Quantum channel wavelength allocation method adaptive to tidal effect and related device

Abstract

The invention belongs to the field of quantum key distribution networks, and discloses a quantum channel wavelength allocation method adaptive to a tidal effect and a related device, and the method comprises the steps: predicting the classical traffic demand of a next time window through constructing a PON service traffic model, and calculating the number of needed wavelengths; judging whether the requirement is met or not according to the current wavelength distribution state: if not, preferentially recovering wavelength resources of a low-frequency quantum channel for classical signal transmission, and if yes, preferentially distributing idle high-frequency wavelength to the quantum signal, and meanwhile, completing key distribution by using the quantum channel to supplement a key pool; and finally, updating the network load state to realize loop optimization. By adopting the method, tidal flow fluctuation can be effectively adapted, resource competition in peak periods can be reduced, classic service congestion and service quality reduction can be avoided, the channel utilization rate in valley periods can be optimized, and efficient coordination of quantum secret communication and classic service transmission can be realized.

Description

Technical Field

[0001] This invention belongs to the field of quantum key distribution network technology, and particularly relates to a quantum channel wavelength allocation method and related device adapted to tidal effects. Background Technology

[0002] Traditional encryption technologies rely on the computational complexity of specific mathematical problems for security. However, with the continuous improvement of computing power, especially the rapid development of quantum computers, traditional encryption algorithms are facing a serious risk of being cracked. Quantum Key Distribution (QKD) technology, based on Heisenberg's immeasurability principle and the no-cloning theorem of single-photon in quantum mechanics, possesses theoretically unconditional security. Even against attackers with infinite computing power, it can guarantee key security. By generating secure keys using this technology and combining them with "one-time pad" encryption, absolute security of information transmission can be achieved. With the in-depth development of QKD technology, key exchange modes have evolved from simple point-to-point to complex networked quantum key distribution and sharing. As a core component of optical communication networks, optical access networks directly carry network data from end users and are a critical link in ensuring data security. Especially in scenarios such as industrial parks and confidential units, data information is mostly confined within the access network, making the need for secure communication extremely urgent. Among them, Continuous Variable-QKD (CV-QKD) technology has become the preferred solution for accessing quantum secure communication networks due to its good compatibility with classical coherent optical communication systems, high key rate over access network distance, and significant advantages in cost, performance, and integrability. Passive Optical Network (PON), as the mainstream networking architecture of current access networks, has become a research hotspot in this field, promoting the integration of CV-QKD and PON architectures and realizing the coexistence of quantum signals and classical data signals on the same optical fiber infrastructure.

[0003] However, PON networks exhibit significant tidal effects in uplink and downlink traffic. Traffic peaks during daytime work hours and evening entertainment hours, leading to intense resource competition between classical and quantum signals. Conversely, traffic drops significantly at night, with most users offline, resulting in extremely low network load and idle channel resources. In CV-QKD access networks, the periodic fluctuations in classical traffic necessitate dynamic adjustments to quantum wavelength resource allocation. However, current research on network resource allocation in CV-QKD access networks is limited, and traditional routing mechanisms struggle to adapt to the dynamic fluctuations in access network traffic. In scenarios where classical and quantum signals are transmitted on the same fiber, resource competition between quantum signals and classical services is particularly pronounced during peak periods, easily causing congestion in classical services and severely impacting their quality of service, thus hindering the coordinated optimization of quantum secure communication and classical service transmission.

[0004] It is evident that existing transmission methods lack quantum wavelength resource allocation strategies adapted to the tidal characteristics of PON services in quantum secure communication scenarios that integrate CV-QKD and PON. They are unable to flexibly schedule resources by sensing fluctuations in classical service traffic, resulting in fierce competition for classical services and quantum signal resources during peak periods and damage to the service quality of classical services. Summary of the Invention

[0005] The purpose of this invention is to provide a quantum channel wavelength allocation method and related device adapted to tidal effects. This method can solve the problem that existing transmission methods lack a quantum wavelength resource allocation strategy adapted to the characteristics of PON tidal services in quantum secure communication scenarios that integrate CV-QKD and PON, and cannot achieve flexible resource scheduling by sensing the fluctuation of classical service traffic.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A quantum channel wavelength allocation method adapted to tidal effects includes: Based on a pre-built PON service traffic model, the classic traffic of each link in the next time window is predicted in combination with the current service load, and the number of wavelengths required for the classic traffic is calculated; wherein, the PON service traffic model is constructed based on the original data of classic traffic within a preset period of each link, and is used to reflect the fluctuation pattern of classic traffic in different time windows within the period. Obtain the number of wavelengths allocated to classical and quantum signals for each link. Determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If not, execute the wavelength recycling process of dynamic resource scheduling. If it does, execute the wavelength allocation process of dynamic resource scheduling. In the wavelength recycling process, low-frequency quantum channel wavelength resources are recycled first, and the recycled channels are switched to classical optical signal transmission; in the wavelength allocation process, idle high-frequency wavelengths are allocated to quantum signals first; key distribution and negotiation between nodes are completed through quantum channels to supplement the key pool; The wavelength allocation results for each time window are updated to the network load status, and the updated wavelength allocation results are used as input for traffic prediction and resource allocation in the next cycle to achieve iterative optimization.

