Quantum computing assisted cloud storage encryption system and anti-quantum attack algorithm

Abstract

The application discloses a quantum computation assisted cloud storage encryption system and an anti-quantum attack algorithm, and is suitable for the field of intelligent park security, and the system comprises a quantum key dynamic management module, a security data layered encryption module, a distributed cloud edge collaborative storage module, a quantum enhanced access control module and a security situation quantum sensing module, each module is connected through a quantum channel and an encryption bus, and the system realizes the collaborative operation of key generation, data encryption, storage, access control and dynamic defense, and the algorithm steps are as follows: S1, collecting environmental parameters through an environmental quantum disturbance unit to generate disturbance factors; S2, calling a key to encrypt video streams in combination with an AES-256 algorithm to generate dynamic quantum labels containing time stamps and device characteristics; S3, assigning storage nodes according to data sensitivity levels and adjusting storage positions in real time; S4, generating decryption sub-keys with biological characteristics, and decrypting after biological and spatial double verification; and S5, constructing an attack risk model and dynamically adjusting key update frequency and encryption strength.

Description

Technical Field

[0001] This invention relates to the field of quantum computing technology applications, and in particular to a quantum computing-assisted cloud storage encryption system and a quantum attack resistant algorithm. Background Technology

[0002] Currently, with the rapid development of quantum computing technology, traditional encryption algorithms based on mathematical complexity face the risk of being cracked. Smart parks, as complex systems integrating video surveillance, access control, data storage, and other security devices, place higher demands on data confidentiality, integrity, and resistance to attacks. In the context of the integration of quantum computing and smart security, how to construct a new encrypted storage system that can resist quantum attacks, adapt to dynamic changes in the park environment, and ensure data security throughout its entire lifecycle has become a key direction for technological development.

[0003] Common technologies for quantum computing-assisted cloud storage encryption suffer from the following shortcomings: First, traditional encryption systems often employ a single classical encryption algorithm or a static quantum key distribution mechanism, making it difficult to simultaneously achieve both encryption efficiency and quantum security. Especially under attack scenarios such as quantum channel eavesdropping and physical layer interference, the key generation process lacks environmental adaptability and is easily stolen or tampered with. Second, encrypted data lacks effective dynamic identification and integrity verification methods; traditional timestamps or hash values ​​are easily forged, making it difficult to trace data tampering during storage and transmission. Third, storage management often employs fixed-level allocation or single-node storage strategies, failing to dynamically adjust data distribution based on real-time node status, thus posing a single point of failure risk. Furthermore, access control mechanisms rely on single biometric features or static keys, making them vulnerable to quantum simulation attacks and lacking real-time response capabilities for spatial location verification and attack risks. This makes it difficult to form a complete, closed-loop security protection system, failing to meet the operational requirements of quantum computing technology applications. Therefore, this paper proposes a quantum computing-assisted cloud storage encryption system and a quantum attack-resistant algorithm. Summary of the Invention

[0004] This invention provides the following technical solution: a quantum computing-assisted cloud storage encryption system, comprising: The system includes a quantum key dynamic management module and a cloud storage module. The quantum key dynamic management module establishes a quantum bit transmission link with the security equipment nodes in the smart park through its quantum channel. The quantum key dynamic management module is connected to a security data layered encryption module through an encryption bus. The quantum key dynamic management module also integrates an environmental quantum perturbation unit. The security data layered encryption module is used to receive key data from the quantum key pool and encrypt video stream data in the smart park in real time using the AES-256 symmetric encryption algorithm combined with the quantum key. The security data layered encryption module is connected to the cloud storage module, which is used for distributed storage and management of the encrypted data. The security data layered encryption module integrates a time-series quantum tag unit. The distributed cloud-edge collaborative storage module is connected to the data output end of the security data hierarchical encryption module via a VPN tunnel. The distributed cloud-edge collaborative storage module is used to distribute encrypted data to storage nodes according to levels. Both the quantum key dynamic management module and the distributed cloud-edge collaborative storage module are connected to the quantum-enhanced access control module via signal lines. The quantum-enhanced access control module is used to generate unique decryption subkeys for different permission roles through a quantum key derivation algorithm. The data receiving end of the quantum key dynamic management module is connected to a security situation quantum sensing module. The security situation quantum sensing module is used to collect quantum channel transmission data, encryption module operation logs, and storage node access records through traffic mirroring technology to construct a quantum attack risk assessment model. The quantum-enhanced access control module is equipped with a biological quantum binding unit and a spatial quantum verification unit.

[0005] This invention provides a quantum-resistant algorithm for a quantum computing-assisted cloud storage encryption system. Based on the aforementioned quantum computing-assisted cloud storage encryption system, the system includes the following steps: S1 quantum key generation and perturbation: First, real-time environmental parameters of the smart park are collected through the environmental quantum disturbance unit, which are then converted into physical disturbance factors. These factors are combined with the qubits transmitted through the quantum channel to generate a basic quantum key. Finally, the key is distributed to each security device node through the quantum key dynamic management module. S2 Data Layered Encryption and Tag Generation: Subsequently, the security data layered encryption module calls the quantum key generated in step S1 and combines it with the AES-256 symmetric encryption algorithm to encrypt the video stream data in real time. At the same time, the time-series quantum tag unit generates dynamic quantum tags containing timestamps and device characteristics, which are then associated with the encrypted data. S3 encrypted data hierarchical storage: The distributed cloud-edge collaborative storage module allocates encrypted data to storage nodes with corresponding security levels based on the data sensitivity level. Subsequently, the storage node health monitoring subunit evaluates the node status in real time and dynamically adjusts the data storage location. S4 Access Control and Decryption: The quantum-enhanced access control module generates a decryption subkey with biometric features through a quantum key derivation algorithm. During access, the biometric features are first verified by the bio-quantum binding unit, and then the legality of the access location is confirmed by the spatial quantum verification unit. After the double verification is passed, decryption is allowed using the subkey. S5 Dynamic Defense Adjustments: The security situation quantum sensing module constructs an attack risk model based on the collected transmission data and access records, and adjusts the key update frequency and encryption strength according to the attack risk model results.

