High-orbit and low-orbit satellite communication and conduction integrated method and system

Abstract

The invention relates to the technical field of satellite conduction integration, and discloses a high and low orbit satellite conduction integration method and system, and the method comprises the steps: carrying out the spatial characteristic analysis of a high orbit navigation satellite signal and a low orbit communication satellite signal, and obtaining visible relation data and Doppler frequency shift characteristic data; executing high-orbit and low-orbit satellite conduction switching analysis, and generating a self-adaptive threshold parameter of conduction service switching between the high-orbit and low-orbit satellites; a high-orbit and low-orbit integrated inter-satellite communication network is established between the high-orbit navigation satellite cluster and the low-orbit communication satellite cluster; conducting resource collaborative allocation is executed, and a resource scheduling instruction is generated; according to the method, deep integration of communication and navigation functions is realized, the limitation of function separation of a traditional satellite system is broken through, and the problem of unstable control caused by dynamic change of network topology of the high-orbit and low-orbit satellites is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite communication and navigation integration, and particularly to a method and system for high and low orbit satellite communication and navigation integration. Background Art

[0002] In traditional satellite systems, the navigation and communication functions are usually completely separated. High orbit satellites mainly provide navigation services, while low orbit satellites are mainly responsible for communication services. This functional separation results in high system redundancy, low resource utilization, and limited comprehensive service capabilities. Under the condition of high-speed user movement, there are significant differences in the signal characteristics of high and low orbit satellites. In particular, the Doppler frequency shift effect has a more obvious impact on low orbit satellites. However, in existing technologies, fixed handover parameters are usually adopted, and it is impossible to flexibly meet the heterogeneous handover requirements between satellites in different orbit layers.

[0003] Existing satellite systems lack an effective cooperation mechanism between high and low orbits, resulting in easy signal interruption and service quality degradation during service handover. Especially in complex scenarios such as cities, oceans, and polar regions, the signal quality of high and low orbit satellites received by high-speed mobile users fluctuates greatly. Traditional handover algorithms often cannot respond to this change in a timely manner, resulting in low handover success rates. In addition, the inter-satellite communication protocols in existing technologies are mostly designed in a general manner and are not optimized for the application scenario of communication and navigation integration, and cannot effectively support the deep integration of navigation enhancement information and communication services. Summary of the Invention

[0004] The present invention provides a method and system for high and low orbit satellite communication and navigation integration. The present invention realizes the deep integration of communication and navigation functions, breaks the limitation of the functional separation of traditional satellite systems, and solves the problem of unstable control caused by the dynamic change of the high and low orbit satellite network topology.

[0005] In a first aspect, the present invention provides a method for high and low orbit satellite communication and navigation integration, and the method for high and low orbit satellite communication and navigation integration includes: Performing spatial characteristic analysis on high orbit navigation satellite signals and low orbit communication satellite signals to obtain visibility relationship data and Doppler frequency shift characteristic data; Performing high and low orbit satellite communication and navigation handover analysis according to the visibility relationship data and the Doppler frequency shift characteristic data, and generating an adaptive threshold parameter for handover of communication and navigation services between high and low orbit satellites; Establishing a high and low orbit integrated inter-satellite communication network between the high orbit navigation satellite cluster and the low orbit communication satellite cluster according to the visibility relationship data; Performing coordinated allocation of communication and navigation resources by using the high and low orbit integrated inter-satellite communication network and the Doppler frequency shift characteristic data, and generating a resource scheduling instruction; Based on the adaptive threshold parameter and the resource scheduling instruction, perform seamless handover of communication and navigation services between high and low orbit satellites, and adjust the communication link and navigation positioning service in real time.

[0006] In a second aspect, the present invention provides a communication and navigation integrated system for high and low orbit satellites, the communication and navigation integrated system for high and low orbit satellites comprising: A space characteristic analysis module, configured to perform space characteristic analysis on high orbit navigation satellite signals and low orbit communication satellite signals to obtain visibility relationship data and Doppler frequency shift characteristic data; A handover analysis module, configured to perform communication and navigation handover analysis between high and low orbit satellites according to the visibility relationship data and the Doppler frequency shift characteristic data, and generate an adaptive threshold parameter for handover of communication and navigation services between high and low orbit satellites; An establishment module, configured to establish a high and low orbit integrated inter-satellite communication network between a high orbit navigation satellite cluster and a low orbit communication satellite cluster according to the visibility relationship data; A cooperative allocation module, configured to perform cooperative allocation of communication and navigation resources by using the high and low orbit integrated inter-satellite communication network and the Doppler frequency shift characteristic data, and generate a resource scheduling instruction; A real-time adjustment module, configured to perform seamless handover of communication and navigation services between high and low orbit satellites based on the adaptive threshold parameter and the resource scheduling instruction, and adjust the communication link and navigation positioning service in real time.

[0007] In the technical solution provided by the present invention, by establishing a three-dimensional relationship model of Doppler frequency shift, orbital altitude, and handover success rate, the limitation of the traditional fixed parameter handover method is broken through. The designed heterogeneous adaptive handover algorithm for communication and navigation integration of high and low orbit satellites can perform adaptive adjustment for the influence of Doppler frequency shift, and effectively solves the handover reliability problem under the condition of high-speed movement of users. Modeling high orbit navigation satellites and low orbit communication satellites as a heterogeneous multi-agent system, the deep integration of communication and navigation functions is realized for the first time, breaking through the limitation of the separation of functions in traditional satellite systems. The designed smooth terminal time regulator and global prescribed time-varying sliding mode control strategy solve the problem of control instability caused by the dynamic change of the network topology of high and low orbit satellites. The constructed communication and navigation fusion resource optimization model can take into account navigation accuracy, communication delay, and energy utilization efficiency at the same time, and effectively solves the inherent contradiction between navigation accuracy and communication delay. It has good adaptability in various typical scenarios such as cities, oceans, and polar regions, and can maintain a high handover success rate even in extreme Doppler environments.

[0008] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the specification, claims, and drawings.

[0009] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings and describes them in detail as follows. Description of the Drawings

[0010] Figure 1 It is a schematic diagram of an embodiment of the high-low orbit satellite communication-navigation integration method in an embodiment of the present invention; Figure 2 It is a schematic diagram of an embodiment of the high-low orbit satellite communication-navigation integration system in an embodiment of the present invention. Detailed Embodiment

[0011] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0012] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other unlisted steps or units, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0013] For the convenience of understanding this embodiment, first, a high-low orbit satellite communication-navigation integration method disclosed in the embodiments of the present invention will be introduced in detail. As Figure 1 shown, the method includes the following steps: 101. Analyze the spatial characteristics of high-orbit navigation satellite signals and low-orbit communication satellite signals to obtain visibility relationship data and Doppler frequency shift characteristic data; It can be understood that the execution subject of the present invention can be a high-low orbit satellite communication-navigation integration system, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present invention will be described by taking the server as the execution subject as an example.

[0014] Specifically, by setting two groups of preset orbital altitude intervals, satellites with orbital altitudes ranging from 20,000 kilometers to 36,000 kilometers are classified into the high-orbit navigation satellite set, and satellites with orbital altitudes ranging from 500 kilometers to 1,500 kilometers are classified into the low-orbit communication satellite set. A globally unique identifier is assigned to each satellite, and its core orbital parameters such as semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and mean anomaly are recorded. For geostationary orbit satellites, the station-keeping deviation in the east-west and north-south directions is additionally recorded, and the corresponding station-keeping window threshold is set. Based on the high- and low-orbit satellite orbital parameter database, the spatial orbital state of the satellites is accurately modeled and dynamically managed. By combining this database with inputs such as the geographical location and time information of ground users or target areas, the visibility state between each pair of high-orbit navigation satellites and low-orbit communication satellites at any given moment is dynamically calculated using celestial mechanics and line-of-sight analysis algorithms, generating visibility relationship data and forming a time-varying reachability dynamic matrix between satellites-satellites and satellites-ground. At the same time, for the mobile user environment, the raw data of the high-orbit navigation satellite signals and low-orbit communication satellite signals received by the user terminal are collected and recorded in real time. The collection content includes carrier frequency, signal strength, arrival time, noise level, etc., and particular attention is paid to the signal variation under different mobile speeds. The Doppler frequency shift value is extracted and analyzed from the collected signal raw data. Using the principle of the Doppler effect, by measuring the frequency drift of each satellite signal and combining the actual motion speed range of the user, the Doppler frequency shift characteristics under different satellite orbits are extracted. Through large-sample statistics and probability distribution modeling, due to the greater relative motion speed with the user, the Doppler frequency shift distribution range of low-orbit communication satellites is significantly higher than that of high-orbit navigation satellites. On this basis, by summarizing and statistically analyzing the Doppler frequency shift observation data of high- and low-orbit satellites in different scenarios (such as cities, oceans, polar regions), a Doppler frequency shift probability distribution model is constructed to characterize the frequency shift characteristics of high-orbit navigation satellites and low-orbit communication satellites in the space propagation environment, forming a Doppler frequency shift characteristic database.