[0007] Furthermore, before predicting the classic traffic of each link in the next time window based on the pre-built PON service traffic model and combining it with the current service load, and calculating the number of wavelengths required for the classic traffic, the method further includes: Collect raw data of classic traffic within a preset period for each link. The raw data includes the arrival time of the service and the required bandwidth. Preprocessing and aggregation operations are performed on the raw data to obtain low-frequency feature data with controllable data volume; Construct a PON service traffic model based on feature data.

[0008] Furthermore, based on the pre-built PON service traffic model, the classic traffic of each link in the next time window is predicted in conjunction with the current service load, and the number of wavelengths required for the classic traffic is calculated, including: Collect current service load; Input the current service load into the pre-built PON service traffic model and output the classic traffic of each link in the next time window; The classical traffic of each link in the next time window is calculated based on the classical traffic of each link in the next time window, and the number of wavelengths required for the classical traffic is output.

[0009] Further, the process of obtaining the number of wavelengths allocated to classical and quantum signals for each link, determining whether the current wavelength allocation meets the classical traffic demand in the next time window based on the required number of wavelengths for classical traffic, and executing a wavelength reclamation process for dynamic resource scheduling if it does not, and executing a wavelength allocation process for dynamic resource scheduling if it does, includes: Read the current network status and count the wavelength allocation of classical and quantum signals on each link to obtain the number of wavelengths allocated to classical and quantum signals on each link; Based on the calculated number of wavelengths required for classical traffic, determine whether the current wavelength allocation meets the transmission requirements of classical traffic in the next time window. If not, execute the wavelength reclamation process of dynamic resource scheduling; if so, execute the wavelength allocation process of dynamic resource scheduling.

[0010] Furthermore, in the wavelength recovery process, priority is given to recovering the wavelength resources of low-frequency quantum channels, and the recovered channels are switched to classical optical signal transmission, including: In the wavelength reclamation process, a request to reclaim the wavelength resources occupied by the quantum signal is sent to the quantum channel. The quantum channel then stops transmitting the quantum signal based on the received request to reclaim the wavelength resources occupied by the quantum signal, releasing the corresponding wavelength to meet the needs of classical services. Priority is given to sending the request to reclaim the wavelength resources occupied by the quantum signal to the low-frequency quantum channel.

[0011] Furthermore, in the wavelength allocation process, idle high-frequency wavelengths are preferentially allocated to quantum signals; key distribution and negotiation between nodes are completed through a quantum channel to supplement the key pool, including: Based on the calculated number of wavelengths required for classical traffic, the idle wavelength resources in the network are allocated to quantum signals, with priority given to high-frequency wavelengths. The key pool is supplemented by key distribution and negotiation between nodes through quantum channels.

[0012] Furthermore, updating the wavelength allocation results of each time window to the network load status, and using the updated wavelength allocation results as input for the next cycle's traffic prediction and resource allocation, to achieve iterative optimization, includes: The wavelength allocation results for each time window are updated to the network load status, and the wavelength allocation results include wavelength occupancy and load distribution; The updated wavelength allocation results are used as input for the next cycle of flow prediction and resource allocation to complete the iteration and achieve cyclic optimization.

[0013] A quantum channel wavelength allocation system adapted to tidal effects, comprising: The prediction module is used to predict the classic traffic of each link in the next time window based on a pre-built PON service traffic model and the current service load, and to calculate the number of wavelengths required for the classic traffic. The PON service traffic model is constructed based on the original data of classic traffic within a preset period of each link and is used to reflect the fluctuation pattern of classic traffic in different time windows within the period. The judgment module is used to obtain the number of wavelengths allocated to classical and quantum signals for each link, and to determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If it does not meet the needs, the wavelength recycling process of dynamic resource scheduling is executed; if it does meet the needs, the wavelength allocation process of dynamic resource scheduling is executed. The allocation module is used to prioritize the recovery of low-frequency quantum channel wavelength resources in the wavelength recovery process and switch the recovered channel to classical optical signal transmission; in the wavelength allocation process, it prioritizes the allocation of idle high-frequency wavelengths to quantum signals; and it completes key distribution and negotiation between nodes through quantum channels to supplement the key pool. The loop module updates the wavelength allocation results of each time window to the network load status, and uses the updated wavelength allocation results as input for the next cycle of traffic prediction and resource allocation to achieve loop optimization.

[0014] A quantum channel wavelength allocation device adapted to tidal effects, comprising: Memory, used to store computer programs; A processor is used to implement the steps of the above-described quantum channel wavelength allocation method adapted to tidal effects when executing the computer program.