[0006] Preferably, the environmental quantum perturbation unit is connected to IoT sensors in the smart park via a quantum encryption interface. This unit collects real-time environmental parameters of the park and converts them into physical perturbation factors for quantum keys. The time-series quantum tag unit is connected to a high-precision NTP time server in the park via a quantum synchronization channel. This unit generates dynamic quantum tags containing nanosecond-level timestamps and device vibration fingerprints. The environmental quantum perturbation unit, connected to IoT sensors via a quantum encryption interface, collects real-time environmental parameters of the park and converts them into physical perturbation factors, enabling the quantum key generation process to dynamically adapt to environmental changes and effectively resist quantum channel eavesdropping. The time-series quantum tag unit, connected to the high-precision NTP time server via a quantum synchronization channel, generates dynamic quantum tags containing nanosecond-level timestamps and device vibration fingerprints, ensuring the temporal continuity and device uniqueness of data during storage and transmission. Even if data is intercepted, it cannot be forged due to the quantum non-cloning property of the tag, significantly improving data integrity and anti-tampering capabilities.

[0007] Preferably, the distributed cloud-edge collaborative storage module further includes a storage node health monitoring subunit. This subunit detects the communication latency, computing load, and hardware status of each storage node and generates a node security level assessment result. By real-time monitoring of node communication latency, computing load, and hardware status, the storage node health monitoring subunit in the distributed cloud-edge collaborative storage module generates dynamic security level assessment results, enabling the system to accurately grasp the operating status of storage nodes. When node performance degrades or anomalies occur, the data storage location can be quickly adjusted to avoid single-point-of-failure risks. Simultaneously, storage resource allocation is optimized to ensure that highly sensitive data is always stored in nodes with matching security levels, improving the reliability and response efficiency of the system storage.

[0008] Preferably, the bio-quantum binding unit establishes an encrypted connection with the biometric data collection device in the park. The bio-quantum binding unit is internally equipped with a quantum hash algorithm. This algorithm converts biometric information into an irreversible feature code, which is then embedded into the decryption subkey. The encrypted connection between the bio-quantum binding unit and the biometric data collection device, along with the use of the quantum hash algorithm to convert biometric information into an irreversible feature code embedded in the decryption subkey, solves the problem of traditional biometric identification being easily forged. The quantum encrypted connection ensures the security of biometric transmission. Combined with irreversible feature code technology, even if biometric information is leaked, the original data cannot be reversed, fundamentally improving access control security and preventing unauthorized users from obtaining decryption privileges by forging biometrics.

[0009] Preferably, in step S1, when the environmental quantum perturbation unit collects real-time environmental parameters, it first classifies different types of parameters and assigns primary weight to abnormal parameters that affect park security. Simultaneously, during key distribution, the quantum key dynamic management module verifies the identity of each security device node, distributing keys only to verified nodes, and the entire distribution path is protected by quantum encryption. The environmental quantum perturbation unit classifies real-time environmental parameters and assigns primary weight to abnormal parameters, enabling the key generation process to prioritize responses to critical environmental changes affecting park security and improving key targeting. Furthermore, the quantum key dynamic management... Before distribution, the module verifies the identity of nodes and protects the distribution path with quantum encryption, ensuring that only legitimate nodes can obtain the key and that there is no risk of eavesdropping during transmission. This guarantees the security and reliability of key distribution from the source. The environmental quantum perturbation unit classifies real-time environmental parameters and assigns primary weight to abnormal parameters, enabling the key generation process to prioritize responses to critical environmental changes affecting park security and improve the targeting of keys. At the same time, the quantum key dynamic management module verifies the identity of nodes before distribution and protects the distribution path with quantum encryption, ensuring that only legitimate nodes can obtain the key and that there is no risk of eavesdropping during transmission. This guarantees the security and reliability of key distribution from the source.

[0010] Preferably, in step S2, when the security data layered encryption module calls the quantum key to encrypt the video stream data, it first performs differentiated processing based on the source region of the video stream. For video streams from the core area of ​​the park, 3-6 rounds of quantum hash verification are added on the basis of conventional encryption, so that the dynamic quantum tags generated by the time-series quantum tag unit form a one-to-one binding relationship with the encrypted data. At the same time, when the encrypted data is interrupted or abnormal during transmission, the time-series quantum tag unit will generate a new supplementary tag containing the time point and location information of the data interruption. The security data layered encryption module will perform integrity verification on the interrupted encrypted data based on the supplementary tag. Only after the verification passes can transmission or storage continue. The security data layered encryption module adds quantum hash verification to the video stream from the core area of ​​the park and strengthens the encryption rounds on the basis of conventional encryption, so that the dynamic quantum tags form a strict binding relationship with the encrypted data. When the transmission is interrupted, the time-series quantum tag unit generates a supplementary tag containing the interruption information and ensures the consistency of the data after recovery through integrity verification. This not only enhances the security of the core data, but also solves the problem of data loss or tampering caused by transmission interruption, and improves the system's fault tolerance capability for abnormal situations.

[0011] Preferably, in step S3, when classifying data sensitivity levels, the distributed cloud-edge collaborative storage module comprehensively judges the data based on its generation scenario, purpose, and the degree of privacy involved. For highly sensitive data involving core security of the park, it not only allocates the data to the storage node with the highest security level but also adopts a multi-node backup storage strategy. Furthermore, communication between backup nodes is encrypted using quantum key distribution. When the storage node health monitoring subunit detects an abnormal node status, it immediately notifies the distributed cloud-edge collaborative storage module to initiate a data migration process. During the migration process, the encrypted data is first encrypted again using a temporary quantum key before being transmitted. The data is transferred to a new storage node. Once the data is stored and verified on the new node, the data in the original node is completely deleted, and the temporary quantum key is destroyed synchronously. The distributed cloud-edge collaborative storage module classifies the sensitivity level based on the data generation scenario, purpose, and privacy requirements. For highly sensitive data, a multi-node backup and quantum encrypted communication strategy is adopted to avoid single-point storage risks. When a node is in an abnormal state, the data is migrated by re-encrypting it with a temporary quantum key, and the original data is completely deleted after verification on the new node. This not only ensures the physical isolation of highly sensitive data, but also improves storage flexibility through a dynamic migration mechanism, ensuring that the data is always in a secure storage state.

