Spatial quantum communication dynamic optimization method

Abstract

The invention discloses a dynamic optimization method for space quantum communication, and particularly relates to the technical field of quantum communication. The space-time continuous optimal distribution of quantum entanglement resource blocks is realized through the steps of load difference monitoring, rotation driving path selection, four-image base intersection verification, path dynamic mapping, task density driving scheduling and the like. The system constructs a dynamic mapping table based on a path state, and introduces a stationary path marking mechanism to realize intelligent, low-delay and high-fidelity transmission of resource scheduling; according to the method, the stability, the scheduling efficiency and the resource utilization rate of quantum communication in an on-orbit complex environment are improved, and the method has good engineering expandability and self-adaptive capability.

Description

Technical Field

[0001] This invention relates to the field of quantum communication technology, and more specifically to a dynamic optimization method for space quantum communication. Background Technology

[0002] Existing technologies suffer from the following shortcomings: In space quantum communication, the high-speed relative motion and orbital drift between nodes cause instantaneous path degradation during channel state transmission. This phenomenon manifests as a sudden deterioration of the originally optimal octagonal quantum state transmission path within certain time windows, even leading to irreversible decoupling of entangled states between qubits, severely impacting the fidelity and error correction efficiency of the overall communication system. Traditional path selection algorithms cannot identify and switch to the optimal path in real time, lacking dynamic robustness and spatiotemporal adaptability, thus limiting the practical deployment of multi-node space quantum networks. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic optimization method for space quantum communication to address the shortcomings of the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a dynamic optimization method for space quantum communication, comprising: The payload difference parameter ΔL between each communication node pair in the space communication network is acquired in real time, and the quantum state transfer operation is determined based on ΔL. Set a preset threshold value θ. When ΔL>θ, execute the collapse-de-stationary quantum state transition protocol. Obtain the current curl tensor and calculate its F-norm. If the F-norm is greater than θ, then enable the curl-driven path selection mechanism and select the set of node links with the highest path fidelity in the curl-driven path. Spatiotemporal partitioning crossover verification is performed on the candidate path set. Based on the four-element basis set, cross-operation is performed on the entangled inner product between nodes in the path. The inner product error of any pair of elements is less than 10⁻ 6 If so, the path is valid; A dynamic path mapping table is constructed in the legal path, and a temporal mapping is performed on the entangled resource blocks according to the mapping table to generate a continuous spatiotemporal distribution path. Based on the path and system task density function, the optimal scheduling order of entangled resource blocks is calculated to achieve dynamic resource reallocation; Output the updated path index table and mark the current path state as a stationary state for use in the next round of quantum communication iteration.

[0005] Preferably, the step of obtaining the load difference parameter ΔL and determining whether a quantum state transition operation is triggered includes: Based on the node physical load monitoring unit in the quantum relay link, the multi-dimensional time-series operation indicators of each node are obtained, including communication frequency, entangled state transmission rate and average energy consumption fluctuation. The node state vector Σi is generated through a weighted aggregation function, where Σi represents the comprehensive operating load of the i-th node in the current time slot. For any two communication nodes i and j, calculate their load difference parameter ΔLij, which is defined as the product of their state vector Euclidean distance and distance weighting coefficient k, where k is dynamically updated by the estimation model of the path loss of the channel between the two nodes and the relative motion speed. Determine whether ΔLij exceeds the dynamic threshold η(τ) set by the system, where η(τ) is the load threshold that is dynamically adjusted with the task time density function. If ΔLij>η(τ), then trigger the quantum state migration operation from the stationary state to the detached state associated with this node.

[0006] Preferably, the step of executing the collapse-de-resident quantum state transition protocol includes: A preset quantum load threshold θ is used as the Taiji value, and θ is dynamically adjusted in each scheduling cycle in combination with the current operating state of the system. When the load difference parameter ΔL of any node pair exceeds the value of θ, the quantum survival probability of entangled particle pairs in the current stationary quantum link is estimated. This probability is evaluated by Bayesian inference based on their residence time, current link energy fluctuation and temperature perturbation conditions. If the survival probability is lower than the system tolerance threshold, a state transition warning is triggered. Based on the warning state, the collapse-de-stationary quantum state transition protocol is executed.

[0007] Preferably, obtaining the current energy curl tensor and calculating its F-norm includes: In the topology of a space quantum communication network, the rate of change of the path energy potential of each node per unit time is obtained, and the quantum potential field gradient matrix P on each link is constructed. Perform three-dimensional curl operations on matrix P to obtain the curl tensor, that is, calculate the partial derivatives of the entangled gradients in the paths of adjacent nodes in each spatial dimension in a non-self dimension to form a curl vector field. The overall F-norm is calculated by summing the squares of the moduli of each component in the curl tensor. Specifically, the energy density index of the curl is obtained by summing the squares of all elements of the three-dimensional matrix formed by each curl component and taking the square root. The calculated F-norm is compared with the Taiji value θ. If it is greater than θ, the system is considered to be in the curl-dominant stage, and the earthquake-driven curl-driven path selection mechanism is activated.

[0008] Preferably, the spatiotemporal segmentation crossover verification includes: A four-element basis set consisting of four orthogonal quantum states is constructed and named Spring Elephant State, Summer Elephant State, Autumn Elephant State and Winter Elephant State respectively. This set is pairwise orthogonal in Hilbert space and serves as the standard entangled state template of the system in different time slots. The path to be verified is divided into equal-length segments according to the time axis, and each segment represents a communication window in the system scheduling cycle. For the node pair state in each time segment, the inner product operation is performed with two or more states in the four-dimensional basis to extract its projection value under the standard basis. Cross-compare the node states in different spatiotemporal slices within the same path to form the inner product error matrix between all pairs of objects, and calculate the deviation between the inner product value of each pair of objects and the theoretical value. When the inner product error value of any pair in the error matrix is ​​less than 10⁻ 6 When the path passes the cross-validation check, it is considered a valid path and marked as such.