[0015] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the above-described quantum channel wavelength allocation method adapted to tidal effects.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a quantum channel wavelength allocation method adapted to tidal effects. It predicts classical traffic demand in the next time window by constructing a PON service traffic model and calculates the required number of wavelengths. Then, it determines whether the current wavelength allocation status meets the demand: if not, it prioritizes reclaiming low-frequency quantum channel wavelength resources for classical signal transmission; if so, it prioritizes allocating idle high-frequency wavelengths to quantum signals. Simultaneously, it utilizes the quantum channel to complete key distribution to replenish the key pool. Finally, it updates the network load status to achieve cyclical optimization. This method dynamically schedules wavelength resources using the periodic fluctuations of the PON service traffic model. During peak traffic periods, it reclaims low-frequency quantum wavelengths to alleviate classical service resource pressure, and during off-peak periods, it allocates high-frequency wavelengths to improve quantum resource utilization. Furthermore, it ensures quantum communication security through a key management mechanism. This method effectively adapts to tidal traffic fluctuations, reduces resource competition during peak periods, avoids classical service congestion and service quality degradation, and optimizes channel utilization during off-peak periods, achieving efficient coordination between quantum secure communication and classical service transmission. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the implementation of a quantum channel wavelength allocation method adapted to tidal effects, provided in an embodiment of the present invention; Figure 2 A flowchart of a quantum channel wavelength allocation method adapted to tidal effects provided by the present invention; Figure 3This is a schematic diagram of a quantum channel wavelength allocation system adapted to tidal effects provided by the present invention. Detailed Implementation

[0018] To facilitate a deeper understanding of the technical solution of this invention, the following explanations are provided for the technical terms: QKD: Quantum Key Distribution is a technology that uses the principles of quantum mechanics to achieve secure key distribution. It can generate theoretically unconditionally secure shared keys between communicating parties, providing security guarantees that cannot be eavesdropped on or cracked for encrypted communication.

[0019] CV-QKD: Continuous Variable – QKD.

[0020] PON: Passive Optical Network.

[0021] Optical Access Network: The optical access network is the last mile connecting the backbone network and end users, and the most common implementation is PON. PON adopts a point-to-multipoint topology, where the optical line terminal shares the fiber optic infrastructure with multiple user-side optical network units through a passive optical splitter. CV-QKD technology, with its unique technical architecture, has shown great potential for large-scale deployment in access networks. However, the services carried by optical access networks exhibit a significant tidal effect. Network traffic is highly concentrated during peak hours in the day and night, leading to channel resource shortages; while during off-peak hours such as late at night, traffic drops significantly, resulting in a large number of wavelength channels and bandwidth resources being underutilized. Optical access networks based on CV-QKD technology can utilize its flexible wavelength tuning characteristics to tune the position of quantum channels using idle wavelength resources during off-peak hours, transmit quantum signals, implement peak-shifting resource allocation strategies, and efficiently and cost-effectively provide information-theoretical security for the massive number of user terminals in the access network.

[0022] Traffic modeling and prediction: Traditional network management and resource allocation strategies struggle to cope with the complex and ever-changing service demands of optical networks, often leading to network congestion, degraded service quality, poor user experience, and high operating costs and resource waste. Accurate modeling and prediction of network traffic has become a core technology for network resource optimization. Traffic modeling aims to deeply analyze and characterize traffic behavior patterns, periodicities, and statistical characteristics using mathematical or statistical methods. Based on this, traffic prediction technology utilizes historical data and established models to extrapolate and predict traffic within a future time window. Accurate traffic prediction capabilities enable networks to shift from passive to proactive scheduling.

[0023] As mentioned in the background section, research on network resource allocation in continuous-variable quantum key distribution access networks is relatively scarce. In scenarios where classical and quantum signals are transmitted on the same fiber, traditional routing mechanisms struggle to adapt to the dynamic fluctuations in access network traffic. Resource competition exists between quantum signals and classical services during peak periods, leading to congestion in classical services and consequently affecting their service quality.

[0024] To achieve the above objectives, this embodiment provides a quantum channel wavelength allocation method adapted to tidal effects. This method addresses the uneven but periodic traffic characteristics in optical access networks by sensing classical optical service traffic in PON networks, constructing a PON service traffic prediction model to dynamically predict link load, and proposing a wavelength staggered allocation method to adjust wavelength resources for quantum and classical signals. While meeting the needs of classical services, channel resources are allocated for quantum key distribution during off-peak hours, prioritizing the quality of service for classical services while staggering wavelength allocation to overcome resource competition between quantum signals and classical services during peak periods, thereby improving the overall network operating efficiency and security performance.

[0025] like Figure 2 As shown, this embodiment provides a quantum channel wavelength allocation method adapted to tidal effects, including: Based on a pre-built PON service traffic model, the classic traffic of each link in the next time window is predicted in combination with the current service load, and the number of wavelengths required for the classic traffic is calculated. The PON service traffic model is constructed based on the original data of classic traffic within a preset period of each link, and is used to reflect the fluctuation pattern of classic traffic in different time windows within the period. The number of wavelengths required for the classic traffic is obtained by dividing the total traffic of each link in the next time window by the service traffic that a single wavelength can carry and rounding up.