[0015] 102. Perform the communication-navigation handover analysis of high- and low-orbit satellites based on the visibility relationship data and Doppler frequency shift characteristic data, and generate the adaptive threshold parameters for the communication-navigation service handover between high- and low-orbit satellites; Specifically, with the correlation between Doppler frequency shift and orbital altitude as the core, a three-dimensional relationship model of the high- and low-orbit satellite system is established. This modeling process not only relies on the statistical data of the characteristic data of the signals of high-orbit navigation satellites and low-orbit communication satellites in the early stage, but also non-linearly fits the Doppler frequency shift observation results of satellites at different orbital altitudes with the actual service handover success rate, so as to deduce the mathematical relationship among Doppler frequency shift, orbital altitude, and handover success rate. This function is parameterized in the form of a composite of exponential and power functions, enabling the handover success rate to be dynamically adjusted with the changes in the Doppler frequency shift amplitude and satellite orbital altitude, and fine-tuning and correction are carried out for environments such as cities, oceans, and polar regions in specific scenarios to improve the universality and accuracy of the modeling. Using the Doppler frequency shift characteristic data, by analyzing the Doppler frequency shift distribution exhibited by high- and low-orbit satellite signals in the actual link and combining with the orbital altitude parameter, the decision threshold of the Doppler frequency shift is set. The Doppler frequency shift threshold is adaptively adjusted using a power-law decreasing or increasing model with the change in orbital altitude. For example, the threshold of high-orbit satellites is lower due to their small relative velocity, while the threshold of low-orbit satellites is higher due to their high relative velocity, thus ensuring the link stability during handover between different orbital levels. Based on the three-dimensional relationship model and the Doppler frequency shift threshold, differential and transformation operations are performed on the handover success rate function to construct a handover threshold adjustment basic function and a handover trigger delay basic function. Among them, the handover threshold adjustment basic function adaptively compensates the traditional fixed hysteresis threshold according to the amplitude change of the Doppler frequency shift and the actual signal quality, enabling the system to flexibly respond to signal jitter and environmental interference in a changing link environment; while the handover trigger delay basic function achieves an optimal balance between the response speed and misjudgment probability of the handover decision by introducing a smoothing factor and a piecewise adaptive factor, effectively preventing the degradation of positioning accuracy and communication interruption caused by frequent handovers or slow handovers. Based on the above adjustment functions, the handover hysteresis threshold between high- and low-orbit satellites is dynamically adjusted to generate handover hysteresis threshold adaptive parameters. According to the handover hysteresis threshold adaptive parameters and the handover trigger delay basic function, delay optimization adjustments are respectively carried out for two typical handover scenarios: from high orbit to low orbit and from low orbit to high orbit. For example, when switching from high orbit to low orbit, the handover trigger delay is appropriately shortened to cope with the characteristics of the changing and frequent low-orbit satellite signals; while when switching from low orbit to high orbit, the delay is extended to avoid unnecessary handovers caused by short-term Doppler anomalies. Through the above process, the system dynamically generates and adjusts the adaptive threshold parameters for the communication and navigation service handover between high- and low-orbit satellites based on real-time Doppler frequency shift data and visibility relationships under a changing space environment and complex user movement states.

[0016] In this embodiment, the handover success rate function is expressed as a joint function of Doppler shift and orbital altitude, such as SR(Δf, h), where Δf is the Doppler shift variable and h is the orbital altitude variable. On this basis, the first-order partial derivatives of the handover success rate function with respect to Doppler shift and orbital altitude are calculated respectively. Taking the first-order partial derivative of the Doppler shift variable, the first sensitivity function of the handover success rate to the change in Doppler shift is obtained, which reflects the response sensitivity of the handover success rate at different Doppler shift amplitudes; while taking the first-order partial derivative of the orbital altitude variable, the second sensitivity function of the handover success rate to the change in orbital altitude is obtained, and this function reveals the influence degree of the change in satellite orbital altitude on the handover success rate. The calculation results of the first sensitivity function and the second sensitivity function are jointly processed. By setting the scenario weight or introducing a sensitivity normalization factor, the two are synthesized with a certain weight to obtain a weight factor used to characterize the adjustment priority of handover parameters in the current system state, representing the relative importance of Doppler shift and orbital altitude in the handover strategy, and can be dynamically adjusted according to the actual operating environment, channel state, user speed, etc. For example, in a high-dynamic environment, the influence weight of Doppler shift is relatively increased; while in a low-dynamic and wide-coverage scenario, the orbital altitude weight is correspondingly increased. Using the above weight factor and the Doppler shift decision threshold, combined with the original numerical characteristics of the handover success rate function, through iterative operations and dynamic adjustments, the handover threshold reference value and the handover delay reference value in the current link state are calculated. This process uses optimization means such as function iteration method and gradient descent to dynamically correct the initial settings of the threshold and delay, so that it can quickly adapt to the dynamic changes of the satellite constellation and the changes in the user's movement trajectory, and form the threshold and delay parameters that best suit the current environment. Substitute the ratio of the handover threshold reference value to the Doppler shift decision threshold into the non-linear transformation model. The ratio of the handover threshold reference value to the Doppler shift decision threshold, as the input variable of the non-linear transformation model, is transformed through functions such as exponential function, logarithmic function or piecewise increasing function, and the handover threshold adjustment basic function is output, and this function determines the sensitivity and robustness of link handover. For the delay parameter, the inverse transformation of the delay reference value and the Doppler shift decision threshold is used as the input, and through the same non-linear processing as the above threshold adjustment model, the handover trigger delay basic function is obtained.

[0017] 103. Based on the visibility relationship data, establish a high-low orbit integrated inter-satellite communication network between the high-orbit navigation satellite cluster and the low-orbit communication satellite cluster; Specifically, based on systematic and dynamic network modeling, the high-orbit navigation satellite constellation and the low-orbit communication satellite constellation are unified into the network node system. Each satellite is regarded as an independent node in the network. Its status and function are differentiated according to the orbital altitude and service type, but as a whole, it forms a heterogeneous, hierarchical, and multi-agent collaborative satellite network structure. Using the visibility relationship data calculated based on information such as the precise orbital parameters, geographical coverage, and time of the satellites, it is determined in real time which high-orbit satellites and which low-orbit satellites have direct communication capabilities at a certain moment, and then a time-varying inter-satellite communication topology map is dynamically constructed, enabling the network structure to adapt to multiple factors such as satellite movement, orbital changes, and space environment disturbances, and realizing adaptive and seamless inter-satellite link reconstruction. Communication bandwidth, transmission delay, and link reliability parameters are assigned to each link in the time-varying inter-satellite communication topology map. These parameters are not only limited by the physical layer characteristics and inter-satellite distance, but also affected by various factors such as real-time resource utilization rate, remaining energy of the satellite, and link load. A characteristic matrix of the high-low orbit inter-satellite link is constructed for all links to ensure that each link can be dynamically characterized and prioritized under different time and environmental conditions. Based on this link characteristic matrix, an inter-satellite integrated navigation and communication protocol is designed and created. This protocol not only covers the traditional communication data transmission requirements, but also considers comprehensive requirements such as navigation assistance data, clock synchronization information, and service integration parameters, enabling the deep integration of data structures and service modes between high-low orbit satellites at the protocol layer, thus breaking through the boundary between navigation and communication. At the network model level, to achieve intelligent and adaptive resource scheduling and cooperation, the high-orbit navigation satellite constellation and the low-orbit communication satellite constellation are overall modeled as a heterogeneous multi-agent system. Each satellite node is abstracted as an agent, and its state vector includes spatial position, velocity, remaining energy, task load, available bandwidth, etc., and the action space covers various decision-making behaviors such as link selection, bandwidth allocation, energy scheduling, and service switching. The states and action rules of all these agents are standardized based on the inter-satellite integrated navigation and communication protocol. With the help of this multi-agent system model, task division, collaborative compensation, and resource sharing among various satellites are realized in a complex network environment, improving the autonomy and response speed of the entire satellite network. Based on the heterogeneous multi-agent system model and the high-low orbit inter-satellite link characteristic matrix, a resource collaborative allocation algorithm is designed. Through the goal of maximizing the global utility, the limited bandwidth, energy, and computing resources are optimally allocated among various nodes and links. This process is achieved by establishing a navigation and communication integrated resource allocation matrix. Each element in the matrix reflects the resource allocation ratio or priority of a certain satellite to another satellite on a specific link, realizing the priority guarantee for key links and services, and at the same time avoiding local resource waste and global bottlenecks.By utilizing the communication-navigation integrated resource allocation matrix and the inter-satellite integrated communication-navigation protocol, an inter-satellite link between high-orbit navigation satellites and low-orbit communication satellites is established and dynamically adjusted throughout the network. According to factors such as satellite motion, service load, and space environment changes, the link configuration is dynamically optimized to achieve the adaptive reconstruction of the inter-satellite communication network, resulting in an integrated high-low orbit inter-satellite communication network.