[0009] Preferably, the step of constructing the path dynamic mapping table includes: Extract the path identifier, node number, path entanglement fidelity, load change rate, and curl direction component of each node pair from the valid paths that pass the cross-validation test, and construct a path state vector set. Cluster analysis is performed on the state vector of each path, and paths with similar structures and synchronous change trends are grouped into the same mapping cluster; Based on the scheduling priority of node pairs within the path cluster, the current quantum load distribution of the system, and the predicted resource requirements, the path set is encoded to generate a ternary index mapping table, which corresponds to the time slice number, node pair identity identifier, and quantum entanglement resource block number, respectively. The generated path dynamic mapping table is embedded in the communication scheduling controller to drive the subsequent resource distribution module to complete the scheduling and locking of entangled blocks.

[0010] Preferably, the step of performing the timing mapping of entangled resource blocks includes: The path dynamic mapping table is invoked to find the corresponding legal path number and node pair information within the current time slice, and to determine the resource transmission direction and target node within the time slot; Match the transmission request information of entangled resource blocks with the time-series weight function associated with the path in the mapping table; For each entangled resource block to be distributed, weighted queuing is performed according to its priority, the availability of the corresponding path in the mapping table, and the current system state, and its optimal scheduling time window and resource injection time are calculated. Once the resource blocks have been queued, they are sent to the designated path queue and injected in a time-sharing manner according to the mapping table controller, thus realizing the continuous spatiotemporal resource distribution on the path.

[0011] Preferably, the process of constructing the task density function includes: Using time as the basic window, statistics are compiled on the task request frequency, target bandwidth, and communication level indicators within each communication cycle to form a time-varying task distribution map. On each path segment, the local task density value is calculated based on the task statistics of the corresponding region and the path link capacity, and a complete task density function is generated. By introducing a sliding window average and an exponential weighting factor, the task density function is smoothed. The processed task density function is input into the scheduling engine as a real-time decision-making reference for dynamic resource sorting and path allocation.

[0012] Preferably, the step of calculating the optimal scheduling order of entangled resource blocks includes: Obtain the current task density function of the system and establish the scheduling pressure coefficient of each entangled resource block. This coefficient consists of three parts: the value of the task density function on its own path, the remaining fidelity of the resource block, and its allocated delay time. The three are obtained by weighted average combination, with the task density having the largest weight. Sort all resource blocks in ascending order according to their scheduling pressure coefficients to obtain the optimal scheduling sequence of entangled resource blocks in the current communication cycle. The sorted scheduling list is input into the scheduling controller as a priority reference for time-sharing transmission and path resource configuration, thereby realizing the dynamic scheduling under task-driven conditions.

[0013] Preferably, the step of outputting the updated path index table and marking the current path state as stationary includes: After the current communication cycle ends, the system extracts the running result data of each path from the path dynamic mapping table and the scheduling execution log, performs a stability evaluation on each path, and if the path meets the stability evaluation criteria, sets a stability flag for the path in the index table and updates its status code to a stable path; and synchronously writes the updated path index table into the system's path scheduling cache and redundant control nodes.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. The space quantum communication dynamic optimization method provided by this invention introduces technical modules such as curl-driven path selection, four-dimensional basis cross-reference, task density function scheduling and sorting, and path stationary state management. This achieves high-fidelity, spatiotemporal continuity, and dynamically controllable distribution of quantum entangled resources, effectively overcoming the insufficient adaptability of traditional methods under dynamic path changes, link disturbances, and resource imbalances. During scheduling, the system can flexibly select the optimal path and quickly respond to environmental changes based on node states, adaptive task pressure, and quantum state change trends, significantly improving communication stability and scheduling efficiency.

[0015] 2. This invention achieves intelligent reuse and state-memory-based scheduling of verified paths by constructing a dynamic path mapping table and a stationary state index mechanism, significantly reducing redundant computational load and shortening scheduling response time. Simultaneously, the introduction of soft undo and task feedback mechanisms enhances system fault tolerance and link recovery capabilities. The overall solution possesses high precision, high robustness, and engineering practicality, making it suitable for future multi-satellite networking and multi-task concurrent space quantum communication networks. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a mind map of the method of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0019] For examples, please refer to Figure 1 As shown in this embodiment, a dynamic optimization method for space quantum communication includes: The payload difference parameter ΔL between each communication node pair in the space communication network is acquired in real time, and the quantum state transfer operation is determined based on ΔL. Set a preset threshold value θ. When ΔL>θ, execute the collapse-de-stationary quantum state transition protocol. Obtain the current curl tensor and calculate its F-norm. If the F-norm is greater than θ, then enable the curl-driven path selection mechanism and select the set of node links with the highest path fidelity in the curl-driven path. Spatiotemporal partitioning crossover verification is performed on the candidate path set. Based on the four-element basis set, cross-operation is performed on the entangled inner product between nodes in the path. The inner product error of any pair of elements is less than 10⁻ 6 If so, the path is valid; A dynamic path mapping table is constructed in the legal path, and a temporal mapping is performed on the entangled resource blocks according to the mapping table to generate a continuous spatiotemporal distribution path. Based on the path and system task density function, the optimal scheduling order of entangled resource blocks is calculated to achieve dynamic resource reallocation; Output the updated path index table and mark the current path state as a stationary state for use in the next round of quantum communication iteration.