[0026] Obtain the number of wavelengths allocated to classical and quantum signals for each link. Determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If not, execute the wavelength recycling process of dynamic resource scheduling. If it does, execute the wavelength allocation process of dynamic resource scheduling. In the wavelength recycling process, low-frequency quantum channel wavelength resources are recycled first, and the recycled channels are switched to classical optical signal transmission; in the wavelength allocation process, idle high-frequency wavelengths are allocated to quantum signals first; key distribution and negotiation between nodes are completed through quantum channels to supplement the key pool; The wavelength allocation results for each time window are updated to the network load status, and the updated wavelength allocation results are used as input for traffic prediction and resource allocation in the next cycle to achieve iterative optimization.

[0027] The quantum channel wavelength allocation method adapted to tidal effects provided in this embodiment will be further explained below with reference to the accompanying drawings: like Figure 1 As shown, this embodiment provides a quantum channel wavelength allocation method adapted to tidal effects. In a dynamic access scenario integrating PON and CV-QKD, addressing the uneven periodic fluctuations in classical traffic, this method uses classical service traffic perception and data acquisition to model and predict demand, enabling wavelength resource evaluation, decision-making, and dynamic scheduling. This method is applied to a controller capable of implementing a quantum channel wavelength allocation method adapted to tidal effects. The controller is divided into five modules: state perception and data acquisition, traffic modeling and demand prediction, resource supply and demand assessment and decision-making, dynamic resource scheduling, and state update and iteration. During this process, intelligent resource coordination between quantum and classical signals is achieved. Specifically, if the current network channel allocation meets the classical service demand predicted by the model for the next time step, the remaining idle wavelengths can be intelligently allocated to quantum signals. If the current network channel allocation does not meet the classical service demand predicted by the model for the next time step, the channel currently transmitting quantum signals on the existing link can be adaptively cut off and reclaimed, releasing resources. The cut-off channel is then converted to transmit classical optical signals, prioritizing classical services. The specific steps are as follows: Step 1: State Awareness and Data Acquisition Step 1.1: Initialize the network and sense its state: During the initialization phase, the network control layer detects and senses the status of PON nodes, links, and services to obtain initial network operation information.

[0028] Step 1.2: Collect data on classic traffic: The network control layer collects classic traffic data for each PON link within a cycle, including the arrival time of services and the required bandwidth.

[0029] Step 1.3: Preprocess and aggregate the collected raw data: The collected raw data is high-frequency, massive in volume, and subject to momentary fluctuations. First, the data is preprocessed, then aggregated to transform it into low-frequency, manageable feature data. This data is then summarized over time to match the scale of the time window, smoothing it for subsequent traffic modeling.

[0030] Step 2: Traffic Modeling and Demand Forecasting Step 2.1: Model the classical flow to obtain the banded pattern of the classical flow in PON within different time windows of the cycle. A PON service traffic model is established to describe the time-dimensional patterns of classic traffic within a period, which contains multiple time windows. Based on the established PON service traffic model, the load occupancy of the link in each time window can be obtained, reflecting the fluctuation characteristics of classic services in different time windows within the period.

[0031] Step 2.2: Predict the classic traffic for each link in the PON in the next time window: Based on the current service load and the established PON service traffic model, the PON control layer predicts the classic traffic change trend of each link in the next time window and obtains the classic traffic load demand in the next time window.

[0032] Step 2.3: Calculate the wavelength requirements for classical traffic in the next time window for each link: Based on the prediction results, the PON control layer calculates the number of wavelength resources required by each link to meet the transmission requirements of classic services in the next time window, providing a basis for subsequent signal scheduling and resource allocation.

[0033] Step 3: Resource Supply and Demand Assessment and Decision-Making Step 3.1: Obtain the number of wavelengths currently allocated to classical and quantum signals on each link: The PON control layer reads the current network status and calculates the wavelength allocation of classical and quantum signals on each link.

[0034] Step 3.2: Determine whether the allocation of classical flow wavelengths on each link meets the requirements of its next time window: The PON control layer determines whether the wavelength resource allocation of the current link can meet the transmission requirements of classic traffic in the next time window. If the wavelength resource allocation of the current link cannot meet the transmission requirements of classic traffic in the next time window, proceed to the next step; if the wavelength resource allocation of the current link can meet the transmission requirements of classic traffic in the next time window, jump to step 4.3.

[0035] Step 4: Dynamic Resource Scheduling Step 4.1: Reclaim the wavelengths allocated to the quantum channel, prioritizing the reclamation of low-frequency signals: The controller determines that the current wavelength resource allocation of the link cannot meet the transmission requirements of classical traffic in the next moment. In order to ensure the transmission of basic services, the control layer issues a request to reclaim the wavelength resources occupied by the quantum signal.

