Dynamic key fragment storage management method and system for multi-cloud architecture

Abstract

The invention relates to the technical field of key distribution, in particular to a multi-cloud architecture-oriented dynamic key fragmentation storage management method and system, which comprises the following steps of: dividing a manufacturing process of the manufacturing industry into a plurality of time sequence stages corresponding to different data access authority levels and security requirements, and constructing corresponding quantum state evolution operators; driving the key state to evolve among different time sequence stages through a quantum state evolution operator according to the progress of the manufacturing process, and adjusting the access authority of the key; quantum entanglement connection is established between supply chain nodes, the trust degree between the nodes is measured and quantized through the association of the quantum entanglement state, and the distribution weight of the key fragments is adjusted based on the change of the trust degree; collecting constraint parameters of production takt, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraint conditions; and when it is detected that the constraint condition changes, recalculating the key fragment distribution scheme based on the quantum state constraint condition and the credibility, and synchronously distributing the key fragment distribution scheme to each supply chain node.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of key allocation, in particular to a dynamic key fragment storage management method and system for a multi-cloud architecture. BACKGROUND

[0002] Existing multi-cloud key management solutions generally adopt a centralized architecture, storing keys in a single key management service, and then providing key services for each cloud platform through an API interface. This solution has obvious single-point failure risks. Once the central node fails or is attacked, the data security of the entire multi-cloud environment will be seriously threatened. At the same time, the centralized solution cannot meet the data sovereignty and compliance requirements of different regions, and cannot realize the localized storage of keys.

[0003] The prior art lacks effective cross-cloud consistency guarantee mechanisms. Poor key management can lead to unauthorized access, data loss and data leakage. In a multi-cloud environment, network latency, system heterogeneity and fault mode differences between different cloud platforms make it difficult for traditional distributed consistency algorithms to ensure reliable synchronization of key fragments between cloud nodes, which can easily lead to key reconstruction failure or security vulnerabilities.

[0004] Therefore, a dynamic key fragment storage management method that can adapt to the characteristics of a multi-cloud environment is needed to solve the technical defects of the prior art in terms of single-point failure, static fragmentation, cross-cloud consistency, etc.

[0005] To this end, a dynamic key fragment storage management method and system for a multi-cloud architecture are proposed. SUMMARY

[0006] The purpose of the application is to provide a dynamic key fragment storage management method and system for a multi-cloud architecture, which converts constraint parameters such as production rhythm, equipment state, logistics delay and quality indicators in the manufacturing process into quantum state constraint conditions by collecting them. When a change in the constraint condition is detected, the key fragment distribution scheme is recalculated based on the quantum state constraint condition and the trust level, and is synchronized and distributed to each supply chain node.

[0007] To achieve the above purpose, the application provides the following technical solutions:

[0008] A dynamic key fragment storage management method for a multi-cloud architecture, comprising:

[0009] The manufacturing process of the manufacturing industry supply chain is divided into multiple time sequence stages, corresponding to different data access permission levels and security requirements, and corresponding quantum state evolution operators are constructed;

[0010] According to the progress of the manufacturing process, the key state is evolved between different time sequence stages by the quantum state evolution operator, and the key access permission is dynamically adjusted.

[0011] Establishing quantum entanglement connection between supply chain nodes, quantifying the trust degree between nodes through correlation measurement of quantum entangled states, and dynamically adjusting the allocation weight of key fragments based on the change of trust degree;

[0012] Collecting constraint parameters such as production rhythm, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraints;

[0013] When detecting the change of constraint conditions, re-computing the key fragment distribution scheme based on quantum state constraints and trust degree, and synchronously distributing to each supply chain node;

[0014] Continuously monitoring network communication quality and constraint parameter violation, and triggering fragment redistribution and entanglement recovery process when detecting trust degree anomaly and / or serious constraint parameter violation.

[0015] Preferably, the time sequence stage division process includes: collecting production order data, bill of materials data and process route data, analyzing the data types and sensitivity information involved in each stage; based on stage division standard, the manufacturing process is divided into seven time sequence stages: conceptual design stage, detailed design stage, process planning stage, production manufacturing stage, quality control stage, packaging logistics stage and after-sales service stage.

[0016] Preferably, the construction process of the quantum state evolution operator is: collecting characteristic parameters of time sequence stage, including data sharing frequency, number of cooperation partners, information flow complexity and security risk level, as the basic input of quantum state construction; mapping the high security characteristic of the conceptual design stage to the Z direction spin state upward; mapping the multi-party cooperation characteristic of the production manufacturing stage to the X direction spin state; mapping the information transparency characteristic of the packaging logistics stage to the Y direction spin state; by weighted combination of spin operators in different directions to represent the key management characteristics of each stage, forming a multi-dimensional quantum state evolution operator.

[0017] Preferably, the quantum state evolution operator driven key state evolution and permission dynamic adjustment process includes: monitoring key milestone events, including design review pass, raw material arrival confirmation, production task start, quality inspection report completion and product delivery confirmation; when detecting the milestone event trigger, applying the corresponding quantum state evolution operator to the time evolution calculation of the current key state, obtaining the quantum state probability distribution; based on the quantum state probability distribution, re-determining the access permission level of each supply chain node; according to the new access permission level allocation result, updating the reconstruction threshold value of the key fragment and the fragment number quota of each node, generating a new access control strategy and issuing.

[0018] Preferably, the construction process of the quantum entanglement connection comprises: deploying quantum communication equipment at each supply chain node, establishing quantum channels between nodes through fiber links; generating entangled photon pairs using parametric down-conversion process, sending one photon of the entangled photon pair to node A and the other photon to node B to establish quantum entanglement correlation between the two nodes; implementing entanglement distribution protocol to expand the entanglement connection to the entire supply chain network in a cascading manner, with the core manufacturing enterprise as the center node and the first-tier suppliers establishing direct entanglement, and the first-tier suppliers establishing entanglement connection with the second-tier suppliers; establishing entanglement maintenance mechanism to regularly detect entanglement fidelity, and automatically starting entanglement purification when the fidelity is lower than the set threshold.

[0019] Preferably, the trust degree acquisition method and distribution weight adjustment process comprises: performing polarization measurement on the entangled photon pairs between nodes, counting the correlation results, and calculating the degree of violation of Bell inequality as the basic score of quantum trust degree; collecting the business behavior data of the nodes, including historical order fulfillment rate, product quality compliance rate, delivery punctuality rate, safety accident occurrence frequency and response processing efficiency, and establishing a business trust evaluation index system; weighting and averaging the quantum trust degree score and the business trust score according to the weight proportion to obtain the comprehensive trust degree score; and adjusting the key fragment distribution weight of each node according to the comprehensive trust degree score result.