[0020] In space quantum communication systems, multiple communication nodes (such as space satellites and orbital repeaters) maintain entangled state sharing through quantum channels. Due to factors such as orbital drift, attitude changes, and different energy management strategies among nodes, the communication load per unit time can vary significantly. Failure to adjust in time will affect transmission stability, fidelity, and system lifetime. Therefore, it is necessary to monitor the operational status of each communication node in real time and analyze whether its quantum state needs to migrate to a better path based on this status.

[0021] This invention first proposes the definition of a "node state vector". The system collects multi-dimensional time-series operating parameters of the nodes through load monitoring units deployed at each communication node, specifically including: Frequency of communication requests per unit time; success rate of quantum entangled state transmission; average energy consumption per unit time; system redundancy utilization (i.e., the ratio of the number of paths a node participates in to the total number of paths); local gradient information of channel delay variation.

[0022] The five types of data mentioned above are combined to form a high-dimensional vector, denoted as Σi, representing the operating state of communication node i within the current time slice. This vector can be normalized using a weighted aggregation model to ensure that different physical quantities have a unified comparison scale.

[0023] To quantify the load difference between two communication nodes within the current communication cycle, this invention designs a calculation formula that couples distance and channel correction. The approach is as follows: First, calculate the Euclidean distance between the state vectors Σi and Σj of the two nodes to characterize their multidimensional load difference; then, introduce a path weighting factor kij, which comprehensively considers the following two influencing factors: the path loss coefficient of the current channel between the node pair, which can be obtained through the real-time channel estimation module; and the relative motion velocity of the node pair, which can be calculated from the onboard inertial measurement system and the orbital dynamics model.

[0024] Taking all the above factors into account, the load difference parameter ΔLij is defined as the product of the path weighting factor kij and the Euclidean distance between the node state vectors. That is, ΔLij equals kij multiplied by the Euclidean distance between the node state vectors Σi and Σj. The path weighting factor kij can be dynamically updated according to the following logic: when path loss increases or relative speed increases, kij will be increased, thereby strengthening the weight of that path in the load difference; conversely, it will be decreased.

[0025] After the load difference parameter ΔLij is calculated, the system compares it with a set of preset dynamic threshold functions η(τ). Here, η(τ) is a load threshold value dynamically generated based on communication task density and system resource utilization, and τ represents the current time period.

[0026] When the task density is high (i.e., the number of concurrent communication requests is large), the value of η(τ) is reduced to improve the system's sensitivity to changes in node load; when the communication status is stable, η(τ) is automatically increased to avoid excessive migration causing resource fluctuations; η(τ) can also be remotely updated by the ground control station to achieve integrated control of ground intervention and on-board autonomy.

[0027] The decision logic is as follows: if the ΔLij of any node pair is greater than the dynamic threshold η(τ), the system will trigger a protocol migration of the quantum state associated with that node pair from a stationary state to a de-stationary state. Specifically, a stationary state refers to an entangled state in the communication path that remains in a certain node for a long time to maintain sharing; while a de-stationary state indicates that resources will be transferred to a link with a lower load for hop relay, thereby avoiding communication instability caused by nodes with excessive energy consumption or those that are about to be overloaded.

[0028] Once the migration conditions are met, the system invokes the "collapse-deresident quantum state transition protocol" to transfer the current entangled resources to the candidate backup path within the shortest possible time delay, and records the change in the path index table for use in the next iteration.

[0029] Traditional communication load determination often employs a static threshold model, which sets a fixed load difference threshold to determine whether to trigger a scheduling operation. However, in space quantum communication networks, node motion states, path interference, and entangled link states are highly dynamic, and a fixed threshold can easily lead to misjudgments or delayed responses.

[0030] To address this, the present invention introduces a dynamically adjustable quantum load threshold θ, referred to as the "Taiji value," to flexibly determine whether a state switching state has been entered. The initial value of this threshold can be set according to the factory parameters of the communication system, for example, set to the historical mean of ΔL plus one standard deviation.

[0031] After the system is running, the θ value will be dynamically updated based on the following three indicators: ΔL variation trend: The load difference ΔL between nodes within a certain time window in the past is fitted with a trend. If the change is drastic, θ is appropriately reduced to enhance sensitivity. Node load fluctuation frequency: The main frequency component of node load change is extracted by Fourier analysis. When the frequency is high, the θ value is reduced to capture frequent imbalances. Entanglement fidelity decay rate: Fit the decay curve to the historical quantum fidelity data along the communication path. If the decay is accelerated, then decrease the θ value.

[0032] The adjustment of the θ value is accomplished by an exponential moving average algorithm to smooth out the impact of instantaneous fluctuations. Specifically, in each scheduling period t, the Taiji value θt is equal to the previous period's θ(t-1) multiplied by the weighting coefficient α, plus the currently evaluated index composite function value multiplied by (1−α), where α is between 0.7 and 0.9.

[0033] Through the above mechanism, Taiji value possesses adaptive capabilities that combine responsiveness and stability.

[0034] Once the load difference ΔL of any node pair exceeds the dynamic θ value, the system does not immediately perform state transition. Instead, it further calls the entangled state evolution prediction module to perform state survival assessment on the quantum states in the current stationary path to ensure the necessity and rationality of the transition operation.

[0035] The core of the evaluation lies in calculating the "survival probability of entangled particle pairs in a stationary state." This probability represents the likelihood that the entangled state will maintain a high-fidelity state within a specified transmission time limit under current load and interference conditions. It is mainly determined by the following three factors: Duration of stay: The longer the entangled state exists in the original path, the higher the risk of decoherence; Energy fluctuation level: including the average variation and instantaneous peak value of energy consumption at nodes along the path; Environmental thermal disturbance: the temperature differences and random thermal fluctuations experienced by the quantum state during its transmission between nodes in the path.

