An island or coastal ground station-oriented satellite-to-ground quantum key distribution cumulative security code length interval estimation method and system

Abstract

The application discloses a satellite-ground quantum key distribution cumulative security code amount interval estimation method and system for island or coastal ground stations, which comprises the following steps: obtaining target transit weather forecast and multiple sets of historical weather forecast and corresponding measured pairing records, calculating the reference instantaneous security code rate and the reference cumulative security code amount; determining the time weight according to the contribution of each calculation time to the cumulative code amount, and determining the parameter weight according to the cumulative code amount change caused by the representative forecast error scale change of each weather parameter; determining the comprehensive difference of the target forecast and the historical forecast by using the two types of weights, and screening the historical records; applying the forecast error of the selected historical records to the target transit weather forecast as a whole according to the original time and height correspondence, forming multiple sets of modified weather scenarios, and recalculating the cumulative security code amount. The application can improve the task pertinence of historical forecast error correction and the reliability of interval estimation.

Description

Technical Field

[0001] This invention belongs to the field of satellite communication technology, specifically relating to a method and system for predicting the cumulative secure code generation interval of satellite-to-ground quantum key distribution for islands or coastal ground stations. Background Technology

[0002] During satellite-to-ground quantum key distribution, atmospheric conditions can affect the transmission loss of quantum signals and the secure key generation rate. Therefore, before a satellite transit mission is carried out, it is usually necessary to estimate the satellite-to-ground quantum link status and the cumulative secure key generation rate based on the weather forecast for the target transit period.

[0003] However, there is usually a certain forecast error between weather forecasts and the actual weather conditions during satellite transit. For islands or coastal areas, weather conditions such as wind speed, visibility, relative humidity, temperature, air pressure, and sea fog conditions change over time, with some meteorological parameters also exhibiting characteristics that vary with altitude. Simultaneously, marine environmental factors such as the hygroscopic absorption of sea salt aerosols, sea fog, and sea salt deposition at the receiving window can further impact the satellite-to-ground quantum link, causing weather forecast errors to further affect the calculation of the satellite-to-ground quantum link and the cumulative secure code generation. Therefore, when directly using weather forecasts for calculations, the obtained prediction results are easily affected by weather forecast errors.

[0004] To mitigate the impact of weather forecast errors, historical weather forecasts and corresponding measured data can be used to correct the weather forecast for the target transit. However, the impact of different meteorological parameters and different transit times on the cumulative safe coding volume varies, exhibiting complex and interrelated characteristics. Simply applying identical corrections to historical forecast errors can easily overlook the varying importance of different error factors to the actual coding results. Furthermore, independently statistically analyzing and correcting forecast errors for different meteorological parameters, times, or altitudes can easily sever the original correlations between various errors within the same meteorological process, thus affecting the rationality of the weather forecast correction results.

[0005] Therefore, how to avoid disrupting the original correlation between historical weather forecast errors while considering the differences in the impact of different forecast errors on the cumulative secure key generation is a problem that needs to be solved in the current prediction of the cumulative secure key generation in satellite-to-ground quantum key distribution. Summary of the Invention

[0006] One of the objectives of this invention is to at least solve one or more of the aforementioned problems existing in the prior art. In other words, one of the objectives of this invention is to provide a method and system for predicting the cumulative secure code generation range of satellite-to-ground quantum key distribution for island or coastal ground stations that meets one or more of the aforementioned requirements.

[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: This invention provides a method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for island or coastal ground stations, including: Acquire the target transit weather forecast and multiple sets of historical weather forecasts and corresponding measured pairing records, and determine the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link based on the target transit weather forecast, so as to calculate the benchmark instantaneous secure coding rate and benchmark cumulative secure coding amount of the target transit satellite; The weight of each calculation moment is determined based on the contribution of the baseline instantaneous secure coding rate to the baseline cumulative secure coding amount at each calculation moment; Based on historical weather forecasts and corresponding measured paired records, the representative forecast error scales of each meteorological parameter are determined. The meteorological parameters in the target transit weather forecast are changed according to the corresponding representative forecast error scales, and the weights of each meteorological parameter are determined based on the cumulative safe code amount changes caused by this. By using the weights of meteorological parameters and the weights of calculation time, the comprehensive difference value between the target transit weather forecast and each group of historical weather forecasts is determined, and several historical weather forecasts with smaller comprehensive difference values ​​are selected and paired with corresponding measured records. By utilizing the forecast error between each set of selected historical weather forecasts and corresponding measured paired records, the overall weather forecast for the transit of the target is corrected to form several sets of corrected weather scenarios; For each set of revised meteorological scenarios, the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link are re-determined in order to calculate the cumulative secure code generation quantity, and the prediction interval of the cumulative secure code generation quantity is determined based on the obtained cumulative secure code generation quantity.

[0008] As a preferred implementation, the calculation of the baseline instantaneous secure coding rate and the baseline cumulative secure coding amount includes: Obtain the orbital parameters, ground station parameters, and quantum key distribution system parameters of the target overpassing satellite, and determine the actual line-of-sight path of the quantum beam at each calculation moment based on the orbital parameters and ground station parameters; The transmission loss caused by Rayleigh scattering, gas absorption, non-sea salt background aerosol extinction, sea salt aerosol hygroscopic extinction, and sea fog extinction was determined along the actual line-of-sight path, and the additional transmission loss caused by sea salt deposition at the receiving window was also determined. The atmospheric transmission efficiency and the receiving window transmission correction efficiency are determined based on transmission loss and additional transmission loss, and the total link efficiency at each calculation time is determined by combining geometric collection efficiency, tracking efficiency, receiving optical efficiency and detector efficiency. The background response is determined based on the background light count rate, dark count rate, and effective detection gate width. The total link efficiency and background response are input into a preset decoy state quantum key distribution protocol key generation rate calculation model to obtain the benchmark instantaneous secure key generation rate at each calculation moment. The benchmark instantaneous secure key generation rate is integrated over time during the effective transit period to obtain the benchmark cumulative secure key generation amount.

[0009] As a further preferred embodiment, the determination of transmission loss caused by moisture absorption and extinction of sea salt aerosol includes: The dry particle size distribution of sea salt particles near the sea surface was determined based on the wind speed near the sea surface, and the dry particle size distribution of sea salt particles at different heights was determined based on the decay relationship of sea salt particle number concentration with height. The wet particle size and wet complex refractive index of sea salt particles after moisture absorption are determined based on the relative humidity and temperature at different altitudes. The Mie extinction efficiency of sea salt particles is determined based on the wet particle size and wet complex refractive index. The extinction coefficient of sea salt aerosol is determined based on the Mie extinction efficiency and the particle size distribution of sea salt particles at the corresponding altitudes. The transmission loss caused by the hygroscopic extinction of sea salt aerosols was determined based on the extinction coefficient of sea salt aerosols.

[0010] As a further preferred embodiment, determining the additional transmission loss caused by sea salt deposition at the receiving window includes: The dry particle size distribution of sea salt particles near the sea surface is determined based on the near-sea surface wind speed, and the mass concentration of sea salt near the sea surface is determined based on the dry particle size distribution. The sea salt deposition flux is determined based on the near-surface sea salt mass concentration and the equivalent deposition rate corresponding to the installation status of the receiving window. The sea salt deposition amount of the receiving window at each calculation time is determined based on the sea salt deposition amount at the start of the target transit and the newly added sea salt deposition amount during the target transit. The additional transmission loss caused by sea salt deposition in the receiving window is determined based on the quantum signal wavelength, relative humidity, and the amount of sea salt deposition in the receiving window.

