A blockchain-based modeling tool management method and system

Abstract

The application discloses a kind of modeling tool management methods and systems based on blockchain, it is related to blockchain technical field, the method includes uploading model file to distributed file system;Record the upload time of model file and calculate the storage address of model file;Classify model file by data classification model, obtain classification result;According to classification result, the relevant data of model file is added to blockchain;According to blockchain data, the change of model file is managed;Wherein, relevant data includes storage address, adding time, upload time, modeling tool identifier, front block hash value and current block hash value.The application classifies model file, according to the storage address of model file and other relevant data in corresponding blockchain according to classification result, the addition of data is verified by blockchain data, improve the efficiency and accuracy of modeling tool modeling data storage.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and specifically to a blockchain-based method and system for managing modeling tools. Background Technology

[0002] Blockchain is a database technology that stores data in a decentralized manner. It achieves secure and transparent data storage through consensus mechanisms and distributed storage technology. Therefore, there is no single controlling entity in a blockchain; all data is recorded by all nodes on the network and maintained collaboratively by multiple nodes, with data consistency maintained through a consensus mechanism.

[0003] The management of modeling tools involves multiple aspects, among which the secure management of model data is of great significance. Currently, the storage and traceability of modeling data in modeling projects have shortcomings, such as the risk of single points of failure in centralized data management and incomplete historical records of modeling data. Therefore, blockchain technology is used to manage modeling data. However, the diverse types and large volumes of modeling data lead to low storage efficiency in blockchain. Summary of the Invention

[0004] The purpose of this invention is to provide a blockchain-based method and system for managing modeling tools. This invention classifies model files and stores the storage addresses of the model files and other related data into the corresponding blockchain based on the classification results. The addition of data is verified through blockchain data, thereby improving the efficiency and accuracy of data storage for modeling tools.

[0005] The objective of this invention is achieved through the following technical means:

[0006] In a first aspect, the present invention provides a blockchain-based method for managing modeling tools, comprising:

[0007] Upload the model file to the distributed file system;

[0008] Record the upload time of the model file and calculate the storage address of the model file;

[0009] The model files are classified using a data classification model to obtain classification results;

[0010] Based on the classification results, the relevant data of the model file is added to the blockchain;

[0011] Changes to the model file are managed based on blockchain data.

[0012] The relevant data includes the storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value.

[0013] Preferably, before classifying the model file using the data classification model to obtain the classification result, the method further includes: training the data classification model, specifically including:

[0014] Obtain historical model files and preprocess them to obtain datasets;

[0015] The number of model objects and components in the historical model files in the dataset is used as features for feature extraction to obtain the training set;

[0016] The data classification model is trained using the training set.

[0017] Preferably, the step of extracting features from the number of model objects and components in the historical model files of the dataset to obtain a training set includes:

[0018] The historical model file is parsed to construct a syntax tree;

[0019] Traverse the nodes of the syntax tree to obtain the list of components and the number of components;

[0020] A feature vector is constructed based on the list of components and the number of components.

[0021] Preferably, adding the relevant data of the model file to the blockchain based on the classification result includes:

[0022] Select the blockchain that matches the classification result;

[0023] Create a new block within the blockchain and record the addition time;

[0024] The current block hash value is calculated based on the storage address, the addition time, and the previous block hash value;

[0025] Add the addition time, the upload time, the modeling tool identifier, the previous block hash value, and the current block hash value to the block header of the newly created block.

[0026] Preferably, calculating the current block hash value based on the storage address, the addition time, and the previous block hash value includes:

[0027] A hash value is calculated on the storage address to obtain a first hash value;

[0028] The hash value of the added time is calculated to obtain a second hash value;

[0029] The first hash value and the second hash value are concatenated and the hash value is calculated to obtain the third hash value;

[0030] The third hash value and the previous block hash value are concatenated and the hash value is calculated to obtain the current block hash value.

[0031] Preferably, before managing changes to the model file based on blockchain data, the method further includes:

[0032] A key-value database is constructed to manage the storage address, using the modeling tool identifier as the key and the storage address as the value.

[0033] Preferably, managing changes to the model file based on blockchain data includes:

[0034] Obtain the upload time of the newly created block, the modeling tool identifier, and the hash value of the preceding block, and verify the changes to the model file;

[0035] When the verification is successful, the newly created block will be synchronized to all nodes.

