Data distributed storage and reading method and system based on multi-modal protection

Abstract

The invention discloses a data distributed storage and reading method and system based on multi-modal protection, and relates to the technical field of multi-modal data encryption storage, and the method comprises the steps: receiving input data, and preprocessing the input data into multi-modal segmentation data; executing multi-key cooperative encryption on the data; carrying out distributed storage on the encrypted data; signing and broadcasting the distributed stored node information based on the block chain; and receiving a data query request of a user, obtaining the corresponding encrypted segmented data through the permission vector of the user, and decrypting and recombining the encrypted segmented data to obtain query data. Through the multi-modal feature fusion technology, the security of data storage is improved, meanwhile, a multi-level encryption system is constructed through multi-key cooperation encryption, the security of key distribution is effectively guaranteed, a distributed storage strategy based on dynamic weight is adopted, intelligent selection and load balancing of storage nodes are achieved, and the security of data storage is improved. And the data storage reliability and the system expansibility are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multimodal data encryption storage, and in particular to a data distributed storage and reading method and system based on multimodal protection. Background Art

[0002] With the development of big data and artificial intelligence technology, the demand for storage and sharing of multimodal data (such as images, text, video, etc.) is growing. There are many problems with traditional data storage and reading methods. When storing or encrypting multimodal data, it is necessary to effectively separate and identify it, and execute corresponding data processing schemes for each module data. However, existing technologies are usually unable to handle it fully. For example, the feature fusion and segmentation accuracy of multimodal data is insufficient, resulting in the inability to effectively retain the original semantic information after data segmentation, affecting the subsequent encryption and storage efficiency. When encrypting the processed data, traditional encryption methods mostly use a single key, which is difficult to meet the differentiated security requirements of different modal data. When storing data, the storage node selection only relies on static indicators such as storage capacity and bandwidth. For a variety of different multimodal data, it is difficult to meet the encryption storage efficiency and security of multimodal data using a single static indicator. Therefore, a technical solution that can encrypt and store multimodal data is of great value to innovation in the field of data storage. Summary of the invention

[0003] In view of this, the present invention proposes a data distributed storage and reading method and system based on multimodal protection, which can significantly improve the security, efficiency and privacy protection capabilities of data storage. The present invention provides the following technical solutions: A method for distributed storage and reading of data based on multimodal protection, the method comprising: receiving input data, and preprocessing the data to obtain multimodal segmented data; performing multi-key collaborative encryption on the multimodal segmented data; performing distributed storage on the encrypted multimodal segmented data; signing and broadcasting distributed stored node information based on blockchain; receiving a user's data query request, obtaining the corresponding encrypted segmented data through the user's authority vector, and performing decryption and reorganization on the encrypted segmented data to obtain query data.

[0004] Optionally, the receiving of input data and preprocessing thereof to obtain multimodal segmentation data includes: identifying each modal data and the corresponding index in the input data, and extracting the feature vectors of the modal data respectively; using an attention mechanism to perform feature fusion and calculate the attention weight of each modal feature; calculating the fused feature vector corresponding to each modal feature based on the attention weight; and performing clustering segmentation according to the feature vector to obtain multimodal segmentation data.

[0005] Optionally, performing multi-key collaborative encryption on the multimodal segmentation data includes: generating a basic key based on a chaotic map and a system private key; performing hash processing on a feature vector corresponding to the multimodal segmentation data to obtain a corresponding hash value; performing an XOR operation on the hash value and the basic key to obtain a key corresponding to the multimodal segmentation data; and encrypting the corresponding multimodal segmentation data using the key.

[0006] Optionally, before encrypting the corresponding multimodal segmented data using the key, it also includes: establishing an entangled quantum pair between the key receiver and the key sender; performing joint measurement on the key information to be transmitted by the sender and a part of the entangled quantum pair to obtain a measurement result; sending the measurement result to the receiver through a classical channel; the receiver recovers the key information based on the measurement result and its entangled quantum pair.

[0007] Optionally, the distributed storage of the encrypted multimodal segmentation data includes: obtaining configuration information of the storage node, the configuration information including: storage capacity, network bandwidth, remaining storage space and node reputation value; calculating dynamic weights based on the strength of the key and the configuration information; calculating comprehensive scores corresponding to the storage nodes based on the dynamic weights; sorting the comprehensive scores, and selecting storage nodes with higher rankings to store the multimodal segmentation data.

