Cloud platform data security verification method and device

Abstract

The application discloses a kind of cloud platform data security verification method and device, the method includes: the original data on cloud platform is divided into multiple data segments, and each data segment is encrypted, and encrypted data segment is obtained;Create the block of each encrypted data segment, add block to blockchain, block includes encrypted data segment and corresponding hash value;The hash value of encrypted data segment in block added to blockchain is recalculated;All recalculation hash values are converted into quantum state hash value;According to quantum state hash value and the hash value of encrypted data segment in all blocks, construct quantum search circuit;Run the quantum search circuit constructed, obtain quantum calculation result;Compare each calculation data segment recalculated hash value and quantum calculation result, obtain the verification result of each encrypted data segment.The application can effectively improve the accuracy and efficiency of data verification on cloud platform, and ensure the consistency and integrity of data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data security and network security, and in particular to a cloud platform data security verification method and device. BACKGROUND

[0002] This section is intended to provide background or context to the embodiments of the application recited in the claims. The description herein does not constitute admission that the prior publication, square, or subject matter described herein and / or the material contained therein are prior art to the claimed application.

[0003] In today's digital era, cloud computing and big data technology are developing at an unprecedented speed. Cloud platforms, with their powerful storage and efficient processing capabilities, attract a large amount of data, covering enterprise core business data, scientific research data, and personal user photos, documents, and other types of information. According to relevant data statistics, the amount of global cloud storage data has grown exponentially in the past few years, with an annual growth rate of tens of percentage points.

[0004] However, as the amount of data increases dramatically, the data security verification problem of uploading to the cloud platform becomes increasingly difficult. Traditional data verification mostly uses centralized methods, relying on a single central node to verify and manage data. This mode can barely meet the demand when the amount of data is relatively small and the application scenario is simple. However, under the impact of today's complex and variable network environment and massive data, its disadvantages are gradually exposed. The single point of failure problem is the first to be addressed. Once the central node fails to work normally due to hardware failure, software vulnerability or malicious attack, the entire data verification system will be paralyzed, resulting in data unable to be verified normally and business being forced to stop. At the same time, the centralized management mode also faces serious trust problems. Since all data verification power is concentrated in one place, if the management of the central node has internal personnel operating abnormally, data is easy to be tampered with and leaked, and users are difficult to detect, and the authenticity and integrity of the data cannot be effectively guaranteed. In addition, with the continuous growth of data volume and the increasing demand of users for real-time data processing, the centralized data verification method is also inadequate in processing efficiency and scalability, and cannot meet the growing business needs.

[0005] In view of the many disadvantages of traditional centralized data verification methods, there is an urgent need for a new and effective solution to realize the security verification of data on the cloud platform, protect the authenticity, integrity and confidentiality of data, maintain the legitimate rights and interests of users, and promote the healthy and sustainable development of the cloud computing industry. SUMMARY

[0006] In a first aspect, the embodiments of the present application provide a cloud platform data security verification method to effectively improve the accuracy and efficiency of data verification on the cloud platform, and ensure the consistency and integrity of data in the transmission and storage process, which comprises:

[0007] The raw data on the cloud platform is divided into a plurality of data segments, and each data segment is encrypted to obtain an encrypted data segment;

[0008] A block of each encrypted data segment is created, and the block is added to a block chain, the block including the encrypted data segment and a corresponding hash value;

[0009] The encrypted data segment in the block added to the block chain is re-calculated for a hash value;

[0010] All re-calculated hash values are converted into quantum state hash values;

[0011] A quantum search circuit is constructed according to the quantum state hash values and the hash values corresponding to the encrypted data segments in all blocks;

[0012] The constructed quantum search circuit is run to obtain a quantum calculation result;

[0013] The re-calculated hash value of each calculation data segment is compared with the quantum calculation result to obtain a verification result of each encrypted data segment.

[0014] In a second aspect, the embodiments of the present application also provide a cloud platform data security verification device to effectively improve the accuracy and efficiency of data verification on the cloud platform and ensure the consistency and integrity of data in the transmission and storage process, and the device comprises:

[0015] An encryption module is configured to divide raw data on a cloud platform into a plurality of data segments, and encrypt each data segment to obtain an encrypted data segment;

[0016] A block creation module is configured to create a block of each encrypted data segment, and add the block to a block chain, the block including the encrypted data segment and a corresponding hash value;

[0017] A quantum state conversion module is configured to re-calculate a hash value of an encrypted data segment in a block added to the block chain, and convert all re-calculated hash values into quantum state hash values;

[0018] A quantum search circuit construction module is configured to construct a quantum search circuit according to the quantum state hash values and the hash values corresponding to the encrypted data segments in all blocks;

[0019] A quantum search circuit running module is configured to run the constructed quantum search circuit to obtain a quantum calculation result;

[0020] A security verification module is configured to compare the re-calculated hash value of each calculation data segment with the quantum calculation result to obtain a verification result of each encrypted data segment.

[0021] In a third aspect, the embodiments of the present application further provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the cloud platform data security verification method when executing the computer program.

[0022] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, which stores a computer program, and the computer program implements the cloud platform data security verification method when executed by a processor.

[0023] In a fifth aspect, the embodiments of the present application further provide a computer program product, which comprises a computer program, and the computer program implements the cloud platform data security verification method when executed by a processor.

[0024] In the embodiments of the present application, the original data on the cloud platform is divided into a plurality of data segments, and each data segment is encrypted to obtain an encrypted data segment; a block of each encrypted data segment is created, and the block is added to a block chain, the block comprising the encrypted data segment and a corresponding hash value; the encrypted data segment in the block added to the block chain is re-calculated for a hash value; all re-calculated hash values are converted into quantum state hash values; a quantum search circuit is constructed according to the quantum state hash values and the hash values corresponding to the encrypted data segments in all blocks; the constructed quantum search circuit is run to obtain a quantum calculation result; each re-calculated hash value of the data segment is compared with the quantum calculation result to obtain a verification result of each encrypted data segment. Through the above steps, the consistency and integrity of the data in the transmission and storage process are ensured by combining the block chain technology, and the data security, the accuracy and the efficiency of the verification are further improved by verifying the encrypted data segment through the quantum search circuit. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor. In the drawings:

[0026] Figure 1 The flow chart of the cloud platform data security verification method in the embodiments of the present application;

[0027] Figure 2 The flow chart of creating a block in the embodiments of the present application;

[0028] Figure 3 The flow chart of adjusting the quantum search circuit in the embodiments of the present application;

[0029] Figure 4 A flowchart for dynamically adjusting the storage location of an encrypted data segment based on a decentralized intelligent agent proxy in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of a cloud platform data security verification generation device in an embodiment of the present application;

[0031] Figure 6 A schematic diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0032] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer and more apparent, the embodiments of the present application will be further described in detail below with reference to the drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation of the present application.