[0036] Secondly, the present invention provides a blockchain-based modeling tool management system, applied to the aforementioned blockchain-based modeling tool management method, comprising:

[0037] The file upload module is used to upload model files to the distributed file system;

[0038] The data processing module is used to record the upload time of the model file and calculate the storage address of the model file;

[0039] The file classification module is used to classify the model files using a data classification model to obtain classification results;

[0040] The file adding module is used to add relevant data of the model file to the blockchain based on the classification results.

[0041] The file management module is used to manage changes to the model file based on blockchain data;

[0042] The relevant data includes the storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value.

[0043] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes the aforementioned blockchain-based modeling tool management method.

[0044] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor of an electronic device, cause the processor to perform the aforementioned blockchain-based modeling tool management method.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] This invention improves the efficiency and accuracy of data storage for modeling tools by classifying model files and storing the storage addresses of the model files and other related data in the corresponding blockchain based on the classification results. The blockchain data is used to verify the addition of data.

[0047] This invention improves the accuracy of the data classification model in classifying model files by preprocessing historical model files through correction and deduplication, and then training the data classification model through feature extraction.

[0048] This invention improves the accuracy of data classification models in classifying model files by using syntax trees to extract features from historical model files and construct feature vectors.

[0049] This invention improves the traceability of modeling data by adding model files to the corresponding blockchain according to the classification results, and adding the addition time, upload time, modeling tool identifier, previous block hash value and current block hash value to the block body;

[0050] In this embodiment, a three-layer Merkle tree structure is used to calculate the current block hash value based on the storage address, added events, and previous block hash values, thereby improving the security of the modeling data of the modeling tool.

[0051] This embodiment constructs a key-value database using modeling tool identifiers as keys and storage addresses as values, providing a fast retrieval method when the model file of the corresponding modeling tool needs to be obtained, thereby improving the retrieval efficiency of modeling tool modeling data;

[0052] In this embodiment, changes to the model file are verified by acquiring and using the upload time of the new block, the modeling tool identifier, and the hash value of the previous block. When the verification is successful, the new block is synchronized to each node, thus ensuring the security of the modeling data of the modeling tool. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating a blockchain-based modeling tool management method provided in this embodiment;

[0056] Figure 2 This is a flowchart illustrating step S5 provided in this embodiment;

[0057] Figure 3 This is a flowchart illustrating step S42 provided in this embodiment;

[0058] Figure 4 This is a flowchart illustrating step S7 provided in this embodiment;

[0059] Figure 5 This is a flowchart illustrating step S73 provided in this embodiment;

[0060] Figure 6 This is a flowchart illustrating step S9 provided in this embodiment;

[0061] Figure 7 This is a schematic diagram of the structure of a blockchain-based modeling tool management system provided in this embodiment;

[0062] Figure 8 This is a schematic diagram of the structure of an electronic device provided in this embodiment. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0064] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0065] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" and "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0066] This embodiment provides a blockchain-based method for managing modeling tools, such as... Figure 1 As shown, it includes the following steps:

[0067] S1, upload the model file to the distributed file system;

[0068] S3 records the upload time of the model file and calculates the storage address of the model file;

[0069] S5, classify the model files using the data classification model to obtain the classification results;

[0070] S7. Based on the classification results, add the relevant data of the model file to the blockchain;

[0071] S9 manages changes to model files based on blockchain data;

[0072] The relevant data includes storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value.

[0073] It's important to note that model files are files obtained after modeling using modeling tools. They possess their own structure and semantics, recording the model's geometric information. Because model files record all geometric information, including vertex coordinates, face definitions, edge connections, and other data, their large data volume would consume significant storage space on the blockchain. Furthermore, the large amount of data would hinder subsequent data processing, slowing down computation and resulting in low storage efficiency. Therefore, uploading model files to a distributed file system for storage, and storing the model file's address on the blockchain, reduces the amount of data the blockchain needs to store. The model file's storage address can be calculated based on its content, using a hash value that serves as the model file's storage address.

