Data encryption method and device, storage medium and electronic equipment

Abstract

The invention discloses a data encryption method and device, a storage medium and electronic equipment, and relates to the field of financial science and technology. The method comprises the following steps: receiving to-be-encrypted data; converting the to-be-encrypted data into a binary sequence to obtain a target sequence; based on the numerical value corresponding to each data bit in the target sequence, compression states in different layered areas in the target three-dimensional model are determined, an encrypted three-dimensional model carrier is obtained, the target three-dimensional model is a model obtained after an initial three-dimensional model is updated through preprocessing operation, the target three-dimensional model is used for bearing data to be encrypted, and the encrypted three-dimensional model carrier is used for carrying the data to be encrypted. The preprocessing operation is used for adjusting the initial three-dimensional model into a model meeting a preset requirement, the encrypted three-dimensional model carrier is a target three-dimensional model after the to-be-encrypted data is embedded, and the preset requirement is used for constraining geometric attributes of the model. The technical problem of low security of bank sensitive data in network transmission in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of fintech, and in particular, to a data encryption method, device, storage medium, and electronic device. Background Art

[0002] In today's highly digital financial environment, the secure transmission of bank sensitive data has become a crucial task. With the progress of technology, especially the popularization of the Internet and the application of cloud computing technology, the processing and transmission speed of bank data have been significantly improved, but at the same time, it also faces unprecedented security challenges.

[0003] In traditional data transmission, sensitive data often flows in the network in plain text or simple encrypted form, making it extremely vulnerable to network attacks. Especially in the face of advanced persistent threats and zero-day vulnerability attacks, traditional firewalls and encryption means are stretched thin, and the security of data is difficult to be fundamentally guaranteed. In addition, the complexity and high value of bank data make the protection of data during transmission a double-edged sword. Excessive protection measures will reduce the efficiency of data transmission and the real-time performance of business processing, while insufficient protection will directly threaten financial security and personal privacy.

[0004] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] This application provides a data encryption method, device, storage medium, and electronic device to at least solve the technical problem of low security of bank sensitive data in network transmission in the prior art.

[0006] According to one aspect of this application, a data encryption method is provided, including: receiving data to be encrypted; converting the data to be encrypted into a binary sequence to obtain a target sequence; determining the compression states in different layered regions of a target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain an encrypted three-dimensional model carrier, where the target three-dimensional model is a model obtained by updating an initial three-dimensional model through a preprocessing operation, the target three-dimensional model is used to carry the data to be encrypted, the preprocessing operation is used to adjust the initial three-dimensional model to a model that meets preset requirements, the encrypted three-dimensional model carrier is the target three-dimensional model after embedding the data to be encrypted, and the preset requirements are used to constrain the geometric attributes of the model.

[0007] Optionally, the preprocessing operation includes at least a first operation, a second operation, and a third operation. The target three-dimensional model is obtained in the following manner: determining an initial three-dimensional model based on the data volume of the data to be encrypted; determining a vertex set in the initial three-dimensional model, where the vertex set includes N vertices in the initial three-dimensional model, and N is an integer greater than 1; performing a first operation on the initial three-dimensional model according to the vertex set to obtain a first model, where the first operation is used to move the initial three-dimensional model in a three-dimensional coordinate system and update the vertex coordinates corresponding to the N vertices in the moved initial three-dimensional model; performing a second operation on the first model to obtain a second model, where the second operation is used to adjust the direction of the first model in the three-dimensional coordinate system; performing a third operation on the second model to obtain the target three-dimensional model, where the third operation is used to adjust the size of the second model in the three-dimensional coordinate system.

[0008] Optionally, the first operation includes the following steps: determining the centroid of the initial three-dimensional model according to the vertex coordinates of the N vertices in the vertex set; moving the centroid of the initial three-dimensional model to the origin of the three-dimensional coordinate system, and taking the coordinates corresponding to each vertex in the moved initial three-dimensional model as the first vertex coordinates to obtain N first vertex coordinates; updating the initial three-dimensional model based on the N first vertex coordinates to obtain the first model.

[0009] Optionally, the second operation includes the following steps: constructing a target matrix according to the N first vertex coordinates, where the target matrix is used to quantify the distribution characteristic information of each vertex in the first model; determining the target eigenvector of the target matrix, where the target eigenvector is used to characterize the direction in which the distance change range between the N vertices in the first model is greater than a preset range; rotating the first model according to the target eigenvector corresponding to the first model and updating the coordinates corresponding to the N vertices in the rotated first model to obtain the updated first model, where the rotation is used to align the target eigenvector corresponding to the first model with any one coordinate axis in the three-dimensional coordinate system; taking the updated first model as the second model.

[0010] Optionally, the third operation includes the following steps: determining the distance between each vertex in the second model and the centroid to obtain N distances; taking the distances greater than or equal to a first preset threshold among the N distances as the target distances; adjusting the size of the second model in the three-dimensional coordinate system according to the target distances to obtain the target three-dimensional model.

[0011] Optionally, before determining the compression states in different layered regions of the target 3D model based on the values corresponding to each data bit in the target sequence to obtain the encrypted 3D model carrier, the method further includes: determining a set of boundary points of the target 3D model, where the set of boundary points includes M vertices, and the set of boundary points includes a first subset of boundary points and a second subset of boundary points. The first subset of boundary points is used to represent the set of vertices of the target 3D model whose distances from the coordinate origin in each coordinate axis direction of the 3D coordinate system are greater than a second preset threshold, and the second subset of boundary points is used to represent the set of vertices of the target 3D model whose distances from the coordinate origin in each coordinate axis direction of the 3D coordinate system are less than a third preset threshold, where M is an integer less than N; generating a target bounding box of the target 3D model based on the set of boundary points, where the target bounding box is used to determine the spatial range of the target 3D model.

[0012] Optionally, based on the values corresponding to each data bit in the target sequence, adjusting the compression states in different layered regions of the target 3D model to obtain the encrypted 3D model carrier includes: determining a target thickness of the target 3D model according to the data volume of the data to be encrypted, where the target thickness is used to determine the slice spacing when dividing the target 3D model; dividing the target 3D model according to the target thickness to obtain S layered regions corresponding to the target 3D model, where S is an integer greater than 1; extracting a first feature point and a second feature point in each of the S layered regions to obtain a first set of feature points and a second set of feature points corresponding to each layered region, where the first feature point is a vertex whose influence value on the model structure is greater than a third preset value, and the second feature point is a vertex whose influence value on the model structure is less than or equal to the third preset value; based on the values corresponding to each data bit in the target sequence, determining the compression states in different layered regions of the target 3D model according to the first set of feature points and the second set of feature points corresponding to each layered region of the target 3D model to obtain the encrypted 3D model carrier.

[0013] Optionally, based on the values corresponding to each data bit in the target sequence, determining the compression states in different layered regions of the target 3D model according to the first set of feature points and the second set of feature points corresponding to each layered region of the target 3D model to obtain the encrypted 3D model carrier includes: setting a compression ratio, where the compression ratio is used to determine the number of vertices after compression for each layered region; based on the first set of feature points in each layered region, determining the compression state of the layered region corresponding to the data bit according to the compression ratio, the values corresponding to each data bit in the target sequence, and the second set of feature points corresponding to each layered region to obtain the encrypted 3D model carrier.

[0014] Optionally, based on the first set of feature points in each hierarchical region, determine the compression state of the hierarchical region corresponding to each data bit according to the compression ratio, the value corresponding to each data bit in the target sequence, and the second set of feature points corresponding to each hierarchical region, including: if it is detected that the value corresponding to the i-th data bit in the target sequence is 0, then prohibit compressing the hierarchical region corresponding to the i-th data bit, where i is an integer greater than or equal to 1, and each data bit in the target sequence respectively corresponds to a hierarchical region in the target three-dimensional model; if it is detected that the value corresponding to the i-th data bit in the target sequence is 1, then use the number of vertices in the hierarchical region corresponding to the i-th data bit as the first value; based on the first set of feature points in the hierarchical region corresponding to the i-th data bit, determine T second feature points from the second set of feature points in the hierarchical region corresponding to the i-th data bit according to the compression ratio and the first value, where T is an integer greater than or equal to 1; compress the T second feature points in the hierarchical region corresponding to the i-th data bit.