[0036] The system uses Bayesian inference to model these factors as a prior distribution, and updates the posterior distribution by monitoring data in real time, thereby calculating the "fidelity stability probability" of the particle pair. If this probability is lower than the tolerance limit (e.g., 95%), the system determines that the stationary state is at risk of decay and enters the quantum migration early warning stage.

[0037] Upon entering the early warning phase, the system will invoke the path selection module of the collapse-destationary quantum state transition protocol to find alternative non-stationary relay paths, enabling rapid migration of entangled states. This invention innovatively introduces a "thermal perturbation stability factor" as a core indicator for path selection.

[0038] This stability factor measures the coupling strength between each node in the path and the environmental temperature difference and link load within a specific time window, and is defined as follows: For a path, the thermal disturbance weight Hi of all node pairs involved will be calculated based on the current ambient temperature Ti and the node load Li; The system uses a weighted average method, combined with the historical stability scores of the nodes, to obtain the thermal perturbation stability factor Fpath for the entire path; The higher the value, the stronger the path's resistance to interference, making it suitable for stationary state migration.

[0039] The system selects the path with the largest Fpath from the candidate path set as the target path, and then calls the photon transition control unit to migrate the stationary quantum resources to the new path, realizing the switching between stationary and departing states. The transition process uses controlled photon pulses to control entangled particles to jump to the new node, achieving seamless path switching.

[0040] At the same time, update the path index table and set the original path status to "pending recovery" so that it can be re-included in the scheduling resources after load balancing.

[0041] In this invention, a quantum communication path field is first defined, which is the set of all feasible paths in the system and their corresponding energy gradients. Each link has a corresponding quantum potential energy value in a certain time slot. This potential energy is modeled by factors such as the entanglement fidelity of the path, channel loss, and load fluctuation, and is denoted as P(i,j), representing the path energy state from node i to node j.

[0042] To obtain curl information in the system, we treat the path energy P as a three-dimensional tensor structure, construct the path gradient matrix in a three-dimensional coordinate system, and calculate the curl using the following method: For each path segment, calculate the energy gradient change of its adjacent paths in the non-principal direction dimension, i.e.: The partial derivatives of the rate of change of energy along the X-axis on the Y and Z axes; Energy perturbations along the Y-axis on the X and Z axes; The path along the Z-axis varies along the X and Y axes.

[0043] The above partial derivative results are organized into a three-dimensional curl tensor, where each position corresponds to a path curl vector, representing the rotational trend of path energy perturbation in local space.

[0044] This curl tensor reflects the structural information of path perturbations in the network, especially the overall effect of link replacement on system perturbations during quantum state transitions, and can more accurately reveal whether the system is in a state of drastic change.

[0045] After obtaining the curl tensor, this invention employs a tensor norm calculation method to evaluate the overall perturbation energy density of the system.

[0046] The F-norm, or Frobenius norm, was originally used to find the square root of the sum of squares of elements in a matrix. To adapt it to tensor fields, we sum the squares of the magnitudes of all curl vectors in the three-dimensional curl tensor and then take the square root to obtain the global F-norm, which serves as a quantitative indicator of global curl perturbation.

[0047] The computational logic can be expressed as: For each vector component in the curl tensor (i.e., the curl value of a certain path segment), calculate the square of its magnitude. Sum the squares of all moduli; The system's curl energy density, or F-norm, is obtained by taking the square root of the sum.

[0048] The F-norm is used as a reference value for comparison with the "Tai Chi" threshold θ. If it exceeds θ, it indicates that the current system has a significant curl trend and path reconstruction operation is required.

[0049] When the F-norm is greater than θ, the system enters the "spin-driven" state, triggering the curl-driven path selection mechanism ΦROT, the process of which is as follows: Curl direction analysis and candidate path generation: In the three-dimensional curl tensor, the system filters path segments with consistent curl directions and continuously increasing magnitudes to form an initial path candidate set Λ0. This step ensures that the paths have a "coherent curl structure," that is, consistency in the direction of perturbation, thereby enhancing the stability of path scheduling.

[0050] Entanglement fidelity integral calculation: For each path in Λ0, calculate its entanglement fidelity integral. This integral is obtained by weighted averaging the cosine function of the angle between the entanglement fidelity values ​​of each segment of the path and the curl direction. The more consistent the curl direction is with the propagation direction in the path, the higher the weight.

[0051] Path validity filtering: The system sets a lower limit for entanglement fidelity δfid (e.g., 0.92). Paths with fidelity integrals lower than this value will be eliminated, resulting in the final path set Λ.

[0052] Path uniformity sorting and main path determination: In the set Λ, the path with the most stable growth of the F-norm along the path, i.e. the smallest local perturbation difference, is selected as the current primary path to realize the safe migration and scheduling of quantum entanglement resources.

[0053] This mechanism ensures that the perturbation trend of the path is consistent with the direction of the system's dynamic evolution, thereby improving the long-term stability of the communication link.

[0054] To address the misjudgment of short-period fluctuations in the F-norm, this invention also designs an F-norm correction mechanism based on the time harmonic factor, which is implemented as follows: Disturbance identification: The system continuously monitors the rate of change of the F-norm. If it changes drastically over multiple periods (e.g., greater than 20% for three consecutive periods), the system is judged to be in a disturbance state.

[0055] Harmonization factor introduction: A time harmonic factor μ (typically ranging from 0.3 to 0.7) is introduced to smooth the F-norm difference between the current cycle and historical cycles.

[0056] The corrected formula is calculated by multiplying the difference between the current F-norm and its historical average by μ, adding it to the current F-norm, and taking the average to obtain the smoothed F-norm.

[0057] Further comparison and judgment: Use the smoothed F-norm for comparison with θ to prevent invalid path switching caused by misjudgment of instantaneous peak values.