[0020] Preferably, the step of converting the constraint parameters into quantum state constraint conditions comprises: collecting real-time manufacturing constraint data, including device efficiency percentage, production rhythm deviation time, logistics distribution delay hours and product quality unqualified rate; standardizing the collected constraint parameters, converting device efficiency into efficiency loss rate, converting rhythm deviation into relative deviation ratio, converting distribution delay into delay severity index, and converting unqualified rate into quality risk coefficient; mapping the standardized constraint parameters into eigenvalues of quantum operators, converting classical constraint conditions into energy eigenstate description of quantum state by constructing constraint Hamiltonian operator; establishing the correspondence between constraint operators and Pauli matrices, mapping production efficiency constraint into Z direction operator, mapping time rhythm constraint into X direction operator, mapping logistics delay constraint into Y direction operator, and mapping quality constraint into tensor product form of multiple operators to form complete quantum state constraint conditions.

[0021] Preferably, the key fragment distribution scheme acquisition process comprises: based on the current quantum trust evaluation result and the quantum state constraint condition, a multi-objective optimization model of key fragment distribution is established, the optimization objectives include system security maximization, communication cost minimization and load balancing optimization; a quantum approximate optimization algorithm is used to solve the fragment distribution problem, the number of quantum bits is set to be equal to the logarithmic value of the number of network nodes, the number of optimization layers is set to three, and the global optimal solution is found through variational parameter iteration; in the optimization process, the distribution constraint condition is applied to ensure that the number of fragments stored by a single node does not exceed one third of the total number of fragments, the fragment combination of any adjacent nodes cannot independently reconstruct the complete key, and the high-trust nodes are preferentially allocated key fragments; the final fragment distribution matrix is output, the storage location of each key fragment in different nodes is determined, the corresponding access permission table and reconstruction threshold parameter are generated, and a real-time synchronization mechanism of the fragment state is established to ensure the consistency of the whole network.

[0022] A dynamic key fragment storage management system for a multi-cloud architecture comprises:

[0023] A time sequence mapping module is configured to divide the manufacturing process of the manufacturing industry supply chain into multiple time sequence stages corresponding to different data access permission levels and security requirements, and construct corresponding quantum state evolution operators.

[0024] A quantum key space-time evolution protocol construction module is configured to drive the evolution of key states between different time sequence stages through quantum state evolution operators according to the progress of the manufacturing process, and dynamically adjust the key access permission.

[0025] A quantum trust evaluation network construction module is configured to establish quantum entanglement connections between supply chain nodes, quantify the trust degree between nodes through correlation measurement of quantum entangled states, and dynamically adjust the allocation weight of key fragments based on the change of the trust degree.

[0026] A manufacturing constraint perception module is configured to collect constraint parameters such as production rhythm, equipment state, logistics delay and quality index in the manufacturing process, and convert the constraint parameters into quantum state constraint conditions.

[0027] A key fragment reconstruction module is configured to recalculate the key fragment distribution scheme based on the quantum state constraint condition and the trust degree when detecting a change in the constraint condition, and synchronously distribute it to each supply chain node.

[0028] A fragment state synchronization updating module is configured to continuously monitor the network communication quality and constraint parameter violation, and trigger the fragment redistribution and entanglement recovery process when detecting trust degree anomalies and / or serious constraint parameter violations.

[0029] Compared with the prior art, the beneficial effects of the present application are:

[0030] 1. The application introduces quantum state evolution operators into the key fragment storage management of the multi-cloud architecture. By dividing the manufacturing supply chain process into different time stages and constructing dedicated quantum state evolution operators for each stage, the key state is probabilistically distributed and evolved with the progress of the business. The key access permission can be automatically adjusted according to the design review, raw material arrival, quality inspection completion, and other key milestone events. The security and collaboration characteristics of the concept design, production, manufacturing, and logistics stages are described using the Pauli matrix Hamiltonian operator, effectively implementing multi-dimensional key permission management. Throughout the entire cycle of the supply chain operation, the security risks and business status are continuously perceived, the access strategy is highly matched with the data sensitivity, the data protection capability is strengthened, and the security risks caused by redundant permission configuration are reduced.

[0031] 2. By deploying quantum communication devices at the main nodes of the supply chain, a quantum channel based on entangled photon pairs is established to realize quantum entanglement connection between nodes. The degree of violation of Bell's inequality is used to quantify the entanglement correlation to obtain a quantum trust score. Meanwhile, combining with business indicators such as historical compliance rate, quality standard rate, and delivery punctuality rate, a comprehensive trust system is constructed, which is used as the core basis for key fragment allocation weight. The trust score can reflect the comprehensive performance of the node in real-time communication quality and business compliance. By preferentially allocating key fragments to high-trust nodes, the probability of low-trust nodes holding core key shares is reduced, effectively preventing key leakage or reconstruction risks caused by abnormal or malicious behavior of nodes. The introduction of quantum entanglement ensures that the trust quantification process has physical layer tamper-proofing characteristics, and the dynamic allocation weight strategy ensures that the system maintains stable and efficient security collaboration capability in complex supply chain environments.

[0032] 3. The application proposes to map manufacturing constraint parameters such as device efficiency, beat deviation, logistics delay, and quality unqualified rate through standardization and quantum operator to construct quantum state constraint conditions, and introduce constraint Hamiltonian operator in key fragment distribution solution. Combined with quantum approximate optimization algorithm, the key fragment distribution is optimized in multiple objectives, considering system security, communication cost and load balancing. Using the global search and superposition characteristics of quantum algorithm, the solution efficiency and global optimization rate are significantly improved. When the network communication quality is abnormal or the constraint condition changes dramatically, the system can quickly trigger fragment redistribution and entanglement recovery process to maintain network consistency and robustness. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A dynamic key fragment storage management method flowchart for a multi-cloud architecture is provided for the application.

[0034] Figure 2 A dynamic key fragment storage management system structure diagram for a multi-cloud architecture is provided for the application.

[0035] Figure 3 A key fragment storage flowchart provided by the present application is shown. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0037] Example one:

[0038] Please refer to Figure 1 The present application provides a dynamic key fragment storage management method for a multi-cloud architecture, and the technical solution is as follows:

[0039] The manufacturing process of the manufacturing industry supply chain is divided into multiple time sequence stages, corresponding to different data access permission levels and security requirements, and corresponding quantum state evolution operators are constructed;

[0040] The time sequence stage division process includes:

[0041] Collect production order data, bill of materials data and process route data, analyze the data types and sensitivity information involved in each stage;

[0042] Based on the stage division standard, the manufacturing process is divided into seven time sequence stages: conceptual design stage, detailed design stage, process planning stage, production manufacturing stage, quality control stage, packaging logistics stage and after-sales service stage;

[0043] The data access permission level and security requirement acquisition process includes:

[0044] The highest confidential level permission is set for the conceptual design stage, and only the core R&D team is allowed to access; the confidential level permission is set for the detailed design stage, and the design department and technical director are allowed to access; the restricted level permission is set for the process planning stage, and the process engineer and production planning personnel are allowed to access; the collaboration level permission is set for the production manufacturing stage, and the production line operator and quality inspector are allowed to access; the verification level permission is set for the quality control stage, and the quality department and customer representative are allowed to access; the limited public level permission is set for the packaging logistics stage, and the logistics supplier is allowed to access the shipping information; the public level permission is set for the after-sales service stage, and the customer and service provider are allowed to access the product usage instructions.