[0015] Optionally, after determining the compression states in different hierarchical regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain the encrypted three-dimensional model carrier, the method further includes: performing a preprocessing operation on the encrypted three-dimensional model carrier to obtain a fourth model; determining the number of vertices in each hierarchical region of the fourth model; determining the compression ratio corresponding to each hierarchical region of the fourth model according to the number of vertices in each hierarchical region of the fourth model and the number of vertices in each hierarchical region of the target three-dimensional model; determining the target sequence according to the compression ratio corresponding to each hierarchical region of the fourth model and obtaining the data to be encrypted according to the target sequence.

[0016] According to another aspect of the present application, there is also provided a data encryption device, including: a receiving unit, configured to receive the data to be encrypted; a conversion unit, configured to convert the data to be encrypted into a binary sequence to obtain a target sequence; a determination unit, configured to determine the compression states in different hierarchical regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain an encrypted three-dimensional model carrier, where the target three-dimensional model is a model obtained by updating the initial three-dimensional model through a preprocessing operation, the target three-dimensional model is used to carry the data to be encrypted, the preprocessing operation is used to adjust the initial three-dimensional model to a model that meets the preset requirements, and the encrypted three-dimensional model carrier is used to represent the target three-dimensional model embedded with the data to be encrypted, where the preset requirements are used to constrain the geometric attributes of the model.

[0017] According to another aspect of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored. When the computer program runs, it causes the device where the computer-readable storage medium is located to execute the above data encryption method.

[0018] According to another aspect of the present application, an electronic device is further provided, including one or more processors and a memory. The memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above data encryption method.

[0019] According to another aspect of the present application, a computer program product is further provided, including computer instructions that implement the steps of the above data encryption method when executed by a processor.

[0020] In the present application, first, the data to be encrypted is received. Then, the data to be encrypted is converted into a binary sequence to obtain a target sequence. Then, based on the numerical value corresponding to each data bit in the target sequence, the compression states in different layered regions of the target three-dimensional model are determined to obtain an encrypted three-dimensional model carrier. The target three-dimensional model is a model obtained by updating the initial three-dimensional model through a preprocessing operation. The target three-dimensional model is used to carry the data to be encrypted. The preprocessing operation is used to adjust the initial three-dimensional model into a model that meets the preset requirements. The encrypted three-dimensional model carrier is the target three-dimensional model after embedding the data to be encrypted, where the preset requirements are used to constrain the geometric attributes of the model. That is, by mapping the binary sequence to the compression states of the three-dimensional model layered regions, the purpose of associating the data bit numerical values with the compression states of the model layered regions is achieved, thereby realizing the technical effect that data is not easily detected and extracted during network transmission, and further solving the technical problem of low security of bank sensitive data during network transmission in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0022] Figure 1 is a flowchart of an optional data encryption method according to an embodiment of the present application;

[0023] Figure 2 is a schematic diagram of an optional data encryption method according to an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of an optional data encryption device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0027] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Moreover, the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure and application, complies with relevant laws, regulations and standards, takes necessary confidentiality measures, does not violate public order and good customs, and provides corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set between this system and relevant users or institutions to provide corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making. If the user chooses to refuse, the expert decision-making process will be entered.

[0028] According to the embodiments of this application, a method embodiment of a data encryption method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described here can be executed in a different order than here.

[0029] It should be noted that an intelligent encryption system can be used as the execution subject of the data encryption method in the embodiments of this application. It can be understood that the data encryption method provided in the embodiments of this application can also be executed by other systems or devices as the execution subject, and the embodiments of this application do not make specific limitations in this regard.

[0030] Figure 1 is a schematic diagram of an optional data encryption method according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0031] Step S101: Receive the data to be encrypted.

[0032] Optionally, the data to be encrypted refers to sensitive information that the bank needs to protect, such as customer identity information, transaction records, account information, etc.

[0033] Step S102: Convert the data to be encrypted into a binary sequence to obtain a target sequence.

[0034] Optionally, the binary sequence is a sequence of 0s and 1s that can be recognized by a computer, and the target sequence is the data form to be embedded in the three-dimensional model.

[0035] Optionally, the intelligent encryption system converts the data to be encrypted into a binary sequence through technologies such as Huffman coding, facilitating the embedding and hiding of data in subsequent steps.

[0036] Step S103: Based on the value corresponding to each data bit in the target sequence, determine the compression state in different hierarchical regions of the target three-dimensional model to obtain an encrypted three-dimensional model carrier.

[0037] In step S103, the target three-dimensional model is the model obtained by updating the initial three-dimensional model through a preprocessing operation. The target three-dimensional model is used to carry the data to be encrypted, and the preprocessing operation is used to adjust the initial three-dimensional model to a model that meets the preset requirements. The encrypted three-dimensional model carrier is the target three-dimensional model after embedding the data to be encrypted.

[0038] In step S103, the preset requirements are used to constrain the geometric attributes of the model.

[0039] Optionally, the target three-dimensional model is a three-dimensional model that meets the preset requirements after preprocessing operations. The preprocessing operations include, but are not limited to, translation, normalization, rotation to generate the minimum bounding box, etc. The preset requirements are to ensure that the geometric attributes of the model meet the needs of data embedding and extraction. The preprocessing operations ensure that the model has a unified coordinate system, size, and direction, avoiding the influence of the geometric features of the model itself on data hiding and making the data embedding process more reliable and secure.

[0040] Optionally, the compression state in different hierarchical regions refers to determining whether to compress a specific hierarchical region of the three-dimensional model according to the value in the binary sequence.

[0041] Optionally, the encrypted three-dimensional model carrier is a three-dimensional model after embedding the data to be encrypted, and the data is hidden by changing the compression state of specific hierarchical regions. The encrypted three-dimensional model carrier not only contains all the information of the original three-dimensional model but also contains the data to be encrypted that has been transformed and hidden, making it difficult to detect the data during transmission. Even if intercepted, it is difficult to decrypt. It combines the physical properties of the three-dimensional model and data hiding technology, enhancing the security and anti-tampering ability of data transmission.

[0042] Optionally, this step is the core of data hiding. By associating the values in the target sequence with the hierarchical regions of the target three-dimensional model, it is determined which regions need to be compressed to represent 1 and 0 in the binary sequence. Specifically, when implementing, if the value in the target sequence is 1, the hierarchical region of the model is compressed; if the value is 0, no compression is performed on this region. In this way, the data is embedded into the compression state of the three-dimensional model, forming the encrypted three-dimensional model carrier.

[0043] From the content of steps S101 to S103, it can be seen that in this application, first, the data to be encrypted is received, then the data to be encrypted is converted into a binary sequence to obtain the target sequence, and then based on the values corresponding to each data bit in the target sequence, the compression states in different hierarchical regions of the target three-dimensional model are determined to obtain the encrypted three-dimensional model carrier. Among them, the target three-dimensional model is the model obtained by updating the initial three-dimensional model through preprocessing operations. The target three-dimensional model is used to carry the data to be encrypted, and the preprocessing operation is used to adjust the initial three-dimensional model to meet the preset requirements. The encrypted three-dimensional model carrier is the target three-dimensional model after embedding the data to be encrypted, where the preset requirements are used to constrain the geometric properties of the model. That is, by mapping the binary sequence to the compression state of the three-dimensional model hierarchical region, the purpose of associating the data bit values with the compression state of the model hierarchical region is achieved, thus realizing the technical effect that the data is not easily detected and extracted during network transmission, and further solving the technical problem of low security of bank sensitive data during network transmission in the prior art.