[0058] This embodiment first defines a four-element basis set as a standard template for the legality of path states in the system. The four-element basis consists of four mutually orthogonal quantum states, named "Spring State," "Summer State," "Autumn State," and "Winter State," respectively. In Hilbert space, these four states satisfy the following conditions: The inner product between any two image bases is zero, meaning they are pairwise orthogonal; Each image basis can serve as a representative of a typical type of entangled state in the system, for example: The spring phase is a Bell state (EPR state). The summer phase is a three-particle GHZ state. The autumn phase is a W-state (weakly entangled asymmetric structure). The winter phase is a symmetrical superposition state (such as GHZ-W superposition).

[0059] During the initial communication deployment phase, the system sets up an image base template and registers it in the image base dictionary of the path selection subsystem for subsequent path mapping and verification.

[0060] To detect the entanglement consistency of the states of each node along the quantum link transmission path, the system divides the path into multiple equal-length segments along the time dimension, each segment being called a "communication time slot window". Within each window, the system extracts the quantum state vectors (e.g., represented in the form of qubit superposition coefficients or density matrices) of all active node pairs in the path.

[0061] The system then performs image-based projection operations on these quantum states, mapping the current nodal state to the orthogonal space containing the four image bases, and obtaining its projection intensity in each image base direction. This projection reflects the degree of consistency between the current nodal state and the standard image state.

[0062] For example, if a node state has the largest projection amplitude with the "Xia Xiang state", then it can be temporarily considered to be similar to the Xia Xiang type structure.

[0063] After completing the image base mapping in each slice, the system performs cross-calculation of the inner product values ​​between the image states of different slices in the same path. This step is called image pair cross-alignment. The specific steps are as follows: Perform dot product calculation on the node state vectors in two different time slots; By using normalization, the inner product value is converted into a similarity index between [0,1], which approximately represents the quantum consistency between two states; Fill the inner product deviation values ​​between all image pairs into the error matrix. The dimension of the matrix is ​​a combination of the number of slices multiplied by the number of image bases.

[0064] The system assumes that, ideally, the inner product between pairs of objects should be 1 (completely identical) or 0 (completely orthogonal). Therefore, the difference between the actual inner product value and the theoretical value is called the "intersection error".

[0065] To determine whether a path is "intersection valid", the system sets a precision threshold ε, typically with an initial value of 10 to the power of -6. If the errors of all pairs of objects in the error matrix are less than this threshold, the path passes the intersection check and is marked as a "valid path".

[0066] This threshold can be dynamically adjusted based on network load, environmental disturbances, and task urgency. For example: During periods of high interference and strong decoherence, the threshold can be relaxed to 10 to the power of -5. In intensive communication phases where high accuracy is required for mission transmission, the speed can be tightened to 10 to the power of -7.

[0067] Through this verification mechanism, the system not only judges the current state of the path, but also indirectly evaluates its quantum structural continuity and logical stability throughout the entire communication window, thereby significantly improving the robustness of path selection.

[0068] To enhance the system's adaptability to changing environments, this invention also introduces a base reconstruction mechanism. The system triggers base updates in the following scenarios: During multiple path selections, the error of the object pair frequently exceeded the limit. Severe disturbances in the external environment, such as solar storms and magnetosphere disturbances; The system load scheduling structure has undergone fundamental adjustments, such as the introduction of new relay nodes.

[0069] The refactoring process includes: Re-collect the current sub-state distribution of the main links in the network; High-frequency entanglement patterns are extracted using principal component analysis (PCA) or quantum clustering algorithms; Based on the above analysis, the four-dimensional basis is redefined to ensure that it still maintains orthogonality; The Schmidt orthogonalization method is applied to adjust the new image basis so that it meets the requirements for orthogonal space construction.

[0070] Once the image base reconstruction is complete, the system will update the image base mapping strategy and cross-validation logic for all paths to ensure continuous and effective verification of path validity.

[0071] This invention first performs structured modeling on the set of legal paths that have passed the cross-validation test, constructing a path state vector set. Each path state vector contains the following key parameters: Unique path number; start and end node IDs; current average entanglement fidelity; rate of change of load between node pairs; curl direction component (e.g., from the analysis results of the curl tensor ∇×P in the main claim); historical trend of communication success rate; resource dwell time (referring to the average time required for entangled resources to complete communication from injection).

[0072] The above information is used to construct a multi-dimensional state vector. Subsequently, the system employs density-based clustering algorithms (such as DBSCAN or K-means) to cluster the path state vectors, identifying path clusters with similar dynamic behaviors or state evolution patterns. This approach structurally aggregates multiple redundant paths in the system, effectively compressing the scheduling space and avoiding abrupt changes in path state during resource injection.

[0073] After clustering is completed, the system generates a triplet index mapping table based on the path cluster structure, where each mapping entry consists of the following triplet: Time slice number (marks the communication scheduling period); Node identification (unique code); Entangled resource block number (which may refer to a pair of particles or logically entangled units).

[0074] The rules for creating the mapping table are as follows: Only paths that are "scheduling feasible" within that period are allocated within each time slice; Each resource block is bound to the optimal time window and path segment based on its priority and distribution delay limit; If multiple nodes compete for the same resource block, the system will calculate a "path weight score" based on path fidelity, success rate, and historical scheduling hit rate, and the node with the highest score will be bound first.

[0075] This table enables the system to achieve refined, temporal, and predictable mapping of resource blocks to paths, significantly improving resource utilization and ensuring path stability.

[0076] After generating the dynamic mapping table, the system distributes and schedules entangled resource blocks on a timed basis during runtime. The specific process is as follows: Path identification and scheduling startup: Before each time slice starts, the system automatically queries the mapping table to obtain the active paths and resource block numbers to be injected within that period.