[0011] As a preferred implementation, the determination of the weights at each calculation time point includes: Based on the time integration rules used to calculate the cumulative secure code generation of the benchmark, the time integration coefficients corresponding to each calculation moment are determined; the integral contribution of each calculation moment to the benchmark cumulative secure code generation is determined based on the benchmark instantaneous secure code generation rate and the corresponding time integration coefficient at each calculation moment, and the weight of each calculation moment is determined based on the integral contribution.

[0012] As a preferred implementation method, the determination of the weights of each meteorological parameter includes: Based on the differences between the forecast and measured values ​​of corresponding meteorological parameters in historical weather forecasts and corresponding measured records, the representative forecast error scale for each meteorological parameter is determined. While keeping other meteorological parameters unchanged, each meteorological parameter in the target transit weather forecast is changed according to the corresponding representative forecast error scale, and the cumulative safe code amount is recalculated. Based on the degree of change of the recalculated cumulative safe code amount relative to the baseline cumulative safe code amount, the weight of each meteorological parameter is determined.

[0013] As a preferred implementation method, the determination of the overall difference value includes: The forecast differences between the target transit weather forecast and each group of historical weather forecasts are determined for various meteorological parameters and calculation times. The forecast differences are weighted using the weights of the corresponding meteorological parameters and the corresponding calculation times to obtain the comprehensive difference value corresponding to each group of historical weather forecasts.

[0014] As a preferred implementation method, the overall correction of the target transit weather forecast includes: for each set of selected historical weather forecasts and corresponding measured paired records, determining the forecast error between the measured value and the forecast value of each meteorological parameter at each calculation time and corresponding altitude position, and applying the forecast errors of each set of paired records as a whole to the target transit weather forecast according to the original time and altitude correspondence.

[0015] As a preferred implementation, determining the cumulative secure code quantity prediction interval based on the obtained multiple sets of cumulative secure code quantities includes: Based on the comprehensive difference between the historical weather forecasts and the target transit weather forecasts selected by each group, the statistical weights corresponding to the modified weather scenarios of each group are determined. The statistical weights are negatively correlated with the comprehensive difference values. Based on the cumulative safe code generation amount and its statistical weight corresponding to each group of modified meteorological scenarios, a weighted empirical distribution is constructed. The lower limit and upper limit of the cumulative safe code generation amount are determined according to the preset lower quantile probability and preset upper quantile probability of the weighted empirical distribution, respectively. The range between the lower limit and the upper limit of the prediction is determined as the prediction interval of the cumulative safe code generation amount. The lower limit of the prediction is determined as the conservative amount of code generated, and the probability of mission achievement is determined based on the statistical weights of each group of modified meteorological scenarios whose cumulative secure code generation reaches the preset minimum secure key amount.

[0016] On the other hand, the present invention also provides a system for predicting the cumulative secure key generation interval of satellite-to-ground quantum key distribution for island or coastal ground stations, comprising: The benchmark calculation module acquires the target transit weather forecast and multiple sets of historical weather forecasts and corresponding measured pairing records. Based on the target transit weather forecast, it determines the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link in order to calculate the benchmark instantaneous secure coding rate and benchmark cumulative secure coding amount of the target transit satellite. The calculation time weight determination module determines the weight of each calculation time based on the contribution of the baseline instantaneous secure coding rate to the baseline cumulative secure coding amount at each calculation time. The meteorological parameter weight determination module determines the representative forecast error scale of each meteorological parameter based on historical weather forecasts and corresponding measured paired records. It then changes each meteorological parameter in the target transit weather forecast according to the corresponding representative forecast error scale, and determines the weight of each meteorological parameter based on the resulting cumulative safe code amount change. The historical record selection module uses the weights of meteorological parameters and the weights of calculation time to determine the comprehensive difference value between the target transit weather forecast and each group of historical weather forecasts, and selects several historical weather forecasts with smaller comprehensive difference values ​​to pair with corresponding measured records. The modified meteorological scenario generation module uses the forecast error between each set of selected historical meteorological forecasts and corresponding measured paired records to make overall corrections to the target transit meteorological forecasts, thereby forming several sets of modified meteorological scenarios. The prediction module redetermines the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link for each set of corrected meteorological scenarios, calculates the cumulative secure code generation, and determines the prediction interval of the cumulative secure code generation based on the obtained cumulative secure code generation.

[0017] Compared with existing technologies, the method and system for predicting the cumulative secure code generation interval of satellite-to-ground quantum key distribution for islands or coastal ground stations provided by this invention have the following beneficial effects: This invention corrects the target transit weather forecast based on forecast errors in historical weather forecasts and corresponding measured paired records. During the historical weather record selection process, it considers both the impact of different meteorological parameters on the cumulative secure code generation and the contribution of different calculation times to the cumulative secure code generation. This ensures that the selected historical weather records highlight meteorological differences relevant to the actual code generation results of the current satellite-to-ground quantum key distribution mission, reducing interference from meteorological differences with weak correlation to the cumulative secure code generation. Furthermore, when using selected historical records for correction, it maintains the original forecast error correspondence between different meteorological parameters, different calculation times, and different altitude positions within the same historical meteorological process. This results in a corrected meteorological scenario that is both highly task-specific and maintains the overall variation characteristics of historical forecast errors, providing a more reasonable and reliable meteorological input for subsequent estimation of the target transit cumulative secure code generation. Attached Figure Description

[0018] Figure 1 This is a flowchart of a method for predicting the cumulative secure code generation range of satellite-to-ground quantum key distribution for islands or coastal ground stations, according to an embodiment of the present invention. Figure 2 This is a diagram illustrating the satellite-to-ground quantum key distribution link and its impact on the marine and atmospheric environment, according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the instantaneous safe coding rate corresponding to each modified meteorological scenario in the verification embodiment of the present invention; Figure 4 This is a schematic diagram of the accumulated secure code volume over time in a verification embodiment of the present invention; Figure 5 This is a schematic diagram of the weighted cumulative probability distribution of the code generation quantity in a verification embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the task achievement probability of a verification embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0021] Embodiments of the present invention provide a method for predicting the cumulative secure code generation interval of satellite-to-ground quantum key distribution for island or coastal ground stations, the flowchart of which is shown below. Figure 1 As shown, it includes steps S100-S600.

[0022] S100 acquires the target transit weather forecast and multiple sets of historical weather forecasts and corresponding measured pairing records, and determines the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link based on the target transit weather forecast, so as to calculate the reference instantaneous secure coding rate and reference cumulative secure coding amount of the target transit satellite.

[0023] For ease of understanding, Figure 2 The effects of satellite-to-ground quantum key distribution links and the marine and atmospheric environment on them are shown.

[0024] Specifically, in step S100, the orbit prediction data of the target quantum satellite is acquired, and the effective transit period of the target satellite relative to the ground station is determined based on the orbit prediction data, including the start and end times of the satellite transit. and And set the calculation time according to the preset time interval within the effective transit period. ,in This represents the total number of calculated time slots within the valid transit period.

[0025] Furthermore, the system acquires the target satellite's transit weather forecast, multiple sets of historical weather forecasts and corresponding measured pairing records, as well as the target satellite's orbital parameters, ground station parameters, quantum key distribution system parameters, and environmental parameters used to determine the environmental loss of the satellite-to-ground quantum link.