[0012] Preferably, in step S4, when the quantum-enhanced access control module generates a decryption subkey using the quantum key derivation algorithm, it simultaneously sets different validity periods for subkeys of different permission roles. That is, the validity period of a temporary visitor's subkey matches the validity period of their entity access permission, and it automatically expires after expiration. When the biometric quantum binding unit embeds biometric features into the subkey, it simultaneously performs irreversible encryption processing on the biometric information. The quantum-enhanced access control module sets decryption subkeys with matching validity periods for different permission roles using the quantum key derivation algorithm, so that the temporary visitor's permission is automatically synchronized with the entity's access validity period, and it automatically expires after expiration, preventing permission abuse. The biometric quantum binding unit performs irreversible encryption processing on the biometric information, so even if the subkey is leaked, it is impossible to reverse-engineer the biometric features, thus improving the security of access control from both permission management and data encryption levels.

[0013] Preferably, in step S5, when constructing the attack risk model, the security situation quantum sensing module simultaneously and comprehensively analyzes multi-dimensional indicators such as the transmission stability of the quantum channel, changes in the computational efficiency of the encryption module, and abnormal access frequency of storage nodes. Then, it compares these multi-dimensional indicators with historical attack patterns to identify potential attack trends. Furthermore, when adjusting the key update frequency, it considers the current risk level—the higher the risk, the shorter the update cycle—and employs a seamless switching mechanism during the update process. The security situation quantum sensing module's comprehensive analysis of multi-dimensional indicators such as quantum channel transmission stability, encryption module computational efficiency, and storage node access frequency to construct the attack risk model enables more accurate identification of potential attack trends. By dynamically adjusting the key update frequency (shorter updates for higher risks) and combining it with a seamless switching mechanism, the system can promptly enhance its defense strength when dealing with quantum attacks without affecting normal services due to key switching, achieving a balance between security protection and system continuity.

[0014] In summary, compared with existing technologies, this invention provides a quantum computing-assisted cloud storage encryption system and a quantum attack-resistant algorithm, which has the following beneficial effects: 1. This invention achieves a dual improvement in data confidentiality and anti-attack capability in a quantum computing environment through deep collaboration between a quantum key dynamic management module and a security data layered encryption module. The environmental quantum perturbation unit collects park environmental parameters in real time and converts them into physical perturbation factors, making the quantum key generation process environmentally adaptable and effectively resisting eavesdropping and interference at the quantum channel level. Combined with the hybrid encryption mechanism of AES-256 symmetric encryption algorithm and quantum key, it retains the efficiency of classical encryption algorithms and solves the defect of traditional algorithms being easily cracked by quantum computing through the unconditional security of quantum keys. The dynamic tags generated by the time-series quantum tag unit bind timestamps to device characteristics, ensuring the integrity and traceability of data during storage and transmission. Even if an attacker intercepts the data, the quantum non-cloning property of the tags will prevent the forgery of legitimate data packets. At the same time, the distributed cloud-edge collaborative storage module allocates storage nodes according to the data sensitivity level and, together with the real-time evaluation of the storage node health monitoring subunit, ensures the physical isolation of high-security data and avoids the risk of single point of failure through dynamic migration mechanism, significantly improving the reliability of the system. 2. This invention forms a security protection system that combines active and passive approaches through a quantum-enhanced access control module and a dynamic defense adjustment mechanism. The bio-quantum binding unit converts user biometrics into irreversible feature codes using a quantum hash algorithm and deeply binds them with subkeys generated by a quantum key derivation algorithm, solving the problem of traditional biometric identification being easily forged. The spatial quantum verification unit verifies the location of user devices using 3D map data of the park, preventing unauthorized remote access caused by quantum simulation attacks. When the security situation quantum sensing module detects a quantum attack risk, the system automatically triggers key update frequency adjustment and encryption strength upgrade. In addition, the entire quantum key distribution and data encryption process is quantum-safe, with each step from key generation to data destruction possessing quantum security characteristics, forming a closed-loop anti-attack chain that effectively addresses the security challenges brought about by the rapid development of quantum computing technology. Attached Figure Description

[0015] Figure 1 This is a system structure block diagram of the present invention.

[0016] Figure 2 This is a flowchart of the algorithm of this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides a technical solution: a quantum computing-assisted cloud storage encryption system, comprising: The system includes a quantum key dynamic management module and a cloud storage module. The quantum key dynamic management module establishes a quantum bit transmission link with the security equipment nodes in the smart park through its quantum channel. The quantum key dynamic management module is connected to the security data layered encryption module through an encryption bus. The quantum key dynamic management module integrates an environmental quantum perturbation unit, which is connected to the IoT sensors in the smart park through a quantum encryption interface. The environmental quantum perturbation unit is used to collect real-time environmental parameters of the park and convert them into physical perturbation factors for quantum keys. The time-series quantum tag unit is connected to the park's high-precision NTP time server through a quantum synchronization channel. The time-series quantum tag unit is used to generate dynamic quantum tags containing nanosecond-level timestamps and device vibration fingerprints. The security data layered encryption module is used to receive key data from the quantum key pool and encrypt video stream data in the smart park in real time using the AES-256 symmetric encryption algorithm combined with the quantum key. The security data layered encryption module is connected to the cloud storage module, which is used for distributed storage and management of encrypted data. The security data layered encryption module integrates a time-series quantum tag unit. The distributed cloud-edge collaborative storage module is connected to the data output end of the security data hierarchical encryption module via a VPN tunnel. The distributed cloud-edge collaborative storage module is used to distribute encrypted data to storage nodes according to their levels. Both the quantum key dynamic management module and the distributed cloud-edge collaborative storage module are connected to the quantum-enhanced access control module via signal lines. The distributed cloud-edge collaborative storage module also has an additional storage node health monitoring subunit. The storage node health monitoring subunit is used to detect the communication latency, computing load and hardware status of each storage node, and generate node security level assessment results. The quantum-enhanced access control module generates unique decryption subkeys for different permission roles using a quantum key derivation algorithm. The data receiver of the quantum key dynamic management module is connected to a security situation quantum sensing module. The security situation quantum sensing module collects quantum channel transmission data, encryption module operation logs, and storage node access records through traffic mirroring technology to build a quantum attack risk assessment model. The quantum-enhanced access control module internally includes a biological quantum binding unit and a spatial quantum verification unit. The biological quantum binding unit establishes an encrypted connection with the park's biometric data collection equipment. The biological quantum binding unit internally uses a quantum hash algorithm to convert biological information into an irreversible feature code and embeds this feature code into the decryption subkey.