[0044] In an alternative embodiment, the preprocessing operation includes at least a first operation, a second operation, and a third operation. The target 3D model is obtained in the following manner: The intelligent encryption system first determines an initial 3D model based on the data volume of the data to be encrypted, and then determines the vertex set in the initial 3D model. The vertex set includes N vertices in the initial 3D model, where N is an integer greater than 1. Then, the first operation is performed on the initial 3D model according to the vertex set to obtain a first model. The first operation is used to move the initial 3D model in the 3D coordinate system and update the vertex coordinates corresponding to the N vertices in the moved initial 3D model. After that, the second operation is performed on the first model to obtain a second model. The second operation is used to adjust the direction of the first model in the 3D coordinate system. Finally, the third operation is performed on the second model to obtain the target 3D model. The third operation is used to adjust the size of the second model in the 3D coordinate system.

[0045] Optionally, the preprocessing operation includes three key steps: the first operation (translation), the second operation (rotation), and the third operation (scaling). The finally formed target 3D model can efficiently and securely embed and transmit the data to be encrypted.

[0046] Optionally, the vertex set contains the coordinate information of all vertices in the initial 3D model, and N is the number of vertices; the first operation is the translation operation, which moves the center of gravity of the model to the origin of coordinates; the second operation is the rotation operation, which rotates the model so that its maximum eigenvector coincides with the z-axis of the coordinate system to achieve direction standardization; the third operation is the scaling operation, which adjusts the size of the model according to the preprocessing requirements to make it reach the preset standard size.

[0047] Optionally, the intelligent encryption system selects a 3D model that can carry the data and has a matching complexity according to the size of the data to be encrypted, ensuring that the model after data embedding is not too complex or simple, which may affect the concealment and transmission efficiency of the data. Then, it determines the vertex set in the initial 3D model. By obtaining the vertex set, the system can accurately grasp the geometric structure of the 3D model, providing the necessary data support for the subsequent first operation and ensuring the accurate position of the model after translation. Next, a translation operation is performed on the initial 3D model. By translating the model, the interference of the model position on the subsequent data hiding process is eliminated, ensuring the consistency and reproducibility of data embedding. The updated vertex coordinates make the model have a definite and standardized position in the coordinate system, facilitating subsequent processing. Then, a rotation operation is performed on the initial 3D model after the translation operation. The rotation operation ensures that the direction of the model does not affect the extraction of feature points and the embedding of data, enhancing the robustness of data hiding. For the rotated model, its eigenvectors are aligned with the coordinate axes, which helps to unify the processing standards in the subsequent data embedding process. Finally, a scaling operation is performed on the initial 3D model that has undergone the translation and rotation operations to obtain the target 3D model. Scaling the model ensures that its size does not become an obstacle to data embedding and extraction when transmitting between different systems, further enhancing data security and transmission efficiency. Through scaling, the model is adjusted to a unit size, facilitating data embedding and extraction between different systems and devices.

[0048] As can be seen from the above, through the implementation of the above steps, sensitive data (data to be encrypted) can be securely and covertly embedded into a specific hierarchical area of the 3D model. The three steps of the preprocessing operation (translation, rotation, and scaling) not only standardize the geometric attributes of the 3D model but also ensure the stability and robustness of the data embedding process. The preprocessed target 3D model can effectively carry and transmit sensitive data. Even if attacked during data transmission, since the data is cleverly hidden in the structural changes of the model, it is difficult for attackers to directly identify and utilize it.

[0049] In an optional embodiment, the first operation includes the following steps: First, the intelligent encryption system determines the centroid of the initial 3D model according to the vertex coordinates of N vertices in the vertex set. Then, it moves the centroid of the initial 3D model to the origin of the 3D coordinate system and takes the coordinates corresponding to each vertex in the moved initial 3D model as the first vertex coordinates to obtain N first vertex coordinates. Then, the initial 3D model is updated based on the N first vertex coordinates to obtain the first model.

[0050] Optionally, the centroid is the geometric mean position of all vertex coordinates of the 3D model.

[0051] Optionally, based on the vertex coordinates of N vertices in the vertex set, the intelligent encryption system calculates the centroid of the initial 3D model, which is the basis for the translation operation and ensures that the model has a clear reference point in the coordinate system. By adjusting the coordinates of each vertex, the centroid of the model is moved to the origin of the coordinates. This operation makes the positioning of the model unified and deterministic, providing convenient conditions for subsequent rotation and data embedding. When moving the centroid, the coordinates of all N vertices need to be updated. These updated coordinates are called the first vertex coordinates and are used to represent the state of the model after translation. This step ensures that all vertices move with the adjustment of the centroid of the model, maintaining the integrity of the model structure. By using the updated first vertex coordinates for the initial 3D model, the intelligent encryption system finally obtains the first model preprocessed by translation. The position of the first model in the 3D coordinate system is standardized, eliminating the influence of position differences on subsequent processing steps such as data embedding and feature point extraction.

[0052] As can be seen from the above, by implementing the centroid translation operation, the position of the 3D model in the 3D coordinate system is accurately standardized, ensuring the consistency and predictability of the geometric properties of the model in subsequent processing steps. Moving the centroid to the origin of the coordinates not only simplifies subsequent rotation and scaling operations but also provides a unified coordinate reference for data embedding, enhancing the robustness and security of data hiding. Therefore, this embodiment provides a more stable and reliable 3D model basis for the embedding and transmission of sensitive data, significantly improving the technical level of secure data transmission. At the same time, due to the introduction of the centroid translation operation, the efficiency and accuracy of the entire data embedding process are also improved.

[0053] In an optional embodiment, the second operation includes the following steps: The intelligent encryption system first constructs a target matrix according to the N first vertex coordinates, where the target matrix is used to quantify the distribution characteristic information of each vertex in the first model. Then, it determines the target eigenvector of the target matrix, where the target eigenvector is used to characterize the direction in which the distance change range between N vertices in the first model is greater than a preset range. Then, based on the target eigenvector corresponding to the first model, the first model is rotated, and the coordinates corresponding to the N vertices in the rotated first model are updated to obtain the updated first model, where the rotation is used to align the target eigenvector corresponding to the first model with any one of the coordinate axes in the 3D coordinate system. After that, the updated first model is used as the second model.

[0054] Optionally, the target matrix is obtained by quantifying the distribution characteristic information of each vertex in the first model.

[0055] Optionally, the intelligent encryption system constructs a target matrix based on N first vertex coordinates. This matrix can reflect the relative position relationship between the vertices of the model, providing a data basis for subsequent analysis of the vertex distribution characteristics. Then, by performing eigenvalue decomposition on the target matrix, the target eigenvector corresponding to the largest eigenvalue is determined. This vector indicates the dominant direction of the vertex distribution in the model, that is, the direction in which the distance between vertices changes the most. Selecting this direction for rotation alignment can eliminate the influence of the model's directionality on subsequent data embedding. Then, the intelligent encryption system rotates the first model according to the target eigenvector to ensure that this eigenvector is aligned with one of the coordinate axes (such as the z-axis) in the three-dimensional coordinate system. After rotation, the direction of the model is standardized, eliminating the influence of the model's direction on data embedding and extraction. Next, the coordinates of all N vertices are updated to obtain the first model updated after rotation. After rotating the first model and updating the vertex coordinates, the resulting model is the second model. The direction of the second model is standardized, providing a more stable and consistent model pose for subsequent data embedding steps, which is beneficial to improving the accuracy and security of data embedding.