[0018] 104. Utilize the integrated high-low orbit inter-satellite communication network and Doppler frequency shift characteristic data to perform collaborative allocation of communication-navigation resources and generate resource scheduling instructions; Specifically, based on the integrated high and low orbit inter-satellite communication network, through systematic information collection and state estimation, the communication and navigation states and actual operating trajectories of each satellite are tracked in real time, generating the communication and navigation state error vectors of high and low orbit satellites, including the deviations of the satellite from the desired position, velocity, and service load, as well as multi-dimensional indicators such as navigation positioning accuracy, communication quality fluctuations, and energy margin. During the state error calculation process, combined with Doppler frequency shift characteristic data, the signal frequency shift and link perturbation caused by factors such as high-speed user movement and satellite orbit changes are dynamically corrected to ensure the timeliness and accuracy of the state error. After obtaining the state error vector, a sliding mode surface is constructed based on the sliding mode control theory. By designing a time-varying sliding mode control law with a global prescribed time, the entire high and low orbit satellite system achieves error convergence and control goal achievement within a limited time. This sliding mode control law takes the Doppler frequency shift characteristic data as the core input parameter, combines the influence of Doppler frequency shift on link quality with the satellite resource scheduling strategy, and constructs an adaptive control mechanism with global robustness and fast response ability. The sliding mode surface not only performs multi-dimensional mapping on the state error but also can adaptively adjust the control strategy according to the dynamic changes of the high and low orbit constellations, enabling the system to autonomously optimize the navigation and communication cooperation services for different orbit levels and environmental states. Based on the global prescribed time-varying sliding mode control law and Doppler frequency shift characteristic data, an adaptive adjustment matrix for high and low orbit satellite control parameters is established. This adjustment matrix dynamically allocates key resources such as power and bandwidth for each satellite, and incorporates the three major indicators of navigation performance, communication performance, and energy utilization efficiency into the weight system to achieve global optimal communication and navigation resource scheduling. To improve the optimization efficiency and accuracy, a multi-objective particle swarm optimization algorithm is used to solve the communication and navigation resource optimization equation. Through large-scale population search and historical elite solution retention mechanism, it continuously approaches the global optimal resource allocation scheme. The position and velocity of the particle represent the allocation ratios of different satellites in terms of power, bandwidth, etc., and the fitness function comprehensively scores according to indicators such as navigation accuracy, communication delay, and energy utilization to achieve multi-objective balance and dynamic weight adjustment. Integrate the high and low orbit satellite power allocation scheme and bandwidth allocation scheme obtained through particle swarm optimization to form a communication and navigation resource scheduling strategy with multiple levels of priorities and a dynamic adaptive mechanism, and use the Doppler frequency shift characteristic data as a real-time feedback signal to continuously dynamically optimize the resource scheduling scheme and generate resource scheduling instructions.

[0019] 105. Based on the adaptive threshold parameter and the resource scheduling instruction, perform seamless switching of communication and navigation services between high and low orbit satellites, and adjust the communication link and navigation positioning service in real time.

[0020] Specifically, the core parameters of the particle swarm algorithm are initialized to generate a set of initial parameters for the particle swarm, including the number of particles, the maximum number of iterations, the initial inertia weight, the individual learning factor, and the swarm learning factor. These parameters determine the coverage breadth and convergence speed of the algorithm's search space. The initial positions and velocities of each particle are randomly set within the constraints of power, bandwidth, etc., so that the search starting point has global diversity, which is conducive to jumping out of local optima. Combining the actual requirements of the optimization of high- and low-orbit communication and navigation resources, a multi-objective particle swarm fitness function suitable for the system is constructed. This function is based on the mathematical equation for optimizing communication and navigation resources, and embeds the geometric dilution of precision constraint, the maximum time delay constraint, the total power constraint, and the total bandwidth constraint into the fitness evaluation system. The fitness function measures whether the geometric accuracy of satellite navigation (such as the GDOP value) meets the positioning requirements, and real-time evaluates whether the maximum time delay of the communication link is lower than the threshold, while ensuring that the total amount of resource allocation does not exceed the physical constraints of the system's total power and total bandwidth, forming a joint consideration of all key indicators. The multi-constraint fitness mechanism can guide the particle swarm to converge steadily to the optimal solution domain. In the optimization main loop stage, based on the set of initial parameters and the fitness function, the particle swarm algorithm continuously performs update iterations of the positions and velocities of each particle in the multi-dimensional resource allocation space. In each iteration, the position of the particle represents the power and bandwidth allocation scheme of each node under the current satellite network, and the adjustment of the velocity comprehensively refers to the individual historical optimal solution and the swarm historical optimal solution, enabling the particle to efficiently search globally. The algorithm generates intermediate results of iterative optimization during the evolution process, and these results reflect the resource allocation trade-offs under different constraints and performance indicators. To improve the diversity of solutions and the global convergence ability, a crowding degree sorting mechanism is introduced for the intermediate results. By calculating the crowding degree values of particles in the target space, the particle swarm is sorted. This mechanism effectively avoids the over-concentration of solutions, improves the coverage ability of the solution space, and at the same time performs elite individual retention operations based on the high and low crowding degrees to ensure that the most representative high-quality solutions will not be eliminated during the evolution process, improving the multi-objective optimization effect. As the iteration progresses, the algorithm continuously calculates the current global optimal particle position according to the distribution and fitness of the target particle swarm, and performs convergence judgment after each iteration, with the fitness improvement degree, the particle displacement amplitude, or the maximum number of iterations as the criterion. When the convergence criterion is met, the system obtains the optimal resource allocation scheme. The optimal solution obtained from the particle swarm evolution is mapped to the power and bandwidth allocation domains of high-orbit navigation satellites and low-orbit communication satellites, and specific satellite power allocation schemes and bandwidth allocation schemes are formulated according to the corresponding components of each node in the optimal particle.

[0021] In the embodiment of the present invention, by establishing a three-dimensional relationship model of Doppler frequency shift, orbital altitude, and handover success rate, the limitations of traditional fixed-parameter handover methods are broken through. The designed heterogeneous adaptive handover algorithm for integrated communication and navigation of high and low orbit satellites can adaptively adjust to the influence of Doppler frequency shift, effectively solving the problem of handover reliability under the condition of high-speed movement of users. Modeling high-orbit navigation satellites and low-orbit communication satellites as a heterogeneous multi-agent system realizes the deep integration of communication and navigation functions for the first time, breaking the limitations of the functional separation of traditional satellite systems. The designed smooth terminal time regulator and global fixed-time time-varying sliding mode control strategy solve the problem of control instability caused by the dynamic change of the network topology of high and low orbit satellites. The constructed integrated communication and navigation resource optimization model can take into account navigation accuracy, communication delay, and energy utilization efficiency at the same time, effectively solving the inherent contradiction between navigation accuracy and communication delay. It has good adaptability in various typical scenarios such as cities, oceans, and polar regions, and can maintain a high handover success rate even in extreme Doppler environments.