[0074] When managing the storage of model files, it's necessary to consider the type of the model file, i.e., the modeling tools used during modeling; it's also necessary to consider modeling different objects using the same modeling tools, as well as the model creator or modifier, to facilitate model file retrieval and version tracking. Therefore, the model files are input into a data classification model, which identifies and classifies the model files based on their content. After obtaining the classification results, the model file's storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value are added to the corresponding classification result's blockchain. The model file addition time is the time when the relevant data of the model file is added to the blockchain; the modeling tool identifier is the marker of the modeling tool used for modeling; the previous block hash value is the hash value of the block preceding the model file's storage block in the blockchain; and the current block hash value is the hash value of the block where the model file is stored.

[0075] In this embodiment, by classifying the model files, the storage address of the model files and other related data are stored in the corresponding blockchain according to the classification results. The addition of data is verified by the blockchain data, which improves the efficiency and accuracy of data storage for modeling tools.

[0076] In some embodiments, before step S5, classifying the model file using a data classification model to obtain the classification result, the method further includes: step S4, training the data classification model, such as... Figure 2 As shown, the specific steps include:

[0077] S41, Obtain historical model files, and preprocess the historical model files to obtain the dataset;

[0078] S42, use the number of model objects and components in the historical model files in the dataset as features to extract the training set;

[0079] S43 trains the data classification model using the training set.

[0080] It should be noted that the dataset is constructed by acquiring existing model files and grouping them with classification results. The preprocessing of this data group involves removing duplicate historical model files and correcting any erroneous ones. Feature extraction is then performed on the number of model objects and components in the historical model files to train the data classification model.

[0081] In this embodiment, by preprocessing historical model files through correction and deduplication, and by performing feature extraction to train the data classification model, the accuracy of the data classification model in classifying model files is improved.

[0082] In some embodiments, step S42 involves extracting features from the number of model objects and components in the model files of the dataset to obtain a training set, such as... Figure 3 As shown, it includes the following steps:

[0083] S421, parse the historical model file and construct a syntax tree;

[0084] S422, traverse the nodes of the syntax tree to get the list of components and the number of components;

[0085] S423, construct feature vectors based on the component list and the number of components.

[0086] It should be noted that by parsing the syntax structure of historical model files, the structure and writing logic of historical models are understood, and efficient machine code is generated. Each node in the syntax tree can be the definition of each component in the model file. By traversing the nodes of the syntax tree, the list and number of components in the historical model file are obtained, and feature vectors are constructed, such as 3D feature vectors for the number of vertices, faces, and normals. These feature vectors are then used to train the data classification model.

[0087] In this embodiment, by using a syntax tree to extract features from historical model files and construct feature vectors, the accuracy of the data classification model in classifying model files is improved.

[0088] In some embodiments, step S7 involves adding relevant data from the model file to the blockchain based on the classification results, such as... Figure 4 As shown, it includes the following steps:

[0089] S71, Select blockchains with the same classification results;

[0090] S72: Create a new block within the blockchain and record the time of its creation;

[0091] S73, calculate the current block hash value based on the storage address, addition time, and previous block hash value;

[0092] S74 adds the addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value to the block header of the new block.

[0093] It should be noted that the blockchain used to store model file data is not limited to a single one. Multiple blockchains can be used as appropriate, depending on the model files of different modeling objects. By classifying the model files, relevant data is uploaded to the blockchain storing model file data with the same classification results. A new block is created after the latest block in this blockchain to store the relevant model file data. Simultaneously, the hash value of the current block is calculated for data verification in subsequent blocks. The upload time, modeling tool identifier, previous block hash value, and current block hash value are added to the header of the new block for data retrieval and verification. The storage address is added to the block body of the new block.

[0094] In this embodiment, by adding the model file to the corresponding blockchain according to the classification results, and adding the addition time, upload time, modeling tool identifier, previous block hash value and current block hash value to the block body, the traceability of modeling tool modeling data is improved.

[0095] In some embodiments, step S73 involves calculating the current block hash value based on the storage address, addition time, and previous block hash value, such as... Figure 5 As shown, it includes the following steps:

[0096] S731, calculate the hash value of the storage address to obtain the first hash value;

[0097] S732, calculate the hash value of the added time to obtain the second hash value;

[0098] S733, concatenate the first hash value and the second hash value and calculate the hash value to obtain the third hash value;

[0099] S734: Concatenate the third hash value and the previous block hash value and calculate the hash value to obtain the current block hash value.