[0026] Specifically, for the target transit weather forecast, the weather forecast parameters corresponding to each calculation time are obtained based on the effective transit time of the target satellite.

[0027] In one implementation, the meteorological parameters involved in subsequent historical forecast error correction can be expressed as: .

[0028] in, This refers to the wind speed forecast value at a height of 10m above the sea surface or land. This is the horizontal visibility forecast value. This is the predicted elevation of the top layer of sea fog when it is present. , and They are respectively the height positions Forecast values ​​for relative humidity, temperature, and air pressure at the location.

[0029] Furthermore, a pairing record between multiple sets of historical weather forecasts and corresponding measured data is established. For any set of historical weather records, it should include at least hourly forecast values ​​and corresponding measured values ​​of wind speed, horizontal visibility, and sea fog top elevation at a height of 10m above the sea surface or ground, as well as hourly forecast profiles and corresponding measured profiles of relative humidity, temperature, and air pressure at different altitudes. The forecast release time, target period, season, and sea fog state corresponding to this set of historical weather records should also be recorded.

[0030] Maintaining a temporal and altitude correspondence between historical weather forecasts and corresponding measured data enables subsequent determination of the forecast error between measured and forecast values ​​at the same calculation time and altitude for the same historical weather process.

[0031] When organizing historical data, historical weather forecast data and corresponding measured data from different sources can be converted to a unified data format. Specifically, a unified time interval, altitude coordinates, and parameter units can be used, and historical weather forecast data and corresponding measured data can be preprocessed according to unified rules for handling missing data. This ensures that the forecast values ​​and measured values ​​in the same historical weather record can correspond one-to-one according to the calculation time and altitude position.

[0032] Orbital parameters are used to determine the actual line-of-sight path of the quantum beam from the satellite to the ground station at each calculation moment. For the target satellite's orbital parameters, the satellite's position and elevation angle are obtained at each calculation moment. Azimuth Distance between stars and Earth .

[0033] For quantum key distribution systems, obtaining the wavelength of quantum signals Transmitted beam waist, receiver aperture, tracking error, receiver optical efficiency Detector efficiency Dark count rate Effective detection gate width Pulse repetition frequency The parameters of the decoy state quantum key distribution protocol used are also specified. These parameters are used to subsequently determine data such as the system transmission efficiency and background response of the quantum beam, and to further calculate the instantaneous secure key generation rate at each computation moment.

[0034] After obtaining the above parameters, the target transit weather forecast is used as the baseline weather scenario corresponding to the target transit. Based on the target satellite's orbital parameters and ground station parameters, the actual line-of-sight path of the quantum beam from the satellite to the ground station at each calculation time is determined, and the transmission loss of the quantum signal caused by the atmospheric environment is determined along the actual line-of-sight path.

[0035] Specifically, transmission loss includes at least the losses caused by Rayleigh scattering, gas absorption, non-sea salt background aerosol extinction, sea salt aerosol hygroscopic extinction, and sea fog extinction; for island or coastal ground stations, additional transmission loss caused by sea salt deposition in the receiving window is also determined based on the near-sea surface sea salt environment and the sea salt deposition state of the receiving window.

[0036] The calculation time is obtained based on the various transmission losses determined along the actual line-of-sight path. The corresponding atmospheric transmission efficiency is obtained, and the transmission correction efficiency of the receiving window is obtained based on the additional transmission loss caused by sea salt deposition in the receiving window. Furthermore, the total link efficiency corresponding to each calculation time is determined by combining the geometric collection efficiency, tracking efficiency, receiving optical efficiency, and detector efficiency of the quantum beam.

[0037] Furthermore, the background response is determined based on the background light count rate, dark count rate, and effective detection gate width at each calculation moment. The total link efficiency and background response are then input into a preset decoy state quantum key distribution protocol key generation rate calculation model to obtain the instantaneous secure key generation rate at each calculation moment.

[0038] If the target transit weather forecast has not been corrected for historical forecast errors, the instantaneous safe code generation rate calculated according to the above process will be recorded as the baseline instantaneous safe code generation rate. Then, according to the trapezoidal integral rule, the baseline instantaneous secure coding rate at each calculation time within the effective transit period is integrated over time to obtain the baseline cumulative secure coding amount corresponding to this transit of the target satellite. .

[0039] Among them, the benchmark instantaneous secure coding rate Used to subsequently determine the contribution of different calculation times to the baseline cumulative secure code generation, the baseline cumulative secure code generation. This is used to evaluate the impact of changes in different meteorological parameters within the representative forecast error scale on the cumulative safe code generation, and serves as a benchmark for comparing the cumulative safe code generation corresponding to each group of corrected meteorological scenarios.

[0040] S200 determines the weight of each calculation moment based on the contribution of the baseline instantaneous secure coding rate to the baseline cumulative secure coding amount.

[0041] Specifically, in order to ensure that the weight of each calculation moment is consistent with the trapezoidal integral rule used in step S100 for calculating the cumulative secure code amount of the benchmark, the time integration coefficient corresponding to each calculation moment is determined according to the time interval between adjacent calculation moments.

[0042] First, let two adjacent calculation times... and The time interval between them is: .

[0043] Furthermore, based on the trapezoidal integral rule, each calculation time is defined. Corresponding time integral coefficient for: .

[0044] Wherein, time integral coefficient This corresponds to the time contribution of each calculation moment in the trapezoidal integral when calculating the cumulative secure code amount of the benchmark.

[0045] Based on the baseline instantaneous secure coding rate at each calculation time and its corresponding time integral coefficient Determine the calculation time weight : .

[0046] Therefore, the weight of each calculation moment is determined by the benchmark instantaneous secure coding rate of that calculation moment and its time integral coefficient in the time integral of the benchmark cumulative secure coding amount. Calculation moments with higher benchmark instantaneous secure coding rates and longer corresponding integration times have greater weights.

[0047] S300 determines the representative forecast error scale of each meteorological parameter based on historical weather forecasts and corresponding measured paired records, changes each meteorological parameter in the target transit weather forecast according to the corresponding representative forecast error scale, and determines the weight of each meteorological parameter based on the cumulative safe code amount change caused by the result.

[0048] Specifically, for each meteorological parameter involved in the correction of historical forecast errors, historical records with similar seasons, sea fog conditions, weather types, and forecast lead times are selected from multiple sets of historical weather forecasts and corresponding measured records. Based on the differences between the forecast values ​​and measured values ​​of the corresponding meteorological parameters in each historical record, the representative forecast error scale of the meteorological parameter is determined.

[0049] For wind speed, horizontal visibility at a height of 10m above sea level or ground, and the altitude of the top layer of sea fog, the first of these parameters is... Meteorological parameters at each calculation time Based on the differences between the forecast values ​​and corresponding measured values ​​of this meteorological parameter in multiple historical records included in the statistics, its representative forecast error scale is determined: .

[0050] in, and The first The first set of historical meteorological records Meteorological parameters at the calculation time The predicted and measured values, For use in determining the first The number of historical meteorological records representing the forecast error scale of each meteorological parameter.

[0051] thus, Characterizing the first Meteorological parameters at the calculation time The corresponding historical forecast error scale.

[0052] For relative humidity, temperature, and air pressure, the first... For the vertical profile parameters, since their meteorological values ​​vary with both the calculation time and altitude, historical forecast values ​​are mapped to corresponding measured values ​​at a unified time and altitude location, and representative forecast error scales are determined for each altitude location. .

[0053] in, and The first The first set of historical meteorological records Item vertical profile parameter at height position Calculation time The corresponding forecast and measured values.