[0022] In a specific embodiment, the process of executing step 101 may specifically include the following steps: Classify and identify the satellites at the first preset orbital altitude to obtain a set of high-orbit navigation satellites, and classify and identify the satellites at the second preset orbital altitude to obtain a set of low-orbit communication satellites; Assign a unique identifier to each satellite in the set of high-orbit navigation satellites and the set of low-orbit communication satellites and record the orbital parameters to obtain a high-low orbit satellite orbital parameter database; According to the high-low orbit satellite orbital parameter database, calculate the visibility status between high-orbit navigation satellites and low-orbit communication satellites, and generate visibility relationship data; Collect the high-orbit navigation satellite signals and low-orbit communication satellite signals received by the mobile user to obtain the original data of the high-low orbit satellite signal characteristics; Extract and analyze the Doppler frequency shift values of the original data of the high-low orbit satellite signal characteristics, establish the Doppler frequency shift characteristic distribution of high-orbit navigation satellites and low-orbit communication satellites, and generate Doppler frequency shift characteristic data based on the Doppler frequency shift characteristic distribution.

[0023] Specifically, stratification is carried out by combining satellite orbital mechanics parameters with system service attributes. For the high-orbit navigation satellite constellation, based on satellites with an orbital altitude in the range of 20,000 kilometers to 36,000 kilometers, they are classified into the high-orbit satellite category. This orbital altitude range covers typical geostationary navigation satellites (such as Beidou, GPS, etc.) and related high-orbit navigation platforms. Satellites in this category have advantages such as long orbital periods, wide ground coverage, and stable signals, and are suitable for space positioning, timekeeping, and navigation enhancement. The low-orbit communication satellite constellation mainly ranges from 500 kilometers to 1,500 kilometers, covering low-orbit high-capacity broadband communication constellations and some remote sensing and Internet of Things satellites. Satellites in this orbital range are mainly characterized by high moving speeds, frequent surface crossings, short signal transmission delays, and strong space link dynamics, and are suitable for high-speed and low-latency data communication and distributed link reconstruction. For each satellite in the high-orbit navigation satellite constellation and the low-orbit communication satellite constellation, a unique identifier is assigned. The identifier is assigned through methods such as space mission numbers, orbital sequence numbers, or constellation IDs to ensure the uniqueness and retrievability of each satellite in subsequent data processing and network scheduling. At the same time, for each satellite, its orbital parameters are recorded, including the semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, mean anomaly, etc. These orbital elements determine the satellite's trajectory and relative motion state in three-dimensional space. For high-orbit satellites in the geostationary orbit or quasi-geostationary orbit, dynamic parameters such as east-west and north-south station-keeping deviations are supplemented, and a station-keeping window is set to facilitate state anomaly detection and attitude control. All the above orbital and state information is standardized and encoded, and then summarized to generate a high-low orbit satellite orbital parameter database. Based on the high-low orbit satellite orbital parameter database, the spatial geometric relationship between high-orbit navigation satellites and low-orbit communication satellites is calculated to evaluate their visibility status. This process integrates multiple physical bases such as celestial mechanics, line-of-sight analysis, Earth rotation, and occlusion models to determine the visibility of each high-orbit satellite and each low-orbit satellite at different times and different spatial positions. Specifically, the system traverses all satellite orbital parameters. At a specific observation time, the spatial position vectors of high-orbit satellites and low-orbit satellites are calculated, and through coordinate projection and obstacle detection algorithms, it is judged whether there is a direct line-of-sight link between them. If the link is not blocked by the Earth's body, terrain, other satellites, etc., it is determined that they are visible at that moment, and a value of 1 is assigned; otherwise, it is 0. A time-varying visibility relationship data matrix is generated according to all times and all satellite pairs, reflecting the dynamic topological evolution of the satellite constellation. The signals of high-orbit navigation satellites and low-orbit communication satellites received by mobile user terminals in different scenarios and at different moving speeds are collected. The collection process relies on multi-dimensional and multi-regional comprehensive sampling such as ground monitoring stations, mobile terminal prototypes, and aviation test platforms. The original data includes information such as signal strength, carrier-to-noise ratio, signal arrival time, frequency drift, etc., especially the data performance at different user speeds and in various environments (urban, ocean, polar).Extract and analyze the Doppler frequency shift values from the original data of the signal characteristics of high and low orbit satellites. Based on the Doppler effect theory, combined with the instantaneous relative velocity and signal frequency between the satellite and the user, measure the frequency drift of each communication and navigation signal. For high orbit navigation satellites, due to the relatively small relative velocity with the user, their Doppler frequency shift is within ±4.5 kHz and changes relatively smoothly; while for low orbit communication satellites, the relative velocity with the user is large and changes rapidly, the amplitude of the Doppler frequency shift can reach ±15 kHz, and the rate fluctuation is significantly larger. By summarizing and statistically analyzing a large number of Doppler frequency shift observation data with different orbital altitudes, different user movement speeds, and different environments, construct the respective Doppler frequency shift characteristic distributions of high orbit navigation satellites and low orbit communication satellites. This distribution not only reflects the physical limits of various satellites in the space channel, but also reveals the parameter boundaries of handover and compensation under different service types. Based on the above Doppler frequency shift characteristic distributions, through statistical modeling or probability distribution analysis, extract the statistical characteristics of the Doppler frequency shift under different orbits and different scenarios, such as mean, variance, extreme value range, and distribution density, etc., and generate Doppler frequency shift characteristic data based on this.

[0024] In a specific embodiment, the process of executing step 102 may specifically include the following steps: Based on the Doppler frequency shift characteristic data and the corresponding orbital altitude data, perform three-dimensional relationship modeling on the Doppler frequency shift, orbital altitude, and handover success rate to obtain the handover success rate function; Use the Doppler frequency shift characteristic data to perform Doppler frequency shift threshold calculation to generate the Doppler frequency shift determination threshold; According to the Doppler frequency shift determination threshold, perform differential and transformation operations on the handover success rate function to obtain the handover threshold adjustment basic function and the handover trigger delay basic function; Based on the handover threshold adjustment basic function, adjust the high and low orbit handover hysteresis threshold to generate the high and low orbit handover hysteresis threshold adaptive parameter; According to the high and low orbit handover hysteresis threshold adaptive parameter and the handover trigger delay basic function, adjust the trigger delays for high orbit to low orbit and low orbit to high orbit handovers respectively to generate the adaptive threshold parameters for the communication and navigation service handover between high and low orbit satellites.

[0025] Specifically, based on the Doppler frequency shift characteristic data obtained from large-scale field measurements and simulations and the corresponding orbital altitude data, the physical relationship between the Doppler frequency shift and the orbital altitude is modeled, and both are correlated with the actual handover success rate. Taking the Doppler frequency shift Δf, the orbital altitude h, and the actually observed handover success rate SR as parameters, a three-dimensional handover success rate function SR(Δf, h) is established based on regression analysis or fitting algorithms. This function selects a non-linear expression to fit the actual link characteristics. During the modeling process, the essential differences in the Doppler frequency shift between high-orbit and low-orbit satellites are utilized to quantify the internal logical relationship between the frequency shift amplitude, the orbital altitude, and the link handover performance. Based on the established three-dimensional relationship function, using the Doppler frequency shift characteristic data, the decision threshold of the Doppler frequency shift is dynamically calculated for different orbital altitudes and service requirements. The Doppler frequency shift threshold is designed in the form of a power-law change with the orbital altitude. This design can ensure that as the orbital altitude increases, the Doppler threshold shows a reasonable attenuation trend, enabling the high-orbit satellite system to avoid misjudgment due to high sensitivity, and the low-orbit satellite system not to have frequent mis-handovers due to too low a threshold. Based on the three-dimensional handover success rate function and the Doppler frequency shift decision threshold, differential and transformation operations are performed on the handover success rate function to obtain the adaptive basic function of the threshold and the delay. By taking the first partial derivatives of SR(Δf, h) with respect to Δf and h respectively, the sensitivity functions of the handover success rate to the Doppler frequency shift and the orbital altitude are obtained. Combining the sensitivity functions with the Doppler frequency shift decision threshold, non-linear transformations (such as square terms, exponential or logarithmic transformations, etc.) are used to generate the handover threshold adjustment basic function and the handover trigger delay basic function. Using the handover threshold adjustment basic function, the hysteresis threshold for high-low orbit handover is dynamically adjusted. The introduction of adaptive parameters enables the system to perceive the changes in the Doppler environment in real time, compensate the threshold sensitively, and effectively prevent misjudgment and link jitter caused by sudden changes in the frequency shift. For example, when the Doppler frequency shift is close to the threshold, the jump of the threshold is avoided through a smoothing adjustment mechanism to ensure the continuity of the system handover process and the smoothness of the service experience. Combining the handover trigger delay basic function and the adaptive threshold parameters, the trigger delays for high-orbit to low-orbit handover and low-orbit to high-orbit handover are dynamically adjusted respectively. High-orbit to low-orbit handover has higher requirements for the timeliness of the link, so the delay response is appropriately shortened; while low-orbit to high-orbit handover needs to enhance stability to avoid unnecessary high-orbit resource consumption and link jitter caused by frequent handovers of low-orbit satellites. For different scenarios and handover directions, strategies such as combined exponential-logarithmic adjustment and multi-scenario compensation are introduced to perform scenario-based and state-based adjustment on the trigger delay basic function to ensure that the handover mechanism can not only respond quickly to emergencies but also avoid mis-handovers and resource waste. Dynamically generate the adaptive threshold parameters for the communication-navigation service handover between high-low orbit satellites.