[0045] In this embodiment, by finely dividing the manufacturing process of the manufacturing supply chain into seven time stages, combining the data types and sensitivity of each stage, a hierarchical access authority and security requirement system is established. The quantum state evolution operator is used to drive the key state to dynamically adjust with the business process, realize the precise matching of data security and authority in the whole process from concept design to after-sales service, and improve the flexibility and overall security of key management in the multi-cloud environment of the supply chain.

[0046] The construction process of the quantum state evolution operator includes:

[0047] Collecting characteristic parameters of the time sequence stage, including data sharing frequency, number of collaboration partners, information flow complexity, and security risk level, and taking these parameters as the basic input for quantum state construction;

[0048] The high confidentiality of the concept design stage is mapped to the Z-direction spin state upward; the multi-party collaboration characteristics of the production and manufacturing stage are mapped to the X-direction spin state; the information transparency characteristics of the packaging and logistics stage are mapped to the Y-direction spin state; Z-direction represents the vertical hierarchical relationship and confidentiality characteristics, X-direction represents the horizontal collaboration interaction characteristics, and Y-direction represents the transparency characteristics of information flow.

[0049] A Hamiltonian operator based on the Pauli matrix is constructed, and different direction spin operators are combined by weighting to represent the key management characteristics of each stage, forming a multi-dimensional quantum state evolution operator; the quantum state evolution operator includes spatial dimension information (through X, Y, Z three direction spin operators), and also integrates the evolution characteristics of time dimension, the evaluation results of trust dimension, and the restriction conditions of constraint dimension;

[0050] Obtain the weight of three directions, the weight allocation is not static and fixed, but dynamically adjusted according to the actual progress of the manufacturing process, and the weight of each direction spin operator will change when the manufacturing process is transferred from one stage to another. For example, when transitioning from the concept design stage to the detailed design stage, the confidentiality requirement may decrease and the collaboration demand begins to increase, which will cause the Z-direction weight to decrease moderately and the X-direction weight to increase accordingly;

[0051] After obtaining the weight coefficients of each direction, the system integrates the spin operators of the three directions into a comprehensive Hamiltonian operator through weighted linear combination; analyze the coupling relationship between different manufacturing characteristics, such as the trade-off between confidentiality requirements and collaboration degree, and the balance between information transparency and security control, and then introduce the corresponding cross terms and interaction terms in the combination process;

[0052] The time evolution operator of the Schrödinger equation is used to describe the quantum state conversion process of the key state from one manufacturing stage to the next stage, ensuring the unitary and reversible evolution process; the time evolution operator is used to describe the evolution trajectory of the key state as the manufacturing process advances, which contains time dependence and can predict what state the manufacturing process should be at a specific time point and how the corresponding key management strategy should be adjusted; meanwhile, the construction of the evolution operator takes into account the typical time period of the manufacturing process, key milestone nodes, possible delays or accelerations, forming a complete space-time evolution description framework.

[0053] In this embodiment, by constructing a quantum state evolution operator that integrates space, time, trust and constraint dimensions, the confidentiality, collaboration and information transparency characteristics of each stage of the manufacturing process are accurately mapped, and dynamic weights and cross-coupling terms are used to optimize the Hamiltonian operator. With the help of the Schrödinger equation, the space-time evolution trajectory of the key state as the process advances is described, so that the key management strategy keeps pace with business progress, ensuring system security and flexibility.

[0054] According to the progress of the manufacturing process, the key state is evolved between different time sequences by the quantum state evolution operator, and the key access rights are dynamically adjusted;

[0055] The quantum state evolution operator driven key state evolution and dynamic adjustment process includes: monitoring key milestone events, including design review approval, raw material arrival confirmation, production task start, quality inspection report completion and product shipment confirmation; when a milestone event trigger is detected, the corresponding quantum state evolution operator is applied to perform time evolution calculation on the current key state to obtain the quantum state probability distribution; based on the quantum state probability distribution, the access right level of each supply chain node is re-determined; according to the new access right level allocation result, the reconstruction threshold value of the key fragment and the fragment quantity quota of each node are updated, a new access control strategy is generated and issued;

[0056] The process of applying the quantum state evolution operator for time evolution calculation includes: first, obtaining the quantum state representation of the current key state, encoding the key fragment holding situation, access right level and security state of each supply chain node into a complex quantum state of multiple quantum bits, where each quantum bit corresponds to a specific key management dimension; select the corresponding quantum state evolution operator according to the detected milestone event type, different events correspond to different operator parameter configurations, the design review approval event corresponds to a confidentiality weight reduction evolution operator, the production start event corresponds to a collaboration weight increase evolution operator, and the product shipment event corresponds to a transparency improvement evolution operator;

[0057] The selected evolution operator is applied to the current quantum state to simulate the natural evolution of the key state over time and the manufacturing progress through a unitary transformation process of the quantum state, and the calculation process considers the noise interference and decoherence effect of the manufacturing environment; the measurement basis selection and probability calculation are performed on the evolved quantum state, the square of the modulus of the complex amplitude is converted into the occurrence probability of various possible key configuration states to form a complete probability distribution matrix, and thus the quantum state probability distribution is obtained.

[0058] In this embodiment, the key state is driven by the quantum state evolution operator to realize dynamic evolution with the key milestone events in the manufacturing process, and the unitary transformation and probability distribution calculation are combined to accurately reflect the changes in node permissions and key configuration. The access level and shard threshold are adjusted in real time in the design review, production start, delivery and other links to ensure that the key management is synchronized with the business status and improve the data security and management flexibility in the multi-cloud environment of the supply chain.

[0059] A quantum entanglement connection is established between the nodes of the supply chain, the trust degree between the nodes is quantified through the correlation measurement of the quantum entangled state, and the allocation weight of the key shard is dynamically adjusted based on the change of the trust degree;

[0060] The construction process of the quantum entanglement connection includes: deploying quantum communication equipment at each supply chain node, including a laser, a beam splitter, a polarizer, and a single-photon detector, and establishing a quantum channel between the nodes through an optical fiber link; generating an entangled photon pair using a parametric down-conversion process, sending one photon of the entangled photon pair to node A and the other photon to node B to establish a quantum entanglement correlation between the two nodes; implementing an entanglement distribution protocol to expand the entanglement connection to the entire supply chain network in a cascading manner, with the core manufacturing enterprise as the center node and the first-tier suppliers establishing direct entanglement, and the first-tier suppliers establishing entanglement connections with the second-tier suppliers; establishing an entanglement maintenance mechanism to periodically detect the entanglement fidelity, and automatically starting entanglement purification when the fidelity is below a set threshold;

[0061] The entanglement maintenance mechanism includes: deploying a quantum state tomography device to periodically measure the polarization correlation and phase correlation of the entangled photon pair, and calculating the entanglement fidelity index by statistically measuring the results; establishing a fidelity monitoring system and setting the fidelity threshold to 95%, triggering the early warning mechanism when the detected fidelity drops below the threshold; starting the entanglement purification protocol to extract a few high-fidelity entangled pairs from multiple low-fidelity entangled pairs through joint measurement and post-selection of multiple entangled states; using a distillation algorithm to optimize the efficiency of entanglement purification, and combining error detection and correction mechanisms to ensure that the purified entangled state meets the quality requirements of key distribution and maintains reliable quantum connections between the nodes of the supply chain.