[0054] After determining the representative forecast error scale of each meteorological parameter, one meteorological parameter in the target transit weather forecast is changed while keeping the other meteorological parameters unchanged, in order to determine the impact of the change of the meteorological parameter within its representative forecast error scale on the cumulative safe code generation.

[0055] Specifically, regarding wind speed, horizontal visibility, and the altitude of the top layer of sea fog, the first... For each meteorological parameter, the representative forecast error scale for the corresponding calculation time is increased or decreased in the target transit weather forecast. ; .

[0056] For relative humidity, temperature, and air pressure, the first... For the vertical profile parameters, the corresponding representative forecast error scales are increased and decreased at each calculation time and at each altitude position, respectively: ; .

[0057] Among them, in the case of the first When the above-mentioned meteorological parameters are adjusted, the other meteorological parameters in the target transit weather forecast remain unchanged.

[0058] The target transit weather forecasts, after increasing and decreasing the representative forecast error scale respectively, are re-substituted into the cumulative secure code generation calculation process in step S100 to redetermine the corresponding satellite-to-ground quantum link environmental loss, link efficiency, and instantaneous secure code generation rate, and the cumulative secure code generation amount is obtained respectively. and .

[0059] Specifically, when different meteorological parameters are changed, the environmental losses associated with the changes in those meteorological parameters are recalculated. Specifically, when wind speed or relative humidity is changed, the effects of dry-state sea salt particle size distribution, hygroscopic state of sea salt particles, sea salt aerosol extinction, and the impact of newly deposited sea salt during the target's passage are recalculated. When the vertical profiles of temperature or air pressure are changed, Rayleigh scattering, gas absorption, and environmental losses associated with the hygroscopic state of sea salt particles are recalculated. When horizontal visibility or the altitude of the top layer of sea fog is changed, the sea fog coverage and the environmental losses caused by sea fog extinction are recalculated.

[0060] Furthermore, the cumulative safe coding quantity obtained after increasing and decreasing the representative forecast error scale is compared with the baseline cumulative safe coding quantity obtained in step S100. The change determines the first The impact of meteorological parameters on the cumulative safe code volume of this transit Thus, the first The cumulative change in safe code quantity caused by the change of a meteorological parameter along the increasing and decreasing directions by a representative forecast error scale: .

[0061] Normalize the influence quantities corresponding to each meteorological parameter to obtain the first... Weight of meteorological parameters : .

[0062] in, The number of meteorological parameters involved in the correction of historical forecast errors.

[0063] In step S300, the weight of each meteorological parameter is determined by the change in the cumulative safe code generation caused by the change of the meteorological parameter within the historical representative forecast error scale. For meteorological parameters that cause a large change in the cumulative safe code generation after changing within the corresponding representative forecast error scale, the corresponding meteorological parameter weight is larger; for meteorological parameters that cause a small change in the cumulative safe code generation, the corresponding meteorological parameter weight is smaller.

[0064] S400 uses the weights of meteorological parameters and the weights of calculation time to determine the comprehensive difference value between the target transit weather forecast and each group of historical weather forecasts, and selects several historical weather forecasts with smaller comprehensive difference values ​​to pair with corresponding measured records.

[0065] It should be noted that if historical meteorological records are selected solely based on the numerical similarity of various meteorological parameters, the impact of different meteorological parameters on the cumulative safe coding volume, as well as the contribution of different transit times to the cumulative safe coding volume, are not differentiated. In reality, the impact of meteorological parameter differences of the same magnitude on the cumulative safe coding volume may differ when occurring at different meteorological parameters; similarly, when forecast differences for the same meteorological parameter occur at different transit times, their reference significance for selecting historical records also differs because the contribution of each time to the cumulative safe coding volume of this transit is different.

[0066] Therefore, this invention does not simply select historical records based on the meteorological distance between the target transit weather forecast and historical weather forecasts. Instead, it uses the meteorological parameter weights determined in step S300 and the calculation time weights determined in step S200 to weight the forecast differences at different meteorological parameters and different calculation times. This allows the comprehensive difference value to primarily reflect the differences in meteorological parameters at calculation times that contribute significantly to the cumulative safe code generation of this transit and have a significant impact on the cumulative safe code generation.

[0067] When comparing historical meteorological records, each set of historical meteorological forecast data is matched with the target transit meteorological forecast in terms of time interval and altitude. This ensures that each set of historical meteorological records includes the forecast values ​​of wind speed, horizontal visibility, and sea fog top elevation at each calculation time, as well as the forecast vertical profiles of relative humidity, temperature, and air pressure.

[0068] In one implementation, historical meteorological records can first be screened for comparability based on the season, weather type, sea fog condition, and forecast lead time of the target transit. Priority is given to historical meteorological records with the same or similar season, weather type, sea fog condition, and forecast lead time. A comprehensive difference value is then calculated from the comparable historical meteorological records.

[0069] To eliminate the differences in units and numerical ranges among different meteorological parameters, the standard deviation of the historical forecast error for each meteorological parameter is determined based on the historical forecast error between historical weather forecasts and corresponding measured values.

[0070] For ease of explanation, the first... The first set of historical meteorological records Meteorological parameters at the calculation time The forecast value is denoted as The corresponding measured value is recorded as Then its forecast error is: .

[0071] The historical forecast average error for wind speed, horizontal visibility, and the altitude of the top layer of sea fog is: .

[0072] The corresponding standard deviation of historical forecast error is: .

[0073] For relative humidity, temperature, and air pressure, the first... Vertical profile parameter, number Group history at height position Calculation time The corresponding forecast error is: .

[0074] Its historical forecast error average is: .

[0075] The corresponding standard deviation of historical forecast error is: .

[0076] Historical forecast error standard deviation This is used to standardize the scale of forecast differences corresponding to different meteorological parameters, so as to reduce the impact of different meteorological parameters on the comprehensive difference value due to different units and value ranges.

[0077] Furthermore, the forecast differences between the target transit weather forecast and each group of historical weather forecasts at various meteorological parameters and calculation times were determined.

[0078] For wind speed, horizontal visibility, and the altitude of the top layer of sea fog, the first parameter in the target transit weather forecast is... Meteorological parameters at the calculation time The forecast value is Then it is related to the first The differences between the historical weather forecasts are as follows: .

[0079] For vertical profile parameters such as relative humidity, temperature, and air pressure, the actual oblique path length of the quantum beam at different altitudes is considered, and the forecast differences at each altitude are synthesized. Target transit weather forecast and the first The first set of historical weather forecasts The vertical profile parameters of the term are calculated at time... The differences are: .

[0080] in, For height position At the calculation time The corresponding path weight is determined based on the length of the adjacent actual diagonal path represented by that height position, and satisfies: .

[0081] The differences calculated above, for meteorological parameters with vertical profile characteristics, no longer represent the differences between two sets of weather forecasts solely by the parameter differences at a single altitude. Instead, they are combined with the situation where the quantum beam passes through different altitude regions along the actual line of sight, thus comprehensively considering the differences in the entire vertical profile.

[0082] After obtaining the forecast differences for each meteorological parameter and the corresponding calculation time, the meteorological parameter weights determined in step S300 are used. and the calculation time weights determined in step S200 Determine the first The combined difference between historical weather forecasts and the target transit weather forecast : .

[0083] in, The number of meteorological parameters included in historical weather forecast comparisons. For the first The standard deviation of the historical forecast error corresponding to each meteorological parameter.