[0026] In a specific embodiment, the process of performing differential and transformation operations on the handover success rate function according to the Doppler frequency shift determination threshold to obtain the handover threshold adjustment basic function and the handover trigger delay basic function may specifically include the following steps: Perform a first-order partial derivative calculation on the handover success rate function based on the Doppler frequency shift variable to obtain the first sensitivity function of the handover success rate to the Doppler frequency shift, and perform a first-order partial derivative calculation on the handover success rate function based on the orbital altitude variable to obtain the second sensitivity function of the handover success rate to the orbital altitude; Perform a joint sensitivity weight calculation on the first sensitivity function and the second sensitivity function to obtain the handover parameter adjustment weight factor; Use the handover parameter adjustment weight factor and the Doppler frequency shift determination threshold to obtain the handover threshold reference value and the delay reference value through function iterative operations; Substitute the ratio of the handover threshold reference value to the Doppler frequency shift determination threshold into the nonlinear transformation model to obtain the handover threshold adjustment basic function, and substitute the inverse transformation of the delay reference value to the Doppler frequency shift determination threshold into the nonlinear transformation model to obtain the handover trigger delay basic function.

[0027] Specifically, analyze the Doppler frequency shift variable, extract the changing trend from a large amount of data on the relationship between the link handover success rate and the Doppler frequency shift amplitude, and construct a function model expressing the mapping relationship between the Doppler frequency shift and the handover success rate based on mathematical modeling. Based on this model, through the first-order change analysis of the Doppler frequency shift variable, the first sensitivity function of the handover success rate to the Doppler frequency shift is obtained. This sensitivity function is used to describe the rate and direction of the change in the handover success rate when the Doppler frequency shift changes slightly at a fixed orbital altitude. If the function value is large, it indicates that the Doppler frequency shift has a significant impact on the link performance, and the system improves the response priority and control accuracy in this direction; if the function value is small, it indicates that this variable has a weak impact on the change in the link state, and relatively reduces its weight in the handover parameter adjustment. Conduct sensitivity analysis for the orbital altitude variable. The impact of the orbital altitude on the link performance is not only reflected in the signal attenuation caused by the physical distance, but also involves the change in the relative motion speed between satellites, the expansion and contraction of the ground visible area, and the fluctuation of the link interference probability. Therefore, in the existing handover success rate function, perform the first-order change analysis on the orbital altitude variable to obtain the second sensitivity function of the handover success rate to the orbital altitude. This function reflects the immediate response intensity of the handover success rate when the orbital altitude changes slightly, and can help the system determine whether it is necessary to actively adjust the link handover strategy or reconfigure the available link resources due to the altitude change. Perform the joint sensitivity weight calculation on the first sensitivity function and the second sensitivity function to form the global weight factor for the handover parameter adjustment. The generation of this weight factor needs to comprehensively consider multiple factors such as the scenario type, user speed, link historical stability, and current network load. In a high-dynamic traffic scenario, the fluctuation of the Doppler frequency shift will become the main inducement for unstable handovers. Therefore, the system automatically increases the influence weight of the first sensitivity function. In a complex constellation structure with frequent cross-orbit handovers or significant orbital differences, the system pays more attention to the impact of the orbital altitude change and increases the participation degree of the second sensitivity function. Through the dynamic combination weight mechanism, the adjustment ratio of each type of physical variable in the handover control is updated in real time according to the current physical state and service requirements to achieve adaptive parameter adjustment. Combine the joint sensitivity weight factor with the Doppler frequency shift determination threshold currently evaluated by the environmental monitoring system, and use this as the core input for function iterative operations to generate the most representative handover threshold reference value and delay reference value under the current system operating state. It is a continuous correction process in which the system continuously receives environmental data, performs handover operations, and feedbacks the handover results during operation. In each iteration, according to the latest sensitivity level and frequency shift threshold feedback, fine-tune the handover threshold and trigger delay so that these two parameters always fit the current link state and service stability requirements to achieve dynamic optimal adjustment based on the real operating state.When the iteration converges, the switching threshold reference value and the delay reference value at the current stage are obtained. These two reference values are input into a non-linear transformation model to obtain a basis function that is more smooth, controllable, and stable in practical applications. For the switching threshold parameter, the ratio of the threshold reference value to the Doppler frequency shift determination threshold is input into a non-linear transformation model with slow-varying characteristics and a saturation section to ensure that when the frequency shift change approaches the critical threshold, the threshold change can remain continuous and stable, and false judgments or frequent switching will not be caused by parameter mutations. The non-linear processing mechanism is applicable to high-frequency fluctuation links and can effectively prevent the system from oscillating frequently due to excessive sensitivity to frequency shift. In the processing of the switching trigger delay parameter, the system adopts the opposite method to the threshold processing. The inverse ratio of the delay reference value to the Doppler frequency shift determination threshold is used as the input variable and substituted into another type of non-linear transformation model to form a delay basis function with characteristics of early response or delayed trigger. When the frequency shift intensity increases and the link change intensifies, the system automatically shortens the switching response time through this basis function to improve service continuity; while in an environment with stable channels and less interference, the switching response time is extended to reduce the system resource switching frequency, thereby improving the overall resource scheduling efficiency and link persistence.

[0028] In a specific embodiment, the process of executing step 103 may specifically include the following steps: Regarding the high-orbit navigation satellite set and the low-orbit communication satellite set as network nodes, a time-varying inter-satellite communication topology graph is constructed based on the visibility relationship data; Communication bandwidth, transmission delay, and link reliability parameters are assigned to each link in the time-varying inter-satellite communication topology graph to obtain a high-low orbit inter-satellite link characteristic matrix; An inter-satellite integrated navigation and communication protocol is created based on the high-low orbit inter-satellite link characteristic matrix; The high-orbit navigation satellite set and the low-orbit communication satellite set are modeled as a heterogeneous multi-agent system, and the agent state vector and action space are defined according to the inter-satellite integrated navigation and communication protocol to obtain a high-low orbit satellite heterogeneous multi-agent system model; Inter-satellite resource collaborative allocation is performed based on the high-low orbit satellite heterogeneous multi-agent system model and the high-low orbit inter-satellite link characteristic matrix to obtain a navigation and communication integrated resource allocation matrix; An inter-satellite link is established using the navigation and communication integrated resource allocation matrix and the inter-satellite integrated navigation and communication protocol, and the link configuration between the high-orbit navigation satellite and the low-orbit communication satellite is dynamically adjusted to obtain a high-low orbit integrated inter-satellite communication network.