[0062] In this embodiment, by deploying quantum communication devices at supply chain nodes, entangled photon pairs are generated and distributed using parametric down-conversion to build a full-network quantum entanglement connection. Combined with fidelity monitoring and entanglement purification mechanisms, quantum channel quality is detected and repaired in real time to ensure stable and high-fidelity transmission of key distribution, improving the security and reliability of key sharing and communication in the supply chain multi-cloud environment.

[0063] The trust degree acquisition method and distribution weight adjustment process includes: performing polarization measurement on the entangled photon pairs between nodes, counting the correlation results, and calculating the violation degree of Bell inequality as the basic score of quantum trust degree; collecting the business behavior data of the nodes, including historical order fulfillment rate, product quality compliance rate, delivery punctuality rate, security incident frequency and response processing efficiency, establishing a business trust evaluation index system; weighting and averaging the quantum trust degree score and the business trust score according to the weight ratio to obtain the comprehensive trust degree score; adjusting the key fragment distribution weight of each node according to the comprehensive trust degree score result;

[0064] Specifically, on the basis of the established entangled photon pair connection between nodes, precise polarization measurement is performed on the entangled photons, and the measurement process uses randomly selected polarization bases, including linear polarization bases and circular polarization bases. The two components of the entangled photon pair are measured synchronously at different nodes; the correlation of a large number of measurement results is counted to evaluate the strength of quantum entanglement. When two nodes independently measure the entangled photon pair, if the quantum entanglement relationship is good, the measurement results will show strong correlation that cannot be explained by classical physics; the violation degree of Bell inequality is calculated, and the greater the violation degree, the stronger the entanglement relationship, and the higher the quantum trust degree score; this trust evaluation method based on quantum physics laws is theoretically unforgeable, providing a solid physical foundation for the entire trust system;

[0065] The data acquisition module continuously collects historical business data of each supply chain node to obtain key indicators reflecting the reliability and cooperation ability of the node; the historical order fulfillment rate is evaluated by counting the accuracy and timeliness of the node in completing the order, including order completion, specification compliance and delivery timeliness; the product quality compliance rate evaluation is a long-term tracking of the quality of products or services provided by the node, which is comprehensively judged by quality inspection reports, customer feedback and rework rate; the delivery punctuality rate not only considers the final delivery time, but also includes response speed of intermediate nodes, logistics distribution efficiency, abnormal situation handling capacity and other factors; the security incident frequency evaluation covers information security events, production safety accidents, data leakage events and other security risk indicators; the response processing efficiency is measured by evaluating the emergency response speed, solution quality, communication and coordination ability of the node when facing problems;

[0066] The fusion process adopts a dynamic weight distribution mechanism, which adjusts the weight proportion of the two types of trust degree according to the characteristics and safety requirements of the current manufacturing stage; in the design stage with high safety requirements, the quantum trust degree occupies a higher weight, emphasizing the physical layer security guarantee; in the production stage with intensive cooperation, the weight of the business trust degree is relatively increased, and more attention is paid to the evaluation of actual cooperation ability; the weight distribution also considers the historical performance stability and current state of the node; for the newly added nodes, the quantum trust degree evaluation is used due to the lack of sufficient business historical data; for the mature nodes with long-term cooperation, the weight of the business trust degree is appropriately increased, and the two types of scores are integrated into a single comprehensive trust degree score through a weighted average algorithm to form a unified trust evaluation result for each node;

[0067] Based on the comprehensive trust degree score result, the key fragment distribution weight of each node is allocated; the high trust degree node will obtain more key fragment distribution weight and be allowed to store more critical key fragments;

[0068] All nodes are sorted and grouped according to the trust degree score, and the core trust node group, the general trust node group and the observation trust node group correspond to different weight distribution strategies; the core trust node enjoys priority distribution right and can obtain the storage right of critical key fragments; the general trust node undertakes the management responsibility of regular key fragments; the observation trust node mainly undertakes auxiliary key management tasks, and its fragment quantity and importance are relatively low; the adjustment process also considers the load balancing and fault tolerance capability of the entire supply chain network; even if a node has a high trust degree, the system will limit the concentration degree of its key fragments to avoid single point failure risk; at the same time, for the nodes with decreasing trust degree, the system will gradually reduce the key fragment weight of the nodes and start the fragment migration process to transfer the important fragments of the nodes to more reliable nodes, ensuring the safety and stability of the entire key management system;

[0069] In the embodiment, through the dual evaluation of quantum trust degree and business trust degree, a comprehensive trust score system is constructed, and the key fragment distribution weight is dynamically adjusted based on the score; the Bell inequality violation degree and business indicators such as order fulfillment rate and quality compliance rate are fused to realize comprehensive measurement of the safety and cooperation ability of the nodes; combined with the hierarchical classification strategy and load balancing mechanism, the distribution of key fragments in the supply chain network is ensured to be safe and efficient, and the overall management stability is improved.

[0070] The constraint parameters of production rhythm, equipment state, logistics delay and quality indicators in the manufacturing process are collected, and the constraint parameters are converted into quantum state constraint conditions;

[0071] The step of converting the constraint parameters into quantum state constraint conditions comprises: collecting real-time manufacturing constraint data, including equipment operation efficiency percentage, production tact bias time, logistics distribution delay hours and product quality unqualified rate; standardizing the collected constraint parameters, converting equipment efficiency into efficiency loss rate, converting tact bias into relative deviation ratio, converting distribution delay into delay severity index, and converting unqualified rate into quality risk coefficient; mapping the standardized constraint parameters into eigenvalues of quantum operators, converting classical constraint conditions into energy eigenstate description of quantum state by constructing constraint Hamiltonian operator; establishing the correspondence between constraint operators and Pauli matrices, mapping production efficiency constraint into Z direction operator, mapping time tact constraint into X direction operator, mapping logistics delay constraint into Y direction operator, and mapping quality constraint into tensor product form of multiple operators to form complete quantum state constraint conditions;

[0072] Specifically, sensor networks and data acquisition devices are deployed at each key node of the manufacturing supply chain to monitor core constraint indicators in real time during the manufacturing process; the equipment operation efficiency percentage is obtained by analyzing the ratio of actual output to theoretical maximum capacity of the equipment through machine learning algorithm, considering factors such as equipment utilization rate, downtime due to failure, maintenance time, etc.; the production tact bias time is obtained by comparing the difference between actual production cycle and standard production tact, including waiting time between processes, material preparation time, quality inspection and acceptance time, etc.; the logistics distribution delay hours involve monitoring the entire logistics chain from raw material procurement to finished product delivery, and real-time location and distribution status information of goods is obtained through GPS positioning, RFID tag tracking, warehouse management system, etc.; the product quality unqualified rate is obtained by combining automatic detection of quality inspection system with manual sampling inspection, including multi-dimensional quality evaluation data such as size deviation, performance indicators, appearance quality, safety standards, etc.

