Modularization-based Beidou navigation satellite continuous observation station lightweight design method

Abstract

The invention relates to the technical field of digital twinning, in particular to a modularization-based Beidou navigation satellite continuous observation station lightweight design method. The method comprises the following steps: reducing the construction period and cost of a Beidou satellite continuous observation station through a modular and lightweight design, so as to efficiently obtain a high-quality Beidou navigation satellite signal source initial collection signal; performing signal preprocessing on the initial acquisition signal of the satellite signal source to generate a purified signal matrix; performing signal space-time feature extraction on the purification signal matrix to obtain signal time feature data and signal space feature data; and performing signal fusion optimization on the purified signal matrix based on the signal time feature data and the signal space feature data to generate a satellite observation comprehensive signal. According to the invention, the rapid construction capability of the observation pillar of the Beidou navigation satellite continuous observation station is realized by using a novel high-strength temperature-deformation-resistant material and a modular design, the light weight is realized, the applicable scenes of the observation pillar in a roof station, a ground station, an island station and the like are expanded, the design of a receiving antenna module of the Beidou navigation satellite continuous observation station is optimized, and the application range of the receiving antenna module is widened. And the signal purity is improved.

Description

Lightweight Design Method of BeiDou Navigation Satellite Continuous Observation Station Based on Modularity Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a lightweight design method for a modular BeiDou navigation satellite continuous observation station. Background Technology

[0002] The BeiDou Navigation Satellite System (BDS) is a global satellite navigation system independently developed by China and has become one of the four major global satellite navigation systems. A BeiDou continuous observation station is a device that uses BeiDou navigation satellite signals to obtain precise spatial position, meeting the needs of applications requiring high spatial accuracy. With the continuous improvement and widespread application of the BeiDou system, the demand for continuous observation stations is increasing. Generally, to ensure sufficient stability, BeiDou continuous observation stations are typically built as ground stations, consisting of heavy and robust reinforced concrete observation piers on the ground, upon which receiving antennas are erected to observe BeiDou navigation satellite positioning signals. This method involves large-scale construction and high costs, is difficult to acquire land for, and is not suitable for widespread distribution. However, in certain specific scenarios where land acquisition is not feasible, BeiDou continuous observation stations, commonly known as rooftop stations, need to be built on existing buildings. Rooftop stations, due to the limited load-bearing capacity and space of building roofs, must ensure sufficient strength of the observation piers while also requiring short construction periods, lightweight construction, stability, and resistance to deformation. Although land acquisition is not required, the requirements for stability and lightweight construction are very high.

[0003] However, in recent years, with the rapid development of modular construction technology, communication technology, sensor technology, and data processing technology, lightweight and intelligent observation stations have become a research hotspot. In particular, with the rise of the Internet of Things and edge computing, it has become possible to construct small, distributed, lightweight BeiDou navigation rooftop observation stations using these emerging technologies. With technological advancements, ground stations have evolved from large, centralized monitoring systems towards distributed, miniaturized designs, especially through the application of advanced digital signal processing technology and artificial intelligence algorithms, enabling efficient processing and transmission of observation data. Furthermore, the introduction of rooftop stations provides more flexible deployment methods, particularly suitable for urban environments with high requirements for accuracy and coverage. By integrating these technologies, the BeiDou Navigation Satellite System can achieve more efficient global positioning and navigation services, laying a solid foundation for the construction of lightweight continuous observation stations. However, current traditional methods for constructing terrain models of the observation space typically require large amounts of data and complex calculations, making it difficult to dynamically adapt to terrain changes in actual applications. Simultaneously, when selecting observation sites, there is often a lack of comprehensive consideration of spatial signal coherence and environmental factors, leading to inaccurate site selection, affecting observation results, and consequently resulting in low construction efficiency and adaptability of the observation stations. Summary of the Invention