[0008] Optionally, the blockchain-based signing and broadcasting of distributed storage node information includes: encoding the information of the storage node into a transaction, and signing the transaction using an elliptic curve digital signature algorithm; broadcasting the signed transaction to the blockchain network for verification and recording; dividing the blockchain network into multiple shards, each shard independently processing the transaction; achieving transaction synchronization and data sharing between shards through a cross-shard communication protocol; and using zero-knowledge proof technology to encrypt and verify sensitive information in the transaction to ensure the privacy and security of storage nodes and data.

[0009] Optionally, the receiving of a user's data layer query request, obtaining the corresponding encrypted segmented data through the user's permission vector, and decrypting and reorganizing the encrypted segmented data to obtain the query data includes: defining the user's permission vector, biometric vector and timestamp; parsing the permission vector and determining whether the user has the right to access the encrypted segmented data in combination with the smart contract; if the user has the right to access, obtaining the node information of the distributed storage of the encrypted segmented data from the blockchain according to the permission vector; obtaining the encrypted segmented data according to the node information of the distributed storage; decrypting the encrypted segmented data; and splicing and reorganizing the decrypted segmented data according to the sorting rules to obtain the query data.

[0010] The present invention further discloses a data distributed storage and reading system based on multimodal protection, comprising: a preprocessing module, used to receive input data and preprocess it to obtain multimodal segmented data; an encryption module, used to perform multi-key collaborative encryption on the multimodal segmented data; a storage module, used to perform distributed storage on the encrypted multimodal segmented data; a signature module, used to sign and broadcast the distributed stored node information based on the blockchain; a data reorganization module, receiving a user's data query request, obtaining the corresponding encrypted segmented data through the user's authority vector, and performing decryption and reorganization on the encrypted segmented data to obtain query data.

[0011] The present invention further discloses a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0012] The present invention further discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the program.

[0013] According to the technical solution of the present invention, through multimodal feature fusion technology, fine-grained processing of multimodal data is achieved, which significantly improves the security of data storage and access efficiency. At the same time, combined with multi-key collaborative encryption and quantum key distribution technology, a multi-level encryption system is constructed to effectively ensure the security of key distribution. Furthermore, a distributed storage strategy based on dynamic weights is adopted to achieve intelligent selection and load balancing of storage nodes, which improves the reliability of data storage and system scalability. On this basis, fine-grained control of user permissions and high efficiency of data reorganization are ensured. On the whole, a full life cycle security protection system is formed from data segmentation, encryption, storage to access, which significantly improves the security, reliability and availability of multimodal data sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] For the purpose of illustration and not limitation, the present invention is now described in conjunction with the embodiments of the present invention and the accompanying drawings, in which: Figure 1 It is a flowchart of a method for distributed data storage and reading based on multimodal protection in an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a data distributed storage and reading system based on multimodal protection in an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the implementation mode of the present application. Obviously, the described implementation mode is only a part of the implementation mode of the present application, not all the implementation modes. Based on the implementation mode in the present application, all other implementation modes obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0016] It should be noted that, in the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0017] refer to Figure 1 This embodiment discloses a data distributed storage and reading method based on multimodal protection, comprising the following steps: S100: receiving input data and preprocessing it to obtain multimodal segmentation data. In this embodiment, the input data is multimodal data for encrypted storage. Exemplarily, the multimodal data includes text data, image data and video data. For text data, a pre-trained model based on the Transformer architecture (such as BERT) is used to extract a semantic feature vector T i , where i represents the index of the text segment. For image data, a convolutional neural network (such as ResNet) is used to extract the visual feature vector I j , where j represents the index of the image region. For video data, a 3D convolutional neural network (such as C3D) and an optical flow algorithm are combined to extract the spatiotemporal feature vector V k , where k represents the index of the video segment.

[0018] After extracting the feature vectors and corresponding indexes of each modal data, the attention mechanism is used to perform feature fusion and calculate the attention weight of each modal feature to integrate the complementary information of different modal data and achieve more accurate data segmentation and access control. Specifically, taking text and images as an example, the attention weight of text on the image is calculated. , ,in is the learnable attention matrix, is the number of image regions. Furthermore, the fused feature vector for: .