[0056] As can be seen from the above, by implementing the rotation alignment operation, the directionality of the three-dimensional model is standardized, ensuring the robustness and consistency of the geometric properties of the model during the data embedding process. The alignment of the target eigenvector with the coordinate axis eliminates the interference of the model's direction on data embedding and extraction, improving the efficiency and accuracy of the data embedding process. Therefore, this embodiment provides a more stable and reliable three-dimensional model pose for the embedding and transmission of sensitive data, significantly enhancing the security and anti-interference ability of data hiding. At the same time, due to the introduction of the rotation alignment operation, the standardization and robustness of the entire data embedding process have also been significantly improved.

[0057] In an alternative embodiment, the third operation includes the following steps: First, the intelligent encryption system determines the distance between each vertex in the second model and the centroid, obtaining N distances. Then, the distances greater than or equal to the first preset threshold among the N distances are used as target distances. Finally, the size of the second model in the three-dimensional coordinate system is adjusted according to the target distances to obtain the target three-dimensional model.

[0058] Optionally, the intelligent encryption system calculates the distance between each vertex in the second model and the center of gravity of the model. This step helps to identify the spatial scalability of the model and provides a basis for subsequent size adjustment. By screening the N distances, the intelligent encryption system determines the distances of all vertices whose distance from the center of gravity is greater than or equal to the first preset threshold as the target distances (i.e., the maximum distances). The target distances represent the boundary expansion characteristics of the model and are the key references for adjusting the model size. Then, the intelligent encryption system adjusts the size of the second model according to the target distances to ensure that all vertices whose distance from the center of gravity is greater than or equal to the first preset threshold meet the preset standardized size requirements. This adjustment process usually involves scaling operations to make the boundaries of the model conform to the preset size range, thereby ensuring that the model after data embedding can be accurately recognized and processed among different devices and systems. After the size adjustment is completed, the updated model is the target three-dimensional model. This model not only retains all the geometric characteristics of the second model but also eliminates the influence of model size differences on data embedding and subsequent processing through standardized adjustment. The target three-dimensional model provides a unified carrier for the data hiding algorithm, enhancing the robustness and consistency of data embedding.

[0059] As can be seen from the above, by implementing the size adjustment operation, the sizes of the three-dimensional models are unified and standardized, ensuring the compatibility and accuracy of data embedding and transmission among different systems and devices. The formation of the target three-dimensional model provides a carrier for data hiding that not only maintains the model characteristics but also meets the size standardization requirements, thereby improving the success rate and security of data embedding. This implementation directly optimizes the model preparation stage before data embedding, simplifies the subsequent data embedding and extraction processes, and also enhances the security and efficiency of the entire data transmission process.

[0060] In an optional embodiment, the intelligent encryption system determines the set of boundary points of the target three-dimensional model. Among them, the set of boundary points includes M vertices. The set of boundary points includes a first subset of boundary points and a second subset of boundary points. Among them, the first subset of boundary points is used to represent the set of vertices whose distance from the origin of coordinates in each coordinate axis direction of the target three-dimensional model is greater than the second preset threshold, and the second subset of boundary points is used to represent the set of vertices whose distance from the origin of coordinates in each coordinate axis direction of the target three-dimensional model is less than the third preset threshold. Among them, M is an integer less than N. Then, the target bounding box of the target three-dimensional model is generated according to the set of boundary points, where the target bounding box is used to determine the spatial range of the target three-dimensional model.

[0061] Optionally, the set of boundary points is a set of M vertices selected from the target three-dimensional model, where M is less than N, and N is the total number of vertices of the model. The first subset of boundary points and the second subset of boundary points respectively represent different sets of vertices of the model far from and close to the origin of coordinates.

[0062] Optionally, the target bounding box is the minimum volume polyhedron generated around the target 3D model, which is used to define the exact range of the model in 3D space.

[0063] Optionally, the intelligent encryption system analyzes the target 3D model, determines the set of vertices whose distance from the origin exceeds the second preset threshold as the first subset of boundary points, and at the same time, determines the set of vertices whose distance from the origin is less than the third preset threshold as the second subset of boundary points. These two subsets together constitute the set of boundary points, and their existence marks the outer contour boundary and the inner concave boundary of the model, thus helping the system to more accurately understand the spatial characteristics of the model. Then, based on the vertex information in the set of boundary points, the intelligent encryption system generates the target bounding box of the target 3D model. This bounding box not only defines the spatial range of the model, but also provides a geometric constraint framework for the subsequent data embedding and transmission processes, ensuring that the data embedding operation is only carried out within the valid area of the model, and avoiding data errors or omissions caused by the uncertainty of the model size and shape.

[0064] As can be seen from the above, by implementing the operations of determining the set of boundary points and generating the target bounding box, the intelligent encryption system can clearly define the spatial range and boundary of the 3D model, providing the necessary spatial information for the accurate embedding of bank sensitive data. The division of the set of boundary points, especially the definition of the first subset of boundary points and the second subset of boundary points, helps to more carefully understand the geometric characteristics of the model, and the generation of the target bounding box ensures the accuracy of the subsequent data embedding operation and avoids the out-of-bounds problem that may occur during data embedding. This implementation directly improves the positioning accuracy and reliability of data embedding, and plays a crucial role in ensuring the security and integrity of data throughout the transmission process. In addition, the use of the target bounding box also optimizes the efficiency of data transmission, because it can quickly indicate the size information of the model, facilitating the rapid estimation of the required transmission bandwidth and storage space.

[0065] In an alternative embodiment, the intelligent encryption system first determines the target thickness of the target three-dimensional model according to the data volume of the data to be encrypted, where the target thickness is used to determine the slice spacing when dividing the target three-dimensional model. Then, the target three-dimensional model is sliced according to the target thickness to obtain S hierarchical regions corresponding to the target three-dimensional model, where S is an integer greater than 1. Then, the first feature points and the second feature points in each of the S hierarchical regions are extracted to obtain a first feature point set and a second feature point set corresponding to each hierarchical region. The first feature points are vertices whose influence value on the model structure is greater than a third preset value, and the second feature points are vertices whose influence value on the model structure is less than or equal to the third preset value. Then, based on the value corresponding to each data bit in the target sequence, according to the first feature point set and the second feature point set corresponding to each hierarchical region in the target three-dimensional model, the compression state in different hierarchical regions of the target three-dimensional model is determined to obtain the encrypted three-dimensional model carrier.

[0066] Optionally, the data volume refers to the size of the bank sensitive data to be encrypted; the target thickness refers to the predetermined thickness or slice spacing of each layer when dividing the target three-dimensional model; the first feature points (key feature points) and the second feature points (non-key feature points) are feature points classified according to the influence degree of the vertices on the model structure; the influence value refers to the influence degree of the change of the model morphology after the vertex is deleted or modified.

[0067] Optionally, the intelligent encryption system calculates an appropriate target thickness based on the data volume of the data to be encrypted. The larger the data volume, the smaller the target thickness may be, which means that the model will be sliced into more layers to provide sufficient space for data embedding, and vice versa. This operation ensures a good match between data embedding and model capacity, avoiding data overflow or waste caused by too coarse or too fine slicing of the model. Then, by setting the slice spacing, the target three-dimensional model is accurately divided into multiple hierarchical regions, and each hierarchical region is a potential data embedding region. This step creates a hierarchical space for subsequent data embedding and feature point recognition, enabling data to be embedded into different levels of the model in an orderly manner. Then, the intelligent encryption system performs feature point extraction on each hierarchical region of the target three-dimensional model, distinguishing the first feature points with greater influence on the model structure and the second feature points with less influence. The first feature points are often located at the boundaries or key positions of the model, while the second feature points may be inside the model or in non-conspicuous parts. By identifying these two types of feature points, the system can select appropriate feature points for compression operations without affecting the overall structure of the model, realizing the concealed embedding of data.