[0077] Weight function matching: For each path, the system calculates a path time series weight function based on its historical fidelity curve, communication success rate trend and resource dwell time on the path, which represents the scheduling priority of the current path.

[0078] Queue scheduling and injection timing determination: The system takes resource block priority and path weight function as input, and determines the queuing order and injection time of resource blocks through a weighted queuing algorithm to ensure that high-priority tasks are completed first.

[0079] Time-sharing injection and continuous scheduling: According to the scheduling results, entangled resource blocks are sent to the corresponding path queue, and the system controller issues resources with precision down to the fragment clock, achieving "seamless" cross-path continuous distribution.

[0080] This mechanism can avoid problems such as resource injection congestion, path conflicts, and state overload, thereby improving the stability and efficiency of the overall system scheduling.

[0081] Considering the significant changes in path state over time in space communication systems, this invention designs a dynamic update mechanism for the mapping table. The triggering methods are as follows: Periodic trigger mode: The system automatically evaluates path status changes every T communication cycles and decides whether to update; Event-triggered mode: When the system detects a sudden degradation of the path (such as a sharp drop in fidelity, node abnormality, path cross-connectivity failure, etc.), it immediately interrupts scheduling and starts the mapping table update process.

[0082] The update mechanism includes the following steps: Freeze scheduling window: Pause the current path scheduling and lock the resource distribution window to prevent conflicts caused by modifying mappings during scheduling; Generate a new mapping draft: Regenerate the path state vector based on the current path state, re-cluster and construct the ternary index structure to form a new mapping draft; Legality verification: Perform cross-validation on the draft (from claims 7-9) to detect resource conflicts, path switching costs, and minimum scheduling steps; "Soft transition" application: If the draft is legal, the system will activate the new table in the next cycle, while performing migration or buffering operations on the original resource blocks to ensure that the task is not interrupted.

[0083] This dynamic mechanism enables the system to maintain continuous scheduling while providing flexibility to cope with environmental fluctuations and link evolution.

[0084] The system task density function is the scheduling basis of this mechanism, used to characterize the concentration of communication requests on different paths or regions within a certain time period in the network. The construction process is as follows: Task data acquisition: The system collects relevant parameters of all communication requests in the network during each scheduling cycle, including target node location, required entanglement resources, task priority, and expected transmission window. Path association calculation: Map tasks to their associated path segments and accumulate the task density on the path per unit time to form a path-task mapping table. Density function generation: The local task density of a path is obtained by dividing the number of task mappings in a path segment by the path capacity and the time window length. Smoothing: To avoid interference with the global scheduling strategy due to a certain peak instantaneous value, a sliding window averaging and exponential weighted filtering method is used to smooth the density value and form a continuous function.

[0085] The generated task density function is dynamically updated over time as a decision-making basis during the scheduling cycle, reflecting the evolution trend of tasks in the entire network.

[0086] In actual operation, different entangled resource blocks have different usage priorities due to differences in scheduling paths, injection times, transmission delays, and the urgency of the target task. Therefore, this invention introduces a scheduling pressure coefficient to finely sort resource blocks. This coefficient consists of the following three parts: Task density factor: The task density function value on the original planned delivery path of this resource block. The higher the value, the more urgent the path demand and the higher the priority. Resource fidelity margin: Represented by the difference between the current fidelity of a resource and the minimum acceptable fidelity threshold of the system. The lower the margin, the earlier it should be scheduled and used. Delay factor: The number of cycles that have been delayed since the resource was generated to the current cycle, representing the waiting time.

[0087] The three factors are combined according to preset weighting coefficients to form a normalized scheduling pressure value, with higher values ​​indicating higher scheduling priority. After sorting, the system generates a resource block scheduling sequence table.

[0088] During the execution of the scheduling cycle, the system performs the following operations according to the sorted resource block scheduling sequence, combined with the path status and time slot arrangement in the mapping table: Resource time-sharing injection: The system starts with high-priority resources and injects resource blocks into the path queue according to the available paths within the current time window; Conflict determination: If multiple high-priority resources compete for the same path, the system will perform scheduling arbitration based on the current bandwidth and estimated success rate of the path, and temporarily suspend the enqueuing of some resources; Remaining scheduling is delayed: Resource blocks that fail to be injected retain their scheduling sequence number and are included in the next cycle of scheduling, but their delay factor is adjusted accordingly, increasing their priority in the next round of sorting; Full-cycle coverage guarantee: The system ensures that all available resource blocks are scheduled into the network within multiple consecutive cycles, guaranteeing task integrity.

[0089] This scheduling mechanism has dynamic response capabilities and can adjust the sorting strategy in real time according to the path status and task load fluctuations, realizing integrated scheduling control of "task density driven + fidelity fault tolerance + latency self-adjustment".

[0090] During operation, some paths may fail due to node failure, quantum state collapse, or intersection mismatch. To improve resource utilization, this invention introduces a dynamic reallocation mechanism for entangled resources. The specific steps are as follows: Abnormal path detection and marking: The system monitors parameters such as path communication success rate and quantum state verification index in real time. If abnormal behavior is detected, such as fidelity falling below the threshold or path activation failure, it is immediately marked as "blocked path". Resource assessment: Extract the entangled resource blocks originally planned to be transmitted on this path, and assess their remaining fidelity and remaining effective lifetime (i.e., the number of remaining cycles that can maintain a communicable state under the current environment). Available pool generation: If the evaluation results show that the resource is still available, it will be included in the "reallocatable resource pool" for subsequent rescheduling; Task density rematch: Call the latest task density function, select regions with high resource density but empty paths for rematching, and remap available resources to new paths; Soft injection and time adjustment: To avoid path congestion caused by sudden reassignment, a soft injection mechanism is adopted, that is, the system reserves an idle time slot window for new resource blocks and inserts them for execution without affecting the original task scheduling.