[0084] Overall difference value The smaller the value, the higher the value. The closer the historical weather forecasts are to the target transit weather forecast under the above weighted comparison, the better. The historical weather forecasts included in the comparison are then ranked according to their comprehensive difference value. Arrange them from smallest to largest, and select the first one. A set of historical weather forecasts and their corresponding measured paired records are used as historical weather records for the subsequent generation of revised weather scenarios.

[0085] S500 uses the forecast error between each set of selected historical weather forecasts and corresponding measured paired records to make overall corrections to the target transit weather forecast, so as to form several sets of corrected weather scenarios.

[0086] Specifically, for each set of historical weather forecasts and corresponding measured pairing records selected in step S400, the forecast error between the measured values ​​and forecast values ​​of various meteorological parameters in the set of historical records is determined according to the calculation time and altitude position corresponding to the target transit weather forecast. The forecast errors of various historical records in the same set are applied to the target transit weather forecast as a whole according to their original time correspondence and altitude correspondence to form a set of corrected meteorological scenarios.

[0087] The reason for selecting a set of historical weather forecasts for correction is that meteorological parameters such as wind speed, visibility, sea fog top elevation, relative humidity, temperature, and air pressure within the same historical weather event exhibit corresponding changes. Furthermore, the forecast errors of each meteorological parameter show continuous variation over time and continuous distribution with altitude. If the forecast errors from different historical weather events were broken down and recombined, it might disrupt the original correspondences between different meteorological parameters, different calculation times, and different altitude positions within the same actual weather event. Therefore, this invention uses a complete set of paired historical weather forecasts and measured records as the basic unit for a single overall correction.

[0088] For the M groups of historical weather forecasts and their corresponding measured records finally selected in step S400, they are numbered sequentially, with the first group being... Group A is a set of selected historical meteorological records, for the first group For a set of historical meteorological records, the forecast errors for wind speed, horizontal visibility, and the top elevation of sea fog at a height of 10m above the sea or ground at each calculation time are first determined. The forecast error is determined by the difference between the measured value and the forecast value at the corresponding calculation time.

[0089] For example, for the first The above meteorological parameters, the first A set of historical meteorological records at the calculation time The corresponding forecast error can be expressed as: .

[0090] in, For the first The first set of historical meteorological records Meteorological parameters at the calculation time The corresponding forecast value, This corresponds to the measured value.

[0091] Following the same time correspondence, the aforementioned forecast error is added to the meteorological forecast value at the corresponding calculation time in the target transit meteorological forecast, thereby obtaining the meteorological parameter at the [number]th [time value]. Values ​​in the group-corrected weather scenario .

[0092] .

[0093] For meteorological parameters with vertical profiles, such as relative humidity, temperature, and air pressure, the calculation is performed simultaneously according to the calculation time and altitude. For the first... The first in the group of historical meteorological records The vertical profile parameter at the calculation time and height position The forecast error at a given location is determined by the difference between the measured value and the forecast value at that location, i.e.: .

[0094] Furthermore, according to the original calculation time and height position The corresponding relationship is used to apply the forecast error to the corresponding meteorological parameter forecast values ​​at the same calculation time and altitude position in the target transit weather forecast, thereby obtaining the corrected vertical profiles of relative humidity, temperature, and air pressure. .

[0095] Right now .

[0096] Through the above-mentioned overall correction method, each set of corrected meteorological scenarios does not involve independent error perturbations to each meteorological parameter. Instead, it transfers the complete forecast deviation structure that actually occurred in a historical weather process to the target transit weather forecast, so that the errors of different meteorological parameters, different calculation times, and different altitude positions in a set of corrected meteorological scenarios still originate from the same historical meteorological process.

[0097] Furthermore, the physical range and parameter consistency of each set of modified meteorological scenarios are checked. For example, wind speed and altitude parameters are ensured to be no less than zero, relative humidity is kept within the allowable range, and air pressure, temperature, and visibility are kept within the corresponding physical reasonable range. For modified meteorological scenarios involving sea fog, the consistency of sea fog conditions between the sea fog top elevation, visibility, and relative humidity can also be checked.

[0098] For parameters that exceed the allowable physical range, they can be corrected to the corresponding allowable boundary; for corrected meteorological scenarios with obvious missing measurements, abnormal jumps, or inability to restore the physical consistency of parameters, the corrected meteorological scenarios can be removed.

[0099] After the above processing, several sets of corrected meteorological scenarios are obtained for subsequent recalculation of the environmental loss and cumulative secure code amount of the satellite-to-ground quantum link.

[0100] S600 recalculates the cumulative safe code quantity for each group of corrected meteorological scenarios and determines the prediction interval of the cumulative safe code quantity based on the obtained cumulative safe code quantity of multiple groups.

[0101] Specifically, for each set of corrected meteorological scenarios obtained in step S500, the corrected wind speed, horizontal visibility, sea fog top elevation, and vertical profiles of relative humidity, temperature, and air pressure are used as the meteorological inputs for this calculation. The same satellite-to-ground quantum link loss, link efficiency, and secure code generation calculation process as in step S100 is used to recalculate the instantaneous secure code generation rate and the cumulative secure code generation for one transit at each calculation time corresponding to the set of corrected meteorological scenarios.

[0102] For the The group revised the weather scenario and recalculated the calculation time. The corresponding instantaneous secure coding rate is denoted as The instantaneous safe coding rate within the effective transit period is integrated over time to obtain the cumulative safe coding amount for a single transit corresponding to the modified weather scenario. .

[0103] Therefore, each set of selected historical weather forecasts paired with actual measurements forms a corresponding corrected weather scenario, and further yields a corresponding cumulative safe coding quantity. The changes in meteorological parameters in each group of corrected meteorological scenarios are transmitted to the secure coding rate through the environmental loss and link efficiency calculation process in step S100, thereby forming multiple sets of cumulative secure coding results that reflect different historical forecast error scenarios.

[0104] In some further embodiments, step S600 further includes: Based on the comprehensive difference value between each set of historical weather forecasts and the target transit weather forecast obtained in step S400 Determine the statistical weights corresponding to each group of revised meteorological scenarios. .

[0105] In these embodiments, the statistical weights are determined by normalization based on the reciprocal of the overall difference value: Right now: .

[0106] in, To prevent positive numbers with a denominator of zero, This represents the number of corrected weather scenarios used in the statistics of the forecast interval.

[0107] As can be seen from the above relationships, the overall difference value The smaller the value, the closer the historical weather forecast that forms the modified weather scenario is to the target transit weather forecast, and the greater the statistical weight of the corresponding modified weather scenario; conversely, the greater the overall difference value, the smaller the statistical weight of the modified weather scenario.

[0108] When forming the cumulative safe code volume prediction interval, the correction results corresponding to historical meteorological states that are closer to the target transit weather forecast have a greater statistical contribution, while the correction results corresponding to historical meteorological states that differ significantly from the target transit weather forecast have a smaller statistical contribution.

[0109] Based on the cumulative safe code volume corresponding to each group of revised weather scenarios and their statistical weights We construct a weighted empirical distribution of the cumulative secure code quantity. Specifically, this can be expressed as: .

[0110] in, This is an indicator function.

[0111] Pre-set the lower quantile probability for determining the prediction interval of cumulative safe code quantity. and upper quantile probability Then, based on the weighted empirical distribution, the lower limit of the cumulative safe code quantity prediction is determined respectively. and upper limit of prediction : ; .

[0112] Will be and Limited range The cumulative safe code volume prediction range for this target transit has been determined, and... As a conservative code quantity.