[0036] Step 4.2: The recovered quantum channel stops transmitting quantum signals and switches to transmitting classical optical signals. The control layer will prioritize the recovery of low-frequency quantum channels. The recovered channels will stop transmitting quantum signals and release the corresponding wavelengths to meet the needs of classical services.

[0037] Step 4.3: Allocate the idle channel to the quantum signal: If the current wavelength resource allocation of the link can meet the transmission requirements of classical traffic in the next moment, proceed to this step. Based on the number of wavelengths calculated in step 2.3, the controller allocates idle wavelength resources in the network to quantum signals. If there are also spare idle channels, they are allocated to quantum signals, with priority given to high-frequency wavelengths.

[0038] Step 12: Key distribution and negotiation are performed between nodes to replenish the key pool. The control layer sends a request to both ends of the channel assigned to the quantum signal to transmit the signal. The node pairs at both ends perform key distribution and negotiation through the quantum channel to replenish the key pool.

[0039] Step 5, State Update and Iteration: Step 5.1: Update the wavelength allocation to the network load status: The control layer updates the wavelength allocation for each time window to the network load status, including wavelength occupancy and load distribution.

[0040] Step 5.2: Update the results as input for the next cycle's forecasting and resource allocation: The control layer uses the updated results as input for the next cycle's traffic prediction and resource allocation, and iterates to achieve cyclical optimization.

[0041] The following section provides further explanation of this routing and spectrum allocation optimization method using specific application examples: This embodiment provides a quantum channel wavelength allocation method adapted to tidal effects, and the specific implementation process is as follows: First, state awareness and data acquisition: Step 1: Initialize and sense the network state: During the initialization phase, the network control layer detects and senses the status of PON nodes, links, and services to obtain initial network operation information.

[0042] Step 2: Collect data on classic traffic: The network control layer collects data on the classic traffic of each PON link within a period, including the arrival time of the service and the required bandwidth. In this embodiment, a time period is 24 hours.

[0043] Step 3: Preprocess and aggregate the collected raw data: The collected 24-hour raw data is high-frequency, massive in volume, and subject to momentary fluctuations. First, the data is preprocessed, then aggregated to transform it into low-frequency, manageable feature data. This data is then summarized over time to match the scale of the time window, making it smoother for subsequent traffic modeling. In this embodiment, a time window is one hour.

[0044] Secondly, traffic modeling and demand forecasting: Step 4: Model the classical flow to obtain the banded pattern of the classical flow in the PON at different time windows within the period. Using the collected 24-hour data, a PON service traffic model is established to describe the time-dimensional pattern of classic traffic within a period, which contains multiple time windows. In this embodiment, a time period is 24 hours, and each hour is a time window. Based on the established PON service traffic model, the load occupancy of the link in each time window can be obtained, reflecting the fluctuation characteristics of classic services in different time windows within the period.

[0045] Step 5: Predict the classic traffic for each link in the PON in the next time window: Based on the current service load and the previously obtained PON service traffic model, the PON control layer predicts the classic traffic change trend of each link in the next time window, thus obtaining the classic traffic load demand for the next time window. In this embodiment, the peak and off-peak periods of PON service arrivals can be predicted using the established model, such as... Figure 2 As shown in the right figure, "Time Window 1" is the wavelength allocation status of the link when the model predicts that the PON classic service will face a peak period, and "Time Window 2" is the wavelength allocation status of the link when the model predicts that the PON classic service will face a trough period.

[0046] Step 6: Calculate the wavelength requirements for classical traffic in the next time window for each link: Based on the model predictions, the PON control layer calculates the number of wavelength resources required for classical service transmission on each link in the next time window, providing a basis for subsequent signal scheduling and resource allocation. The calculations show that in time window one, 14 classical signal wavelengths are needed for the next time window; and in time window two, 10 classical signal wavelengths are needed for the next time window.

[0047] Secondly, resource supply and demand assessment and decision-making: Step 7: Obtain the number of wavelengths currently allocated to classical and quantum signals on each link: The PON control layer reads the current network status and calculates the wavelength allocation for classical and quantum signals on each link. In time window one, classical signals are allocated 11 channels, and quantum signals are allocated 6 channels. In time window two, classical signals are allocated 11 channels, and quantum signals are allocated 3 channels.

[0048] Step 8: Determine whether the allocation of classical flow wavelengths on each link meets the requirements of its next time window: The PON control layer determines whether the wavelength resource allocation of the current link can meet the transmission requirements of classic traffic in the next time window. If the wavelength resource allocation of the current link cannot meet the transmission requirements of classic traffic in the next time window, proceed to the next step; if the wavelength resource allocation of the current link can meet the transmission requirements of classic traffic in the next time window, jump to step 11.

[0049] Then, dynamic resource scheduling: Step 9: Reclaim the wavelengths allocated to the quantum channel, prioritizing the recovery of low-frequency signals: The model predicts that the current link will face a peak in classical traffic in the next time period. Based on the calculated wavelength demand, the controller determines that the currently allocated wavelength resources cannot meet the transmission demand of classical traffic in the next time period. To ensure the transmission of basic services, the control layer issues a request to reclaim the wavelength resources occupied by quantum signals.