[0033] Figure 1 A flowchart of a cloud platform data security verification method in an embodiment of the present application, comprising:

[0034] Step 101, dividing the original data on the cloud platform into a plurality of data segments, and encrypting each data segment to obtain encrypted data segments;

[0035] Step 102, creating a block for each encrypted data segment, and adding the block to a block chain, the block including the encrypted data segment and a corresponding hash value;

[0036] Step 103, recalculating the hash value of the encrypted data segment in the block added to the block chain;

[0037] Step 104, converting all recalculated hash values into quantum state hash values;

[0038] Step 105, constructing a quantum search circuit according to the quantum state hash values and the hash values corresponding to the encrypted data segments in all blocks;

[0039] Step 106, running the constructed quantum search circuit to obtain a quantum computing result;

[0040] Step 107, comparing the recalculated hash value of each computing data segment with the quantum computing result to obtain a verification result for each encrypted data segment.

[0041] In the embodiments of the present application, the blockchain technology is combined to ensure the consistency and integrity of data in the transmission and storage process, and the quantum search circuit is used to verify the encrypted data segments, which can further improve the data security, as well as the accuracy and efficiency of verification. Each step will be described in detail below.

[0042] In step 101, the original data on the cloud platform is divided into multiple data segments, and each data segment is encrypted to obtain encrypted data segments;

[0043] Specifically, the raw data D on the cloud platform is divided into multiple fragments. ,in,

[0044]

[0045] Quantum key distribution (QKD) can be used to generate n secure encryption keys. .

[0046] In addition to the key generated by quantum key distribution (QKD), a key mixing algorithm is used to generate the final encryption key, combining the key generated by traditional symmetric encryption algorithms (such as AES). This leverages both the high security of quantum keys and the efficiency of traditional algorithms, allowing for flexible switching between different security requirements and improving encryption efficiency and adaptability. For example, for small data transmissions with high real-time requirements, the weight of the traditional key can be appropriately increased; for highly sensitive data, quantum keys take the lead.

[0047] Encryption keys can be updated periodically or triggered based on information such as blockchain timestamps and transaction frequency. For example, when a certain number of high-risk transactions occur consecutively on the blockchain, the key update process is automatically triggered, a new key is generated through QKD, and the data fragment is re-encrypted to further enhance data confidentiality and prevent the key from being cracked after long-term use.

[0048] Use the generated encryption key For each data segment Encryption is performed to obtain encrypted data fragments. .in,

[0049]

[0050] In step 102, a block is created for each encrypted data fragment, and the block is added to the blockchain. The block includes the encrypted data fragment and the corresponding hash value.

[0051] Figure 2 This is a flowchart illustrating the creation of blocks in an embodiment of the present invention. In one embodiment, creating a block for each encrypted data fragment and adding the block to the blockchain includes:

[0052] Step 201, for each encrypted data segment Generate hash value ;

[0053] in, It is the hash value of the i-th encrypted data fragment;

[0054] Step 202, creating a block containing encrypted data segments and corresponding hash values ;

[0055]

[0056] Wherein, Timestamp is the time stamp, and Prev_Hash is the hash value of the previous block.

[0057] Step 203, adding the created block to the blockchain Chain;

[0058] The blockchain can be represented as

[0059] When added to the blockchain, it is necessary to ensure that all nodes on the blockchain agree on the innovative block.

[0060] In step 103, the hash value of the encrypted data segment added to the block in the blockchain is recalculated;

[0061] In step 104, all recalculated hash values are converted into quantum state hash values ;

[0062] Generally, the integrity of the data can be verified by comparing the hash values of the blocks added to the blockchain and the recalculated hash values.

[0063] However, in order to further optimize the verification step and improve data security, the embodiment of the present application proposes the idea of quantum computing assisted verification. It can be represented as: =Encode(hc i );

[0064] Wherein, Encode is an encoding function for converting hash values into quantum state hash values, and hc i is the recalculated hash value of the i-th encrypted data segment. The Encode encoding function can be improved, and in an embodiment, all recalculated hash values are converted into quantum state hash values, including:

[0065] All recalculated hash values are converted into quantum state hash values using the encoding function, and the encoding function adds redundant quantum bits when encoding.

[0066] The above redundant quantum bits are used to: when the quantum state hash value is disturbed by noise during transmission or storage, the correct quantum state can be recovered using quantum error correction mechanism, improving the reliability and verification accuracy of the quantum state hash value.

[0067] In step 105, a quantum search circuit is constructed according to the quantum state hash value and the hash values corresponding to the encrypted data segments in all blocks;

[0068] The quantum search circuit can be represented as: Grover( ,)

[0069]

[0070] wherein H is a set of hash values corresponding to the encrypted data segments in all blocks, Index i is the index position of the hash value corresponding to the i-th encrypted data segment in the set.

[0071] In the embodiments of the present application, the structure and parameters of the quantum search circuit can be dynamically adjusted according to factors such as the amount of data on the blockchain, the data update frequency, etc. For example, when the amount of data increases significantly, the number of quantum bits and the number of search iterations are increased; when the data is updated frequently, the search algorithm is optimized to adapt to data changes faster, improving the verification efficiency and effect of quantum computing in different scenarios. An embodiment is given below to illustrate the adjustment process.

[0072] Figure 3 For the flowchart of the quantum search circuit adjustment in the embodiments of the present application, in an embodiment, the method further comprises:

[0073] Step 301: The monitoring module deployed on each node on the blockchain is used to count the amount of data and the data update frequency of the blockchain stored on each node.

[0074] The monitoring module can periodically (e.g., every 1 hour) count the amount of data stored on the node, including the total size of the stored encrypted data segments, the number of blocks, etc.