[0029] Specifically, the high-orbit navigation satellite constellation and the low-orbit communication satellite constellation are used as the basic nodes of the network for unified modeling. Each high-orbit navigation satellite and low-orbit communication satellite undertakes independent positioning, timing, or communication tasks and is regarded as an intelligent node in the inter-satellite network that can actively participate in information exchange, resource scheduling, and dynamic cooperation. By comprehensively considering the physical positions, orbital parameters, current service loads, and resource status of each satellite, and then through dynamic analysis of the visibility relationship data, a time-varying inter-satellite communication topology graph is established. This topology graph takes time as a sequence to determine and dynamically record whether there is an available direct link between all high-orbit navigation satellites and low-orbit communication satellites at each moment, and reflects the topological structure changes of the entire satellite constellation network in real time. Based on the time-varying inter-satellite communication topology graph, specific communication parameters are assigned to each actual inter-satellite link in the network, including communication bandwidth, transmission delay, and link reliability, etc. The allocation of communication bandwidth is dynamically adjusted according to the remaining resources, service requirements, and link physical characteristics of the satellite nodes, while the transmission delay is related to the distance between satellites, the number of signal relaying times, and the current link load. The link reliability comprehensively considers multi-dimensional data such as orbital altitude, space interference, and historical bit error rate. All these link parameters are summarized and sorted to form a high-low orbit inter-satellite link characteristic matrix. Based on the high-low orbit inter-satellite link characteristic matrix, an inter-satellite integrated navigation and communication protocol is created. This protocol needs to meet the efficient transmission requirements of navigation, communication, and integrated data, and be compatible with complex scenarios of different orbital altitudes, different satellite models, and diverse service loads. The protocol structure includes core fields such as data header, protocol type, quality of service identifier, priority, data length, source and destination satellite IDs, timestamp, service load, and integrity check, and differentiates and extends navigation enhancement packets, communication service packets, and integrated packets. Through standardized and modular protocol design, the system supports cross-orbit level information collaboration, service switching, and data integration. The high-orbit navigation satellite constellation and the low-orbit communication satellite constellation are modeled as a heterogeneous multi-agent system. Under this model, each high-orbit navigation satellite and low-orbit communication satellite participates in global cooperation as an agent node. The state vector of the agent covers physical and service information such as spatial position, speed, remaining energy, computing power, and task load, while the action space includes controllable behaviors such as link establishment, resource allocation, and service switching. The entire multi-agent system takes the inter-satellite integrated navigation and communication protocol as the communication standard, enabling all nodes to perform collaborative optimization according to unified service and communication standards. The system continuously senses environmental changes, node states, and link characteristics to globally model and predict all satellites and links. Based on the heterogeneous multi-agent system model and the high-low orbit inter-satellite link characteristic matrix, a resource collaborative allocation algorithm is constructed. This algorithm aims at the optimal utilization of the whole network resources. By setting the weight parameters of navigation, communication, and integrated services, it dynamically allocates key resources such as computing power, energy, and bandwidth for each satellite and each link. All agent nodes form a navigation and communication integrated resource allocation matrix according to their own states and the global network requirements.Based on the communication-navigation integrated resource allocation matrix and the inter-satellite communication-navigation fusion protocol, an actual inter-satellite link is established. Each link establishment, adjustment, and reconstruction are dynamically scheduled in combination with the current satellite status, service priority, and physical link characteristics. For example, when there is a business peak or link congestion in a high-orbit navigation satellite, according to the fusion protocol, the computing and bandwidth resources of a low-orbit communication satellite are quickly allocated, and the navigation data is diverted or compensated via the low-orbit link; when the link quality of a low-orbit communication satellite deteriorates due to spatial position or resource constraints, the high-orbit satellite acts as a supplementary node to temporarily improve the navigation data transmission ability or cooperate to support communication services. An integrated high-low orbit inter-satellite communication network is obtained.

[0030] In a specific embodiment, the process of executing step 104 may specifically include the following steps: Based on the integrated high-low orbit inter-satellite communication network, smooth terminal time adjustment is performed to generate the communication-navigation state error vector of high-low orbit satellites; Combined with the Doppler frequency shift characteristic data, a sliding mode surface is constructed for the communication-navigation state error vector of high-low orbit satellites to obtain a globally prescribed time-varying sliding mode control law; According to the globally prescribed time-varying sliding mode control law and the Doppler frequency shift characteristic data, an adaptive adjustment matrix for the control parameters of high-low orbit satellites is constructed; Based on the adaptive adjustment matrix for the control parameters of high-low orbit satellites, a communication-navigation resource optimization solution equation including navigation performance indicators, communication performance indicators, and energy utilization efficiency indicators is constructed; Perform particle swarm optimization on the communication-navigation resource optimization solution equation to obtain the power allocation scheme and bandwidth allocation scheme for high-low orbit satellites; Integrate the power allocation scheme and bandwidth allocation scheme for high-low orbit satellites into a communication-navigation resource scheduling strategy for high-low orbit satellites, and perform dynamic optimization according to the Doppler frequency shift characteristic data to generate a resource scheduling instruction.

[0031] Specifically, based on the integrated high- and low-orbit inter-satellite communication network, a smooth terminal time adjustment mechanism is established through the dynamic perception of multi-dimensional state information such as the position, velocity, task load, and remaining energy of each satellite node in the whole network. This mechanism not only focuses on the time synchronization of a single node and local clock drift, but also emphasizes the unified adjustment of the overall time series of the satellite constellation through network-level data exchange and collaborative control. Through this step, the difference between the actual state of each satellite and its expected trajectory is quantified to form a navigation and communication state error vector for high- and low-orbit satellites. Combining the Doppler frequency shift characteristic data, a sliding mode surface is constructed for the navigation and communication state error vector of high- and low-orbit satellites. The globally prescribed-time time-varying sliding mode strategy in sliding mode control theory is adopted to allocate dynamically adjusted control objectives for each satellite and its associated links. The sliding mode surface not only reflects the change trend of the error state, but also can automatically set a control law with strong robustness and fast response speed for different service links according to the strength, change rate, and uncertainty of the Doppler frequency shift. The introduction of the globally prescribed-time time-varying sliding mode control law solves the problem of slow response or unstable control of traditional satellite link management in extreme environments, enabling the high- and low-orbit satellite system to be forced to converge to the optimal service state within a limited time. Regardless of how large the disturbances and uncertainties are, it can achieve fine control and active optimization of navigation and communication resources. With the real-time evolution of the sliding mode control law, the system constructs an adaptive adjustment matrix for the control parameters of high- and low-orbit satellites based on the physical and service states of each satellite, each link, and each moment, combined with the dynamic feedback of the Doppler frequency shift characteristics. This matrix is supported by the multi-agent system theory and dynamically updates the control parameters, link bandwidth, transmission power, and energy allocation of each satellite. Incorporating the cooperation and competition mechanisms between different satellite nodes into a unified resource scheduling framework can not only ensure the continuity of the whole network service and service balance, but also make self-adaptation and elastic adjustment according to emergencies or service peaks, improving the system's response ability to external environmental changes and sudden service loads. Based on the adaptive adjustment matrix of the control parameters of high- and low-orbit satellites, an optimization equation for navigation and communication resources is constructed, which includes navigation performance indicators, communication performance indicators, and energy utilization efficiency indicators. This optimization equation comprehensively considers multiple objectives and constraints such as the geometric dilution of precision, link bandwidth utilization rate, delay constraint, energy consumption balance, and cooperation benefits between nodes, and realizes the multi-objective joint optimization of navigation and positioning accuracy, communication link bandwidth, and overall energy utilization rate. For the multi-objective optimization problem, a particle swarm optimization algorithm is used for global solution. The particle swarm optimization algorithm effectively avoids falling into local optima and improves the solution efficiency through population parallel search, historical optimal solution memory, and crowding degree sorting mechanism, and obtains the global optimal combination of the power allocation scheme and the bandwidth allocation scheme. The position and velocity of the particles represent the resource allocation ratios of each satellite respectively, and the fitness function evaluates the comprehensive performance of navigation accuracy, communication delay, and energy utilization rate in real time. After the optimization solution is completed, the power allocation scheme and the bandwidth allocation scheme are integrated into the navigation and communication resource scheduling strategy for high- and low-orbit satellites.This strategy not only globally allocates the transmission power, link bandwidth, and service priority of each satellite according to the current status of the whole network resources and service requirements, but also performs dynamic optimization in combination with the Doppler shift characteristic data. The process of dynamic optimization is reflected in the sensitive capture of the real-time changes in the Doppler shift and the adaptive update of parameters. The system can adjust the resource scheduling instructions of each node at any time according to the complexity of the link environment, interference risk, and service handover pressure, maintaining the flexibility of the network structure and the high availability of the service quality.