[0050] Step 10: The recovered quantum channel stops transmitting quantum signals and switches to transmitting classical optical signals. The control layer will prioritize the recovery of low-frequency quantum channels. In the embodiment, channels 3, 8, and 9 are recovered. The recovered channels stop transmitting quantum signals and release the corresponding wavelengths to meet the needs of classical services.

[0051] Step 11: Allocate the idle channel to the quantum signal: The model predicts that the current link will face a low point in classical traffic in the next time period. Based on the calculated wavelength demand, the controller determines that the currently allocated wavelength resources can meet the transmission needs of classical traffic in the next time period and proceeds to this step. According to the number of wavelengths calculated in step 6, the controller allocates idle wavelength resources in the network—channels 8, 11, and 14—to quantum signals. Any remaining idle wavelengths are also allocated to quantum signals, prioritizing high-frequency signals. In this embodiment, the controller switches channel 17, which will be idle in the next time window, from transmitting classical signals to transmitting quantum signals.

[0052] Step 12: Key distribution and negotiation are performed between nodes to replenish the key pool. The control layer sends a request to both ends of the channel assigned to the quantum signal to transmit the signal. The nodes at both ends perform key distribution and negotiation through the quantum channel to replenish the key pool.

[0053] Finally, state updates and iterations: Step 13: Update the wavelength allocation to the network load status: The control layer updates the wavelength allocation for each time window to the network load status, including wavelength occupancy and load distribution.

[0054] Step 14: Update the results as input for the next cycle of forecasting and resource allocation: The control layer uses the updated results as input for the next cycle's traffic prediction and resource allocation, and iterates to achieve cyclical optimization.

[0055] like Figure 3 As shown, this embodiment also provides a quantum channel wavelength allocation system adapted to tidal effects, including: a prediction module, used to predict the classical traffic of each link in the next time window based on a pre-built PON service traffic model and combined with the current service load, and to calculate the number of wavelengths required for the classical traffic; wherein, the PON service traffic model is constructed based on the original data of classical traffic within a preset period of each link, and is used to reflect the fluctuation pattern of classical traffic in different time windows within the period. The judgment module is used to obtain the number of wavelengths allocated to classical and quantum signals for each link, and to determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If it does not meet the needs, the wavelength recycling process of dynamic resource scheduling is executed; if it does meet the needs, the wavelength allocation process of dynamic resource scheduling is executed. The allocation module is used to prioritize the recovery of low-frequency quantum channel wavelength resources in the wavelength recovery process and switch the recovered channel to classical optical signal transmission; in the wavelength allocation process, it prioritizes the allocation of idle high-frequency wavelengths to quantum signals; and it completes key distribution and negotiation between nodes through quantum channels to supplement the key pool. The loop module updates the wavelength allocation results of each time window to the network load status, and uses the updated wavelength allocation results as input for the next cycle of traffic prediction and resource allocation to achieve loop optimization.

[0056] The present invention also provides a quantum channel wavelength allocation device adapted to tidal effects, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the quantum channel wavelength allocation method adapted to tidal effects.

[0057] When the processor executes the computer program, it implements the above-mentioned steps of quantum channel wavelength allocation adapted to tidal effects, for example: based on a pre-built PON service traffic model, it predicts the classical traffic of each link in the next time window based on the current service load, and calculates the number of wavelengths required for the classical traffic; wherein, the PON service traffic model is constructed based on the original data of classical traffic within a preset period of each link, and is used to reflect the fluctuation pattern of classical traffic in different time windows within the period. Obtain the number of wavelengths allocated to classical and quantum signals for each link. Determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If not, execute the wavelength recycling process of dynamic resource scheduling. If it does, execute the wavelength allocation process of dynamic resource scheduling. In the wavelength recycling process, low-frequency quantum channel wavelength resources are recycled first, and the recycled channels are switched to classical optical signal transmission; in the wavelength allocation process, idle high-frequency wavelengths are allocated to quantum signals first; key distribution and negotiation between nodes are completed through quantum channels to supplement the key pool; The wavelength allocation results for each time window are updated to the network load status, and the updated wavelength allocation results are used as input for traffic prediction and resource allocation in the next cycle to achieve iterative optimization.