[0019] Furthermore, based on the fused feature vectors, the spectral clustering algorithm is used to segment the data. Specifically, the similarity matrix S between the feature vectors is calculated: ,in, is the bandwidth parameter. The similarity matrix S is further normalized to obtain the Laplace matrix L, and the eigenvector of L is solved, and then cluster segmentation is performed according to the eigenvector to obtain multimodal segmentation data.

[0020] S200: Perform multi-key collaborative encryption on the multimodal segmented data. Select a chaotic mapping model. In this embodiment, Logistic mapping is selected, and its mathematical expression is: ,in is the control parameter to control the system in a chaotic state, is the value of the nth iteration, select an initial value As the seed of the chaotic map, the seed can be generated by the system according to the current timestamp, random number and other factors to ensure that the chaotic sequence generated each time is random and unpredictable. Get the private key b of the current system, encode it, convert it into a binary sequence, and then perform an XOR operation on the chaotic sequence generated by the chaotic map and the binary sequence of the system private key b to generate the basic key .

[0021] Furthermore, the feature vector corresponding to the multimodal segmentation data is obtained, and the secure hash algorithm (SHA-256) is used to calculate it to obtain the corresponding hash value, exemplarily: , and , in order to convert the feature vector into a fixed-length hash value, which is convenient for subsequent key generation and encryption operations, and also increases the security of the data. The hash value corresponding to each multimodal segmentation data is compared with the basic key Perform an XOR operation to obtain the key corresponding to the multimodal segmentation data. For text data, the key is: , for image data, the key is: , for video data, the key is: Through XOR operation, the basic key and the characteristic information of each modal data are combined, so that each modal data has its own unique key, which enhances the security of encryption. , and That is, the key of the multi-modal segmented data is sharded to generate key shards Keyshares. Based on this, in the subsequent key recovery scheme, a sufficient number of shards need to be collected to recover the original key.

[0022] After obtaining the key corresponding to the multi-modal segmented data, an entangled quantum pair is established between the key receiver and the key sender, so that when one quantum state is measured, the other quantum state will change accordingly instantly, thereby ensuring the security of key distribution. Further, the key distribution is executed, and the sender sends the key information to be transmitted (such as , , ) and a part of the entangled quantum pair to obtain a measurement result M, which contains the key information and the association information of the quantum state. The sender sends the measurement result M to the receiver through a classical channel, and the receiver recovers the key information through a quantum algorithm based on the received measurement result M and the other part of the entangled quantum pair it holds. Exemplarily, the receiver uses the entanglement characteristics of the quantum state and the association of the measurement result to operate on the quantum state it holds, thereby obtaining the same key as the sender , , Furthermore, the corresponding multi-modal segmentation data is encrypted by using the key of each modal data obtained.

[0023] S300: Distribute the encrypted multimodal segmented data. Specifically, first obtain the configuration information of the storage node, which includes: storage capacity, network bandwidth, remaining storage space and node reputation. Among them, storage capacity determines the total amount of data that the node can store; network bandwidth affects the reading and writing speed of data; remaining storage space reflects the size of the space currently available for the node to store new data; the reputation of the node is based on the node's past performance, such as the integrity of data storage, response time, whether there have been security issues and other factors. The dynamic weight is calculated based on the strength of the key and the configuration information, and the calculation formula is: ,in, is the remaining storage space of the nth node, is the total storage capacity of all nodes, is the network bandwidth of the nth node, is the maximum network bandwidth among all nodes, is the reputation value of the nth node, is a quantitative value of the key strength, , , , is a pre-set weight coefficient, and . For the quantification of key strength, the formula is: ,in, is the key entropy value, is the security level of the selected encryption algorithm. For example, the present embodiment selects the AES-256 encryption algorithm, and its security level is 3. In contrast, the security level of the RSA-3072 encryption algorithm is 4. Calculate the comprehensive score corresponding to each storage node , the formula is: ,in, It is a monotonically increasing function, which ensures that the nodes with large dynamic weights also have high comprehensive scores. After obtaining the comprehensive score, the comprehensive scores of all storage nodes are sorted, and the storage nodes with the highest ranking are selected to store the multimodal segmentation data. The appropriate number of nodes can be selected according to the size of the data and the storage requirements. For example, if the amount of data is large, multiple nodes can be selected for distributed storage to improve the reliability and performance of the storage. In order to further improve the reliability of the data, a data redundancy strategy is adopted. The encrypted multimodal segmentation data can be copied and stored on different nodes. For example, for important data fragments, two copies can be made and stored on nodes in different geographical locations to prevent data loss due to failure or damage of a single node. After completing the storage of the encrypted multimodal segmentation data, the key shards Keyshares corresponding to the multimodal segmentation data are assigned to the corresponding storage nodes. At the same time, the same key shards are copied to multiple nodes in different geographical locations to cooperate with the redundant storage of multimodal segmentation data. Exemplary, multimodal segmentation data of text data By key encryption, The generated key shards will be Assign to the same or related storage node to ensure that the corresponding decryption key shard can be obtained when the data shard needs to be accessed.