[0075] The nodes send the amount of data information obtained by counting to a central coordination node (or share among the nodes through a distributed consistency algorithm) through a secure communication protocol (e.g., a message passing protocol based on blockchain encryption technology).

[0076] The central coordination node (or the nodes jointly) summarizes and analyzes the collected network-wide data amount information, and calculates the current total amount of data of the entire blockchain network.

[0077] The monitoring module can also record the generation timestamp of each block and the update timestamp of the encrypted data segment in real time.

[0078] The data update frequency is determined by calculating the time interval between adjacent timestamps and counting the number of data updates per unit time (e.g., per minute). For example, the total number of data updates in the last 10 minutes is calculated, and then divided by 10 to obtain the average data update frequency per minute.

[0079] Each node also sends the calculated data update frequency information to the central coordination node (or distributed sharing), which integrates and analyzes the data update frequency of the entire network to obtain the statistical results.

[0080] Step 302, when the data volume of the blockchain meets the data volume adjustment trigger condition, determine the qubit number increase strategy, and adjust the increase amplitude of the search iteration number of the quantum search circuit based on the qubit number increase strategy;

[0081] Data volume trigger condition determination: a threshold value of data volume is pre-set, for example, when the total data volume of the entire blockchain exceeds 80% of the initial capacity (which can be flexibly adjusted according to actual needs), it is considered that the data volume has increased significantly, and the current total data volume of the entire network is compared with the pre-set threshold value. If it exceeds the threshold value, the data volume adjustment trigger condition is triggered, and the quantum search circuit structure and parameter adjustment is required.

[0082] Increase the number of qubits: determine the strategy for increasing the number of qubits, for example, increase the number of qubits according to the proportion of the current blockchain data volume to the initial capacity. Assuming that the initial number of qubits is n, the current data volume is D, and the initial capacity is D0, then the increased number of qubits n' = n x (D / D0) (rounded up). Send instructions to the quantum computer to reconfigure the qubit resources and expand the number of qubits to the calculated n'. During the configuration process, ensure that the coupling and other physical characteristics between the newly added qubits and the original qubits meet the requirements of quantum computing, and avoid introducing too much noise to affect the accuracy of the calculation.

[0083] Increase the search iteration number: according to the increased number of qubits and the change of data volume, determine the increase amplitude of the search iteration number. For example, an empirical formula can be set, such as search iteration number I' = I x log10(D / D0) (I is the original iteration number, I' is the adjusted iteration number, rounded up).

[0084] Update the parameter settings in the quantum search circuit that control iteration, so that the quantum search algorithm runs according to the newly determined iteration number I', to more fully search the large amount of increased data on the blockchain and improve the verification efficiency.

[0085] Step 303: When the data update frequency of each blockchain meets the frequency adjustment trigger condition, adjust the search algorithm and corresponding parameters of the quantum search circuit.

[0086] Data update frequency trigger condition judgment: Set a threshold for data update frequency, such as when the average number of data updates per minute in the entire network exceeds a certain set value (such as 50 times, which can be adjusted as needed), compare the statistical data update frequency of the entire network with the preset threshold. If it exceeds the threshold, it indicates that the data is updated frequently, and the frequency adjustment trigger condition is met.

[0087] Optimize the search algorithm: Analyze the performance bottlenecks of the current quantum search algorithm (such as Grover algorithm) when processing frequently updated data, such as the efficiency problems in the data matching search method, quantum state initialization process, etc. According to the analysis results, the search algorithm is optimized. For example, use a more efficient data indexing method to speed up the matching process of quantum states and blockchain data hash values; or improve the quantum state initialization strategy to make it faster to adapt to the characteristics of newly updated data. Machine learning algorithms can be introduced to automatically learn and adjust the parameters and processes of the search algorithm based on historical data update conditions and algorithm performance feedback, to achieve faster adaptation.

[0088] Algorithm parameter adjustment and verification: Adjust the parameters related to the optimized search algorithm, such as adjusting the parameters of quantum gate operations and controlling quantum state evolution, to further improve the running efficiency of the algorithm in the context of frequent data updates. After adjusting the parameters, test and verify the optimized quantum search circuit using a small amount of newly updated data, compare the test results with the expected results (such as the number of correctly verified hash values, verification time, etc.), and evaluate the optimization effect. If the effect is not ideal, continue to fine-tune the algorithm parameters or further optimize the algorithm structure until the verification efficiency and effect requirements in the context of frequent data updates are met.

[0089] Step 304: Deploy the adjusted quantum search circuit on the quantum computer.

[0090] Deploy the quantum search circuit that has been adjusted in structure and parameters to the quantum computer, ensuring that the circuit can run correctly and interact stably with the blockchain system. Update the relevant interfaces and programs involved in quantum computing verification in the blockchain network to adapt to the adjusted quantum search circuit, ensuring smooth data verification process.

[0091] A real-time monitoring mechanism for the running state of the adjusted quantum search circuit is established to monitor key indicators such as the running time of quantum computing, resource occupation (such as the utilization rate of qubits, the frequency of quantum gate operations, etc.), verification accuracy, etc. The data obtained by monitoring is fed back to the central coordination node (or shared among the nodes in the blockchain network), and the adjustment effect is evaluated according to the feedback data. If it is found that the adjusted quantum search circuit still has low efficiency or poor effect in the running process, analysis and adjustment are performed again to form a dynamic optimization closed loop.

[0092] In step 106, the constructed quantum search circuit is run to obtain a quantum computing result.

[0093] Specifically, the constructed quantum search circuit can be run on a quantum computer.

[0094]

[0095] wherein Run is a function of running a quantum circuit on a quantum computer, and Result is a quantum computing result.

[0096] In step 107, the hash value recalculated for each piece of computing data is compared with the quantum computing result to obtain a verification result for each piece of encrypted data.

[0097]

[0098] wherein Verify is a verification function, hci i is the hash value recalculated for the i-th piece of encrypted data, Result is the quantum computing result, and H[Index i ] is the hash value corresponding to the index position in the set.

[0099] In an embodiment, the method further comprises:

[0100] After generating the hash value of each piece of encrypted data, a plurality of hash value combinations are divided, each hash value combination including a preset number of adjacent hash values.