[0068] Optionally, after feature extraction of the model, the intelligent encryption system determines the compression state of each hierarchical region in the target 3D model according to each data bit in the target sequence. If the data bit is 1, the second feature points (less influential feature points) in this hierarchical region will be compressed to embed data; if it is 0, the region remains unchanged. In this way, data is secretly stored in the structural changes of the model, increasing the difficulty of data extraction. After the above steps, some hierarchical regions of the model have undergone feature point compression while other regions remain unchanged, forming the final encrypted 3D model carrier. This carrier not only looks no different from an ordinary 3D model on the outside, but actually contains sensitive bank data, achieving the covert transmission of data.

[0069] As can be seen from the above, the intelligent encryption system significantly improves the ability to securely embed sensitive data in 3D models by implementing data volume-driven model segmentation and feature point compression operations. The target 3D model is finely segmented into multiple hierarchical regions, the feature points of each region are accurately identified, and selective compression is performed according to the binary bits of the preprocessed data, ensuring the accuracy of data hiding and the robustness of the model. This method not only effectively prevents the direct observation and interception of data during transmission, but also greatly increases the difficulty of data extraction by changing the subtle structure rather than the appearance of the model, thus significantly enhancing data security and anti-interference ability.

[0070] In an optional embodiment, the intelligent encryption system sets a compression rate, where the compression rate is used to determine the number of vertices after compression of each hierarchical region, and then based on the set of first feature points in each hierarchical region, according to the compression rate, the value corresponding to each data bit in the target sequence, and the set of second feature points corresponding to each hierarchical region, determines the compression state of the hierarchical region corresponding to this data bit, obtaining the encrypted 3D model carrier.

[0071] Optionally, the compression rate is a ratio used to determine the number of vertices after compression of each hierarchical region, reflecting the balance between the depth of data embedding and the robustness of the model.

[0072] Optionally, the compression rates corresponding to each hierarchical region can be the same or different.

[0073] Optionally, the intelligent encryption system sets a specific compression ratio for each hierarchical region. This compression ratio is set based on the characteristics of the data and the carrying capacity of the model, ensuring the security and concealment of the data during the embedding process while maintaining the ability of the model to be accurately decoded after compression. Then, for each data bit in the target sequence, the system analyzes its corresponding value (0 or 1) and the set of first feature points of the hierarchical region associated with this data bit. According to the set compression ratio, if the value corresponding to the data bit is 1, the system will compress the selected hierarchical region, retaining some key feature points in the set of second feature points; if the value is 0, no compression is performed, and the number of vertices in this region remains unchanged. This process realizes the close association between the data and the model features, and secretly carries information by changing the model structure. Through the above steps, the intelligent encryption system corresponds each data bit in the target sequence to the compression state of the hierarchical region of the model, and the finally obtained three-dimensional model carrier is the encrypted three-dimensional model carrier. This carrier is similar to the original model in appearance, but contains data information in its internal structure, effectively realizing the concealed transmission of data.

[0074] As can be seen from the above, by implementing the association between the data bit and the compression state of the hierarchical region, the three-dimensional model carrier is endowed with the ability to carry bank-sensitive data while maintaining the structural integrity and visual similarity of the model. The determination of the compression state is not only related to the preset compression ratio, but more directly depends on the value of the data bit in the target sequence, ensuring the flexibility and concealment of data embedding. The above steps directly enhance the security and anti-tampering of data embedding and transmission. In addition, by dynamically adjusting the compression ratio, the intelligent encryption system can flexibly select the depth of data embedding according to the characteristics of the data, further enhancing the robustness and adaptability of the algorithm.

[0075] In an optional embodiment, if it is detected that the value corresponding to the i-th data bit in the target sequence is 0, the intelligent encryption system prohibits compressing the hierarchical region corresponding to the i-th data bit, where i is an integer greater than or equal to 1, and each data bit in the target sequence corresponds to a hierarchical region in the target three-dimensional model; if it is detected that the value corresponding to the i-th data bit in the target sequence is 1, the intelligent encryption system takes the number of vertices in the hierarchical region corresponding to the i-th data bit as the first value, and then, based on the set of first feature points in the hierarchical region corresponding to the i-th data bit, according to the compression ratio and the first value, determines T second feature points from the set of second feature points in the hierarchical region corresponding to the i-th data bit, where T is an integer greater than or equal to 1, and finally compresses the T second feature points in the hierarchical region corresponding to the i-th data bit.

[0076] Optionally, the intelligent encryption system detects the value of each data bit in the target sequence one by one. If the value is 0, it is decided not to compress the hierarchical area corresponding to the i-th data bit; if the value is 1, subsequent compression processing is performed. This detection step ensures the precise correspondence between the data and the model's hierarchical areas and is the key to data embedding control. When the value corresponding to the i-th data bit is detected as 1, the system takes the original number of vertices as the first value and determines T vertices from the second set of feature points for compression based on this value and the preset compression rate. This process ensures that the compressed model still maintains its geometric features while embedding information by changing the distribution of non-critical feature points. After determining the T second feature points, the intelligent encryption system performs a compression operation on these vertices, specifically by removing or slightly adjusting the positions of the vertices, causing subtle changes in the geometric characteristics of a specific hierarchical area of the model without affecting the overall structure and visual effect of the model. This operation realizes the covert embedding of data and is not easily detected by external observers.

[0077] As can be seen from the above, by implementing the hierarchical area compression operation based on data bit control, the intelligent encryption system can accurately embed bank-sensitive data in the non-significant features of the target 3D model without damaging the appearance or geometric characteristics of the model. The above steps directly enhance the covertness and security of data embedding, avoid changing the significant features of the model, and thus reduce the risk of data being discovered and exploited. In addition, by controlling the conditions for compression or not, the system can flexibly respond to the values of different data bits, providing a dynamic control mechanism for data hiding. This operation not only enhances the security of the data but also optimizes the efficiency of data embedding, ensuring the integrity and confidentiality of the data during embedding and transmission.

[0078] In an alternative embodiment, the intelligent encryption system performs a preprocessing operation on the encrypted 3D model carrier to obtain a fourth model, then determines the number of vertices in each hierarchical area of the fourth model, and then determines the compression rate corresponding to each hierarchical area of the fourth model based on the number of vertices in each hierarchical area of the fourth model and the number of vertices in each hierarchical area of the target 3D model. Finally, the target sequence is determined based on the compression rate corresponding to each hierarchical area of the fourth model, and the data to be encrypted is obtained according to the target sequence.

[0079] Optionally, during the decryption process, the intelligent encryption system first preprocesses the encrypted 3D model carrier, including but not limited to operations such as translation, rotation, and scaling, to generate a standardized fourth model. Although the sender preprocessed the model before embedding the data, during network transmission, the model may undergo slight changes due to various factors (such as packet loss, network latency, decoding errors, etc.). Therefore, after receiving the encrypted 3D model carrier, preprocessing operations need to be performed again. These standardization operations can eliminate the changes caused by transmission and ensure that the model is in the expected standardized state.

[0080] Optionally, after preprocessing the encrypted 3D model carrier, the fourth model is then sliced and layered, and the number of vertices in each layered area is counted. Then, based on the number of vertices in each layered area of the fourth model and the number of vertices in the corresponding layered area of the target 3D model, the intelligent encryption system calculates the compression ratio of each layered area. Based on the calculated compression ratio of each layered area, the intelligent encryption system determines the target sequence. This sequence encodes the information on whether the layered area is compressed. Finally, the intelligent encryption system reverse-parses the data to be encrypted according to the target sequence. This process is actually the process of decoding the data embedding pattern into the original sensitive data and is a key step in data extraction.

[0081] Optionally, after obtaining the encrypted 3D model carrier, the intelligent encryption system preprocesses the model and extracts the information of each layered area in the robust area. By calculating the compression ratio of each sliced layered area, it is determined whether the layered area is compressed, and the final complete data (the original data to be encrypted) is obtained by arranging them in order.