[0073] The process of converting device efficiency into efficiency loss rate first establishes a theoretical optimal efficiency benchmark for the device, then calculates the difference between actual efficiency and benchmark efficiency, and normalizes the difference into a loss rate index between zero and one. When converting production tempo deviation into relative deviation proportion, the system takes the standard production tempo as the benchmark and calculates the relative deviation of the actual tempo. Positive deviation indicates that the production speed is slower than expected, and negative deviation indicates that the production speed is faster than expected. These deviation values are normalized into relative proportion form. Converting logistics distribution delay into delay severity index requires establishing a delay impact evaluation model, considering the cascading effect of delay on subsequent production links, and converting time delay into an impact degree index on overall manufacturing progress. The process of converting product quality unqualified rate into quality risk coefficient is more complex and requires weighted processing according to the severity of unqualified types. Minor cosmetic defects and serious safety hazards have different weights in risk coefficient calculation. Various quality problems are converted into a unified risk coefficient representation through a risk assessment matrix.

[0074] The standardized constraint parameters need to be mapped to the eigenvalues of quantum operators, which is a key step in the conversion of classical constraints to quantum constraints. Each standardized constraint parameter is assigned a corresponding quantum operator, and the numerical value of the constraint parameter directly corresponds to the eigenvalue of the operator. When efficiency loss rate is mapped to the eigenvalue of Z-direction Pauli operator, loss rate of zero corresponds to eigenvalue positive one, and loss rate of one corresponds to eigenvalue negative one, and intermediate values are determined by linear interpolation. The mapping process also considers the interdependence between constraint parameters. When multiple constraints exist simultaneously, a joint eigenstate is constructed to describe the composite constraint condition.

[0075] Based on the eigenvalue mapping, a Hamiltonian operator is constructed to describe all constraint conditions: production efficiency constraint is mapped to Z-direction operator, reflecting the binary opposition characteristic of efficiency; tempo constraint is mapped to X-direction operator, reflecting the coordination feature of production speed; logistics delay constraint is mapped to Y-direction operator, representing the dynamic nature of logistics state; quality constraint involves multiple quality dimensions, and a multi-operator tensor product form is used to represent it; different quality indicators are respectively mapped to different Pauli operators, and then a composite operator is formed through tensor product operation to describe the overall quality state; this multi-operator tensor product form can describe multiple quality dimension constraints simultaneously, while maintaining the independence and correlation between dimensions; the Pauli operators in different directions are combined into a complete constraint Hamiltonian operator through weighted summation, and the weight coefficients reflect the relative importance of different constraints in the current manufacturing stage

[0076] In this embodiment, the precision description and dynamic optimization of manufacturing constraints are realized by quantum modeling, the multi-source parameters such as equipment efficiency, production rhythm, logistics delay and quality risk are standardized and mapped into the eigenvalues of Pauli operators, the constraint Hamiltonian operator is constructed, and the unified quantum state constraint condition is formed; the operator comprehensively reflects the multidimensional influence and evolution trend of efficiency, time, logistics and quality, realizes the global constraint analysis and prediction of the manufacturing process, provides quantitative decision support for production rhythm coordination, equipment scheduling, logistics optimization and quality improvement, and significantly enhances the resilience and intelligent level of the manufacturing system.

[0077] When the constraint condition changes are detected, the key fragment distribution scheme is recalculated based on the quantum state constraint condition and the trust degree, and is synchronously distributed to each supply chain node;

[0078] The obtaining process of the key fragment distribution scheme includes: based on the current quantum trust degree evaluation result and the quantum state constraint condition, a multi-objective optimization model of key fragment distribution is established, the optimization objectives include system security maximization, communication cost minimization and load balancing optimization; a quantum approximate optimization algorithm is used to solve the fragment distribution problem, the number of quantum bits is set to be equal to the logarithmic value of the number of network nodes, the number of optimization layers is set to three, and the global optimal solution is found through variational parameter iteration; the distribution constraint condition is applied in the optimization process to ensure that the number of fragments stored by a single node does not exceed one-third of the total number of fragments, the fragment combination of any adjacent nodes cannot independently reconstruct the complete key, and the key fragments are preferentially allocated to high-trust nodes; the final fragment distribution matrix is output, the storage location of each key fragment in different nodes is determined, the corresponding access permission table and reconstruction threshold parameter are generated, and a real-time synchronization mechanism of fragment state is established to ensure the consistency of the whole network; specifically, a comprehensive multi-objective optimization model needs to be established for the key fragment distribution scheme; the system security maximization objective is realized by quantifying security indicators such as the dispersion degree of key fragments, the reconstruction difficulty, and the access control strictness, to ensure that even if part of the nodes are broken, the complete key information cannot be obtained; the communication cost minimization objective considers the matching degree between fragment storage location and access demand, reduces unnecessary cross-node communication overhead, and optimizes the network bandwidth utilization efficiency; the load balancing optimization objective aims to avoid excessive concentration of key management burden on a few nodes, and improves the stability and scalability of the whole system by uniformly distributing storage tasks and computing tasks; the improvement of system security of the three optimization objectives may lead to an increase in communication cost, and the requirement of load balancing may conflict with the optimal configuration of security, so a balance point needs to be found among the multi-objectives.

[0079] The number of quantum bits is set equal to the logarithm of the number of network nodes, avoiding the problem of computational complexity explosion caused by excessive number of quantum bits; the design of the three-layer optimization structure balances the algorithm accuracy and computational efficiency, the first layer performs coarse-grained global search, the second layer performs refined search, and the third layer performs local optimization adjustment; the variational parameter iteration process adopts the mode of cooperative work of classical optimizer and quantum processor, the classical optimizer is responsible for adjusting the variational parameter, and the quantum processor is responsible for evaluating the objective function value corresponding to the parameter, the convergence condition is monitored in the iteration process, and when the improvement amplitude of the objective function is less than the set threshold, the iteration is stopped, and the current optimal parameter configuration is output as an approximation of the global optimal solution.