[0101] The hash value of each hash value combination is calculated to obtain an aggregated hash value of each hash value combination.

[0102] Before recalculating the hash value of the encrypted data added to the block in the blockchain, the method further comprises:

[0103] The aggregated hash value of each hash value combination is recalculated.

[0104] All recalculated aggregated hash values are converted into quantum state aggregated hash values.

[0105] ​According to the quantum state aggregation hash value and the aggregation hash value, an aggregation quantum search circuit is constructed;

[0106] The constructed aggregation quantum search circuit is run to obtain an aggregation quantum calculation result;

[0107] The re-calculated aggregation hash value of each hash value combination is compared with the aggregation quantum calculation result to obtain a verification result of each hash value combination;

[0108] If there is a hash value combination that does not pass the verification, the hash value of each hash value corresponding to the encrypted data segment in all hash value combinations that do not pass the verification is re-calculated.

[0109] In the above embodiment, the hash value of each hash value combination is calculated, and a hierarchical hash structure can be formed. The upper layer hash value not only contains single segment information, but also reflects the association between segments. During verification, the integrity of the data group can be quickly judged through the upper layer hash, and if there is a problem, the hash value of the single encrypted data segment is further checked, the verification efficiency of large-scale data is improved, and the calculation resource consumption is reduced.

[0110] The hash algorithm (such as RSA-SHA3) resistant to quantum computing attacks is introduced in parallel with the existing hash calculation method in the embodiment of the application. When the quantum computing technology develops and threatens the current hash algorithm, the hash value can be quickly recalculated and verified by switching to the quantum-resistant hash algorithm, thereby ensuring the security and integrity of the blockchain data in the future quantum computing environment.

[0111] Figure 4 For the flowchart of dynamically adjusting the storage location of the encrypted data segment based on the decentralized intelligent agent in the embodiment of the application, in an embodiment, the method further comprises:

[0112] Step 401, collecting the environmental data of each encrypted data segment in each block on each blockchain through the decentralized intelligent agent deployed on the blockchain node;

[0113] The purpose of the decentralized intelligent agent is to dynamically adjust and optimize the storage location of the block where the encrypted data segment is located on the blockchain, so as to improve the access efficiency and security.

[0114] The decentralized intelligent agent is deployed on each node and is responsible for monitoring the environmental data of each encrypted data segment and processing access requests.

[0115] The environmental data includes but is not limited to data state, access frequency and network delay.

[0116] At step 402, the storage location of the block where the encrypted data segment is located on the blockchain is dynamically adjusted by a decentralized intelligent agent proxy, the decentralized intelligent agent proxy inputs the environmental data of the encrypted data segment in each block on each blockchain into a trained reinforcement learning model to obtain the optimal storage location of the block where the encrypted data segment is located on the blockchain, the state space of the reinforcement learning model is the environmental data of the encrypted data segment, and the action space of the reinforcement learning model is the storage location of the block where the encrypted data segment is located on the blockchain.

[0117] In a specific implementation, the state space S and the action space A of the decentralized intelligent agent proxy can be represented as follows:

[0118]

[0119] where S ={ }, is the i-th state, including the data state of the encrypted data segment, access frequency, network delay and other environmental data; A={ }, is the j-th action, including the data storage location of the encrypted data segment.

[0120] In an embodiment, the reward function of the reinforcement learning model during training includes data security, access efficiency and resource utilization.

[0121] Before training the reinforcement learning model, a reward function needs to be designed according to the current state and the action taken to give a reward.

[0122]

[0123] wherein, is the data security, is the access efficiency, is the resource utilization.

[0124] The reinforcement learning model is trained using a reinforcement learning algorithm (such as Q-learning, DQN) so that it can dynamically adjust the strategy according to the environmental data. The objective function value (also called value function) of the strategy is represented as follows:

[0125]

[0126] wherein, is the objective function value, θ is the parameter of the reinforcement learning model, is the strategy, is the reward at time step t, and γ is the discount factor.

[0127] After that, the decentralized agent agent dynamically adjusts the storage location of the encrypted data segment according to the trained reinforcement learning, optimizes the resource allocation to obtain the optimal action .

[0128]

[0129] wherein, is the Q value function, which represents the expected cumulative reward of taking action a in state s.

[0130] In an embodiment of the present application, a plurality of decentralized agent agents with different functions can be deployed in the blockchain network, such as security monitoring agents, performance optimization agents, resource scheduling agents, etc. These agents communicate and cooperate through the smart contract of the blockchain, share environmental data and jointly determine the storage location of the encrypted data segment. For example, when the security monitoring agent discovers potential security threats, it cooperates with the resource scheduling agent to migrate the relevant data segment to a safer storage area, while the performance optimization agent adjusts the access path to realize the collaborative optimization of data security and access efficiency.

[0131] In an embodiment, the decentralized agent agent includes a security monitoring agent, a performance optimization agent, and a resource scheduling agent, the environmental data collected by the performance optimization agent includes at least one of data state, access frequency, and network delay, and the environmental data collected by the resource scheduling agent includes at least one of storage capacity, used space, and free space of each node; the environmental data collected by the security monitoring agent includes at least one of network traffic, login logs of nodes, and transaction data;

[0132] Through the decentralized agent agent, the storage location of the block where the encrypted data segment is located on the blockchain is dynamically adjusted, including:

[0133] Through the security monitoring agent, based on the security rule base and the threat detection model, it is analyzed whether there is abnormal behavior in the blockchain network, if there is, a reminder information is sent to the performance optimization agent and the resource scheduling agent, and a collaborative process is entered, in which the performance optimization agent and the resource scheduling agent jointly determine the storage location of the block where the encrypted data segment is located on the blockchain.

[0134] On each node of the blockchain network, a decentralized intelligent agent proxy with different functions is deployed according to the demand and node resource situation. For example, a security monitoring intelligent agent is deployed on a node with relatively abundant computing resources, which is responsible for real-time monitoring of security threats in the blockchain network, including but not limited to abnormal access requests, malicious code injection, etc. A performance optimization intelligent agent is deployed on a node with high network bandwidth, which is used to analyze and optimize data access paths to improve data read and write speed. A resource scheduling intelligent agent is deployed on a node with abundant storage resources, which is responsible for managing and deploying data storage resources on the blockchain.