[0024] S400: Sign and broadcast the node information of distributed storage based on blockchain. Collect the configuration information of all storage nodes involved in the distributed storage process, which includes the unique identification of the storage node, the identification of the stored data fragment, the storage location, the configuration information of the node (storage capacity, network bandwidth, remaining storage space, reputation value, etc.), as well as the dynamic weight and comprehensive score. Encode the collected node information in the format specified by the blockchain network and convert it into transaction data suitable for transmission and processing on the blockchain. The encoding process uses binary encoding or JSON format to ensure the simplicity and readability of the data. The system uses the pre-assigned private key of the elliptic curve digital signature algorithm (ECDSA) to sign the encoded transaction data using the ECDSA algorithm. The signing process involves hashing the transaction data and encrypting the private key. Specifically, the transaction data is first hashed to obtain a hash value of a fixed length, and then the hash value is encrypted using the private key to generate a digital signature. The purpose of the signature is to ensure the integrity and non-repudiation of the transaction data, that is, only the system holding the private key can sign the transaction, and the signature can be verified by any node holding the public key. Broadcast the signed transaction data to each node in the blockchain network. Before broadcasting, the system needs to establish a stable connection with the blockchain network. It can communicate with other nodes through P2P network protocols (such as BitTorrent, Kademlia, etc.) to ensure that transaction data can be quickly and accurately propagated to the entire network.

[0025] Furthermore, the blockchain network is divided into multiple shards, each of which is an independent sub-network responsible for processing a part of the transactions. The division of shards is carried out based on factors such as the geographical location, performance, and function of the nodes to improve the concurrent processing capability and scalability of the blockchain network. For example, nodes with similar geographical locations can be divided into one shard, or nodes with strong processing capabilities can be divided into one shard to specifically handle complex transactions. The allocation rules of transaction data are formulated based on the type of transaction, the amount of data, and the nodes involved. For example, for transactions involving a specific node, the shard where the node is located is allocated for processing; for transactions with a large amount of data, it can be allocated to a shard with strong processing capabilities for processing. After the shard allocation is completed, each shard independently processes the allocated transactions. The nodes in the shard verify the transaction, check whether the signature of the transaction is valid, whether the data is complete, etc. If the verification is passed, the transaction is recorded in the local blockchain account book. Among them, when performing communication across shards, a cross-shard communication protocol is used to realize transaction synchronization and data sharing between shards. The communication rules and data formats between shards are defined by the communication protocol to ensure that different shards can interact effectively. When a shard completes a transaction, it will synchronize the transaction results to other related shards through the cross-shard communication protocol. The synchronization process needs to ensure data consistency and integrity to avoid data conflicts and inconsistencies. This implementation method uses a distributed consensus algorithm (such as PBFT, PoS, etc.) to ensure synchronization between shards.

[0026] In order to ensure the security of sensitive information such as the reputation value and dynamic weight of the node during the transaction, zero-knowledge proof technology is used to encrypt it, and only the validity of this information is proved to other nodes without leaking the specific value. Similarly, for other nodes, the validity of sensitive information in the transaction is confirmed by verifying the zero-knowledge proof. The verification process does not need to obtain specific sensitive information, thereby protecting the privacy and security of nodes and data. If the verification is passed, the transaction is considered legal and can continue to be processed. The verified and processed transaction data will be recorded in the blockchain account book. In this embodiment, the account book is a distributed, tamper-proof database that records all transaction history and node information. Each node will maintain a complete copy of the account book to ensure the reliability and traceability of the data. Further, the node will update the local account book status according to the new transaction data, including the update of node information, the update of data storage records, etc. At the same time, the blockchain network will ensure that the account book status of all nodes is consistent through a consensus mechanism.