[0019] Please see Figure 2This invention provides a quantum-computation-assisted cloud storage encryption system with anti-quantum attack algorithm. Based on the aforementioned quantum-computation-assisted cloud storage encryption system, the system includes the following steps: S1 quantum key generation and perturbation: First, real-time environmental parameters of the smart park are collected through the environmental quantum disturbance unit, which are then converted into physical disturbance factors. These factors are combined with the qubits transmitted through the quantum channel to generate a basic quantum key. Finally, the key is distributed to each security device node through the quantum key dynamic management module. When collecting real-time environmental parameters, the environmental quantum disturbance unit first classifies different types of parameters and assigns primary weight to abnormal parameters that affect park security. At the same time, during the key distribution process, the quantum key dynamic management module first verifies the identity of each security device node and only distributes keys to nodes that have passed the verification. The entire distribution path is protected by quantum encryption. The specific implementation process of the above method is as follows: The environmental quantum disturbance unit first initiates the parameter acquisition process, establishing connections with IoT sensors deployed throughout the smart park via a quantum-encrypted interface. These sensors monitor various physical parameters in the park environment in real time, including temperature fluctuations, electromagnetic field strength, equipment vibration frequency, and changes in air humidity. The acquired raw parameters are divided into two categories: one is routine environmental parameters, used to maintain normal system operation; the other is abnormal parameters that affect park safety, such as suddenly increased electromagnetic interference or abnormal equipment vibration. These abnormal parameters are automatically assigned a higher processing priority by the system. After parameter classification, the environmental quantum perturbation unit uses a pre-defined mapping algorithm to convert different categories of physical parameters into perturbation factors for quantum key generation. This process is similar to "encoding" natural changes in the environment into physical conditions that affect quantum state preparation; for example, temperature fluctuations adjust the phase parameters of the qubit transmitter, while electromagnetic field changes affect the initial setting of photon polarization states. In this way, the quantum key generation process is deeply integrated with the real-time environment of the campus, forming a key generation mechanism that dynamically adapts to environmental changes. In the quantum channel transmission stage, the quantum key dynamic management module sends single-photon sequences to security device nodes via fiber optic links. Each photon carries specific quantum state information, and a decoy state method is used to detect potential eavesdropping during transmission. When abnormal photon arrival rates or polarization state abnormalities are detected, the system automatically switches to a backup fiber optic link to ensure the reliability of quantum bit transmission. The generation of the basic quantum key combines environmental perturbation factors with the quantum bits transmitted through the quantum channel. Through quantum state superposition and measurement mechanisms, an initial key pool with environmental adaptability is ultimately formed. Before key distribution, the quantum key dynamic management module initiates a node identity verification process. Each security device node must pass dual authentication via digital certificate verification and quantum random number challenge-response protocol to confirm its legitimate identity before entering the key distribution queue. The distribution path is encrypted using a quantum key negotiation protocol to ensure that even if the transmission is intercepted, the key content cannot be deciphered. For high-security nodes, the system also adds a quantum teleportation protocol to achieve unconditional secure key transmission through quantum entanglement. During key distribution, the environmental quantum perturbation unit continuously monitors changes in the campus environment. When abnormal parameter fluctuations are detected, the key generation strategy is dynamically adjusted. For example, if electromagnetic interference in a certain area suddenly increases, the system will prioritize distributing a key version with stronger anti-interference capabilities to nodes in that area, while simultaneously sending strategy update instructions to relevant nodes via the quantum channel. The entire distribution process is isolated by a trusted execution environment, ensuring that sensitive operations such as key management and node authentication are executed within a hardware-level security domain to prevent external attacks and interference. The generated quantum keys are stored in a quantum key pool and synchronized to the security data layered encryption module via an encryption bus. The key pool adopts a layered management architecture, with frequently used keys stored in a high-speed cache, while keys for long-term storage are encoded with quantum error correction codes and stored in persistent storage. Each key is bound to environmental parameter feature values ​​and a distribution timestamp, forming a complete key lifecycle traceability chain, providing a reliable foundation for subsequent data encryption and access control. S2 Data Layered Encryption and Tag Generation: Subsequently, the security data layered encryption module calls the quantum key generated in step S1 and combines it with the AES-256 symmetric encryption algorithm to encrypt the video stream data in real time. At the same time, the time-series quantum tag unit generates dynamic quantum tags containing timestamps and device characteristics, which are then associated with the encrypted data. When the security data layered encryption module calls the quantum key to encrypt video stream data, it first performs differentiated processing based on the source region of the video stream. For video streams from the core area of ​​the park, 3-6 rounds of quantum hash verification are added on the basis of conventional encryption, so that the dynamic quantum tags generated by the time-series quantum tag unit form a one-to-one binding relationship with the encrypted data. At the same time, when the encrypted data is interrupted or abnormal during transmission, the time-series quantum tag unit will generate a new supplementary tag. The supplementary tag contains the time point and location information of the data interruption. The security data layered encryption module will perform integrity verification on the interrupted encrypted data based on the supplementary tag. Only after the verification is passed can the transmission or storage continue. The specific implementation process of the above method is as follows: After the security data layered encryption module is activated, it first receives quantum key pool data from the quantum key dynamic management module via the encryption bus. Based on the physical location of the video stream acquisition devices, the system divides the park into three categories: core area, key area, and general area. For video stream data in the core area, the module automatically activates an enhanced encryption mode, adding 3 to 6 rounds of quantum hash verification on top of the standard AES-256 symmetric encryption. Specifically, each frame of video data is divided into multiple data blocks before encryption, and each data block undergoes independent quantum key injection and hash calculation, ensuring that even if a single frame is intercepted, the complete content cannot be recovered through partial decryption. During the encryption process, the time-series quantum tag unit synchronously initiates the tag generation process. This unit maintains nanosecond-level time synchronization with the campus's high-precision NTP time server via a quantum synchronization channel, generating a tag header containing a precise timestamp for each frame of encrypted data. Simultaneously, the unit's built-in vibration sensor collects the mechanical vibration characteristics of the video acquisition equipment, converting physical parameters such as vibration frequency and amplitude into a unique vibration fingerprint for the device using a quantum state encoding algorithm. The timestamp and vibration fingerprint are embedded into the header field of the encrypted data packet via a quantum teleportation protocol, forming a dynamic quantum tag uniquely bound to each data packet. When encrypted data is transmitted to the distributed cloud-edge collaborative storage module via the VPN tunnel, the system continuously monitors the integrity of the data stream. If a transmission interruption or anomaly is detected, the timing quantum tag unit immediately triggers a supplementary tag generation mechanism. When an interruption occurs, the unit automatically records the precise time point of the interruption (accurate to milliseconds) and the device identifier of the interruption location, generating a supplementary tag containing this information. After being encrypted with a quantum key, the supplementary tag is stored in a buffer queue along with the encrypted data packets before and after the interruption. During data recovery transmission, the security data layered encryption module calls supplementary tags for integrity verification. The verification process includes time continuity verification (ensuring no jumps in data packet timestamps), device fingerprint matching (confirming the data source device has not been tampered with), and quantum hash value comparison (verifying the data content has not been modified). Only when the supplementary tag forms a complete time chain with the tags of the preceding and following data packets, and the hash values ​​are completely identical, will the system allow data to continue transmission or storage. If verification fails, the module automatically isolates the abnormal data packet and sends a security alert to the administrator via the quantum channel. For video stream data from non-core areas, the system employs a standard encryption process: each frame of data is injected with a quantum key and then encrypted using AES-256; a time-series quantum tag unit generates a basic tag and binds it. Regardless of the data source region, all encrypted data packets are appended with a unique identifier via a quantum random number generator during generation, ensuring that even if the same video stream is captured at different times, the generated encrypted data packets will have completely different quantum tag characteristics. Finally, the encrypted and tag-bound data packets are encapsulated into a standard format conforming to the VPN tunnel transmission protocol, awaiting transmission to the distributed cloud-edge collaborative storage module for further processing. S3 encrypted data hierarchical storage: The distributed cloud-edge collaborative storage module allocates encrypted data to storage nodes with corresponding security levels based on the data sensitivity level. Subsequently, the storage node health monitoring subunit evaluates the node status in real time and dynamically adjusts the data storage location. When classifying data sensitivity levels, the distributed cloud-edge collaborative storage module comprehensively considers the data's generation scenario, purpose, and the degree of privacy involved. For highly sensitive data involving the core security of the park, it is not only allocated to the storage node with the highest security level, but also a multi-node backup storage strategy is adopted. Furthermore, communication between backup nodes is encrypted using quantum key encryption. When the health monitoring subunit of a storage node detects an abnormal node status, it immediately notifies the distributed cloud-edge collaborative storage module to initiate a data migration process. During the migration process, the encrypted data is first re-encrypted using a temporary quantum key before being transmitted to the new storage node. After the data is stored on the new node and verified to be correct, the data in the original node is completely deleted, and the temporary quantum key is destroyed synchronously. The specific implementation process of the above method is as follows: After the distributed cloud-edge collaborative storage module starts, it first receives encrypted data packets from the security data layered encryption module via a VPN tunnel. Based on a preset grading strategy, the system determines the sensitivity level of each encrypted data packet by considering three dimensions: data generation scenario, purpose, and privacy level. For example, data involving monitoring of the main entrance / exit of the park and access records of the core computer room will be marked as highly sensitive; while general patrol videos of public areas will be marked as ordinary. During the determination process, the system automatically extracts metadata from the packet header, including the acquisition device number, data generation time, and associated permission roles, as a reference for grading. After determining the security level, the storage module prioritizes allocating highly sensitive data to core storage nodes that have passed the Trusted Execution Environment (TEX) certification, based on the current security level distribution of the storage nodes. These nodes are equipped with quantum encryption hardware modules, support quantum key negotiation protocols, and are physically deployed in independent racks in the core data center of the campus. For ordinary-level data, the system adopts a hybrid storage strategy of edge nodes and cloud nodes, storing data with high real-time requirements on nearby edge servers and archiving historical data to a cloud storage pool. During data allocation, the storage node health monitoring subunit simultaneously initiates a real-time evaluation process. This unit periodically probes the communication latency of each node via the ICMP protocol, obtains indicators such as CPU utilization and memory usage using the node's built-in computing power monitoring module, and reads hardware status information via the SNMP protocol. The evaluation results dynamically generate a node security level score. The scoring model comprehensively considers node performance, historical failure rate, and current load, ensuring that nodes with high scores are given priority for critical data storage tasks. When a node's security score is detected to be below a preset threshold, the system automatically triggers a data migration