[0082] As can be seen from the above, the decryption process utilizes the compressed state of the hierarchical regions of the 3D model. Even if the model undergoes a certain degree of deformation or data loss, the original data can still be restored based on the compressed state of the hierarchical regions, demonstrating the robustness of this encryption method. Since the data is secretly embedded in the non-significant features (non-critical feature points) of the 3D model, this not only increases the concealment of the data but also improves the anti-tampering ability of the data. Even if the model is artificially modified during transmission, it is difficult for the modifier to access these non-significant features, thus maintaining the integrity of the data. Through the above steps, the receiving party can accurately determine which hierarchical regions are compressed and the degree of compression, which provides the possibility for data tracking and auditing. Once an anomaly occurs in the data, the source of the problem can be traced by checking the compressed state of the hierarchical regions of the model. And the intelligent encryption system, based on the preset model preprocessing steps and feature point determination methods, ensures that only legitimate recipients can understand and execute the data decryption process. Even if an illegal visitor obtains the model, it is difficult to correctly identify and extract the hidden data, thus greatly enhancing the security of the data during network transmission. Restoring the data by calculating the compression ratio of the model hierarchical regions, this mechanism not only ensures the accurate decryption of the data but also takes into account the extraction efficiency. In addition, the preprocessing steps simplify the model structure, making the decryption process more efficient, while the extraction of feature points ensures the accuracy of compression and restoration.

[0083] In an alternative embodiment, Figure 2 is a schematic diagram of an alternative data encryption method according to an embodiment of the present application, as Figure 2 shown. First, sensitive data is received, and then data preprocessing operations are performed, that is, the sensitive data is converted into a binary sequence using Huffman coding technology, and a suitable original model (3D model carrier) is selected as the encryption carrier according to the information volume of the sensitive data. To avoid the influence of factors such as the position, orientation, and size of the 3D model carrier on feature extraction, the carrier model needs to be translated, normalized, rotated, etc. By finding the maximum boundary point and minimum boundary point of the model, the minimum bounding box of the model is generated. The specific steps are as follows:

[0084] Step 1: Place the model in a three-dimensional Cartesian coordinate system and perform operations such as translation, normalization, and rotation on the 3D model carrier. Let the vertex set V of the 3D model carrier be {v1, v2,..., v n}, and the coordinates of any vertex v i in three-dimensional space be (x i , y i , z i ), where i = 1, 2,..., n.

[0085] According to the vertex coordinates of the above model, the coordinates V G of the center of gravity G of the model are obtained, as shown in formula (1):

[0086]

[0087] Among them, x g , y g , z g respectively represent the corresponding values of the centroid G on the coordinate axes x, y, and z.

[0088] After determining the centroid of the model, move the centroid of the model to the origin of coordinates to obtain the new vertex v after translation of the original vertices of the model i ′ , as shown in formula (2):

[0089] v i ′ = (x i ′ , y i ′ , z i ′ ) = (x i - x g , y i - y g , z i - z g ) (2)

[0090] Among them, x i ′ , y i ′ , z i ′ are the updated three-dimensional coordinates of the vertex v i .

[0091] Then construct the covariance matrix K based on the updated vertices, as shown in formula (3):

[0092]

[0093] After that, find its maximum eigenvalue and its corresponding maximum eigenvector T, and then rotate the model so that the eigenvector T coincides with the z-axis to satisfy the rotational invariance of the three-dimensional model. And calculate the maximum distance D from the vertices of the model to the centroid, as shown in formula (4), and then scale the model to unit size based on the maximum distance D.

[0094]

[0095] Step 2: Find the maximum boundary point v max and the minimum boundary point v min , and generate the minimum bounding box of the model, as shown in formula (5):

[0096]

[0097] Among them, L is the maximum boundary length of the model in the X-axis direction, that is, the distance between the maximum boundary point and the minimum boundary point of the model in the X-axis direction; W is the maximum boundary width of the model in the Y-axis direction, that is, the distance between the maximum boundary point and the minimum boundary point of the model in the Y-axis direction; H is the maximum boundary height of the model in the Z-axis direction, that is, the distance between the maximum boundary point and the minimum boundary point of the model in the Z-axis direction.

[0098] Step 3: Determine the layer thickness of the model according to the information amount, so as to slice the model into several slices with the same thickness.

[0099] Then, extract the feature points of the model, use the Meanshift clustering analysis method (a non-parametric clustering technique) to extract the feature points of each layer area, and then compress the non-critical feature points of each area.

[0100] For a given d-dimensional space R n with n points x i (i = 1, 2,..., n) in it, the drift mean vector of the area where the center point x is located is shown in formula (6):

[0101]

[0102] Among them, s h(x) refers to a high-dimensional spherical region with a radius of h, whose center point is x and contains n x data points.

[0103] The specific steps are as follows:

[0104] The first step: Set all data points to the unlabeled state, and randomly select a point c1 on the area Q, and draw a sphere with this point as the center point and h as the radius. Denote the points inside the sphere as set A, set its state cluster as C1, and record the number of times the points are visited.

[0105] The second step: According to the drift mean vector formula, calculate the drift mean vector, and this vector points to c2.

[0106] The third step: Take c2 as the new center point, and repeat the first step to the second step until the drift mean reaches the threshold M t , and at this time, update set A.

[0107] The fourth step: Judge the distance between c1 and c2. If it is less than the threshold M t , update set A at this time.

[0108] The fifth step: Until all points are visited, according to the access frequency of each point to each class, take the class corresponding to the maximum value as the clustering of the current point set.

[0109] After completing the above steps, the compression of the layered region is achieved by deleting some non-critical feature points of the point cloud model of each layer, and the boundary points within the region do not participate in the compression. Suppose the number of vertices of the original layered region is c, and the number of points in the sliced projection region after compression is c'. Then the calculation formula for the compression rate R is: R = (c - c') / c. Calculate the number of vertices after compression through the set compression rate, and then partially remove the non-critical feature points of the already extracted layered region, so as to achieve the purpose of partial region compression. After slicing and layering the three-dimensional model carrier, the information is represented by whether the layered region of the model is compressed: if the data is 0, the layered region is not compressed; if the data is 1, the layered region is compressed. According to the processed binary sequence, the encrypted carrier model is obtained. For example, if the model is divided into 3 layers, then the secret information embedded in the model is 3 bits. Suppose the secret information is "011", then the second and third divided regions of the model are compressed, and the first divided region does not need to be compressed.

[0110] An embodiment of the present application also provides a data encryption device. It should be noted that the data encryption device in the embodiment of the present application can be used to execute the data encryption method provided in the embodiment of the present application. The data encryption device provided in the embodiment of the present application will be introduced below.

[0111] According to an embodiment of the present application, there is also provided a device for implementing the above data encryption method. Figure 3 is a schematic diagram of an optional data encryption device according to an embodiment of the present application, as Figure 3 shown. The device includes: a receiving unit 301, a conversion unit 302, and a determination unit 303.

[0112] Optionally, the receiving unit 301 is configured to receive the data to be encrypted; the conversion unit 302 is configured to convert the data to be encrypted into a binary sequence to obtain a target sequence; the determination unit 303 is configured to determine the compression state within different layered regions of the target three-dimensional model based on the value corresponding to each data bit in the target sequence, so as to obtain an encrypted three-dimensional model carrier, where the target three-dimensional model is a model obtained by updating the initial three-dimensional model through a preprocessing operation, the target three-dimensional model is used to carry the data to be encrypted, the preprocessing operation is used to adjust the initial three-dimensional model to a model that meets the preset requirements, and the encrypted three-dimensional model carrier is used to represent the target three-dimensional model in which the data to be encrypted is embedded, where the preset requirements are used to constrain the geometric attributes of the model.