[0027] S500: Receive a data query request from a user, obtain corresponding encrypted segmented data through the user's authority vector, and decrypt and reassemble the encrypted segmented data to obtain query data.

[0028] The user's permission vector is dynamically generated by the system based on factors such as the user's role, access history, and data sensitivity. , where each element Indicates whether the user has the right to access the i-th multimodal segmentation data. The user's biometric data is further obtained and converted into a feature vector B. The system also records the timestamp t when the user initiates the query request to verify the timeliness of the user's permissions.

[0029] Furthermore, the user's permission vector Perform parsing to extract access rights information. For example, the parsing obtains a set of multimodal segmented data indexes that the user has access to. . Then call the smart contract deployed on the blockchain to verify: Permission validity: Check the user's permission vector Whether it is within the validity period, that is, whether the current timestamp t is within the effective time interval of the permission Internal; Whether the biometrics match: compare the user's feature vector B with the user's biometric template stored in the blockchain and calculate the similarity score. For example, the cosine similarity formula is used The similarity score is calculated, and if the similarity score exceeds the preset threshold, the biometric verification is passed.

[0030] When judging whether the current user has the right to access the encrypted segmented data, the permission vector of the user The collection of multimodal segmentation data indexes that can be accessed in Query the blockchain security ledger and obtain: the encrypted data storage location of each shard , which contains the address, port and path of the storage node; the corresponding key shard ; Shard sorting rules. Based on this, filter out the reputation values ​​of the storage nodes Nodes with a reputation value lower than a preset threshold are selected to ensure that data is extracted from nodes with high reputation values. For the sorting rules of shards, this embodiment provides an example of a sorting rule, that is, when generating encryption key shards, the key shards are bound to the corresponding multimodal data shards. At this time, the sorting rules will be integrated into the key management system, that is, each key shard is not only associated with the encryption information of the data shard, but also with its logical location information in the original data.

[0031] According to storage location Send a data extraction request to the storage node, verify the logical relationship between the storage node and the key shard according to the data extraction request, and extract the target data if the verification is successful. Collect at least t key shards and restore the original key through Lagrange interpolation: ,in, are the interpolation coefficients, which are obtained by the polynomial Sure; is a large prime number; is the shard key stored in each storage node. Exemplarily, this implementation adopts a 3-of-5 threshold scheme, that is, starting from t=3, according to the 3 shard keys collected , and , through the pre-computed interpolation coefficients , and , insert the above formula to calculate and take the modulus, and finally restore the original key .

[0032] Furthermore, a temporary key is generated through the Diffie-Hellman protocol based on the user private key a and the system private key b. The expression is: ,in, is the shared secret value calculated by the Diffie-Hellman algorithm, is the biometric feature vector, Is the timestamp.

[0033] Generate the final decryption key based on the original key and the temporary key: Finally, use Decrypt multimodal segmentation data.

[0034] After obtaining the decrypted multimodal segmentation data, it is sorted according to the sorting rule. The sorting rule here is a preset rule. For example, the video segmentation data is sorted in ascending order by timestamp, or the text segmentation data is sorted by priority of semantic tags.

[0035] Furthermore, the multimodal segmented data is reorganized. In this embodiment, for exemplary text data, an NLP model (such as BERT) is used to restore the semantic structure and process sentence segmentation and grammar; for image data, a super-resolution reconstruction algorithm (such as SRGAN) is used to restore the image; for video data, a time series alignment algorithm (such as optical flow method) is used to restore the temporal continuity of the video. The reorganized data is further converted into a specified format according to user needs.

[0036] After the reorganization of the segmented data is completed, the user ID, accessed shard index and other information are encoded as transactions, written to the blockchain after being signed with ECDSA, and the decrypted shard and temporary key in the secure cache are deleted to prevent data leakage.