[0080] In the optimization process, multiple distribution constraints are applied to ensure the security and feasibility of the sharding scheme; the constraint that the number of shards stored by a single node does not exceed one-third of the total number of shards prevents key over-concentration and reduces the risk of single-point failure; this constraint is implemented by adding a penalty term to the optimization objective function, when a candidate scheme violates the shard number limit, its objective function value will be significantly penalized; the constraint that any combination of shards of adjacent nodes cannot independently reconstruct the complete key ensures the security of the sharding scheme. The system analyzes the network topology, identifies all adjacent node pairs, and then checks whether the combination of shards held by each node pair can independently complete key reconstruction. If a security vulnerability is found, the system will adjust the shard allocation scheme and redistribute the relevant shards to non-adjacent nodes; the constraint that high-trust nodes are preferentially allocated critical shards reflects the differentiated management strategy based on trust. The system sorts all nodes according to the previously calculated comprehensive trust score, and the node with the highest trust score is given the most critical shards for key reconstruction, these critical shards usually have higher weights or occupy more important positions in the reconstruction algorithm.

[0081] After the optimization algorithm is completed, a detailed fragment distribution matrix is output, which clearly shows the storage location of each key fragment in the supply chain network. The distribution matrix not only contains static fragment location information, but also contains dynamic fragment state information such as fragment creation time, last update time, access frequency statistics, etc.; the access permission table accompanying the distribution matrix defines the access permissions of each node to the key fragments under different roles and different manufacturing stages, realizing fine-grained permission control; the setting of the reconstruction threshold parameter is based on the current network size and security requirements to determine how many fragments are needed to reconstruct the complete key, and the threshold parameter will be dynamically adjusted according to the advancement of the manufacturing process and the change of trustworthiness, increasing the reconstruction threshold in the high security requirement stage and appropriately reducing the threshold in the intensive cooperation stage to improve operation efficiency; the fragment state real-time synchronization mechanism ensures the consistent awareness of the network nodes on the fragment distribution information, and uses a distributed consistency protocol, which broadcasts update information to other related nodes when the fragment state of any node changes; the synchronization mechanism also includes conflict detection and resolution functions, which can automatically identify and repair inconsistencies when they occur, maintaining the integrity and reliability of the entire key management system; in this embodiment, the quantum approximate optimization algorithm is used to realize the secure distribution of key fragments, and by combining node trustworthiness and distribution constraints, the optimal fragment matrix and access permission table are dynamically generated, and the reconstruction threshold parameter is set; the fragment state synchronization mechanism ensures network consistency and prevents key concentration and adjacent node independent reconstruction risks.

[0082] The network communication quality and constraint parameter violation are continuously monitored, and when trustworthiness anomalies and / or serious constraint parameter violations are detected, the fragment redistribution and entanglement recovery processes are triggered; specifically:

[0083] Fragment redistribution includes: when the trigger condition is detected, start the fragment redistribution decision program, re-evaluate the security level of each node based on the current network quality, trustworthiness distribution and constraint parameter state; perform fragment recovery phase, send fragment recovery instructions to nodes with abnormal trustworthiness or substandard network quality, requiring them to securely transmit the stored key fragments to designated trusted nodes through encrypted channels; implement new fragment distribution calculation, based on the updated node security evaluation results and network topology conditions, recalculate the optimal fragment distribution scheme to ensure the security and availability of the system are improved; perform fragment redistribution operation, distribute the recalculated fragment distribution scheme to each node and require each node to confirm the fragment reception status and storage integrity; establish a fragment redistribution verification mechanism to verify the effectiveness of the new fragment distribution scheme through key reconstruction testing to ensure that the complete key can be correctly reconstructed when needed; update the fragment distribution mapping table and access control strategy for the entire network to ensure that all nodes can obtain the latest fragment distribution information;

[0084] The entanglement recovery process includes: when detecting that the quality of the quantum entanglement connection decreases or is interrupted, analyzing the current state and degradation degree of the entangled state through quantum state tomography technology; implementing entanglement quality evaluation, measuring key indicators such as entanglement fidelity, Bell state purity and quantum correlation strength, and determining whether the entanglement connection can still maintain effective quantum trust degree measurement function; performing entanglement purification operation, for the entangled state that is partially degraded but can be repaired, using quantum entanglement purification protocol to remove noise and decoherence effect, and recovering the purity and correlation strength of the entangled state; when the entangled state cannot be recovered through purification, starting the entanglement reconstruction program, generating new entangled photon pairs and distributing them to related nodes through quantum channels to establish new quantum entanglement connection; establishing entanglement recovery verification mechanism, verifying the quality of the newly established or recovered entanglement connection through Bell inequality test, and ensuring that it can support reliable quantum trust degree measurement; updating the entanglement network topology graph and trust degree measurement parameters, ensuring that the trust degree evaluation system based on quantum entanglement can operate normally, and providing reliable trust degree basis data for key fragment management.

[0085] The present application provides a comprehensive dynamic security solution for manufacturing supply chain, realizes precise permission control and key distribution from concept design to after-sales service whole process, and refers to Figure 2 Through time sequence stage management driven by quantum state evolution operator, the access permission level is automatically adjusted according to the actual progress of the manufacturing process, which guarantees high security while taking into account the collaboration efficiency. The trust degree evaluation system constructed by quantum entanglement connection technology combines quantum correlation at the physical layer and actual business performance, providing an unforgeable theoretical basis for node trust evaluation and improving the transparency and reliability of supply chain partnership. The complex manufacturing constraint parameters are quantumized modeling, and the multi-dimensional constraint conditions of production efficiency, time beat, logistics state and quality risk are accurately described by Pauli operator and Hamilton operator, realizing real-time monitoring and prediction analysis of the manufacturing process. Quantum approximate optimization algorithm ensures the optimal distribution of key fragments in the network, balances multiple objectives such as system security, communication cost and load balancing, and avoids single point failure and key concentration risk.

[0086] Embodiment two:

[0087] The present application provides a kind of dynamic key fragment storage management method for multi-cloud architecture, it is applied to a kind of dynamic key fragment storage management system for multi-cloud architecture, and refers to Figure 3;Specific content includes a time sequence mapping module for dividing the manufacturing process of a manufacturing supply chain into multiple time sequence stages corresponding to different data access permission levels and security requirements, and constructing the corresponding quantum state evolution operator; a quantum key space-time evolution protocol construction module for driving the evolution of the key state between different time sequence stages through the quantum state evolution operator according to the progress of the manufacturing process, and dynamically adjusting the key access permission; a quantum trust evaluation network construction module for establishing quantum entanglement connection between supply chain nodes, quantifying the trust degree between nodes through correlation measurement of quantum entangled states, and dynamically adjusting the allocation weight of key fragments based on the trust degree change; a manufacturing constraint awareness module for collecting constraint parameters such as production rhythm, equipment state, logistics delay and quality index in the manufacturing process, and converting the constraint parameters into quantum state constraint conditions; a key fragment reconstruction module for recalculating the key fragment distribution scheme based on the quantum state constraint condition and the trust degree when detecting the change of the constraint condition, and synchronously distributing to each supply chain node; a fragment state synchronous updating module for continuously monitoring the network communication quality and the constraint parameter violation, and triggering the fragment redistribution and entanglement recovery process when detecting the trust degree anomaly and / or the constraint parameter serious violation.