[0113] Optionally, the determination unit 303 includes: a first determination subunit, a second determination subunit, a first processing subunit, a second processing subunit, and a third processing subunit. Among them, the first determination subunit is configured to determine an initial three-dimensional model based on the data volume of the data to be encrypted; the second determination subunit is configured to determine a vertex set in the initial three-dimensional model, where the vertex set includes N vertices in the initial three-dimensional model, and N is an integer greater than 1; the first processing subunit is configured to perform a first operation on the initial three-dimensional model according to the vertex set to obtain a first model, where the first operation is used to move the initial three-dimensional model in a three-dimensional coordinate system and update the vertex coordinates corresponding to the N vertices in the moved initial three-dimensional model; the second processing subunit is configured to perform a second operation on the first model to obtain a second model, where the second operation is used to adjust the direction of the first model in the three-dimensional coordinate system; the third processing subunit is configured to perform a third operation on the second model to obtain a target three-dimensional model, where the third operation is used to adjust the size of the second model in the three-dimensional coordinate system.

[0114] Optionally, the first processing subunit includes: a first determination module, a first processing module, and a first update module. Among them, the first determination module is configured to determine the centroid of the initial three-dimensional model according to the vertex coordinates of the N vertices in the vertex set; the first processing module is configured to move the centroid of the initial three-dimensional model to the origin of the three-dimensional coordinate system and use the coordinates corresponding to each vertex in the moved initial three-dimensional model as the first vertex coordinates to obtain N first vertex coordinates; the first update module is configured to update the initial three-dimensional model based on the N first vertex coordinates to obtain a first model.

[0115] Optionally, the second processing subunit includes: a first construction module, a second determination module, a second processing module, and a second update module. Among them, the first construction module is configured to construct a target matrix according to the N first vertex coordinates, where the target matrix is used to quantify the distribution characteristic information of each vertex in the first model; the second determination module is configured to determine the target eigenvector of the target matrix, where the target eigenvector is used to characterize the direction in which the distance change range between the N vertices in the first model is greater than a preset range; the second processing module is configured to rotate the first model according to the target eigenvector corresponding to the first model and update the vertex coordinates corresponding to the N vertices in the rotated first model to obtain an updated first model, where the rotation is used to align the target eigenvector corresponding to the first model with any coordinate axis in the three-dimensional coordinate system; the second update module is configured to use the updated first model as the second model.

[0116] Optionally, a third processing subunit includes: a third determination module, a fourth determination module, and a first adjustment module. Among them, the third determination module is configured to determine the distance between each vertex in the second model and the centroid, obtaining N distances; the fourth determination module is configured to use the distances greater than or equal to the first preset threshold among the N distances as target distances; the first adjustment module is configured to adjust the size of the second model in the three-dimensional coordinate system according to the target distances, obtaining a target three-dimensional model.

[0117] Optionally, the data encryption device further includes: a first determination unit and a first generation unit. Among them, the first determination unit is configured to determine a set of boundary points of the target three-dimensional model. The set of boundary points includes M vertices, and the set of boundary points includes a first subset of boundary points and a second subset of boundary points. The first subset of boundary points is used to represent the set of vertices whose distances from the coordinate origin in each coordinate axis direction of the target three-dimensional model in the three-dimensional coordinate system are greater than the second preset threshold, and the second subset of boundary points is used to represent the set of vertices whose distances from the coordinate origin in each coordinate axis direction of the target three-dimensional model in the three-dimensional coordinate system are less than the third preset threshold. Here, M is an integer less than N; the first generation unit is configured to generate a target bounding box of the target three-dimensional model according to the set of boundary points, where the target bounding box is used to determine the spatial range of the target three-dimensional model.

[0118] Optionally, the determination unit 303 includes: a third determination subunit, a fourth processing subunit, a first extraction subunit, and a fourth determination subunit. Among them, the third determination subunit is configured to determine the target thickness of the target three-dimensional model according to the data volume of the data to be encrypted, where the target thickness is used to determine the slice spacing when dividing the target three-dimensional model; the fourth processing subunit is configured to divide the target three-dimensional model according to the target thickness, obtaining S layered regions corresponding to the target three-dimensional model, where S is an integer greater than 1; the first extraction subunit is configured to extract the first feature points and the second feature points in each of the S layered regions, obtaining a set of first feature points and a set of second feature points corresponding to each layered region. The first feature points are the vertices whose influence values on the model structure are greater than the third preset value, and the second feature points are the vertices whose influence values on the model structure are less than or equal to the third preset value; the fourth determination subunit is configured to determine the compression states in different layered regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence, according to the set of first feature points and the set of second feature points corresponding to each layered region of the target three-dimensional model, obtaining an encrypted three-dimensional model carrier.

[0119] Optionally, the fourth determination subunit includes: a first setting module and a fifth determination module. The first setting module is configured to set a compression ratio, where the compression ratio is used to determine the number of vertices after compression for each hierarchical region; the fifth determination module is configured to, based on the first set of feature points in each hierarchical region, determine the compression state of the hierarchical region corresponding to each data bit according to the compression ratio, the value corresponding to each data bit in the target sequence, and the second set of feature points corresponding to each hierarchical region, to obtain an encrypted three-dimensional model carrier.

[0120] Optionally, the fifth determination module includes: a first processing sub-module, a first determination sub-module, a second determination sub-module, and a first compression sub-module. The first processing sub-module is configured to, if it is detected that the value corresponding to the i-th data bit in the target sequence is 0, prohibit compression of the hierarchical region corresponding to the i-th data bit, where i is an integer greater than or equal to 1, and each data bit in the target sequence corresponds to a hierarchical region in the target three-dimensional model; the first determination sub-module is configured to, if it is detected that the value corresponding to the i-th data bit in the target sequence is 1, use the number of vertices in the hierarchical region corresponding to the i-th data bit as a first value; the second determination sub-module is configured to, based on the first set of feature points in the hierarchical region corresponding to the i-th data bit, determine T second feature points from the second set of feature points in the hierarchical region corresponding to the i-th data bit according to the compression ratio and the first value, where T is an integer greater than or equal to 1; the first compression sub-module is configured to compress the T second feature points in the hierarchical region corresponding to the i-th data bit.

[0121] Optionally, the data encryption device further includes: a first processing unit, a second determination unit, a third determination unit, and a fifth determination unit. The first processing unit is configured to perform a preprocessing operation on the encrypted three-dimensional model carrier to obtain a fourth model; the second determination unit is configured to determine the number of vertices in each hierarchical region of the fourth model; the third determination unit is configured to determine the compression ratio corresponding to each hierarchical region of the fourth model according to the number of vertices in each hierarchical region of the fourth model and the number of vertices in each hierarchical region of the target three-dimensional model; the fifth determination unit is configured to determine a target sequence according to the compression ratio corresponding to each hierarchical region of the fourth model and obtain data to be encrypted according to the target sequence.

[0122] According to another aspect of the present application, there is also provided a computer-readable storage medium storing a computer program, where when the computer program runs, the device where the computer-readable storage medium is located executes the above data encryption method.

[0123] According to another aspect of the present application, there is also provided an electronic device, including one or more processors and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above data encryption method.

[0124] According to another aspect of the present application, there is also provided a computer program product, including computer instructions which, when executed by a processor, implement the steps of the above data encryption method.

[0125] The serial numbers of the above embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0126] In the above embodiments of the present application, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0127] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, the functional units in the respective embodiments of the present application can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0130] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0131] The above are only the preferred embodiments of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. A data encryption method, characterized in that, Including: Receiving data to be encrypted; Converting the data to be encrypted into a binary sequence to obtain a target sequence; Based on the values corresponding to each data bit in the target sequence, determining the compression states in different layered regions of a target 3D model to obtain an encrypted 3D model carrier, where the target 3D model is a model obtained by updating an initial 3D model through a preprocessing operation, the target 3D model is used to carry the data to be encrypted, the preprocessing operation is used to adjust the initial 3D model to a model that meets preset requirements, the encrypted 3D model carrier is the target 3D model after embedding the data to be encrypted, and the preset requirements are used to constrain the geometric attributes of the model.