[0037] In summary, after receiving a user data query request, the user's permission vector, biometric vector and timestamp are first defined. By parsing the permission vector and combining with the smart contract, it is determined whether the user has the right to access the encrypted segmented data; if the user has the right to access, the distributed storage node information of the encrypted segmented data is obtained from the blockchain according to the permission vector, and then the encrypted data is obtained. After the decryption operation is completed, the decrypted segmented data is spliced ​​and reorganized according to the sorting rules, and finally the query data is obtained. Fine-grained access control is achieved through permission vectors and smart contracts to ensure the security and compliance of data access; with the help of blockchain storage node information, the stored information is ensured to be tamper-proof and traceable; combined with biometric verification, dynamic key negotiation and other mechanisms, the security of data transmission and storage is strengthened; and the decryption and reorganization process ensures the integrity and availability of the data obtained by the user, and finally realizes data security, efficiency and controllable query and processing.

[0038] refer to Figure 2 This embodiment further discloses a data distributed storage and reading system based on multimodal protection, including: The preprocessing module 21 is used to receive input data and preprocess it to obtain multimodal segmentation data, including: identifying each modal data and the corresponding index in the input data, and extracting the feature vectors of the modal data respectively; using the attention mechanism to perform feature fusion and calculate the attention weight of each modal feature; calculating the fused feature vector corresponding to each modal feature based on the attention weight; and performing clustering segmentation according to the feature vector to obtain multimodal segmentation data.

[0039] The encryption module 22 is used to perform multi-key collaborative encryption on the multimodal segmented data, including: generating a basic key based on a chaotic map and a system private key; performing hash processing on a feature vector corresponding to the multimodal segmented data to obtain a corresponding hash value; performing an XOR operation on the hash value and the basic key to obtain a key corresponding to the multimodal segmented data; establishing an entangled quantum pair between a key receiver and a key sender; performing a joint measurement on a part of the key information to be transmitted by the sender and the entangled quantum pair to obtain a measurement result; sending the measurement result to the receiver through a classical channel; the receiver recovers the key information based on the measurement result and its entangled quantum pair, and encrypts the corresponding multimodal segmented data through the key.

[0040] The storage module 23 is used for distributed storage of the encrypted multimodal segmentation data, including: obtaining configuration information of the storage node, the configuration information including: storage capacity, network bandwidth, remaining storage space and node reputation value; calculating dynamic weights based on the strength of the key and the configuration information; calculating the comprehensive scores corresponding to the storage nodes based on the dynamic weights; sorting the comprehensive scores, and selecting the storage nodes with the highest sorting to store the multimodal segmentation data.

[0041] The signature module 24 is used to sign and broadcast the node information of distributed storage based on the blockchain. It includes: encoding the information of the storage node into a transaction and signing the transaction using the elliptic curve digital signature algorithm; broadcasting the signed transaction to the blockchain network for verification and recording; dividing the blockchain network into multiple shards, each shard independently processing the transaction; realizing transaction synchronization and data sharing between shards through a cross-shard communication protocol; using zero-knowledge proof technology to encrypt and verify sensitive information in the transaction to ensure the privacy and security of storage nodes and data.

[0042] The data reorganization module 25 receives a user's data query request, obtains the corresponding encrypted segmented data through the user's authority vector, and decrypts and reorganizes the encrypted segmented data to obtain query data, including: defining the user's authority vector, biometric vector and timestamp; parsing the authority vector and determining whether the user has the right to access the encrypted segmented data in combination with the smart contract; if the user has the right to access, obtaining the node information of the distributed storage of the encrypted segmented data from the blockchain according to the authority vector; obtaining the encrypted segmented data according to the node information of the distributed storage; decrypting the encrypted segmented data; and splicing and reorganizing the decrypted segmented data according to the sorting rules to obtain query data.

[0043] Figure 3 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Figure 3 As shown, the electronic device 50 includes: a processor 501 (processor), a memory 502 (memory) and a bus 503; The processor 501 and the memory 502 communicate with each other via the bus 503 ; the processor 501 is used to call program instructions in the memory 502 to execute the methods provided by the above-mentioned method implementation methods.

[0044] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable a computer to execute the methods provided by the above-mentioned method embodiments.

[0045] A person skilled in the art can understand that all or part of the steps for implementing the above-mentioned method implementation method can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method implementation method; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various storage media that can store program codes.

[0046] The device implementation described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present implementation scheme. Those of ordinary skill in the art may understand and implement it without creative effort.

[0047] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each implementation mode or some parts of the implementation mode.