[0088] The construction process of the quantum entanglement connection also includes a multi-layer quantum error correction and fault tolerance mechanism, specifically including: encoding and protecting the quantum entangled state by using quantum stabilizer code, improving the stability of the quantum state in long-distance transmission and long-term storage through redundant encoding; establishing a quantum error detection mechanism, periodically measuring the syndrome of the quantum state of each node, identifying quantum decoherence, bit flip and phase flip error types; when detecting quantum state error, starting an automatic error correction protocol, performing corresponding Pauli operator operation for error correction according to the measurement result of the error syndrome; a multi-layer fault tolerance architecture is established, and the logical quantum bits are distributed and stored on multiple physical nodes, and when a single node fails completely, the quantum state can be reconstructed through the redundant information of other nodes to ensure the continuous availability of the quantum trust degree measurement mechanism.

[0089] The present application provides a multi-layer quantum error correction and fault tolerance mechanism, which improves the anti-interference ability and stability of the quantum entangled state in a complex manufacturing environment through quantum; by actively identifying and repairing common quantum errors such as quantum decoherence and bit flip, the continuous accuracy of quantum trust degree measurement is ensured; when a single node fails, the multi-layer fault tolerance mechanism can quickly reconstruct the quantum state through distributed redundant information to avoid the collapse of the entire trust degree evaluation system; effectively solve the noise interference and equipment failure problems faced by quantum technology in actual industrial applications.

[0090] The trust degree acquisition method and weight distribution adjustment process further include a multi-dimensional security situation awareness and early warning mechanism, specifically including: establishing a multi-dimensional security monitoring system for supply chain nodes, collecting multi-source security data such as network traffic anomalies, system resource occupation rate, access behavior pattern changes, and manufacturing equipment status in real time; using a deep learning algorithm to analyze the multi-dimensional security data, establishing a security situation assessment model for the supply chain network, and quantifying the security risk level of each node and the entire network; combining the quantum trust degree measurement results and the security situation assessment results, establishing a dual verification mechanism, when the quantum trust degree and the security situation assessment are inconsistent, triggering a deep security check process; establishing a security threat early warning system, predicting potential security threats based on the trend of the security situation, and proactively adjusting the key fragment distribution strategy before the threat actually occurs, to achieve preventive security protection; when a serious security threat is detected, an emergency response mechanism is started, including temporarily isolating high-risk nodes, enabling backup key fragments, and rebuilding secure quantum entanglement connections.

[0091] In this embodiment, a three-dimensional supply chain security protection system is constructed through the multi-dimensional security situation awareness and early warning mechanism, and more comprehensive and accurate threat identification capability is achieved by fusing quantum trust degree measurement and traditional network security monitoring means. Intelligent fusion analysis of multi-source security data by a deep learning algorithm can discover complex security threats that cannot be identified by single-dimensional monitoring; the dual verification mechanism effectively avoids the risk of misjudgment that may exist in single trust degree evaluation methods, improving the reliability of security assessment; the early warning system enables the system to proactively adjust the protection strategy before the security threat actually occurs, changing passive defense to active protection; the emergency response mechanism ensures the rapid recovery capability of the system in the case of serious threat; and the security protection level and emergency disposal efficiency of the manufacturing industry supply chain key management system are improved.

[0092] The process of continuously monitoring network communication quality includes: deploying distributed network quality monitoring nodes to collect network performance indicators such as network delay, packet loss rate, bandwidth utilization, connection stability, and transmission error rate between each supply chain node in real time; normalizing and weighting the collected network performance data to calculate the network communication quality score between each node pair; setting a network quality monitoring threshold system, including network delay warning threshold, packet loss rate danger threshold, bandwidth saturation threshold, and connection interruption frequency limit, recording abnormal events when any indicator exceeds the corresponding threshold; establishing a network quality historical trend analysis mechanism to analyze the trend of network performance changes through a sliding window algorithm to identify early signs of network quality deterioration; when detecting continuous deterioration or sudden serious decline in network communication quality, generating a network quality anomaly alarm and triggering corresponding countermeasures.

[0093] The constraint parameter violation monitoring process comprises: continuously collecting key manufacturing indicators such as production rhythm deviation, equipment operation efficiency, logistics delivery delay, product quality pass rate and inventory level; setting a multi-level constraint parameter threshold system, including a normal operation range, a mild abnormality range, a moderate violation range and a serious violation range, and setting different response strategies for each range; using statistical process control methods to analyze the change pattern of the constraint parameter, identify the root cause of the parameter deviation from the normal range, and distinguish between random fluctuations and systematic deviations; establishing a constraint parameter correlation analysis mechanism, when an abnormality of a constraint parameter is detected, automatically analyzing the influence degree and propagation path of the related parameters; when the constraint parameter violation reaches the moderate violation level, a warning information is generated, and when it reaches the serious violation level, an emergency response process is triggered immediately to ensure the stability of the manufacturing process and the coordination and unity of data security.

[0094] The trust degree anomaly detection process comprises: establishing a trust degree baseline model, learning the trust degree fluctuation range and change pattern of each supply chain node in the normal operation state through historical data; using an anomaly detection algorithm to analyze real-time trust degree data, identifying abnormal trust degree changes that exceed the normal fluctuation range, including sharp decline, abnormal fluctuation and long-term depression; establishing a trust degree anomaly classification mechanism, dividing the abnormality into three categories: quantum trust degree anomaly, business trust degree anomaly and comprehensive trust degree anomaly, and setting different processing strategies for each type of anomaly; implementing a trust degree cross-validation mechanism, when an anomaly of the trust degree of a node is detected, cross-checking the trust degree of other nodes associated with the node to confirm the authenticity and influence range of the anomaly; establishing a trust degree anomaly impact assessment system to analyze the influence of trust degree anomaly on the security of the entire supply chain network, and determining whether to immediately take key fragment redistribution measures.

[0095] In the embodiment, a comprehensive security monitoring system covering network quality, manufacturing constraints and trust degree evaluation is constructed through continuous monitoring and abnormality handling mechanisms, realizing the transition from passive response to active prevention; through distributed monitoring nodes and multi-dimensional data fusion analysis, the system can real-time perceive the running state changes of the supply chain network, and timely identify potential security threats and performance anomalies; the multi-level threshold system and statistical process control method ensure the accuracy and timeliness of anomaly detection, avoiding false positives and false negatives; the trust degree cross-validation mechanism and correlation analysis enhance the reliability of anomaly judgment, improving the robustness of the system; the automatic execution capability of fragment redistribution and entanglement recovery process enables the system to have strong self-healing function, and can quickly recover to normal operation state under abnormal conditions, providing reliable quantum security guarantee for the digital transformation of manufacturing supply chain.