[0113] Furthermore, in these implementations, a minimum security key quantity is pre-set for the target transit mission. At that time, based on whether the cumulative secure key generation amount corresponding to each group of modified weather scenarios reaches the minimum secure key amount, the statistical weights corresponding to each group of modified weather scenarios that have reached the preset minimum secure key amount are accumulated to determine the probability of mission achievement for this target transit: .

[0114] The above-mentioned mission success probability is used to characterize the estimated probability that the cumulative amount of secure key generated during this satellite transit will reach the preset minimum security key requirement, taking into account historical weather forecast errors.

[0115] An embodiment of the present invention also provides a method for calculating the baseline instantaneous secure coding rate and the baseline cumulative secure coding amount in step S100.

[0116] In step S100, based on the target transit weather forecast and the target satellite's orbital parameters and ground station parameters, the actual line-of-sight path of the quantum beam at each calculation moment is determined, and the transmission losses caused by Rayleigh scattering, gas absorption, non-sea salt background aerosol extinction, sea salt aerosol hygroscopic extinction, and sea fog extinction are determined along the actual line-of-sight path. Rayleigh scattering, gas absorption, non-sea salt background aerosol extinction, and sea fog extinction can be determined using existing atmospheric optical calculation models.

[0117] Integrating the above transmission losses along the actual line-of-sight path yields the calculation time. Corresponding atmospheric transmission efficiency The transmission correction efficiency of the receiving window is determined based on the additional transmission loss caused by sea salt deposition in the receiving window. .

[0118] Furthermore, the geometric collection efficiency is determined based on the transmitted beam parameters, receiver aperture, and satellite-to-ground distance. The tracking efficiency is determined based on the tracking error. Therefore, the total link efficiency at each calculation time can be expressed as: .

[0119] in, For receiving optical efficiency, For detector efficiency.

[0120] According to the calculation time The background response is determined by the corresponding background light count rate, dark count rate, and effective detector gate width, and then the background response is compared with the total link efficiency. Input a pre-defined key rate calculation model for the decoy state quantum key distribution protocol. The key rate calculation model can employ the secure key rate calculation method of existing decoy state quantum key distribution protocols. Based on parameters such as total link efficiency, background response, average photon count in the signal state and decoy state, transmission probability at various light intensities, error parameters of detection events, and error correction efficiency, it estimates the count rate and error rate of single-photon events and obtains the calculation time. The corresponding secure coding rate.

[0121] When using the target transit weather forecast without historical forecast error correction as input, the resulting safe code rate is recorded as the baseline instantaneous safe code rate. .

[0122] The baseline instantaneous secure coding rate within the effective transit period is integrated over time using the trapezoidal integral rule and the actual time interval to obtain the baseline cumulative secure coding amount. .

[0123] In some further embodiments, the transmission loss caused by the moisture absorption and extinction of sea salt aerosol in step S100 can be determined as follows: First, based on the calculation time Corresponding near-sea wind speed The dry particle size distribution of sea salt particles near the sea surface is determined. Specifically, multiple log-normal sub-distributions can be used to characterize the particle size distribution of sea salt particles near the sea surface. In one embodiment, two log-normal sub-distributions are superimposed to form the dry particle size distribution of sea salt particles. The amplitude coefficient, geometric mean dry particle size, and geometric standard deviation of each sub-distribution are determined according to the wind speed range to which the near-sea surface wind speed belongs, or they can be determined by interpolation based on the corresponding parameters of adjacent wind speed ranges.

[0124] As a feasible example, the Porter-Clarke marine aerosol wind speed banding parameters can be used to determine the aforementioned dry particle size distribution. To illustrate the selection method for the double log-normal particle size spectrum parameters, Table 1 lists the parameters corresponding to some representative 10m wind speed ranges. The first group of parameters... , and The second set of parameters corresponds to the peaks of sea salt particles with larger particle sizes. , and The peak corresponds to sea salt particles with smaller particle sizes.

[0125] Table 1

[0126] Furthermore, considering that the sea salt particle number concentration decreases with increasing altitude, the dry particle size distribution near the sea surface is transformed into the dry particle size distribution of sea salt particles at different altitudes based on the relationship between the sea salt particle number concentration and altitude.

[0127] As a feasible example, the effective extinction height of sea salt aerosol is denoted as... The altitude of the ground station is recorded as Let the proportion of sea salt particle number concentration at the effective extinction height to the near-sea surface number concentration be assumed. The vertical attenuation scale of sea salt particles can then be expressed as: .

[0128] high The dry particle size distribution of sea salt at a given location can be determined from the near-sea surface dry particle size distribution according to an exponential relationship based on the vertical attenuation scale. For example: .

[0129] in, , The dry diameter of the sea salt particles. This represents the dry particle size distribution near the sea surface. We can take... This means that the particle number concentration at the effective extinction height of sea salt aerosols has been reduced to 1% of the near-sea surface value.

[0130] Because sea salt particles are hygroscopic, further analysis is needed based on the relative humidity at different altitudes. and temperature Determine the wet particle size of sea salt particles of various dry particle sizes after moisture absorption. In one embodiment, it can be based on... -Köhler's hygroscopic equilibrium relationship for solving wet particle size .

[0131] After obtaining the wet particle size, the complex refractive index of the wet sea salt particles is determined based on the volume fractions of dry sea salt and adsorbed water in the wet particles. For example, the Lorentz-Lorenz mixing relation can be used to determine the wet complex refractive index based on the complex refractive indices of dry sea salt and liquid water at the quantum signal wavelength and their volume fractions.

[0132] Furthermore, the hygroscopic sea salt particles are approximated as uniform spherical particles. Based on the wet particle size, wet complex refractive index, and quantum signal wavelength, the corresponding Mie extinction efficiency is calculated using Mie scattering. .

[0133] For the wetted diameter is The extinction cross section of sea salt particles can be expressed as: .

[0134] in, For wet sea salt particle size parameters, It represents the complex relative refractive index of wet sea salt particles relative to the surrounding air.

[0135] By integrating the wet particle extinction cross section corresponding to each dry particle size with the number and particle size distribution of sea salt particles at the current height, the height can be obtained. Calculation time The corresponding extinction coefficient of sea salt aerosol: .

[0136] Therefore, the extinction coefficient of sea salt aerosol at each height position along the actual line-of-sight path of the quantum beam is determined. The extinction coefficient of sea salt aerosol is integrated along the actual line-of-sight path to obtain the corresponding optical thickness of sea salt aerosol, and the transmission loss caused by hygroscopic extinction of sea salt aerosol is determined based on the optical thickness.

[0137] In some further embodiments, in step S100, the additional transmission loss caused by sea salt deposition at the receiving window is determined as follows: Based on the aforementioned dry sea salt particle size distribution and dry sea salt density determined by near-surface wind speed, the near-surface sea salt mass concentration is determined. Based on the near-surface sea salt mass concentration and the equivalent deposition rate corresponding to the installation status of the target ground station's receiving window, the sea salt deposition flux per unit time is determined.

[0138] The amount of sea salt deposited per unit area at the start of the target transit window is used as the initial amount, and the amount of sea salt deposited is increased cumulatively based on the sea salt deposition flux corresponding to each calculation time during the target transit period, thereby determining the amount of sea salt deposited at each calculation time window.

[0139] Furthermore, based on the quantum signal wavelength, the relative humidity of the environment where the receiving window is located, and the amount of sea salt deposition at the receiving window, the additional optical thickness caused by sea salt deposition is determined, and the additional transmission loss caused by sea salt deposition at the receiving window and the corresponding transmission correction efficiency of the receiving window are obtained accordingly.