process. Before migration, the distributed cloud-edge collaborative storage module requests a temporary quantum key from the quantum key dynamic management module to perform secondary encryption on the encrypted data to be migrated. This secondary encryption uses a quantum teleportation protocol to ensure that even if the migration path is intercepted, the data cannot be deciphered. During migration, the original node and the new node establish a secure connection through a quantum channel. Data transmission uses a fragmented transmission method, with each fragment accompanied by a checksum to ensure transmission reliability. Once data arrives at the new node, the system performs dual verification: first, it compares the quantum tag in the packet header with the original record; second, it recalculates the data fingerprint using a quantum hash algorithm and matches it with the hash value before migration. Only when both verifications pass will the data be officially written to the new node and the storage location index updated. Old data in the original node is physically deleted immediately after verification, and the temporary quantum key is completely destroyed using a quantum erasure protocol to prevent key residue risks. For highly sensitive data involving core security of the park, the system will additionally employ a multi-node backup strategy during allocation. Backup nodes establish encrypted communication links through a quantum key negotiation protocol. When data is synchronized between the primary and backup nodes, quantum error-correcting coding technology is used to ensure that even if some nodes fail, the data can still be fully recovered through other backup nodes. The selection of backup nodes dynamically avoids nodes with health levels below the standard, forming a fault-tolerant distributed storage network. Ultimately, the location information, data distribution, and health assessment results of all storage nodes are synchronized to the quantum-enhanced access control module via signal lines, providing data support for subsequent access permission verification and dynamic defense adjustments. The entire tiered storage process achieves a balance between data storage security and system operating efficiency through closed-loop management. S4 Access Control and Decryption: The quantum-enhanced access control module generates a decryption subkey with biometric features through a quantum key derivation algorithm. During access, the biometric features are first verified by the bio-quantum binding unit, and then the legality of the access location is confirmed by the spatial quantum verification unit. After the double verification is passed, decryption is allowed using the subkey. When the quantum-enhanced access control module generates a decryption subkey through the quantum key derivation algorithm, it simultaneously sets different validity periods for subkeys of different permission roles. That is, the validity period of the subkey of a temporary visitor matches the validity period of its entity access permission and automatically expires after expiration. When the biological quantum binding unit embeds biometric features into the subkey, it simultaneously performs irreversible encryption processing on the biological information. The specific implementation process of the above method is as follows: When a user initiates a data access request, the quantum-enhanced access control module first initiates the decryption subkey generation process. The module uses a quantum key derivation algorithm to map the master key to a high-dimensional quantum state space and generates a unique initial subkey based on the user's permission level. During this process, the system simultaneously invokes the biometric quantum binding unit, communicating with the park's biometric data collection equipment via an encrypted connection to collect the user's fingerprint, facial, or voiceprint information. The built-in quantum hash algorithm in the biometric quantum binding unit irreversibly transforms the original biometric features, generating a fixed-length feature code. This feature code is then embedded as a quantum state parameter into the qubit sequence of the subkey, ensuring that even if the subkey is leaked, the user's true biometric features cannot be reverse-engineered. After generating subkeys, the system sets their validity period based on user permission type. For example, the validity period of a temporary visitor's subkey is strictly tied to the validity period of their physical pass; the subkey automatically expires when the visitor leaves the park or their permissions expire. Subkeys for long-term staff are set to be valid indefinitely, but require periodic renewal via biometric re-authentication. All subkey generation records are synchronized to the quantum key dynamic management module and backed up to the trusted execution environment via an encrypted bus, forming a complete key lifecycle log. When a user attempts to decrypt data, the system first initiates a biometric verification process. The biometric quantum binding unit sends a biometric collection request to the user's device via a quantum channel. The user must complete fingerprint pressing, face scanning, or voiceprint registration on the designated device. The collected biometric features are immediately converted into a feature code using a quantum hash algorithm and compared with the feature code embedded in the subkey in a quantum state. If the comparison fails, the system automatically rejects the access request and records the abnormal event; if the comparison succeeds, it proceeds to the spatial location verification stage. The spatial quantum verification unit sends a spatial coordinate verification request to the user equipment via a quantum channel, requiring the equipment to return its current physical location information. The unit's built-in 3D campus map model, combined with a quantum random number generator, performs accuracy verification on the returned location data to ensure that the user's actual location matches the declared location and is within a legitimate access area. For example, decryption of core data is only permitted in specific computer rooms or monitoring centers. If a user is detected attempting to decrypt data in an unauthorized area, the system will immediately terminate the process and trigger a security alarm. After both authentications are successful, the quantum-enhanced access control module sends an activation command to the user equipment via a secure channel, unlocking the subkey. The user can then use the activated subkey to decrypt the encrypted data using the security data layered encryption module's decryption engine. During decryption, the system continuously monitors the transmission stability of the quantum channel. If abnormal interference is detected, it automatically switches to the backup quantum key and re-authenticates the user, ensuring the decryption process is unaffected by quantum attacks. After data decryption, the user device must use the decrypted data within a specified time. After the timeout, the system will automatically destroy the temporary cached data copy. Simultaneously, the quantum-enhanced access control module records detailed information about the access, including user identity, access time, operation data, and device location, forming a complete audit log. All data involving biometrics and spatial location are encrypted using quantum keys during storage and transmission to ensure user privacy is not compromised. Finally, the subkey that has completed the decryption process will enter the retirement phase. The system completely removes key remnants through a quantum erasure protocol to prevent key fragments from being maliciously used. The entire access control and decryption process enhances traditional two-factor authentication with quantum technology, forming a triple protection mechanism of biometrics, spatial location, and quantum keys, effectively resisting simulation attacks, man-in-the-middle attacks, and key cracking threats in a quantum computing environment; S5 Dynamic Defense Adjustments: The security situation quantum sensing module constructs an attack risk model based on the collected transmission data and access records, and adjusts the key update frequency and encryption strength according to the attack risk model results; When constructing an attack risk model, the quantum sensing module for security situation simultaneously and comprehensively analyzes multi-dimensional indicators such as the transmission stability of the quantum channel, changes in the computational efficiency of the encryption module, and abnormal access frequency of the storage node. Then, it compares the multi-dimensional indicators with historical attack patterns to identify potential attack trends. Furthermore, when adjusting the key update frequency, it takes into account the current risk level, i.e., the higher the risk, the shorter the update cycle, and a seamless switching mechanism is used during the update process. The specific implementation process of the above method is as follows: After the security situation quantum sensing module is activated, it first collects three types of core data in real time using traffic mirroring technology: quantum channel transmission data, encryption module operation logs, and storage node access records. Quantum channel transmission data includes photon transmission efficiency, qubit error rate, and channel interference strength; these indicators reflect the physical layer security of the quantum key distribution process. The encryption module operation logs record the encryption time, key usage frequency, and hash operation load for each batch of data, used to monitor whether the encryption algorithm is under abnormal computational pressure. The storage node access records include the source IP address, request frequency, and data read volume, helping to identify unauthorized access or brute-force attempts. After data collection is completed, the module enters the attack risk model construction phase. The system divides multi-dimensional indicators into three analysis dimensions: quantum channel stability is assessed through photon transmission efficiency and bit error rate fluctuation range. If the bit error rate suddenly increases and is accompanied by unnatural interference patterns, it is determined that the channel may be subject to quantum eavesdropping attacks; changes in encryption module computational efficiency are assessed by comparing the deviation between real-time encryption time and the baseline value. If the encryption time is abnormally prolonged in a certain period, it may indicate that the encryption algorithm is being simulated and cracked by a quantum computer; abnormal storage node access frequency is identified by statistically analyzing the number of access requests per unit time and combining it with historical access baselines. Such characteristics are often associated with data flooding attacks in quantum attacks. During model building, the system compares current multi-dimensional indicators with a historical attack pattern database. This database contains typical characteristics of past quantum attack events; for example, quantum key distribution attacks are often accompanied by periodic fluctuations in the channel error rate, while quantum simulation attacks cause a sharp increase in the computational load of the encryption module. Through feature matching algorithms, the module can identify whether the current threat is related to known attack patterns and further combine machine learning algorithms to predict potential attack trends. For instance, when the channel interference strength and encryption time increase simultaneously, it may indicate that an attacker is attempting to reduce key quality by interfering with the channel while simultaneously using a quantum computer to accelerate the cracking process. Based on the attack probability and threat level output by the risk model, the system automatically triggers a dynamic defense adjustment mechanism. Regarding key update frequency adjustment, the system adopts a tiered response strategy: when the risk level is low, the original daily update cycle is maintained; for medium risk, it is shortened to hourly updates; and for high risk, real-time dynamic updates are enabled, continuously generating new keys and replacing old keys through the quantum key dynamic management module. During the update process, the old key is encrypted and transmitted to the backup key pool via a quantum channel, and the new key is immediately synchronized to all security device nodes after generation, ensuring that the system still has complete encryption capabilities during key switching. Regarding encryption strength adjustment, the system synchronously modifies the encryption parameters of the security data layered encryption module according to the risk level. At low risk, it maintains basic AES-256 encryption; at medium risk, it increases the number of quantum hash verification rounds, for example, increasing the verification rounds of the core area video stream from 3 rounds to 6 rounds; at high risk, it enables a dual encryption mechanism, superimposing a quantum teleportation protocol on top of AES-256 to perform secondary encryption on critical data packets. All encryption parameter adjustment commands are transmitted through the encryption bus to ensure that the adjustment process is not intercepted by man-in-the-middle attacks. To ensure system continuity, a seamless switching mechanism is employed for key updates and encryption strength adjustments. The quantum key dynamic management module maintains two sets of key pools, a primary and a backup. During updates, the old and new keys are transmitted in parallel via a quantum channel, ensuring that each node can still decrypt data using the old key before receiving the new key. The encryption module uses hot-switching technology, gradually applying the new encryption parameters to subsequent data packets without affecting existing encryption tasks. After the switch is complete, the system sends a switch completion command to all nodes via a quantum synchronization channel and immediately destroys sensitive information in the old key pool to prevent key remnant risks. Ultimately, all data from the dynamic defense adjustment process, including risk model assessment results, key update records, and encryption parameter change logs, is synchronized to the quantum-enhanced access control module and the distributed cloud-edge collaborative storage module. This provides real-time security situation awareness for access permission verification and storage node migration. Through this closed-loop dynamic adjustment mechanism, the system can maintain a higher level of security protection than traditional encryption systems, even as quantum attack methods continue to evolve.