[0091] Through the above mechanism, the present invention ensures that resources are "transferred in a timely manner" from unavailable paths, avoids failure and waste, and enhances the flexibility and efficiency of overall resource scheduling.

[0092] At the end of each scheduling cycle (or communication window), the system will automatically collect the actual operational data of all participating paths, forming a path behavior record set. This record set contains the following dimensions: Path usage frequency: The number of times this path is invoked in the current cycle, reflecting its scheduling frequency; Entangled resource injection count and completion rate: The system records the number of injected resource blocks and the proportion of successfully transmitted tasks; Scheduling offset: The deviation between the actual task execution time and the planned injection time; Fidelity decay rate: The average fidelity decay of all resource blocks in this path before and after transmission; Task completion quality indicators: such as communication success rate, task priority adaptation rate, etc.

[0093] The above data is generated by the system scheduling module and the quantum link monitoring module working together, and is recorded in the communication cycle log in real time.

[0094] To determine whether a path possesses the "stationary" characteristic, the system introduces a stationary evaluation function that takes path behavior data as input and outputs a path status flag. The evaluation function includes the following main decision rules: State fluctuation threshold determination: If the fidelity change rate of the path remains within the tolerance range set by the system (such as ±2%) in multiple consecutive cycles, it indicates that its quantum state transmission stability is high. Scheduling offset is zero or near zero: The scheduling offset reflects whether the task and resource injection are closely coordinated. If the offset time is less than the system time slot precision (e.g., less than 1 millisecond), it is considered "no timing offset". Success rate threshold judgment: If the success rate of resource block transmission on the path is greater than the task adaptation threshold set by the system (usually set to above 95%), it indicates that the path task adaptation is excellent. Unentangled structure collapse record: There are no entanglement collapse or error correction failure events caused by path degradation in the path.

[0095] If a path satisfies all of the above conditions in the most recent N periods (N is set by the system, usually 3 to 5), it is considered a "stationary path".

[0096] The system will add a flag to the path index table of the stable paths determined by the above evaluation mechanism and update the status information fields, including: Set the path status flag to "stationary state"; Mark the "effective fidelity time window" and "reuse priority level" of the path; Establish a path scheduling priority index for rapid scheduling and invocation in subsequent cycles.

[0097] The index table is a structured form with fields including path ID, start and end nodes, status flags, stabilization start time, period validity, and expected stabilization time window. In the next scheduling cycle, the system will first query this table, prioritizing stable paths for task allocation, thus skipping the regular path re-evaluation process and saving computational resources.

[0098] The index table update operation is completed through the index synchronization program executed periodically by the scheduler, and is automatically synchronized to the master control node and disaster recovery backup node to ensure the fault tolerance and high availability of the path scheduling module.

[0099] Considering that the path state may change abruptly due to external disturbances, this invention also designs a mechanism for canceling and re-evaluating a stable path: State fluctuation detection trigger: If a stable path experiences a sudden drop in fidelity, an increase in offset, or an increase in task failure rate in subsequent scheduling cycles, the system will trigger a state abnormality event. Soft cancellation strategy: Cancelling the stationary status will not immediately remove the path, but will first downgrade it to a "candidate path" and include it in the regular evaluation process of the next cycle; Dynamic recovery mechanism: If the path meets the stability assessment criteria again in a subsequent assessment, the stability marker can be automatically restored; Static cache refresh mechanism: The system sets a maximum validity period for static markers. Paths that are not scheduled or exhibit abnormal behavior after the preset period will automatically become invalid.

[0100] This mechanism enables path state management to be dynamically adaptable and state-memory-enabled, achieving efficient path reuse without sacrificing system robustness.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A dynamic optimization method for space quantum communication, characterized in that: include: The payload difference parameter ΔL between each communication node pair in the space communication network is acquired in real time, and the quantum state transfer operation is determined based on ΔL. Set a preset threshold value θ. When ΔL>θ, execute the collapse-de-stationary quantum state transition protocol. Obtain the current curl tensor and calculate its F-norm. If the F-norm is greater than θ, then enable the curl-driven path selection mechanism and select the set of node links with the highest path fidelity in the curl-driven path. Spatiotemporal partitioning crossover verification is performed on the candidate path set. Based on the four-element basis set, cross-operation is performed on the entangled inner product between nodes in the path. The inner product error of any pair of elements is less than 10⁻ 6 If so, the path is valid; A dynamic path mapping table is constructed in the legal path, and a temporal mapping is performed on the entangled resource blocks according to the mapping table to generate a continuous spatiotemporal distribution path. Based on the path and system task density function, the optimal scheduling order of entangled resource blocks is calculated to achieve dynamic resource reallocation; Output the updated path index table and mark the current path state as a stationary state for use in the next round of quantum communication iteration.

2. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The steps of obtaining the load difference parameter ΔL and determining whether a quantum state transfer operation is triggered include: Based on the node physical load monitoring unit in the quantum relay link, the multi-dimensional time-series operation indicators of each node are obtained, including communication frequency, entangled state transmission rate and average energy consumption fluctuation. The node state vector Σi is generated through a weighted aggregation function, where Σi represents the comprehensive operating load of the i-th node in the current time slot. For any two communication nodes i and j, calculate their load difference parameter ΔLij, which is defined as the product of their state vector Euclidean distance and distance weighting coefficient k, where k is dynamically updated by the estimation model of the path loss of the channel between the two nodes and the relative motion speed. Determine whether ΔLij exceeds the dynamic threshold η(τ) set by the system, where η(τ) is the load threshold that is dynamically adjusted with the task time density function. If ΔLij>η(τ), then trigger the quantum state migration operation from the stationary state to the detached state associated with this node.

3. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The steps for executing the collapse-de-resident quantum state transition protocol include: A preset quantum load threshold θ is used as the Taiji value, and θ is dynamically adjusted in each scheduling cycle in combination with the current operating state of the system. When the load difference parameter ΔL of any node pair exceeds the value of θ, the quantum survival probability of entangled particle pairs in the current stationary quantum link is estimated. This probability is evaluated by Bayesian inference based on their residence time, current link energy fluctuation and temperature perturbation conditions. If the survival probability is lower than the system tolerance threshold, a state transition warning is triggered. Based on the warning state, the collapse-de-stationary quantum state transition protocol is executed.

4. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The step of obtaining the current energy curl tensor and calculating its F-norm includes: In the topology of a space quantum communication network, the rate of change of the path energy potential of each node per unit time is obtained, and the quantum potential field gradient matrix P on each link is constructed. Perform three-dimensional curl operations on matrix P to obtain the curl tensor, that is, calculate the partial derivatives of the entangled gradients in the paths of adjacent nodes in each spatial dimension in a non-self dimension to form a curl vector field. The overall F-norm is calculated by summing the squares of the moduli of each component in the curl tensor. Specifically, the energy density index of the curl is obtained by summing the squares of all elements of the three-dimensional matrix formed by each curl component and taking the square root. The calculated F-norm is compared with the Taiji value θ. If it is greater than θ, the system is considered to be in the curl-dominant stage, and the earthquake-driven curl-driven path selection mechanism is activated.

5. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The spatiotemporal segment cross-validation includes: A four-element basis set consisting of four orthogonal quantum states is constructed and named Spring Elephant State, Summer Elephant State, Autumn Elephant State and Winter Elephant State respectively. This set is pairwise orthogonal in Hilbert space and serves as the standard entangled state template of the system in different time slots. The path to be verified is divided into equal-length segments according to the time axis, and each segment represents a communication window in the system scheduling cycle. For the node pair state in each time segment, the inner product operation is performed with two or more states in the four-dimensional basis to extract its projection value under the standard basis. Cross-compare the node states in different spatiotemporal slices within the same path to form the inner product error matrix between all pairs of objects, and calculate the deviation between the inner product value of each pair of objects and the theoretical value. When the inner product error value of any pair in the error matrix is ​​less than 10⁻ 6 When the path passes the cross-validation check, it is considered a valid path and marked as such.

6. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The steps for constructing the dynamic path mapping table include: Extract the path identifier, node number, path entanglement fidelity, load change rate, and curl direction component of each node pair from the valid paths that pass the cross-validation test, and construct a path state vector set. Cluster analysis is performed on the state vector of each path, and paths with similar structures and synchronous change trends are grouped into the same mapping cluster; Based on the scheduling priority of node pairs within the path cluster, the current quantum load distribution of the system, and the predicted resource requirements, the path set is encoded to generate a ternary index mapping table, which corresponds to the time slice number, node pair identity identifier, and quantum entanglement resource block number, respectively. The generated path dynamic mapping table is embedded in the communication scheduling controller to drive the subsequent resource distribution module to complete the scheduling and locking of entangled blocks.

7. The dynamic optimization method for space quantum communication according to claim 6, characterized in that: The timing mapping step for executing entangled resource blocks includes: The path dynamic mapping table is invoked to find the corresponding legal path number and node pair information within the current time slice, and to determine the resource transmission direction and target node within the time slot; Match the transmission request information of entangled resource blocks with the time-series weight function associated with the path in the mapping table; For each entangled resource block to be distributed, weighted queuing is performed according to its priority, the availability of the corresponding path in the mapping table, and the current system state, and its optimal scheduling time window and resource injection time are calculated. Once the resource blocks have been queued, they are sent to the designated path queue and injected in a time-sharing manner according to the mapping table controller, thus realizing the continuous spatiotemporal resource distribution on the path.

8. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The process of constructing the task density function includes: Using time as the basic window, statistics are compiled on the task request frequency, target bandwidth, and communication level indicators within each communication cycle to form a time-varying task distribution map. On each path segment, the local task density value is calculated based on the task statistics of the corresponding region and the path link capacity, and a complete task density function is generated. By introducing a sliding window average and an exponential weighting factor, the task density function is smoothed. The processed task density function is input into the scheduling engine as a real-time decision-making reference for dynamic resource sorting and path allocation.

9. The dynamic optimization method for space quantum communication according to claim 8, characterized in that: The steps for calculating the optimal scheduling order of entangled resource blocks include: Obtain the current task density function of the system and establish the scheduling pressure coefficient of each entangled resource block. This coefficient consists of three parts: the value of the task density function on its own path, the remaining fidelity of the resource block, and its allocated delay time. The three are obtained by weighted average combination, with the task density having the largest weight. Sort all resource blocks in ascending order according to their scheduling pressure coefficients to obtain the optimal scheduling sequence of entangled resource blocks in the current communication cycle. The sorted scheduling list is input into the scheduling controller as a priority reference for time-sharing transmission and path resource configuration, thereby realizing the dynamic scheduling under task-driven conditions.

10. The dynamic optimization method for space quantum communication according to claim 1, characterized in that: The steps of outputting the updated path index table and marking the current path state as stationary include: After the current communication cycle ends, the system extracts the running result data of each path from the path dynamic mapping table and the scheduling execution log, performs a stability evaluation on each path, and if the path meets the stability evaluation criteria, sets a stability flag for the path in the index table and updates its status code to a stable path; and synchronously writes the updated path index table into the system's path scheduling cache and redundant control nodes.

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