[0096] Those skilled in the art can easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A dynamic key sharding storage management method for multi-cloud architectures, characterized in that, include: The manufacturing process of the manufacturing supply chain is divided into multiple time-series stages, corresponding to different data access permission levels and security requirements, and corresponding quantum state evolution operators are constructed. Based on the progress of the manufacturing process, the key state is driven to evolve between different time stages by quantum state evolution operators, and the key access permissions are dynamically adjusted. Establish quantum entangled connections between supply chain nodes, quantify the trust between nodes by measuring the correlation of quantum entangled states, and dynamically adjust the allocation weight of key shards based on changes in trust. Collect constraint parameters of production cycle time, equipment status, logistics delay and quality indicators during the manufacturing process, and convert the constraint parameters into quantum state constraint conditions; When a change in constraints is detected, the key sharding distribution scheme is recalculated based on the quantum state constraints and the degree of trust, and then synchronously distributed to each supply chain node. Continuously monitor network communication quality and constraint parameter violations. When an abnormal trust level and / or a serious violation of constraint parameters are detected, trigger the fragment redistribution and entanglement recovery process.

2. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The process of dividing the time sequence into stages includes: collecting production order data, bill of materials data, and process route data; analyzing the data types and sensitivity information involved in each stage; and dividing the manufacturing process into seven time sequence stages based on the stage division criteria: conceptual design stage, detailed design stage, process planning stage, production and manufacturing stage, quality control stage, packaging and logistics stage, and after-sales service stage.

3. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The construction process of the quantum state evolution operator is as follows: collecting characteristic parameters of the timing stage, including data sharing frequency, number of collaborating partners, information flow complexity and security risk level, as the basic input for quantum state construction; mapping the high confidentiality characteristics of the conceptual design stage to the Z-direction spin-up state; and mapping the multi-party collaboration characteristics of the manufacturing stage to the X-direction spin state. The information transparency characteristics of the packaging and logistics stage are mapped to the spin state in the Y direction; the key management characteristics of each stage are characterized by weighted combination of spin operators in different directions, forming a multi-dimensional quantum state evolution operator.

4. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The process of driving key state evolution and dynamic permission adjustment by the quantum state evolution operator includes: monitoring milestone events; when a milestone event is detected, applying the corresponding quantum state evolution operator to perform time evolution calculation on the current key state to obtain the quantum state probability distribution; based on the quantum state probability distribution, redetermining the access permission level of each supply chain node; and updating the key sharding reconstruction threshold and the sharding quota of each node according to the new access permission level allocation result, generating a new access control policy and issuing it.

5. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The process of constructing the quantum entanglement connection includes: deploying quantum communication devices at each supply chain node and establishing a quantum channel between nodes through fiber optic links; generating entangled photon pairs using a parametric down-conversion process, sending one photon of the entangled photon pair to node A and the other photon to node B, establishing a quantum entanglement association between the two nodes; implementing an entanglement distribution protocol to extend the entanglement connection to the entire supply chain network through cascading, with the core manufacturing enterprise acting as the central node to establish direct entanglement with first-tier suppliers, and the first-tier suppliers then establishing entanglement connections with second-tier suppliers; and establishing an entanglement maintenance mechanism to periodically check the entanglement fidelity, automatically initiating entanglement purification when the fidelity falls below a set threshold.

6. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The trust acquisition method and weighting adjustment process include: performing polarization measurements on entangled photon pairs between nodes, statistically analyzing the correlation results, and calculating the degree of violation of Bell's inequality as the basic score for quantum trust; collecting business behavior data of nodes, including historical order fulfillment rate, product quality compliance rate, on-time delivery rate, frequency of security incidents, and response efficiency, and establishing a business trust evaluation index system; weighting the quantum trust score and the business trust score according to their respective weight ratios to obtain a comprehensive trust score; and adjusting the key sharding allocation weights of each node based on the comprehensive trust score results.

7. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The steps for converting constraint parameters into quantum state constraints include: collecting real-time manufacturing constraint data and performing standardization processing; converting equipment efficiency into efficiency loss rate, cycle time deviation into relative deviation ratio, delivery delay into delay severity index, and non-conformity rate into quality risk coefficient; mapping the standardized constraint parameters to eigenvalues ​​of quantum operators; converting classical constraints into energy eigenstate descriptions of quantum states by constructing constraint Hamiltonian operators; establishing the correspondence between constraint operators and Pauli matrices; mapping production efficiency constraints to Z-direction operators, time cycle time constraints to X-direction operators, logistics delay constraints to Y-direction operators, and quality constraints to tensor product forms of multiple operators, forming complete quantum state constraints.

8. The dynamic key sharding storage management method for multi-cloud architecture according to claim 1, characterized in that: The process of obtaining the key sharding distribution scheme includes: establishing a multi-objective optimization model for key sharding distribution based on the current quantum trust assessment results and quantum state constraints. The optimization objectives include maximizing system security, minimizing communication costs, and optimizing load balancing. A quantum approximation optimization algorithm is used to solve the sharding distribution problem, setting the number of qubits to the logarithm of the number of network nodes, and setting the optimization layer to three layers. The global optimal solution is found through variational parameter iteration. Distribution constraints are applied during the optimization process to ensure that the number of shards stored on a single node does not exceed one-third of the total number of shards, that the combination of shards from any adjacent node cannot independently reconstruct the complete key, and that high-trust nodes are given priority in allocating key shards. The final sharding distribution matrix is ​​output, specifying the storage location of each key shard on different nodes, generating corresponding access permission tables and reconstruction threshold parameters, and establishing a real-time synchronization mechanism for sharding status to ensure network-wide consistency.

9. A dynamic key sharding storage management system for multi-cloud architectures, characterized in that, include: The time-series mapping module is used to divide the manufacturing process of the manufacturing supply chain into multiple time-series stages, corresponding to different data access permission levels and security requirements, and to construct corresponding quantum state evolution operators. The quantum key spatiotemporal evolution protocol construction module is used to drive the key state to evolve between different time stages according to the progress of the manufacturing process through quantum state evolution operators, and dynamically adjust the key access permissions. The quantum trust assessment network construction module is used to establish quantum entangled connections between supply chain nodes, quantify the trust between nodes by measuring the correlation of quantum entangled states, and dynamically adjust the allocation weight of key shards based on changes in trust. The manufacturing constraint sensing module is used to collect constraint parameters of production cycle time, equipment status, logistics delay and quality indicators during the manufacturing process, and convert the constraint parameters into quantum state constraint conditions. The key sharding reconstruction module is used to recalculate the key sharding distribution scheme based on quantum state constraints and trust level when a change in constraints is detected, and then synchronously distribute it to each supply chain node. The sharding status synchronization update module is used to continuously monitor network communication quality and constraint parameter violations. When an abnormal trust level and / or a serious violation of constraint parameters are detected, the sharding redistribution and entanglement recovery process is triggered.

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