[0020] This solution achieves a dual improvement in data confidentiality and anti-attack capability in a quantum computing environment through deep collaboration between the quantum key dynamic management module and the security data layered encryption module. The environmental quantum perturbation unit collects park environmental parameters in real time and converts them into physical perturbation factors, making the quantum key generation process environmentally adaptable and effectively resisting eavesdropping and interference at the quantum channel level. Combined with the hybrid encryption mechanism of AES-256 symmetric encryption algorithm and quantum key, it retains the efficiency of classical encryption algorithms while solving the defect of traditional algorithms being easily cracked by quantum computing through the unconditional security of quantum keys. The dynamic tags generated by the time-series quantum tag unit bind timestamps to device characteristics, ensuring the integrity and traceability of data during storage and transmission. Even if attackers intercept data, they cannot forge legitimate data packets due to the quantum non-cloning property of the tags. At the same time, the distributed cloud-edge collaborative storage module allocates storage nodes according to the data sensitivity level, and with the real-time evaluation of the storage node health monitoring subunit, it not only ensures the physical isolation of high-security data, but also avoids the risk of single point of failure through the dynamic migration mechanism, significantly improving the reliability of the system.

[0021] This solution forms a security protection system that combines active and passive approaches through a quantum-enhanced access control module and a dynamic defense adjustment mechanism. The bio-quantum binding unit converts user biometrics into irreversible feature codes using a quantum hash algorithm and deeply binds them with subkeys generated by a quantum key derivation algorithm, solving the problem of traditional biometric identification being easily forged. The spatial quantum verification unit verifies the location of user devices using 3D map data of the park, preventing unauthorized remote access caused by quantum simulation attacks. When the security situation quantum sensing module detects a quantum attack risk, the system automatically triggers key update frequency adjustment and encryption strength upgrade. In addition, the entire quantum key distribution and data encryption process is quantum-safe, with each step from key generation to data destruction possessing quantum security characteristics, forming a closed-loop anti-attack chain that effectively addresses the security challenges brought about by the rapid development of quantum computing technology.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A quantum computing-assisted cloud storage encryption system, characterized in that, include: The system includes a quantum key dynamic management module and a cloud storage module. The quantum key dynamic management module establishes a quantum bit transmission link with the security equipment nodes in the smart park through its quantum channel. The quantum key dynamic management module is connected to a security data layered encryption module through an encryption bus. The quantum key dynamic management module also integrates an environmental quantum perturbation unit. The security data layered encryption module is used to receive key data from the quantum key pool and encrypt video stream data in the smart park in real time using the AES-256 symmetric encryption algorithm combined with the quantum key. The security data layered encryption module is connected to the cloud storage module, which is used for distributed storage and management of encrypted data. The security data layered encryption module integrates a time-series quantum tag unit. The distributed cloud-edge collaborative storage module is connected to the data output end of the security data hierarchical encryption module via a VPN tunnel. The distributed cloud-edge collaborative storage module is used to distribute encrypted data to storage nodes according to levels. Both the quantum key dynamic management module and the distributed cloud-edge collaborative storage module are connected to the quantum-enhanced access control module via signal lines. The quantum-enhanced access control module is used to generate unique decryption subkeys for different permission roles through a quantum key derivation algorithm. The data receiving end of the quantum key dynamic management module is connected to a security situation quantum sensing module. The security situation quantum sensing module is used to collect quantum channel transmission data, security data layered encryption module operation logs and storage node access records through traffic mirroring technology to construct a quantum attack risk model. The quantum-enhanced access control module is equipped with a biological quantum binding unit and a spatial quantum verification unit. The environmental quantum perturbation unit is connected to the IoT sensors in the smart park through a quantum encryption interface. The environmental quantum perturbation unit is used to collect real-time environmental parameters of the smart park and convert them into physical perturbation factors for quantum keys. The time-series quantum tag unit is connected to the high-precision NTP time server of the smart park through a quantum synchronization channel. The time-series quantum tag unit is used to generate dynamic quantum tags containing nanosecond-level timestamps and device vibration fingerprints. The distributed cloud-edge collaborative storage module also includes a storage node health monitoring subunit. This subunit is used to detect the communication latency, computing load, and hardware status of each storage node, and to generate a node security level assessment result. The biological quantum binding unit establishes an encrypted connection with the biometric data collection device in the smart park. The biological quantum binding unit is equipped with a quantum hash algorithm. The biological quantum binding unit uses the quantum hash algorithm to convert biological information into an irreversible feature code and embeds the feature code into the decryption subkey.