[0058] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, such as: a prediction module, which is used to predict the classic traffic of each link in the next time window based on a pre-built PON service traffic model and combined with the current service load, and calculate the number of wavelengths required for the classic traffic; wherein, the PON service traffic model is constructed based on the original data of the classic traffic within a preset period of each link, and is used to reflect the fluctuation pattern of the classic traffic in different time windows within the period. The judgment module is used to obtain the number of wavelengths allocated to classical and quantum signals for each link, and to determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If it does not meet the needs, the wavelength recycling process of dynamic resource scheduling is executed; if it does meet the needs, the wavelength allocation process of dynamic resource scheduling is executed. The allocation module is used to prioritize the recovery of low-frequency quantum channel wavelength resources in the wavelength recovery process and switch the recovered channel to classical optical signal transmission; in the wavelength allocation process, it prioritizes the allocation of idle high-frequency wavelengths to quantum signals; and it completes key distribution and negotiation between nodes through quantum channels to supplement the key pool. The loop module updates the wavelength allocation results of each time window to the network load status, and uses the updated wavelength allocation results as input for the next cycle of traffic prediction and resource allocation to achieve loop optimization.

[0059] Exemplarily, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, the instruction segments describing the execution process of the computer program in the quantum channel wavelength allocation device adapted to tidal effects. For example, the computer program can be divided into a routing module, a dynamic adjustment module, and a frequency slot release module; the specific functions of each module are as follows: a prediction module, a judgment module, an allocation module, and a loop module, which predict the classical traffic of each link in the next time window based on the current service load and calculate the number of wavelengths required for the classical traffic; wherein, the PON service traffic model is constructed based on the original data of classical traffic within a preset period of each link, used to reflect the fluctuation pattern of classical traffic in different time windows within the period; The judgment module is used to obtain the number of wavelengths allocated to classical and quantum signals for each link, and to determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If it does not meet the needs, the wavelength recycling process of dynamic resource scheduling is executed; if it does meet the needs, the wavelength allocation process of dynamic resource scheduling is executed. The allocation module is used to prioritize the recovery of low-frequency quantum channel wavelength resources in the wavelength recovery process and switch the recovered channel to classical optical signal transmission; in the wavelength allocation process, it prioritizes the allocation of idle high-frequency wavelengths to quantum signals; and it completes key distribution and negotiation between nodes through quantum channels to supplement the key pool. The loop module updates the wavelength allocation results of each time window to the network load status, and uses the updated wavelength allocation results as input for the next cycle of traffic prediction and resource allocation to achieve loop optimization.

[0060] The quantum channel wavelength allocation device adapted to the tidal effect can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above are examples of quantum channel wavelength allocation devices adapted to the tidal effect and do not constitute a limitation on such devices. The device may include more components than described above, or combine certain components, or use different components. For example, the quantum channel wavelength allocation device adapted to the tidal effect may also include input / output devices, network access devices, buses, etc.

[0061] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. This processor is the control center for the tidal effect-adapted quantum channel wavelength allocation, connecting various parts of the entire tidal effect-adapted quantum channel wavelength allocation device via various interfaces and lines.

[0062] The memory can be used to store the computer program and / or modules. The processor implements various functions of the quantum channel wavelength allocation device adapted to tidal effects by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0063] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0064] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the quantum channel wavelength allocation method adapted to tidal effects.

[0065] If the modules / units of the quantum channel wavelength allocation system adapted to the tidal effect are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0066] Based on this understanding, the present invention can implement all or part of the processes in the above-described quantum channel wavelength allocation method adapted to tidal effects, which can also be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described quantum channel wavelength allocation method adapted to tidal effects. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0067] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0068] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0069] Therefore, this invention provides a quantum channel wavelength allocation method adapted to tidal effects, which has the following advantages compared to existing transmission allocation methods: This method addresses dynamic access scenarios integrating PON and CV-QKD. Considering the uneven but periodic fluctuations in classical traffic, it proposes a CV-QKD wavelength resource allocation method and process based on the tidal effect. This invention presents a method and processing flow for intelligently predicting classical traffic and dynamically allocating wavelength resources through modeling. Without affecting the classical communication service quality (QoS), it improves the efficiency of quantum key generation, while ensuring the continuity and stability of classical services and enhancing the security of user information transmission.

[0070] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A quantum channel wavelength allocation method adapted to tidal effects, characterized in that, include: Based on a pre-built PON service traffic model, the classic traffic of each link in the next time window is predicted in combination with the current service load, and the number of wavelengths required for the classic traffic is calculated; wherein, the PON service traffic model is constructed based on the original data of classic traffic within a preset period of each link, and is used to reflect the fluctuation pattern of classic traffic in different time windows within the period. Obtain the number of wavelengths allocated to classical and quantum signals for each link. Determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If not, execute the wavelength recycling process of dynamic resource scheduling. If it does, execute the wavelength allocation process of dynamic resource scheduling. In the wavelength recycling process, low-frequency quantum channel wavelength resources are recycled first, and the recycled channels are switched to classical optical signal transmission; in the wavelength allocation process, idle high-frequency wavelengths are allocated to quantum signals first; key distribution and negotiation between nodes are completed through quantum channels to supplement the key pool; The wavelength allocation results for each time window are updated to the network load status, and the updated wavelength allocation results are used as input for traffic prediction and resource allocation in the next cycle to achieve iterative optimization.