[0048] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data distributed storage and reading method based on multimodal protection, characterized in that: The method comprises: receiving input data and preprocessing the input data to obtain multimodal segmentation data; Performing multi-key collaborative encryption on the multimodal segmented data; Distributed storage of encrypted multimodal segmentation data; Sign and broadcast distributed storage node information based on blockchain; Receive a data query request from a user, obtain the corresponding encrypted segmented data through the user's authority vector, and decrypt and reassemble the encrypted segmented data to obtain the query data.

2. The method for distributed data storage and reading based on multimodal protection according to claim 1, characterized in that: The receiving input data and preprocessing the input data to obtain multimodal segmentation data includes: Identify each modal data and corresponding index in the input data, and extract the feature vectors of the modal data respectively; Use the attention mechanism to perform feature fusion and calculate the attention weight of each modality feature; Calculate the fused feature vector corresponding to each modality feature based on the attention weight; Cluster segmentation is performed according to the feature vector to obtain multimodal segmentation data.

3. The method for distributed data storage and reading based on multimodal protection according to claim 1, characterized in that: The performing multi-key collaborative encryption on the multimodal segmented data comprises: Generate a basic key based on the chaotic map and the system private key; Performing hash processing on the feature vector corresponding to the multimodal segmentation data to obtain a corresponding hash value; Performing an XOR operation on the hash value and the basic key to obtain a key corresponding to the multimodal segmented data; The corresponding multimodal segmented data is encrypted using the key.

4. The method for distributed data storage and reading based on multimodal protection according to claim 3 is characterized in that: Before encrypting the corresponding multimodal segmented data by using the key, it also includes: Establishing an entangled quantum pair between the key receiver and the key sender; Performing joint measurement on the key information to be transmitted by the sender and a part of the entangled quantum pair to obtain a measurement result; Sending the measurement result to a receiver via a classical channel; The receiver recovers the key information based on the measurement results and its entangled quantum pairs.

5. The method for distributed data storage and reading based on multimodal protection according to claim 1, characterized in that: The distributed storage of the encrypted multimodal segmentation data includes: Obtaining configuration information of the storage node, the configuration information including: storage capacity, network bandwidth, remaining storage space and node reputation value; Calculating a dynamic weight based on the strength of the key and the configuration information; Calculate the comprehensive score corresponding to the storage node based on the dynamic weight; The comprehensive scores are sorted, and a storage node with a higher ranking is selected to store the multimodal segmentation data.

6. The method for distributed data storage and reading based on multimodal protection according to claim 1, characterized in that: The signing and broadcasting of distributed storage node information based on blockchain includes: Encoding the information of the storage node into a transaction, and signing the transaction using an elliptic curve digital signature algorithm; Broadcast the signed transaction to the blockchain network for verification and recording; Dividing the blockchain network into multiple shards, each shard independently processing the transaction; Transaction synchronization and data sharing between shards are achieved through cross-shard communication protocols; Zero-knowledge proof technology is used to encrypt and verify sensitive information in transactions to ensure the privacy and security of storage nodes and data.

7. The method for distributed data storage and reading based on multimodal protection according to claim 1, characterized in that: The receiving of a user's data layer query request, obtaining corresponding encrypted segmented data through the user's authority vector, and decrypting and reorganizing the encrypted segmented data to obtain query data includes: Define the user's permission vector, biometric vector, and timestamp; Parsing the permission vector and determining whether the user has the right to access the encrypted segmented data in combination with the smart contract; If the user has access rights, the user obtains the node information of the distributed storage of the encrypted segmented data from the blockchain according to the permission vector; Acquire encrypted segmented data according to the node information of the distributed storage; performing decryption on the encrypted segmented data; The decrypted segmented data is concatenated and reassembled according to the sorting rules to obtain the query data.

8. A data distributed storage and reading system based on multimodal protection, characterized in that: include: A preprocessing module, used for receiving input data and preprocessing the input data to obtain multimodal segmentation data; An encryption module, used for performing multi-key collaborative encryption on the multimodal segmented data; A storage module, used for distributed storage of encrypted multimodal segmentation data; Signature module, used to sign and broadcast distributed storage node information based on blockchain; The data reorganization module receives a user's data query request, obtains the corresponding encrypted segmented data through the user's authority vector, and decrypts and reorganizes the encrypted segmented data to obtain the query data.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

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