[0032] In a specific embodiment, the process of performing particle swarm optimization to solve the communication and navigation resource optimization equation to obtain the power allocation scheme and bandwidth allocation scheme for high and low orbit satellites may specifically include the following steps: Initialize the particle swarm parameters to obtain the particle swarm initialization parameter set; According to the communication and navigation resource optimization equation, construct a particle swarm fitness function including geometric dilution of precision constraint, maximum time delay constraint, total power constraint, and total bandwidth constraint; Based on the particle swarm initialization parameter set and the particle swarm fitness function, perform position and velocity iterative update operations on each particle to generate iterative optimization intermediate results; Introduce a crowding degree sorting mechanism into the iterative optimization intermediate results, calculate the crowding degree value of each particle in the target space, and perform particle sorting based on the crowding degree value, and perform the operation of retaining elite individuals to obtain the target particle swarm iteration result; According to the target particle swarm iteration result, calculate the global optimal particle position, and perform convergence judgment to obtain the optimal resource allocation scheme; Map the optimal resource allocation scheme to the power and bandwidth allocation domains of high orbit navigation satellites and low orbit communication satellites to obtain the power allocation scheme and bandwidth allocation scheme for high and low orbit satellites.

[0033] Specifically, the parameters of the particle swarm are initialized. According to the actual requirements of satellite network resource allocation, key algorithm parameters are set, including the particle swarm size, search space dimension, maximum number of iterations, initial inertia weight, individual best and swarm best learning factors, etc. The size of the particle swarm is selected according to the number of network nodes and task complexity, which should not only ensure full coverage of the search space, but also balance the computing resources and convergence speed. For each particle, its position vector and velocity vector are randomly initialized within the allowable range of satellite network power allocation and bandwidth allocation, so as to ensure good diversity of the population. At the same time, actual factors such as node constraints, historical allocation experience, and current service requirements are considered during the initialization process, so that the initialization results can highly match the network operating state and system objectives. After the initialization of the particle swarm parameter set is completed, the algorithm designs a multi-objective fitness function that can simultaneously reflect navigation accuracy, communication quality, and resource constraints according to the communication and navigation resource optimization solution equation. This function integrates constraint conditions such as geometric dilution of precision, maximum delay, total power, and total bandwidth, so that each solution should not only maximize the network benefit theoretically, but also strictly meet physical and service limitations in actual operation. For example, the geometric dilution of precision is used to constrain the positioning reliability of the navigation system, the maximum delay directly reflects the service experience of the communication system, and the total power and total bandwidth control the resource consumption and load balancing of the entire satellite network. The construction of the fitness function not only focuses on the extreme of a single index, but also emphasizes the trade-off and balance between multiple objectives, prompting the particle swarm to converge efficiently between the global optimal solution and the constraint feasible solution. Based on the particle swarm initialization parameter set and the particle swarm fitness function, position and velocity iterative update operations are performed on each particle. Each particle adjusts its position in the search space continuously according to the preset iterative formula based on its current own position and velocity, as well as its historical best and swarm best solutions. The update of the velocity combines three components: inertia, cognition, and society, which not only ensures the diversity of the search trajectory but also improves the convergence speed towards the global optimal direction. Each iteration of position and velocity is essentially a global search and fine-tuning of all available power and bandwidth combination schemes by the system. After each round of iteration, the fitness of all particles is re-evaluated, and the system continuously updates the global best and individual best to ensure that the best solution is always retained within the population. To improve the balance and diversity of multi-objective optimization, a crowding degree sorting mechanism is introduced. This mechanism sorts all solutions in the population by evaluating the relative density of particles in the objective space, and preferentially retains elite individuals distributed in sparse intervals, representing different objective trade-off points. This strategy effectively avoids the loss of diversity and premature convergence of the algorithm caused by the concentration of the optimal solution in a certain local area, and improves the balance and representativeness of the solution. The elite retention operation ensures that a number of particles with the most global advantages in each generation of the population directly enter the next round of evolution and will not be accidentally lost during the population evolution. As the iteration continues, the particle swarm algorithm updates the global optimal solution according to the fitness performance and crowding degree ranking after each round of evolution.When the global optimal solution tends to be stable in consecutive iterations or reaches a preset convergence threshold, the system considers that the current particle swarm has converged to the optimal resource allocation scheme. On this basis, the parameter vector of the optimal particle is mapped into the specific satellite node and link configuration domain, and refined into the power allocation and bandwidth allocation instructions for the high-orbit navigation satellite and the low-orbit communication satellite within their respective mission intervals.

[0034] The above describes the high-low orbit satellite communication-navigation integration method in the embodiments of the present invention. Next, the high-low orbit satellite communication-navigation integration system in the embodiments of the present invention will be described. Please refer to Figure 2 , an embodiment of the high-low orbit satellite communication-navigation integration system in the embodiments of the present invention includes: A space characteristic analysis module 201, configured to perform space characteristic analysis on the high-orbit navigation satellite signal and the low-orbit communication satellite signal to obtain visibility relationship data and Doppler frequency shift characteristic data; A handover analysis module 202, configured to perform communication-navigation handover analysis between high and low orbit satellites according to the visibility relationship data and the Doppler frequency shift characteristic data, and generate an adaptive threshold parameter for the communication-navigation service handover between high and low orbit satellites; An establishment module 203, configured to establish a high-low orbit integrated inter-satellite communication network between the high-orbit navigation satellite cluster and the low-orbit communication satellite cluster according to the visibility relationship data; A collaborative allocation module 204, configured to utilize the high-low orbit integrated inter-satellite communication network and the Doppler frequency shift characteristic data to perform collaborative allocation of communication-navigation resources and generate resource scheduling instructions; A real-time adjustment module 205, configured to perform seamless handover of communication-navigation services between high and low orbit satellites based on the adaptive threshold parameter and the resource scheduling instructions, and real-time adjust the communication link and the navigation positioning service.

[0035] Through the collaborative cooperation of the above-mentioned various components, by establishing a three-dimensional relationship model of Doppler frequency shift, orbital altitude, and handover success rate, the limitations of the traditional fixed-parameter handover method are broken through. The designed heterogeneous adaptive handover algorithm for high-low orbit satellite communication-navigation integration can perform adaptive adjustment in response to the influence of Doppler frequency shift, effectively solving the handover reliability problem under the condition of high-speed movement of users. Modeling the high-orbit navigation satellite and the low-orbit communication satellite as a heterogeneous multi-agent system, the deep integration of communication and navigation functions is realized for the first time, breaking through the limitation of the separation of functions in traditional satellite systems. The designed smooth terminal time regulator and global prescribed-time time-varying sliding mode control strategy solve the problem of control instability caused by the dynamic change of the high-low orbit satellite network topology. The constructed communication-navigation fusion resource optimization model can take into account navigation accuracy, communication delay, and energy utilization efficiency at the same time, effectively solving the inherent contradiction between navigation accuracy and communication delay. It has good adaptability in various typical scenarios such as cities, oceans, and polar regions, and can maintain a high handover success rate even in extreme Doppler environments.

[0036] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0037] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0038] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for integrated communication and navigation of high and low orbit satellites, characterized in that Including: Performing spatial characteristic analysis on high-orbit navigation satellite signals and low-orbit communication satellite signals to obtain visibility relationship data and Doppler frequency shift characteristic data; Performing high-low orbit satellite communication-navigation handover analysis according to the visibility relationship data and the Doppler frequency shift characteristic data, and generating adaptive threshold parameters for the communication-navigation service handover between high-low orbit satellites; Establishing a high-low orbit integrated inter-satellite communication network between the high-orbit navigation satellite cluster and the low-orbit communication satellite cluster according to the visibility relationship data; Performing collaborative allocation of communication-navigation resources by using the high-low orbit integrated inter-satellite communication network and the Doppler frequency shift characteristic data, and generating resource scheduling instructions; Performing seamless handover of the communication-navigation service between high-low orbit satellites based on the adaptive threshold parameters and the resource scheduling instructions, and real-time adjusting the communication link and the navigation and positioning service.

2. The integrated communication and navigation method for high and low orbit satellites according to claim 1, characterized in that The performing spatial characteristic analysis on high-orbit navigation satellite signals and low-orbit communication satellite signals to obtain visibility relationship data and Doppler frequency shift characteristic data includes: Classifying and identifying satellites at a first preset orbital altitude to obtain a high-orbit navigation satellite set, and classifying and identifying satellites at a second preset orbital altitude to obtain a low-orbit communication satellite set; Assigning a unique identifier to each satellite in the high-orbit navigation satellite set and the low-orbit communication satellite set and recording the orbital parameters to obtain a high-low orbit satellite orbital parameter database; Calculating the visibility state between high-orbit navigation satellites and low-orbit communication satellites according to the high-low orbit satellite orbital parameter database, and generating visibility relationship data; Collecting high-orbit navigation satellite signals and low-orbit communication satellite signals received by mobile users to obtain original high-low orbit satellite signal characteristic data; Extracting and analyzing the Doppler frequency shift values of the original high-low orbit satellite signal characteristic data, establishing a Doppler frequency shift characteristic distribution of high-orbit navigation satellites and low-orbit communication satellites, and generating Doppler frequency shift characteristic data based on the Doppler frequency shift characteristic distribution.