[0135] Each intelligent agent is assigned a unique identity and corresponding permissions, and is registered and recorded through a smart contract on the blockchain to ensure the legality and traceability of the intelligent agent.

[0136] After each intelligent agent is deployed, it is initialized. The security monitoring intelligent agent loads the pre-set security rule library and threat detection model, such as a machine learning-based intrusion detection model, to identify potential security risks. The performance optimization intelligent agent initializes network topology information and performance evaluation indicators to analyze and optimize data access paths later. The resource scheduling intelligent agent reads the current storage resource distribution of the blockchain network. The intelligent agents communicate with each other through the smart contract on the blockchain, exchange initialization information, and establish a basis for mutual contact and cooperation.

[0137] Environmental data collection: The security monitoring intelligent agent continuously monitors the security-related environmental data in the blockchain network, and detects whether there are abnormal behaviors such as abnormally high-frequency transaction requests and access from unknown addresses by analyzing these data. The performance optimization intelligent agent collects real-time environmental data related to data access performance, and evaluates the performance status of the current data access path by collecting and analyzing these data. The resource scheduling intelligent agent regularly collects environmental data on the use of storage resources of each node to accurately grasp the storage resource dynamics of the blockchain network.

[0138] Data sharing: The intelligent agents share the collected environmental data through the smart contract of the blockchain. The smart contract provides a safe and reliable channel for data sharing, ensuring the integrity and confidentiality of the data. For example, the security monitoring intelligent agent uploads the detected abnormal traffic data to the smart contract, and the performance optimization intelligent agent and the resource scheduling intelligent agent can obtain these data through the smart contract to make comprehensive analysis and collaborative decision-making. In order to improve the efficiency and accuracy of data sharing, each intelligent agent preprocesses the collected data, such as data cleaning and format conversion, to make it conform to the data format and standard specified by the smart contract.

[0139] The security monitoring agent uses its loaded security rule base and threat detection model to perform real-time analysis on the collected environmental data. For example, when it detects that a certain node has received a large number of abnormal access requests from different IP addresses in a short period of time, and the frequency of these requests is far beyond the normal range, the security monitoring agent determines that there may be a potential security risk.

[0140] The security monitoring agent records the detected potential security threat information in detail and sends a reminder to other agents through a smart contract. The reminder information includes the type of threat, the location involved (such as the node address), the severity assessment of the threat, etc.

[0141] After receiving the reminder from the security monitoring agent, the performance optimization agent and the resource scheduling agent determine whether to collaborate based on the reminder information. If the security threat may affect the storage security or access efficiency of the data, such as an attack that may cause storage node failure or data transmission obstruction, the performance optimization agent and the resource scheduling agent immediately respond and enter the collaboration process.

[0142] Based on the threat information provided by the security monitoring agent, the resource scheduling agent assesses the data storage security of the threatened area. If it believes that the relevant data segments have a high security risk, the resource scheduling agent analyzes the storage resource situation of other nodes in the blockchain network, finds a safe and suitable storage area as the migration target for the data segments.

[0143] The performance optimization agent also participates in the decision-making process, assessing the impact on the access path after the data segment migration based on the current network topology and data access traffic. Through simulation and calculation of data access delay and throughput under different migration schemes, the performance optimization agent and the resource scheduling agent jointly determine the optimal data segment migration scheme to ensure data security while minimizing the impact on data access efficiency.

[0144] The agents negotiate and make decisions through smart contracts to reach an agreed operation scheme. For example, determine which encrypted data segments to migrate to which specific secure storage area, and how to adjust the subsequent data access path.

[0145] Based on the collaborative decision-making result, the resource scheduling agent initiates the data segment migration operation. Through the distributed storage protocol of the blockchain, it migrates the relevant encrypted data segments from the threatened storage area to the selected secure storage area. During the migration process, the integrity and consistency of the data are ensured, and the data is verified through techniques such as hash checking.

[0146] The performance optimization agent adjusts the data access path, updates the network routing table and cache strategy, etc. For example, the data access request originally directed to the threatened storage area is re-routed to a new secure storage area, the data transmission path is optimized, the access delay is reduced, and the overall access efficiency is improved.

[0147] The security monitoring agent continues to monitor the security status of the related area after the data segment migration and access path adjustment is completed, and evaluates whether the security threat is effectively mitigated. For example, check if the abnormal access request is reduced, and whether the storage node resumes normal operation, etc.

[0148] The performance optimization agent evaluates the data access efficiency, compares the performance indicators such as data access delay and throughput before and after the operation, and judges whether the expected optimization effect of this collaborative operation is achieved. The resource scheduling agent evaluates whether the utilization of storage resources is reasonable, and whether the new storage area can stably carry the migrated data segments.

[0149] According to the effect evaluation result, each agent carries out summary and feedback. If it is found that the security threat still exists or the data access efficiency does not reach the expected target, the agents again negotiate through the smart contract, analyze the problem reason, and adjust the operation strategy. For example, re-evaluate the security of the storage area, further optimize the data access path, etc.

[0150] Each agent continuously learns and accumulates experience, and continuously optimizes and updates its own model, rule and strategy according to the actual running situation. For example, the security monitoring agent updates the threat detection model according to the new type of security threat, the performance optimization agent adjusts the performance evaluation index and optimization algorithm according to the change of network topology structure, and the resource scheduling agent adjusts the resource allocation strategy according to the performance change of the storage device, so as to continuously improve the effect of multi-agent cooperation and the overall performance of the blockchain system.

[0151] In the embodiment of the application, a plurality of different reinforcement learning models (such as A2C, A3C based on policy gradient and Q-learning, DQN based on value function) can be fused. In different scenarios, different model advantages are automatically switched or mixed for decision-making according to the characteristics of the environment data. At the same time, when the blockchain network structure or data characteristics change greatly, the transfer learning technology is used to quickly transfer the model parameters trained in a similar scenario to the new model for fine-tuning, reduce the training time and resource consumption, and speed up the ability of the agent proxy to adapt to the new environment. The following is a step of automatically switching or mixing different model advantages according to the characteristics of the environment data.

[0152] (1) Initialize the A2C (Advantage Actor-Critic), A3C (Asynchronous Advantage Actor-Critic) models based on policy gradient and the Q-learning, DQN (Deep Q-Network) models based on value function respectively. This includes defining the network structure of the model, such as the number of layers and neurons of the neural network; setting the hyperparameters of the model, such as the learning rate and discount factor.