2. The quantum channel wavelength allocation method adapted to tidal effects according to claim 1, characterized in that, Before the pre-built PON service traffic model, combined with the current service load, predicts the classic traffic of each link in the next time window and calculates the number of wavelengths required for the classic traffic, it also includes: Collect raw data of classic traffic within a preset period for each link. The raw data includes the arrival time of the service and the required bandwidth. Preprocessing and aggregation operations are performed on the raw data to obtain low-frequency feature data with controllable data volume; Construct a PON service traffic model based on feature data.

3. The quantum channel wavelength allocation method adapted to tidal effects according to claim 1, characterized in that, Based on a pre-built PON service traffic model, the classic traffic for each link in the next time window is predicted in conjunction with the current service load, and the number of wavelengths required for the classic traffic is calculated, including: Collect current service load; Input the current service load into the pre-built PON service traffic model and output the classic traffic of each link in the next time window; The classical traffic of each link in the next time window is calculated based on the classical traffic of each link in the next time window, and the number of wavelengths required for the classical traffic is output.

4. The quantum channel wavelength allocation method adapted to tidal effects according to claim 1, characterized in that, The process involves obtaining the number of wavelengths allocated to classical and quantum signals for each link, determining whether the current wavelength allocation meets the classical traffic demand for the next time window based on the required number of wavelengths, and if not, executing a dynamic resource scheduling wavelength reclamation process; if so, executing a dynamic resource scheduling wavelength allocation process. This includes: Read the current network status and count the wavelength allocation of classical and quantum signals on each link to obtain the number of wavelengths allocated to classical and quantum signals on each link; Based on the calculated number of wavelengths required for classical traffic, determine whether the current wavelength allocation meets the transmission requirements of classical traffic in the next time window. If not, execute the wavelength reclamation process of dynamic resource scheduling; if so, execute the wavelength allocation process of dynamic resource scheduling.

5. The quantum channel wavelength allocation method adapted to tidal effects according to claim 1, characterized in that, In the wavelength recovery process, priority is given to recovering the wavelength resources of low-frequency quantum channels, and the recovered channels are switched to classical optical signal transmission, including: In the wavelength reclamation process, a request to reclaim the wavelength resources occupied by the quantum signal is sent to the quantum channel. The quantum channel then stops transmitting the quantum signal based on the received request to reclaim the wavelength resources occupied by the quantum signal, releasing the corresponding wavelength to meet the needs of classical services. Priority is given to sending the request to reclaim the wavelength resources occupied by the quantum signal to the low-frequency quantum channel.

6. The quantum channel wavelength allocation method adapted to tidal effects according to claim 1, characterized in that, In the wavelength allocation process, idle high-frequency wavelengths are preferentially allocated to quantum signals; To supplement the key pool, key distribution and negotiation between nodes are performed via quantum channels, including: Based on the calculated number of wavelengths required for classical traffic, the idle wavelength resources in the network are allocated to quantum signals, with priority given to high-frequency wavelengths. The key pool is supplemented by key distribution and negotiation between nodes through quantum channels.

7. The quantum channel wavelength allocation method adapted to tidal effects according to claim 1, characterized in that, The step of updating the wavelength allocation results of each time window to the network load status, and using the updated wavelength allocation results as input for the next cycle's traffic prediction and resource allocation, to achieve iterative optimization, includes: The wavelength allocation results for each time window are updated to the network load status, and the wavelength allocation results include wavelength occupancy and load distribution; The updated wavelength allocation results are used as input for the next cycle of flow prediction and resource allocation to complete the iteration and achieve cyclic optimization.

8. A quantum channel wavelength allocation system adapted to tidal effects, characterized in that, include: The prediction module is used to predict the classic traffic of each link in the next time window based on a pre-built PON service traffic model and the current service load, and to calculate the number of wavelengths required for the classic traffic. The PON service traffic model is constructed based on the original data of classic traffic within a preset period of each link and is used to reflect the fluctuation pattern of classic traffic in different time windows within the period. The judgment module is used to obtain the number of wavelengths allocated to classical and quantum signals for each link, and to determine whether the current wavelength allocation meets the needs of classical traffic in the next time window based on the number of wavelengths required for classical traffic. If it does not meet the needs, the wavelength recycling process of dynamic resource scheduling is executed; if it does meet the needs, the wavelength allocation process of dynamic resource scheduling is executed. The allocation module is used to prioritize the recovery of low-frequency quantum channel wavelength resources in the wavelength recovery process and switch the recovered channel to classical optical signal transmission; in the wavelength allocation process, it prioritizes the allocation of idle high-frequency wavelengths to quantum signals; and it completes key distribution and negotiation between nodes through quantum channels to supplement the key pool. The loop module updates the wavelength allocation results of each time window to the network load status, and uses the updated wavelength allocation results as input for the next cycle of traffic prediction and resource allocation to achieve loop optimization.

9. A quantum channel wavelength allocation device adapted to tidal effects, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the quantum channel wavelength allocation method adapted to tidal effects as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the quantum channel wavelength allocation method adapted to tidal effects as described in any one of claims 1-7.

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