2. The data encryption method according to claim 1, characterized in that The preprocessing operation at least includes a first operation, a second operation, and a third operation, and the target 3D model is obtained in the following manner: Determining the initial 3D model based on the data volume of the data to be encrypted; Determining a vertex set in the initial 3D model, where the vertex set includes N vertices in the initial 3D model, and N is an integer greater than 1; Performing the first operation on the initial 3D model according to the vertex set, to obtain a first model, where the first operation is used to move the initial 3D model in a 3D coordinate system and update the vertex coordinates corresponding to the N vertices in the moved initial 3D model; Performing the second operation on the first model to obtain a second model, where the second operation is used to adjust the direction of the first model in the 3D coordinate system; Performing the third operation on the second model to obtain the target 3D model, where the third operation is used to adjust the size of the second model in the 3D coordinate system.

3. The data encryption method according to claim 2, wherein The first operation includes the following steps: Determining the centroid of the initial 3D model according to the vertex coordinates of the N vertices in the vertex set; Moving the centroid of the initial 3D model to the origin of coordinates in the 3D coordinate system, and taking the coordinates corresponding to each vertex in the moved initial 3D model as first vertex coordinates to obtain N first vertex coordinates; Updating the initial 3D model based on the N first vertex coordinates to obtain the first model.

4. The data encryption method according to claim 3, wherein The second operation includes the following steps: Constructing a target matrix according to the N first vertex coordinates, where the target matrix is used to quantify the distribution characteristic information of each vertex in the first model; Determining the target eigenvector of the target matrix, where the target eigenvector is used to characterize the direction in which the distance change range between the N vertices in the first model is greater than a preset range; Rotating the first model according to the target eigenvector corresponding to the first model, and updating the coordinates corresponding to the N vertices in the rotated first model to obtain the updated first model, where the rotation is used to align the target eigenvector corresponding to the first model with any one coordinate axis in the 3D coordinate system; Taking the updated first model as the second model.

5. The data encryption method according to claim 4, wherein The third operation includes the following steps: Determine the distances between each vertex in the second model and the centroid, obtaining N distances; Take the distances among the N distances that are greater than or equal to the first preset threshold as target distances; Adjust the size of the second model in the three-dimensional coordinate system according to the target distances, obtaining the target three-dimensional model.

6. The data encryption method according to claim 2, wherein Before determining the compression states in different hierarchical regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain the encrypted three-dimensional model carrier, the method further includes: Determine the set of boundary points of the target three-dimensional model, where the set of boundary points includes M vertices, and the set of boundary points includes a first subset of boundary points and a second subset of boundary points. The first subset of boundary points is used to represent the set of vertices whose distances from the coordinate origin in each coordinate axis direction of the target three-dimensional model in the three-dimensional coordinate system are greater than the second preset threshold, and the second subset of boundary points is used to represent the set of vertices whose distances from the coordinate origin in each coordinate axis direction of the target three-dimensional model in the three-dimensional coordinate system are less than the third preset threshold, where M is an integer less than N; Generate a target bounding box of the target three-dimensional model according to the set of boundary points, where the target bounding box is used to determine the spatial range of the target three-dimensional model.

7. The data encryption method according to claim 1, wherein Adjusting the compression states in different hierarchical regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain the encrypted three-dimensional model carrier includes: Determine the target thickness of the target three-dimensional model according to the data volume of the data to be encrypted, where the target thickness is used to determine the slice spacing when dividing the target three-dimensional model; Slice the target three-dimensional model according to the target thickness, obtaining S hierarchical regions corresponding to the target three-dimensional model, where S is an integer greater than 1; Extract the first feature points and the second feature points in each of the S hierarchical regions, obtaining a first set of feature points and a second set of feature points corresponding to each hierarchical region, where the first feature points are vertices whose influence values on the model structure are greater than the third preset value, and the second feature points are vertices whose influence values on the model structure are less than or equal to the third preset value; Based on the values corresponding to each data bit in the target sequence, determine the compression states in different hierarchical regions of the target three-dimensional model according to the first set of feature points and the second set of feature points corresponding to each hierarchical region in the target three-dimensional model, obtaining the encrypted three-dimensional model carrier.

8. The data encryption method according to claim 7, wherein Based on the values corresponding to each data bit in the target sequence, determine the compression states in different hierarchical regions of the target three-dimensional model according to the first set of feature points and the second set of feature points corresponding to each hierarchical region in the target three-dimensional model, obtaining the encrypted three-dimensional model carrier, including: Set the compression ratio, where the compression ratio is used to determine the number of vertices after compression for each hierarchical region; Based on the first set of feature points in each of the hierarchical regions, determine the compression state of the hierarchical region corresponding to each data bit according to the compression ratio, the value corresponding to each data bit in the target sequence, and the second set of feature points corresponding to each of the hierarchical regions, to obtain the encrypted three-dimensional model carrier.

9. The data encryption method according to claim 8, wherein Based on the first set of feature points in each of the hierarchical regions, determining the compression state of the hierarchical region corresponding to each data bit according to the compression ratio, the value corresponding to each data bit in the target sequence, and the second set of feature points corresponding to each of the hierarchical regions includes: If it is detected that the value corresponding to the i-th data bit in the target sequence is 0, then prohibit compressing the hierarchical region corresponding to the i-th data bit, where i is an integer greater than or equal to 1, and each data bit in the target sequence respectively corresponds to a hierarchical region in the target three-dimensional model; If it is detected that the value corresponding to the i-th data bit in the target sequence is 1, then use the number of vertices in the hierarchical region corresponding to the i-th data bit as the first value; Based on the first set of feature points in the hierarchical region corresponding to the i-th data bit, determine T second feature points from the second set of feature points in the hierarchical region corresponding to the i-th data bit according to the compression ratio and the first value, where T is an integer greater than or equal to 1; Compress the T second feature points in the hierarchical region corresponding to the i-th data bit.

10. The data encryption method according to claim 1, wherein After determining the compression states in different hierarchical regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain the encrypted three-dimensional model carrier, the method further includes: Perform the preprocessing operation on the encrypted three-dimensional model carrier to obtain a fourth model; Determine the number of vertices in each of the hierarchical regions in the fourth model; According to the number of vertices in each of the hierarchical regions in the fourth model and the number of vertices in each of the hierarchical regions in the target three-dimensional model, determine the compression ratio corresponding to each of the hierarchical regions in the fourth model; Determine the target sequence according to the compression ratio corresponding to each of the hierarchical regions in the fourth model, and obtain the data to be encrypted according to the target sequence.

11. A data encryption device, characterized in that, Includes: A receiving unit that receives the data to be encrypted; A conversion unit that converts the data to be encrypted into a binary sequence to obtain a target sequence; A determination unit that determines the compression states in different hierarchical regions of the target three-dimensional model based on the values corresponding to each data bit in the target sequence to obtain an encrypted three-dimensional model carrier, where the target three-dimensional model is a model obtained by updating the initial three-dimensional model through a preprocessing operation, the target three-dimensional model is used to carry the data to be encrypted, the preprocessing operation is used to adjust the initial three-dimensional model to a model that meets preset requirements, and the encrypted three-dimensional model carrier is used to represent the target three-dimensional model in which the data to be encrypted is embedded, where the preset requirements are used to constrain the geometric attributes of the model.

12. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium. When the computer program runs, the device where the computer-readable storage medium is located is caused to execute the data encryption method described in any one of claims 1 to 10.

13. An electronic device, characterized in that, It includes one or more processors and a memory. The memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the data encryption method described in any one of claims 1 to 10.

14. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by a processor, the steps of the data encryption method described in any one of claims 1 to 10 are implemented.

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