[0100] It should be noted that the storage address, addition time, and previous block hash value form a Merkle tree structure, with the storage address and addition event serving as the data layer. The storage address is hashed to obtain a first hash value H1, and the addition time is hashed to obtain a second hash value H2. The first hash value H1 and the second hash value H2 serve as the leaf node layer. The first hash value H1 and the second hash value H2 are concatenated to obtain a first concatenated hash value (H1, H2). The first concatenated hash value is hashed to obtain a third hash value H3. The third hash value H3 is concatenated with the previous block hash value Hf to obtain a second concatenated hash value (H3, Hf). The third hash value H3 and the second concatenated hash value serve as the intermediate node layer. The second concatenated hash value is hashed to obtain the current block hash value Hc. The current block hash value Hc serves as the root node layer. After obtaining the storage address of the block body, it is necessary to ensure that the storage address has not been modified. The final hash value is obtained by concatenating and calculating multiple layers of hash values. The final hash value is then compared with the current block hash value Hc of the root node layer. If the two are the same, it can be determined that the stored data has not been modified.

[0101] In this embodiment, a three-layer Merkle tree structure is used to calculate the current block hash value based on the storage address, added events, and previous block hash values, thereby improving the security of the modeling data of the modeling tool.

[0102] In some embodiments, before managing changes to the model file based on blockchain data, step S9 further includes:

[0103] Step S8: Using the modeling tool identifier as the key and the storage address as the value, construct a key-value database to manage the storage address.

[0104] This embodiment constructs a key-value database using modeling tool identifiers as keys and storage addresses as values, providing a fast retrieval method when the model file of the corresponding modeling tool needs to be obtained, thereby improving the retrieval efficiency of modeling tool modeling data.

[0105] In some embodiments, step S9 involves managing changes to the model file based on blockchain data, such as... Figure 6 As shown, it includes the following steps:

[0106] S91: Obtain the upload time of the newly created block, the modeling tool identifier, and the hash value of the previous block, and verify the changes to the model file;

[0107] S92, when verification is successful, synchronizes the newly created block to all nodes.

[0108] It's important to note that after a block is added to a node, the information of the newly created block needs to be synchronized to other nodes. At this point, each node needs to verify the new block. Only after successful verification is the addition of the block allowed, and the block is synchronized to all nodes to ensure data consistency. Each node verifies changes to the model file by obtaining the upload time, modeling tool identifier, and hash value of the preceding block. Specifically, verification is considered successful only when the modeling tool identifier of the new block is the same as that of the preceding block, the hash value of the preceding block is the same as the current hash value of the preceding block, and the upload time of the new block is later than that of the preceding block. In this case, the data is added to the new block, and the new block is synchronized to all nodes.

[0109] In this embodiment, changes to the model file are verified by acquiring and using the upload time of the new block, the modeling tool identifier, and the hash value of the previous block. When the verification is successful, the new block is synchronized to each node, thus ensuring the security of the modeling data of the modeling tool.

[0110] This embodiment provides a blockchain-based modeling tool management system, applied to the aforementioned blockchain-based modeling tool management method, such as... Figure 7 As shown, it includes:

[0111] The file upload module is used to upload model files to the distributed file system;

[0112] The data processing module is used to record the upload time of the model file and calculate the storage address of the model file;

[0113] The file classification module is used to classify model files using a data classification model and obtain classification results.

[0114] The file addition module is used to add relevant data from model files to the blockchain based on the classification results.

[0115] The file management module is used to manage changes to model files based on blockchain data;

[0116] The relevant data includes storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value.

[0117] In this embodiment, by classifying the model files, the storage address of the model files and other related data are stored in the corresponding blockchain according to the classification results. The addition of data is verified by the blockchain data, which improves the efficiency and accuracy of data storage for modeling tools.

[0118] It should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the module division described above is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, each functional module can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0119] This embodiment provides an electronic device 2, such as... Figure 8 As shown, there is a processor 21 and a memory 22. The memory 22 is used to store computer program code, which includes computer instructions. When the processor 21 executes the computer instructions, the electronic device executes the aforementioned blockchain-based modeling tool management method.