[0153] For A2C and A3C models, initialize the parameters of the policy network and the value network. The policy network is used to output the probability of the agent taking each action in different states, and the value network is used to estimate the value of the state.

[0154] For Q-learning and DQN models, initialize the Q-value function network, which is used to calculate the Q-value (i.e., the expected cumulative reward) of taking different actions in different states.

[0155] (2) Analyze the advantages of each reinforcement learning model. A2C and A3C are based on policy gradient, which can directly optimize the policy and has better convergence speed and stability when dealing with continuous action space and complex environments; Q-learning and DQN are based on value function, which are good at estimating action value and quickly learning simple rules in discrete action space.

[0156] For the blockchain scenario, determine the specific circumstances where different models are suitable. For example, when dealing with continuous action decisions related to resource allocation in the blockchain network (such as adjusting the proportion of computing resources allocated to nodes), the policy gradient method of A2C and A3C may be more effective; while when it comes to simple discrete action decisions, such as choosing whether to prioritize the verification of a certain transaction, the value function estimation of Q-learning and DQN may be more appropriate.

[0157] (3) Automatically switch or mix the advantages of different models according to the characteristics of the environment data. Establish an environment feature evaluation module to extract key features such as transaction frequency, node load distribution, etc. from the collected environment data.

[0158] According to these features, judge the nature of the current scenario, for example, when the transaction frequency is high and the node load is uneven, it is judged as a complex resource scheduling scenario, at which time the policy optimization part of the A2C or A3C model is enabled; when the transaction frequency is low and the decision is relatively simple, such as judging the verification order of certain small transactions, the value function estimation part of the Q-learning or DQN model is enabled.

[0159] For the case of mixed use, a weight distribution mechanism is designed. For example, the weights of different model outputs are dynamically adjusted according to the complexity of the scene. In relatively complex scenes, give higher weight to the policy gradient-based model, and in relatively simple scenes, increase the weight of the value function-based model.

[0160] (4) Model decision: according to the judgment result of the environment feature evaluation module, select the corresponding reinforcement learning model or model combination for decision-making. If it is judged as a scene suitable for A2C model, the probability of each action in the current state is output through the policy network of A2C, and the agent selects the action according to the probability; if it is a scene suitable for Q-learning, the Q value of each action is calculated through the Q value function network, and the agent selects the action with the maximum Q value.

[0161] When using model mixing, the action probabilities or Q values output by different models are weighted and summed according to the pre-set weights to obtain the integrated action selection basis, and the agent selects the action accordingly.

[0162] (5) Action execution

[0163] The agent sends the selected action to the blockchain network for execution. For example, the agent decides to adjust the allocation ratio of computing resources of a node, and sends resource adjustment instructions to the node through the smart contract or related interface of the blockchain, and the node adjusts the corresponding resource configuration according to the instructions.

[0164] After executing the action, observe the feedback of the blockchain network environment and obtain new state and reward information. The reward information gives positive reward according to the specific target setting, such as resource utilization rate improvement, transaction delay reduction, etc., and negative reward otherwise.

[0165] (6) Model training and updating

[0166] According to the new state and reward obtained after executing the action, each reinforcement learning model is trained and updated. For A2C and A3C models, the parameters of the policy network and the value network are updated according to the policy gradient algorithm to maximize the cumulative reward; for Q-learning and DQN models, the parameters of the Q value function network are updated according to the Q value update formula (such as Bellman update formula of Q-learning).

[0167] In the training process, the collected environment data and feedback information after executing the action are used to continuously optimize the parameters of the model and improve the decision-making ability of the model in the current scene.

[0168] Adjust the weights and other parameters in the model fusion strategy based on the training effect and decision accuracy of the model in the current scenario. For example, if it is found that the hybrid model performs poorly in certain scenarios, the influence of the better-performing model can be increased by adjusting the weights, or the mapping relationship between environmental features and model selection can be re-evaluated to optimize the model switching conditions.

[0169] (7) Transfer learning preparation

[0170] Similar scenario identification: Establish a scenario similarity evaluation mechanism. By analyzing historical scenario data, extract key features of the scenario, such as transaction type, network topology, data traffic pattern, etc. When there is a significant change in the blockchain network structure or data characteristics, compare the features of the current new scenario with historical scenarios and calculate the similarity. Cosine similarity and other algorithms can be used to measure the similarity between feature vectors to find historical scenarios with high similarity to the current new scenario.

[0171] Determine transferable models: For the similar historical scenarios found, determine the reinforcement learning models that have been trained well and have good performance in that scenario. For example, if it is found that the current new scenario is similar to a historical scenario in terms of transaction type and network load, and the A3C model performs well in that historical scenario, then select the A3C model as the transferable model.

[0172] (8) Transfer learning implementation and fine-tuning

[0173] Parameter transfer: Transfer the selected model parameters trained in similar scenarios to the new model. For neural network models, directly copy the weights of the corresponding layers. For example, copy some or all of the weights of the policy network and value network in the A3C model to the new A3C model as the initial parameters of the new model. When transferring parameters, ensure that the dimensions and structures of the parameters match the new model, and make appropriate adjustments and conversions if necessary.

[0174] Model fine-tuning: Fine-tune the new model with transferred parameters using a small amount of data collected in the new scenario. Through optimization methods such as backpropagation algorithm, adjust the model parameters slightly according to the feedback of the new data, so that the model can better adapt to the characteristics of the new scenario. During the fine-tuning process, closely monitor the performance changes of the model to avoid overfitting to the small amount of data in the new scenario. Cross-validation and other methods can be used to evaluate the generalization ability of the model, and when the model performance reaches a certain standard (such as the reward value on the validation set is stable or reaches the expected target), stop fine-tuning.

[0175] (9) Continuous monitoring and optimization

[0176] Model performance monitoring: During the application of the model, the performance of the model is continuously monitored. By evaluating the decision accuracy of the model in the actual scene, the cumulative reward acquisition, and other indicators, it is determined whether the model can effectively respond to changes in the blockchain network. For example, periodically statistics the resource utilization rate improvement and transaction processing delay reduction brought by the decisions made by the agent in a period of time, as the basis for evaluating the performance of the model.