2. A quantum-computation-assisted cloud storage encryption system's anti-quantum attack method, applied to the quantum-computation-assisted cloud storage encryption system of claim 1, characterized in that, Includes the following steps: S1 quantum key generation and perturbation: First, the real-time environmental parameters of the smart park are collected by the environmental quantum disturbance unit and converted into physical disturbance factors. These factors are then combined with the qubits transmitted through the quantum channel to generate quantum keys. Finally, the quantum keys are distributed to each security device node through the quantum key dynamic management module. S2 Data Layered Encryption and Tag Generation: Subsequently, the security data layered encryption module calls the quantum key generated in step S1 and combines it with the AES-256 symmetric encryption algorithm to encrypt the video stream data in real time. At the same time, the time-series quantum tag unit generates dynamic quantum tags containing nanosecond-level timestamps and device vibration fingerprints, which are then associated with the encrypted data. S3 encrypted data hierarchical storage: The distributed cloud-edge collaborative storage module allocates encrypted data to storage nodes with corresponding security levels based on the data sensitivity level. Subsequently, the storage node health monitoring subunit evaluates the node status in real time and dynamically adjusts the data storage location. S4 Access Control and Decryption: The quantum-enhanced access control module generates a decryption subkey with biometric features through a quantum key derivation algorithm. During access, the biometric features are first verified by the biological quantum binding unit, and then the legality of the access location is confirmed by the spatial quantum verification unit. After the dual verification is passed, decryption is allowed using the decryption subkey. S5 Dynamic Defense Adjustments: The security situation quantum sensing module constructs a quantum attack risk model based on the collected transmission data and access records, and adjusts the key update frequency and encryption strength according to the results of the quantum attack risk model.

