoising, enhancement, and standardization preprocessing are performed on graphic code data and image data.

4. The electronic component authentication method according to claim 1, characterized in that, The process involves associating authentication feature data with corresponding authentication record data and storing it in the system database. Authentication query tags for electronic components are obtained through user terminals. Component authentication features are generated from these tags, imported into an authentication classification model for classification, and the classification results are identified. Based on these classification results, user authentication record data is then filtered from the system database. Specifically: The authentication feature data is associated with the corresponding authentication record data and stored in the system database; Users input authentication query information through their user terminals, and authentication query tags are generated based on this information. Component authentication features are generated by using authentication query tags. These features are then imported into an authentication classification model to perform initial classification of authentication information, resulting in authentication classification results. Based on the authentication classification results, the corresponding authentication record dataset is selected from the system database; A secondary retrieval of records is performed from the authentication record dataset, and user authentication record data is further retrieved.

5. An electronic component authentication system, characterized in that, The system includes: a memory and a processor, wherein the memory includes an electronic component authentication program, and the electronic component authentication program, when executed by the processor, implements the steps of the electronic component authentication method as described in claim 1.

6. The electronic component authentication system according to claim 5, characterized in that, The acquisition of the testing and certification plan for the target batch of electronic components, based on the certification plan, sets the initial batch certification quantity N of components as follows: Obtain the testing and certification plan for the target batch of electronic components, wherein the testing and certification plan includes information on the number of electronic components to be certified, the testing and certification frequency, and the testing and certification mode. By conducting a testing and certification plan, a feasibility analysis is performed on the certification requirements and certification equipment, and the initial batch certification quantity N of components is set.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes an electronic component authentication program, which, when executed by a processor, implements the steps of the electronic component authentication method as described in any one of claims 1 to 4.

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