3. The integrated communication and navigation method for high and low orbit satellites according to claim 1, characterized in that The performing high-low orbit satellite communication-navigation handover analysis according to the visibility relationship data and the Doppler frequency shift characteristic data, and generating adaptive threshold parameters for the communication-navigation service handover between high-low orbit satellites includes: Based on the Doppler frequency shift characteristic data and the corresponding orbital altitude data, performing three-dimensional relationship modeling on the Doppler frequency shift, the orbital altitude, and the handover success rate to obtain a handover success rate function; Performing Doppler frequency shift threshold calculation by using the Doppler frequency shift characteristic data to generate a Doppler frequency shift determination threshold; Performing differential and transformation operations on the handover success rate function according to the Doppler frequency shift determination threshold to obtain a handover threshold adjustment basic function and a handover trigger delay basic function; Adjusting the high-low orbit handover hysteresis threshold based on the handover threshold adjustment basic function to generate an adaptive parameter for the high-low orbit handover hysteresis threshold; Adjusting the trigger delays for the handover from high orbit to low orbit and from low orbit to high orbit respectively according to the adaptive parameter for the high-low orbit handover hysteresis threshold and the handover trigger delay basic function to generate adaptive threshold parameters for the communication-navigation service handover between high-low orbit satellites.

4. The integrated communication and navigation method for high and low orbit satellites according to claim 3, wherein Performing differential and transformation operations on the handover success rate function according to the Doppler frequency shift determination threshold to obtain a handover threshold adjustment basic function and a handover trigger delay basic function, including: Performing a first-order partial derivative calculation on the handover success rate function based on the Doppler frequency shift variable to obtain a first sensitivity function of the handover success rate to the Doppler frequency shift, and performing a first-order partial derivative calculation on the handover success rate function based on the orbital altitude variable to obtain a second sensitivity function of the handover success rate to the orbital altitude; Performing a joint sensitivity weight calculation on the first sensitivity function and the second sensitivity function to obtain a handover parameter adjustment weight factor; Using the handover parameter adjustment weight factor and the Doppler frequency shift determination threshold, obtaining a handover threshold reference value and a delay reference value through function iteration operations; Substituting the ratio of the handover threshold reference value to the Doppler frequency shift determination threshold into a non-linear transformation model to obtain a handover threshold adjustment basic function, and substituting the inverse transformation of the delay reference value to the Doppler frequency shift determination threshold into the non-linear transformation model to obtain a handover trigger delay basic function.

5. The integrated communication and navigation method for high and low orbit satellites according to claim 2, wherein Establishing a high-low orbit integrated inter-satellite communication network between the high-orbit navigation satellite cluster and the low-orbit communication satellite cluster according to the visibility relation data, including: Regarding the high-orbit navigation satellite set and the low-orbit communication satellite set as network nodes, and constructing a time-varying inter-satellite communication topology graph based on the visibility relation data; Allocating communication bandwidth, transmission delay, and link reliability parameters to each link in the time-varying inter-satellite communication topology graph to obtain a high-low orbit inter-satellite link characteristic matrix; Creating an inter-satellite integrated communication and navigation protocol based on the high-low orbit inter-satellite link characteristic matrix; Modeling the high-orbit navigation satellite set and the low-orbit communication satellite set as a heterogeneous multi-agent system, and defining an agent state vector and an action space according to the inter-satellite integrated communication and navigation protocol to obtain a high-low orbit satellite heterogeneous multi-agent system model; Performing inter-satellite resource collaborative allocation based on the high-low orbit satellite heterogeneous multi-agent system model and the high-low orbit inter-satellite link characteristic matrix to obtain a communication and navigation integrated resource allocation matrix; Using the communication and navigation integrated resource allocation matrix and the inter-satellite integrated communication and navigation protocol to establish an inter-satellite link, and dynamically adjusting the link configuration between the high-orbit navigation satellite and the low-orbit communication satellite to obtain a high-low orbit integrated inter-satellite communication network.

6. The integrated communication and navigation method for high and low orbit satellites according to claim 1, characterized in that, Performing communication and navigation resource collaborative allocation by using the high-low orbit integrated inter-satellite communication network and the Doppler frequency shift characteristic data to generate a resource scheduling instruction, including: Performing smooth terminal time adjustment based on the high-low orbit integrated inter-satellite communication network to generate a high-low orbit satellite communication and navigation state error vector; Combining the Doppler frequency shift characteristic data, constructing a sliding mode surface for the high-low orbit satellite communication and navigation state error vector to obtain a globally specified time-varying sliding mode control law; Constructing a high-low orbit satellite control parameter adaptive adjustment matrix according to the globally specified time-varying sliding mode control law and the Doppler frequency shift characteristic data; Based on the high- and low-orbit satellite control parameter adaptive adjustment matrix, construct a communication and navigation resource optimization solution equation that includes navigation performance indicators, communication performance indicators, and energy utilization efficiency indicators; Perform particle swarm optimization on the communication and navigation resource optimization solution equation to obtain the power allocation scheme and bandwidth allocation scheme for high- and low-orbit satellites; Integrate the power allocation scheme and the bandwidth allocation scheme of the high- and low-orbit satellites into a communication and navigation resource scheduling strategy for high- and low-orbit satellites, and perform dynamic optimization according to the Doppler frequency shift characteristic data to generate a resource scheduling instruction.

7. The integrated communication and navigation method for high and low orbit satellites according to claim 6, characterized in that, The performing particle swarm optimization on the communication and navigation resource optimization solution equation to obtain the power allocation scheme and bandwidth allocation scheme for high- and low-orbit satellites includes: Initialize the particle swarm parameters to obtain a particle swarm initialization parameter set; According to the communication and navigation resource optimization solution equation, construct a particle swarm fitness function that includes geometric dilution of precision constraints, maximum delay constraints, total power constraints, and total bandwidth constraints; Based on the particle swarm initialization parameter set and the particle swarm fitness function, perform position and velocity iterative update operations on each particle to generate an iterative optimization intermediate result; Introduce a crowding degree sorting mechanism into the iterative optimization intermediate result, calculate the crowding degree value of each particle in the target space, and perform particle sorting based on the crowding degree value, and perform an elite individual retention operation to obtain an iterative result of the target particle swarm; According to the iterative result of the target particle swarm, calculate the global optimal particle position, and perform a convergence judgment to obtain an optimal resource allocation scheme; Map the optimal resource allocation scheme to the power and bandwidth allocation domains of high-orbit navigation satellites and low-orbit communication satellites to obtain the power allocation scheme and bandwidth allocation scheme for high- and low-orbit satellites.

8. An integrated communication and navigation system for high and low orbit satellites, characterized in that, For implementing the integrated communication and navigation method for high- and low-orbit satellites as described in any one of claims 1-7, the integrated communication and navigation system for high- and low-orbit satellites includes: A space characteristic analysis module for analyzing the space characteristics of high-orbit navigation satellite signals and low-orbit communication satellite signals to obtain visibility relationship data and Doppler frequency shift characteristic data; A handover analysis module for performing integrated communication and navigation handover analysis between high- and low-orbit satellites according to the visibility relationship data and the Doppler frequency shift characteristic data, and generating an adaptive threshold parameter for handover of communication and navigation services between high- and low-orbit satellites; A establishment module for establishing an integrated high- and low-orbit inter-satellite communication network between a high-orbit navigation satellite cluster and a low-orbit communication satellite cluster according to the visibility relationship data; A collaborative allocation module for using the integrated high- and low-orbit inter-satellite communication network and the Doppler frequency shift characteristic data to perform collaborative allocation of communication and navigation resources and generate a resource scheduling instruction; A real-time adjustment module for performing seamless handover of communication and navigation services between high- and low-orbit satellites based on the adaptive threshold parameter and the resource scheduling instruction, and real-time adjusting the communication link and the navigation positioning service.

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