[0120] The electronic device 2 includes a processor 21, a memory 22, an output device 23, and an input device 24. The processor 21, memory 22, output device 23, and input device 24 are coupled together via connectors, which may include various interfaces, transmission lines, or buses, etc., and are not limited in this embodiment of the invention. It should be understood that in various embodiments of the invention, coupling refers to mutual connection through a specific method, including direct connection or indirect connection through other devices, such as through various interfaces, transmission lines, buses, etc.

[0121] The processor 21 can be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Optionally, the processor 21 can also be other types of processors, etc., and this embodiment of the invention is not limited thereto.

[0122] The memory 22 can be used to store computer program instructions, as well as various types of computer program code, including program code for executing the present invention. Optionally, the memory 22 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), and the memory 22 is used for related instructions and data.

[0123] Input device 24 is used to input data and / or signals, and output device 23 is used to output data and / or signals. Output device 23 and input device 24 can be independent devices or an integrated device.

[0124] This embodiment provides a computer-readable storage medium storing a computer program, which includes program instructions. When executed by a processor of an electronic device, the program instructions cause the processor to execute the aforementioned blockchain-based modeling tool management method.

[0125] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A blockchain-based method for managing modeling tools and equipment, characterized in that, include: Upload the model file to the distributed file system; Record the upload time of the model file and calculate the storage address of the model file; The model files are classified using a data classification model to obtain classification results; Based on the classification results, the relevant data of the model file is added to the blockchain; Changes to the model file are managed based on blockchain data. The relevant data includes the storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value; Before classifying the model file using a data classification model to obtain the classification result, the method further includes: training the data classification model, specifically including: Obtain historical model files and preprocess them to obtain datasets; The number of model objects and components in the historical model files in the dataset is used as features for feature extraction to obtain the training set; The data classification model is trained using the training set.

2. The blockchain-based modeling tool management method according to claim 1, characterized in that, The preprocessing includes correcting and deduplicating historical model files.

3. The blockchain-based modeling tool management method according to claim 2, characterized in that, The step of extracting features from the number of model objects and components in the historical model files of the dataset to obtain a training set includes: The historical model file is parsed to construct a syntax tree; Traverse the nodes of the syntax tree to obtain the list of components and the number of components; A feature vector is constructed based on the list of components and the number of components.

4. The blockchain-based modeling tool management method according to claim 1, characterized in that, The step of adding the relevant data of the model file to the blockchain based on the classification result includes: Select the blockchain that matches the classification result; Create a new block within the blockchain and record the addition time; The current block hash value is calculated based on the storage address, the addition time, and the previous block hash value; Add the addition time, the upload time, the modeling tool identifier, the previous block hash value, and the current block hash value to the block header of the newly created block.

5. The blockchain-based modeling tool management method according to claim 2, characterized in that, The step of calculating the current block hash value based on the storage address, the addition time, and the previous block hash value includes: A hash value is calculated on the storage address to obtain a first hash value; The hash value of the added time is calculated to obtain a second hash value; The first hash value and the second hash value are concatenated and the hash value is calculated to obtain the third hash value; The third hash value and the previous block hash value are concatenated and the hash value is calculated to obtain the current block hash value.

6. The blockchain-based modeling tool management method according to claim 1, characterized in that, Before managing changes to the model file based on blockchain data, the process further includes: A key-value database is constructed to manage the storage address, using the modeling tool identifier as the key and the storage address as the value.

7. The blockchain-based modeling tool management method according to claim 1, characterized in that, The management of changes to the model file based on blockchain data includes: Obtain the upload time of the newly created block, the modeling tool identifier, and the hash value of the preceding block, and verify the changes to the model file; When the verification is successful, the newly created block will be synchronized to all nodes.

8. A blockchain-based modeling tool management system, employing the blockchain-based modeling tool management method as described in any one of claims 1 to 7, characterized in that, include: The file upload module is used to upload model files to the distributed file system; The data processing module is used to record the upload time of the model file and calculate the storage address of the model file; The file classification module is used to classify the model files using a data classification model to obtain classification results; The file adding module is used to add relevant data of the model file to the blockchain based on the classification results. The file management module is used to manage changes to the model file based on blockchain data; The relevant data includes the storage address, addition time, upload time, modeling tool identifier, previous block hash value, and current block hash value.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device performs a blockchain-based modeling tool management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform a blockchain-based modeling tool management method as described in any one of claims 1 to 7.

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