[0177] Dynamic adjustment and optimization: According to the model performance monitoring results, dynamically adjust the model fusion strategy and the related parameters of transfer learning. If it is found that a certain model continuously performs poorly in the current scene, re-evaluate the model fusion strategy, adjust the model selection or weight distribution; if it is found that the model after transfer learning is insufficiently adaptable in the new scene, re-perform the similar scene identification and parameter transfer fine-tuning process, continuously optimize the model, and improve the ability of the agent proxy to adapt to the dynamic changes of the blockchain network.

[0178] The embodiment of the application also provides a cloud platform data security verification generation device, which has similar principles to the cloud platform data security verification generation method, and will not be described here.

[0179] Figure 5 A schematic diagram of a cloud platform data security verification generation device in an embodiment of the application is shown in FIG. 1. The device includes:

[0180] An encryption module 501 is configured to divide the original data on the cloud platform into a plurality of data segments, and encrypt each data segment to obtain an encrypted data segment.

[0181] A block creation module 502 is configured to create a block for each encrypted data segment, and add the block to a blockchain. The block includes the encrypted data segment and a corresponding hash value.

[0182] A quantum state conversion module 503 is configured to recalculate the hash value of the encrypted data segment in the block added to the blockchain, and convert all recalculated hash values into quantum state hash values.

[0183] A quantum search circuit construction module 504 is configured to construct a quantum search circuit based on the quantum state hash values and the hash values corresponding to the encrypted data segments in all blocks.

[0184] A quantum search circuit running module 505 is configured to run the constructed quantum search circuit to obtain a quantum computing result.

[0185] A security verification module 506 is configured to compare the recalculated hash value of each computing data segment with the quantum computing result to obtain a verification result for each encrypted data segment.

[0186] In an embodiment, the block creation module is configured to:

[0187] generating a hash value for each encrypted data segment;

[0188] creating a block containing the encrypted data segment and the corresponding hash value;

[0189] adding the created block to the blockchain.

[0190] In an embodiment, the block creation module is further configured to:

[0191] After generating the hash value for each encrypted data segment, dividing a plurality of hash value combinations, each of which includes a preset number of adjacent hash values;

[0192] performing hash calculation on each hash value combination to obtain an aggregated hash value of each hash value combination;

[0193] The quantum state conversion module is further configured to:

[0194] Before recalculating the hash value of the encrypted data segment added to the block in the blockchain, recalculating the aggregated hash value of each hash value combination; and converting all recalculated aggregated hash values into quantum state aggregated hash values;

[0195] The quantum search circuit construction module is further configured to: construct an aggregated quantum search circuit according to the quantum state aggregated hash value and the aggregated hash value;

[0196] The quantum search circuit running module is further configured to: run the constructed aggregated quantum search circuit to obtain an aggregated quantum calculation result;

[0197] The security verification module is further configured to: compare the recalculated aggregated hash value of each hash value combination with the aggregated quantum calculation result to obtain a verification result of each hash value combination;

[0198] The quantum state conversion module is further configured to:

[0199] If there is a hash value combination that fails to pass the verification, recalculating the hash value of the encrypted data segment corresponding to each hash value in all hash value combinations that fail to pass the verification.

[0200] In an embodiment, the quantum state conversion module is configured to:

[0201] Convert all recalculated hash values into quantum state hash values using an encoding function, wherein redundant quantum bits are added during encoding in the encoding function.

[0202] In an embodiment, the apparatus further comprises a dynamic adjustment module configured to:

[0203] Collecting environmental data of the encrypted data segment in each block on each blockchain through a decentralized intelligent agent agent deployed on a blockchain node;

[0204] By means of the decentralized intelligent agent proxy, environment data of the encrypted data segment in each block on each blockchain is input into a trained reinforcement learning model to obtain an optimal storage location of the block on the blockchain, a state space of the reinforcement learning model is the environment data of the encrypted data segment, and an action space of the reinforcement learning model is the storage location of the block on the blockchain.

[0205] In an embodiment, the apparatus further comprises a quantum search circuit updating module configured to:

[0206] By means of the monitoring module deployed on each node on the blockchain, the amount of data and the data update frequency of the blockchain stored on each node are counted.

[0207] When the amount of data of the blockchain meets a data amount adjustment trigger condition, a quantum bit number increasing strategy is determined, and the increase amplitude of the search iteration number of the quantum search circuit is adjusted based on the quantum bit number increasing strategy.

[0208] When the data update frequency of each blockchain meets a frequency adjustment trigger condition, the search algorithm and the corresponding parameters of the quantum search circuit are adjusted.

[0209] The adjusted quantum search circuit is deployed on a quantum computer.

[0210] In an embodiment, the decentralized intelligent agent proxy comprises a security monitoring intelligent agent, a performance optimization intelligent agent, and a resource scheduling intelligent agent, the environment data collected by the performance optimization intelligent agent comprises at least one of a data state, an access frequency, and a network delay, the environment data collected by the resource scheduling intelligent agent comprises at least one of a storage capacity, a used space, and a free space of each node, and the environment data collected by the security monitoring intelligent agent comprises at least one of network traffic, login logs of nodes, and transaction data.

[0211] The quantum search circuit updating module is configured to:

[0212] By means of the security monitoring intelligent agent, whether there is an abnormal behavior in the blockchain network is analyzed based on a security rule library and a threat detection model, if there is, an alarm information is sent to the performance optimization intelligent agent and the resource scheduling intelligent agent, and a collaborative process is entered, in which the performance optimization intelligent agent and the resource scheduling intelligent agent jointly determine the storage location of the block on the blockchain.

[0213] It can be seen that the scheme provided in the embodiments of the present application has the following beneficial effects:

[0214] (1) The original data is divided into multiple segments and encrypted using the secure key generated by quantum key distribution (QKD), improving the security of data during transmission and storage. Even if a certain data segment is known, the entire data set cannot be decrypted.

[0215] (2) By recording the hash value of each data segment of the cloud platform in the blockchain, the consensus mechanism of the blockchain ensures the consistency of data among all nodes. Any attempt to tamper with the data will be rejected by other nodes.

[0216] (3) Through the combination of blockchain and decentralized agent proxy, a decentralized data management architecture is formed, eliminating the risk of single point failure. Even if a node fails, other nodes can still work, ensuring the high availability of the system.