[0140] The relationship between sea salt deposition and additional optical thickness can be determined based on the effective extinction coefficient per unit surface density under the corresponding quantum signal wavelength and humidity conditions; the equivalent deposition rate and the effective extinction coefficient per unit surface density can be obtained from historical observation data of the target ground station or corresponding calibration tests.

[0141] To verify the effectiveness of the cumulative secure code generation interval prediction method of the present invention, the present invention also provides a verification embodiment, which performs simulation verification on the cumulative secure code generation interval prediction.

[0142] In this embodiment, the ground station altitude is set to 50m, the quantum satellite orbital altitude to 500km, and the Earth's equivalent radius to 6371km. The effective transit period is selected as 300s before and after the satellite's relative transit center time, with a calculation time interval of 10s, resulting in 61 calculation times. The satellite elevation angle gradually increases from 20° to 75° and then decreases back to 20°. 101 altitude calculation nodes are set along the upper limit of atmospheric extinction integral from the ground station altitude to 20km, and the satellite-ground skew path corresponding to different calculation times is calculated according to the aforementioned actual line-of-sight path determination method.

[0143] This embodiment employs a decoy-state quantum key distribution protocol with a quantum signal wavelength of 1550 nm. The link transmission efficiency and baseline instantaneous secure key generation rate are calculated at each calculation moment based on the target transit weather forecast, following the aforementioned method. Regarding the impact of sea salt aerosols in the island environment, the effective extinction height of sea salt aerosols is taken as 1200 m, and it is assumed that the sea salt particle number concentration at this height decreases to 1% of the near-sea surface value. The dry particle size distribution, hygroscopic growth, wet complex refractive index, and Mie extinction of sea salt particles are determined according to the aforementioned implementation method.

[0144] To create a historical sample for weather forecast error analysis, this embodiment constructs 60 sets of paired historical weather forecasts and corresponding measured records. Each set includes wind speed, horizontal visibility, and sea fog top elevation at 61 calculation times, as well as forecast and measured values ​​of relative humidity, temperature, and air pressure at 101 altitude locations. The forecast errors in each set of historical records are generated using an error process with temporal and altitude continuity to simulate the continuous variation of actual weather forecast errors over time and altitude.

[0145] Following steps S200-S400, the weights of each meteorological parameter and the weight of the calculation time are determined. The comprehensive difference values ​​between 60 sets of historical weather forecasts and the target transit weather forecast are calculated. These comprehensive difference values ​​are then sorted from smallest to largest, and the top 12 historical records are selected. For each selected historical record, all meteorological parameters involved in the correction, all calculation times, and all forecast errors at all altitude positions are applied to the target transit weather forecast according to the original time and altitude correspondences, thus forming 12 sets of corrected meteorological scenarios. The corresponding cumulative safe coding amount is then recalculated for each scenario.

[0146] After simulation under the above conditions, the range of instantaneous safe key rate variation for each modified meteorological scenario as a function of satellite transit is as follows: Figure 3 As shown, the main contribution to secure coding is concentrated near the transit center at higher satellite elevation angles. Integrating the instantaneous secure coding rate at each calculation moment, the accumulated secure coding amount over time is as follows: Figure 4 As shown, the cumulative weighted cumulative probability distribution of the cumulative code generation is constructed based on the statistical weights of each scenario, as follows: Figure 5 As shown.

[0147] Furthermore, such as Figure 6 As shown, as the minimum security key quantity required for the target transit mission gradually increases, the mission success probability obtained from the statistics of various modified meteorological scenarios decreases accordingly. In this embodiment, the minimum security key requirement is set to 90% of the baseline cumulative security key quantity. When the corresponding minimum security key requirement is reached, the mission success probability approaches 1. Therefore, target satellite transit missions can be screened and key resources can be allocated according to different minimum security key requirements.

[0148] Another embodiment of the present invention provides a system for predicting the cumulative secure code generation interval of satellite-to-ground quantum key distribution for island or coastal ground stations, including a benchmark calculation module, a calculation time weight determination module, a meteorological parameter weight determination module, a historical record selection module, a corrected meteorological scenario generation module, and a prediction module.

[0149] The benchmark calculation module is used to acquire the target transit weather forecast and multiple sets of historical weather forecasts and corresponding measured pairing records, and to determine the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link based on the target transit weather forecast, so as to calculate the benchmark instantaneous secure coding rate and benchmark cumulative secure coding amount of the target transit satellite. The calculation time weight determination module is used to determine the weight of each calculation time based on the contribution of the benchmark instantaneous secure coding rate to the benchmark cumulative secure coding amount at each calculation time. The meteorological parameter weight determination module is used to determine the representative forecast error scale of each meteorological parameter based on the historical weather forecast and the corresponding measured pairing records, change each meteorological parameter in the target transit weather forecast according to the corresponding representative forecast error scale, and determine the weight of each meteorological parameter based on the cumulative safe code amount change caused therefrom. The historical record selection module is used to determine the comprehensive difference value between the target transit weather forecast and each group of historical weather forecasts by using the weights of the meteorological parameters and the weights of the calculation time, and to select several historical weather forecasts with smaller comprehensive difference values ​​and pair them with corresponding measured records. The modified meteorological scenario generation module is used to make overall modifications to the target transit meteorological forecast by utilizing the forecast error between each set of selected historical meteorological forecasts and the corresponding measured paired records, so as to form several sets of modified meteorological scenarios. The prediction module is used to redetermine the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link for each group of modified meteorological scenarios, so as to calculate the cumulative secure code amount and determine the prediction interval of the cumulative secure code amount based on the obtained cumulative secure code amount of multiple groups.

[0150] It is understandable that when the above module performs its function, it can be achieved by executing the steps in the method for predicting the cumulative secure code generation range of satellite-to-ground quantum key distribution for islands or coastal ground stations in the above embodiments.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0152] The above description is merely an exemplary embodiment of this application and should not be construed as limiting the scope of this application. Any equivalent changes and modifications made in accordance with the teachings of this application shall still fall within the scope of this application. Other embodiments of this application will readily conceive of those skilled in the art upon consideration of the specification and practice of the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this application are defined by the claims.

Claims

1. A method for predicting the cumulative secure key generation interval of satellite-to-ground quantum key distribution for island or coastal ground stations, characterized in that, include: Acquire the target transit weather forecast and multiple sets of historical weather forecasts and corresponding measured pairing records, and determine the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link based on the target transit weather forecast, so as to calculate the reference instantaneous secure coding rate and reference cumulative secure coding amount of the target transit satellite; The weight of each calculation moment is determined based on the contribution of the baseline instantaneous secure coding rate to the baseline cumulative secure coding amount at each calculation moment; Based on the historical weather forecasts and corresponding measured paired records, the representative forecast error scales of each meteorological parameter are determined. The meteorological parameters in the target transit weather forecast are changed according to the corresponding representative forecast error scales, and the weights of each meteorological parameter are determined based on the cumulative safe code amount changes caused thereby. Using the weights of the meteorological parameters and the weights of the calculation time, the comprehensive difference value between the target transit weather forecast and each group of historical weather forecasts is determined, and several historical weather forecasts with smaller comprehensive difference values ​​are selected and paired with corresponding measured records. By utilizing the forecast error between each selected set of historical weather forecasts and the corresponding measured paired records, the overall weather forecast for the target transit is corrected to form several sets of corrected weather scenarios. For each group of modified meteorological scenarios, the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link are re-determined to calculate the cumulative secure code generation quantity, and the prediction interval of the cumulative secure code generation quantity is determined based on the obtained cumulative secure code generation quantities.

2. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 1, characterized in that, The calculation of the benchmark instantaneous secure coding rate and the benchmark cumulative secure coding amount includes: The orbital parameters, ground station parameters, and quantum key distribution system parameters of the target passing satellite are obtained, and the actual line-of-sight path of the quantum beam at each calculation moment is determined based on the orbital parameters and ground station parameters. The transmission loss caused by Rayleigh scattering, gas absorption, non-sea salt background aerosol extinction, sea salt aerosol hygroscopic extinction, and sea fog extinction is determined along the actual line of sight path, and the additional transmission loss caused by sea salt deposition in the receiving window is determined. The atmospheric transmission efficiency and the receiving window transmission correction efficiency are determined based on the transmission loss and the additional transmission loss, and the total link efficiency at each calculation time is determined by combining the geometric collection efficiency, tracking efficiency, receiving optical efficiency and detector efficiency. The background response is determined based on the background light count rate, dark count rate, and effective detection gate width. The total link efficiency and background response are then input into a preset decoy state quantum key distribution protocol key generation rate calculation model to obtain the benchmark instantaneous secure key generation rate at each calculation moment. The benchmark instantaneous secure key generation rate is then integrated over time within the effective transit period to obtain the benchmark cumulative secure key generation amount.

3. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 2, characterized in that, The determination of the transmission loss caused by the hygroscopic extinction of sea salt aerosol includes: The dry particle size distribution of sea salt particles near the sea surface was determined based on the wind speed near the sea surface, and the dry particle size distribution of sea salt particles at different heights was determined based on the decay relationship of sea salt particle number concentration with height. The wet particle size and wet complex refractive index of sea salt particles after absorbing moisture are determined based on the relative humidity and temperature at different altitudes. The Mie extinction efficiency of sea salt particles is determined based on the wet particle size and wet complex refractive index. The extinction coefficient of sea salt aerosol is determined based on the Mie extinction efficiency and the particle size distribution of sea salt particles at the corresponding altitude. The transmission loss caused by the hygroscopic extinction of sea salt aerosol is determined based on the extinction coefficient of the sea salt aerosol.

4. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 3, characterized in that, The determination of the additional transmission loss caused by sea salt deposition in the receiving window includes: The dry particle size distribution of near-sea salt particles is determined based on the near-sea wind speed, and the near-sea salt mass concentration is determined based on the dry particle size distribution. The sea salt deposition flux is determined based on the near-sea surface sea salt mass concentration and the equivalent deposition rate corresponding to the installation state of the receiving window. The sea salt deposition amount of the receiving window at each calculation time is determined based on the sea salt deposition amount at the start of the target transit and the newly added sea salt deposition amount during the target transit. The additional transmission loss caused by sea salt deposition in the receiving window is determined based on the quantum signal wavelength, relative humidity, and the amount of sea salt deposition in the receiving window.

5. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 1, characterized in that, The determination of the weights at each calculation time point includes: According to the time integration rule used to calculate the baseline cumulative secure code generation, the time integration coefficient corresponding to each calculation moment is determined; the integral contribution of each calculation moment to the baseline cumulative secure code generation is determined based on the baseline instantaneous secure code generation rate and the corresponding time integration coefficient at each calculation moment, and the weight of each calculation moment is determined based on the integral contribution.

6. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 1, characterized in that, The determination of the weights of the meteorological parameters includes: Based on the differences between the forecast and measured values ​​of the corresponding meteorological parameters in the historical weather forecast and the corresponding measured pairing records, a representative forecast error scale for each meteorological parameter is determined. While keeping other meteorological parameters unchanged, each meteorological parameter in the target transit weather forecast is changed according to the corresponding representative forecast error scale, and the cumulative safe coding quantity is recalculated. Based on the degree of change of the recalculated cumulative safe coding quantity relative to the baseline cumulative safe coding quantity, the weight of each meteorological parameter is determined.

7. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 1, characterized in that, The determination of the comprehensive difference value includes: The forecast differences between the target transit weather forecast and each group of historical weather forecasts are determined for various meteorological parameters and calculation times. The forecast differences are weighted using the weights of the corresponding meteorological parameters and the weights of the corresponding calculation times to obtain the comprehensive difference value corresponding to each group of historical weather forecasts.

8. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 1, characterized in that, The overall correction of the target transit weather forecast includes: for each set of selected historical weather forecasts and corresponding measured paired records, determining the forecast error between the measured and forecast values ​​of each meteorological parameter at each calculation time and corresponding altitude position, and applying the forecast errors of each pair of records in the same set to the target transit weather forecast as a whole according to the original time and altitude correspondence.

9. The method for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations according to claim 1, characterized in that, Based on the obtained cumulative secure code quantity, the predicted range of the cumulative secure code quantity includes: Based on the comprehensive difference between the historical weather forecasts selected by each group and the target transit weather forecast, the statistical weights corresponding to the modified weather scenarios of each group are determined, and the statistical weights are negatively correlated with the comprehensive difference values. A weighted empirical distribution is constructed based on the cumulative safe code generation amount and its statistical weight corresponding to each group of modified meteorological scenarios. The lower limit and upper limit of the cumulative safe code generation amount are determined according to the preset lower quantile probability and preset upper quantile probability of the weighted empirical distribution, respectively. The range between the lower limit and the upper limit of the prediction is determined as the prediction interval of the cumulative safe code generation amount. The prediction lower limit is determined as the conservative code generation amount, and the probability of task achievement is determined based on the statistical weights corresponding to each group of modified meteorological scenarios whose cumulative secure code generation amount reaches the preset minimum secure key amount.

10. A system for predicting the cumulative secure code generation interval for satellite-to-ground quantum key distribution for islands or coastal ground stations, characterized in that, include: The benchmark calculation module acquires the target transit weather forecast and multiple sets of historical weather forecasts and corresponding measured pairing records, and determines the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link based on the target transit weather forecast, so as to calculate the benchmark instantaneous secure coding rate and benchmark cumulative secure coding amount of the target transit satellite. The calculation time weight determination module determines the weight of each calculation time based on the contribution of the benchmark instantaneous secure coding rate to the benchmark cumulative secure coding amount at each calculation time. The meteorological parameter weight determination module determines the representative forecast error scale of each meteorological parameter based on the historical weather forecast and the corresponding measured pairing records, changes each meteorological parameter in the target transit weather forecast according to the corresponding representative forecast error scale, and determines the weight of each meteorological parameter based on the cumulative safe code amount change caused thereby. The historical record selection module uses the weights of the meteorological parameters and the weights of the calculation time to determine the comprehensive difference value between the target transit weather forecast and each group of historical weather forecasts, and selects several historical weather forecasts with smaller comprehensive difference values ​​to pair with corresponding measured records. The modified meteorological scenario generation module uses the forecast error between each set of selected historical meteorological forecasts and the corresponding measured paired records to make overall corrections to the target transit meteorological forecast, so as to form several sets of modified meteorological scenarios. The prediction module redetermines the environmental loss and corresponding link efficiency of the satellite-to-ground quantum link for each group of modified meteorological scenarios, calculates the cumulative secure code generation, and determines the prediction interval of the cumulative secure code generation based on the obtained cumulative secure code generation from multiple groups.

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