3. The quantum-resistant method for a quantum computing-assisted cloud storage encryption system according to claim 2, characterized in that: In step S1, when the environmental quantum disturbance unit collects real-time environmental parameters, it first classifies and processes different types of environmental parameters, and assigns primary weight to abnormal environmental parameters that affect the security of the smart park. At the same time, during the quantum key distribution process, the quantum key dynamic management module first verifies the identity of each security device node, and only distributes quantum keys to nodes that have passed the verification. The distribution path is protected by quantum encryption throughout.

4. The quantum-resistant method for a quantum computing-assisted cloud storage encryption system according to claim 2, characterized in that: In step S2, when the security data layered encryption module calls the quantum key to encrypt the video stream data in real time, it first performs differentiated processing based on the source region of the video stream. For video streams from the core area of ​​the smart park, 3-6 rounds of quantum hash verification are added on the basis of conventional encryption, so that the dynamic quantum tags generated by the time-series quantum tag unit form a one-to-one binding relationship with the encrypted data. At the same time, when the encrypted data is interrupted or abnormal during transmission, the time-series quantum tag unit will generate a new supplementary tag. The supplementary tag contains the time point and location information of the data interruption. The security data layered encryption module will perform integrity verification on the interrupted encrypted data based on the supplementary tag. Only after the integrity verification passes will transmission or storage be allowed to continue.

5. The quantum-resistant method for a quantum computing-assisted cloud storage encryption system according to claim 2, characterized in that: In step S3, when classifying data sensitivity levels, the distributed cloud-edge collaborative storage module comprehensively judges the data based on its generation scenario, purpose, and the degree of privacy involved. For highly sensitive data involving the core security of the smart park, it will not only be allocated to the storage node with the highest security level, but also adopt a multi-node backup storage strategy. Furthermore, the communication between each backup node is encrypted using quantum key encryption. When the storage node health monitoring subunit detects an abnormal node status, it immediately notifies the distributed cloud-edge collaborative storage module to start the data migration process. During the migration process, the encrypted data will be encrypted again using a temporary quantum key before being transmitted to the new storage node. After the data is stored and verified to be correct on the new storage node, the data in the original node will be completely deleted, and the temporary quantum key will be destroyed synchronously.

6. The quantum attack resistant method for a quantum computing-assisted cloud storage encryption system according to claim 2, characterized in that: In step S4, when the quantum-enhanced access control module generates a decryption subkey with biometric features through the quantum key derivation algorithm, it simultaneously sets different validity periods for subkeys of different permission roles. The validity period of the subkey of a temporary visitor matches the validity period of its entity access permission and automatically expires after expiration. When the biometric quantum binding unit embeds biometric features into the subkey, it simultaneously performs irreversible encryption processing on the biometric information.

7. The quantum-resistant method for a quantum computing-assisted cloud storage encryption system according to claim 2, characterized in that: In step S5, when constructing the quantum attack risk model, the security situation quantum sensing module simultaneously and comprehensively analyzes multi-dimensional indicators such as the transmission stability of the quantum channel, the changes in the computational efficiency of the security data layered encryption module, and the abnormal access frequency of the storage node. Then, it compares the multi-dimensional indicators with historical attack patterns to identify potential attack trends. Furthermore, when adjusting the key update frequency, it considers the current risk level. The higher the risk, the shorter the update cycle, and a seamless switching mechanism is used during the update process.

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