[0217] (4) By deploying decentralized agent proxy, the status of data segments and access requests are monitored in real time, and reinforcement learning algorithm is used to dynamically adjust data storage and access strategies. Not only improves the efficiency of data access, but also optimizes the utilization of resources, reduces unnecessary waste of resources.

[0218] The embodiment of the present application also provides a computer device, Figure 6 The computer device 600 includes a memory 610, a processor 620, and a computer program 630 stored on the memory 610 and executable on the processor 620. The processor 620 executes the computer program 630 to implement the cloud platform data security verification method described above.

[0219] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the cloud platform data security verification method.

[0220] The embodiment of the present application also provides a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the cloud platform data security verification method.

[0221] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0222] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0223] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0224] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0225] The above-described specific embodiments, the purpose, technical solutions and advantages of the present application are further described in detail, it should be understood that the above-described is only the specific embodiments of the present application, and is not used to limit the protection scope of the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A cloud platform data security verification method, characterized in that, include: The raw data on the cloud platform is divided into multiple data segments, and each data segment is encrypted to obtain encrypted data segments; Create a block for each encrypted data fragment and add the block to the blockchain. The block includes the encrypted data fragment and its corresponding hash value. Recalculate the hash value of encrypted data fragments added to blocks on the blockchain; Convert all recalculated hash values ​​into quantum state hash values; Construct a quantum search circuit based on the quantum state hash value and the hash values ​​corresponding to the encrypted data fragments in all blocks; Run the constructed quantum search circuit to obtain quantum computing results; By comparing the recalculated hash value of each encrypted data segment with the quantum computing result, the verification result of each encrypted data segment is obtained.

2. The method according to claim 1, characterized in that, Creating a block for each encrypted data fragment and adding the block to the blockchain includes: Generate a hash value for each encrypted data fragment; Create blocks containing encrypted data fragments and their corresponding hash values; Add the created block to the blockchain.

3. The method according to claim 1, characterized in that, Also includes: After generating the hash value for each encrypted data fragment, the data is divided into multiple hash value combinations, each of which includes a preset number of adjacent hash values. Perform hash calculations on each hash value combination to obtain the aggregate hash value for each hash value combination; Before recalculating the hash value of the encrypted data fragments added to the block on the blockchain, the following steps are also included: Recalculate the aggregate hash value for each combination of hash values; Convert all recalculated aggregate hash values ​​into quantum state aggregate hash values; Construct an aggregated quantum search circuit based on the aggregated hash value of the quantum state and the aggregated hash value; Run the constructed aggregated quantum search circuit to obtain aggregated quantum computing results; By comparing the aggregated hash value recalculated for each hash value combination with the aggregated quantum computing result, the verification result for each hash value combination is obtained; If there are unverified hash value combinations, the hash value of the encrypted data fragment corresponding to each hash value in all unverified hash value combinations will be recalculated.

4. The method according to claim 1, characterized in that, Convert all recalculated hash values ​​to quantum state hash values, including: An encoding function is used to convert all recalculated hash values ​​into quantum state hash values, and redundant qubits are added during the encoding process.

5. The method according to claim 1, characterized in that, Also includes: By deploying monitoring modules on each node of the blockchain, the amount of blockchain data stored on each node and the data update frequency are statistically analyzed. When the amount of data in the blockchain meets the triggering condition for data volume adjustment, a strategy for increasing the number of qubits is determined, and based on the strategy for increasing the number of qubits, the increase in the number of search iterations of the quantum search circuit is adjusted. When the data update frequency of each blockchain meets the frequency adjustment triggering condition, the search algorithm and corresponding parameters of the quantum search circuit are adjusted. The modified quantum search circuit is deployed on a quantum computer.

6. The method according to claim 1, characterized in that, Also includes: By using decentralized intelligent agents deployed on blockchain nodes, environmental data is collected from encrypted data fragments in each block on each blockchain. The decentralized intelligent agent dynamically adjusts the storage location of the block containing the encrypted data fragment on the blockchain. The decentralized intelligent agent inputs the environmental data of the encrypted data fragment in each block on each blockchain into the trained reinforcement learning model to obtain the optimal storage location of the block containing the encrypted data fragment on the blockchain. The state space of the reinforcement learning model is the environmental data of the encrypted data fragment, and the action space of the reinforcement learning model is the storage location of the block containing the encrypted data fragment on the blockchain.

7. The method according to claim 6, characterized in that, The decentralized intelligent agent agent includes a security monitoring agent, a performance optimization agent, and a resource scheduling agent. The environmental data collected by the performance optimization agent includes at least one of data status, access frequency, and network latency. The environmental data collected by the resource scheduling agent includes at least one of the storage capacity, used space, and free space of each node. The environmental data collected by the security monitoring agent includes at least one of network traffic, node login logs, and transaction data. Through decentralized intelligent agent agents, the storage location of the block containing the encrypted data fragment on the blockchain is dynamically adjusted, including: Based on a security rule base and threat detection model, the security monitoring agent analyzes whether there is any abnormal behavior in the blockchain network. If so, it sends a reminder to the performance optimization agent and the resource scheduling agent and initiates a collaborative process. In the collaborative process, the performance optimization agent and the resource scheduling agent jointly determine the storage location of the block containing the encrypted data fragment on the blockchain.

8. A cloud platform data security verification device, characterized in that, include: The encryption module is used to divide the raw data on the cloud platform into multiple data segments and encrypt each data segment to obtain encrypted data segments. The block creation module is used to create a block for each encrypted data fragment and add the block to the blockchain. The block includes the encrypted data fragment and the corresponding hash value. The quantum state conversion module is used to recalculate the hash value of encrypted data fragments added to blocks on the blockchain; and convert all recalculated hash values ​​into quantum state hash values. A quantum search circuit construction module is used to construct a quantum search circuit based on the quantum state hash value and the hash values ​​corresponding to the encrypted data fragments in all blocks; The quantum search circuit operation module is used to run the constructed quantum search circuit and obtain quantum computing results; The security verification module compares the recalculated hash value of each encrypted data segment with the quantum computing result to obtain the verification result of each encrypted data segment.

9. A computer 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 computer program, it implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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