[0004] Therefore, it is necessary to provide a lightweight design method for a modular BeiDou navigation satellite continuous observation station to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a lightweight design method for a continuous BeiDou navigation satellite observation station based on modularity is proposed. The method includes the following steps: Step S1: Acquire the initial acquisition signal from the satellite signal source; preprocess the initial acquisition signal to generate a purified signal matrix; extract the spatiotemporal features of the purified signal matrix to obtain signal time feature data and signal spatial feature data; perform signal fusion optimization on the purified signal matrix based on the signal time feature data and signal spatial feature data to generate a comprehensive satellite observation signal; Step S2: Acquire spatial geographic information data; perform three-dimensional reconstruction of the spatial geographic information data to generate a three-dimensional terrain model of the spatial observation area; dynamically divide the three-dimensional terrain model of the spatial observation area into spatiotemporal grids based on the comprehensive satellite observation signal to generate spatial signal grid division regions; Step S3: Perform regional signal coherence analysis on the spatial signal grid division regions to generate a regional signal coherence atlas; and perform spatial signal grid division based on the regional signal coherence atlas. The process involves dividing the area for signal observation station site selection, generating spatial site selection data for signal observation stations; performing environmental perception on the spatial site selection data to generate environmental perception data for the spatial area; confirming the construction type of the observation stations based on the environmental perception data, thus obtaining ground observation station construction type data and rooftop observation station construction type data; step S4: performing conventional ground observation construction simulation on the ground observation station construction type data to generate conventional ground observation station construction simulation data; modularizing the rooftop observation station construction type data for lightweight construction to generate lightweight rooftop observation station construction simulation data; and using the conventional ground observation station construction simulation data and the lightweight rooftop observation station construction simulation data to convert the three-dimensional terrain model of the observation space into engineering drawings, thereby generating construction view drawings for BeiDou navigation satellite continuous observation stations to execute the modularized lightweight design of BeiDou navigation satellite continuous observation stations.

[0006] This invention purifies the initial acquired signals from satellite signal sources, eliminating environmental noise and interference, thereby improving signal purity. Subsequent spatiotemporal feature extraction further enhances the signal's temporal and spatial resolution, ensuring the accuracy and precision of the integrated satellite observation signals. This processing significantly improves the overall monitoring capability of the system and reduces signal distortion. Using geographic information data from the observation space, a high-precision 3D terrain model is created through three-dimensional reconstruction. This model provides a precise geographic basis for subsequent signal grid division, ensuring a high degree of alignment between the divided signal areas and the actual terrain. This not only improves the scientific accuracy of signal distribution but also optimizes resource allocation efficiency. Coherence analysis of the spatial signal grid division areas identifies areas of phase consistency or inconsistency during signal propagation. This analysis helps optimize the site selection of signal observation stations, ensuring optimal signal transmission quality within the selected areas, thereby improving the overall system's signal coverage and stability. The site selection process for signal observation stations incorporates environmental perception data, fully considering the environmental conditions of the selected area, such as building height and meteorological conditions. This measure ensures that the construction types of observation stations can adapt to different geographical and environmental conditions, improving the efficiency of station construction and operation. Furthermore, the construction simulation of ground and rooftop observation stations further optimizes the flexibility of the construction scheme, enabling it to better meet the needs of different scenarios. While ensuring functionality, the system adopts a lightweight design, especially in the construction of rooftop observation stations, reducing the impact on the building structure and lowering construction and maintenance costs. This design allows the continuous observation stations of the BeiDou Navigation Satellite System to be deployed in a wider range of environments, improving the system's adaptability and accessibility. By generating engineering drawings that meet actual operational needs, the system provides detailed construction guidance for the actual construction process. These drawings cover not only the construction of conventional ground observation stations but also the design of lightweight rooftop observation stations, ensuring smooth construction and reducing errors during construction. Integrating multiple data types, from satellite signals to geographic information, from coherence atlases to environmental perception data, each step relies on precise data processing and analysis. This data-driven approach ensures the scientific and rational construction of continuous observation stations, effectively improving the overall service capabilities of the BeiDou Navigation Satellite System. Therefore, this invention improves the construction efficiency and adaptability of the observation station through precise signal processing, dynamic spatial model construction, accurate site selection, and flexible construction schemes.

[0007] Preferably, step S1 includes the following steps: Step S11: Obtain the initial acquisition signal of the satellite signal source using a distributed edge computing network, wherein the initial acquisition signal of the satellite signal source includes BeiDou navigation satellite signals and radiation source interference signals, and the initial acquisition signal of the satellite signal source is represented as: ;in The first Beidou receiving antenna for the Beidou navigation satellite observation station The combined signal vector received by each receiving unit This refers to the number of elements in the BeiDou receiving antenna array. This is a vector of the BeiDou satellite signal. This refers to the BeiDou satellite signal vector received by the BeiDou navigation satellite observation station. For the first Signals from other radiation sources For signals from other radiation sources, To follow the steady-state distribution of background noise, This refers to the number of BeiDou satellites received by a single BeiDou satellite observation station. For the number of other sources of interfering radiation, Step S12: Perform signal cleaning on the initial acquired signals of the satellite signal sources to generate a cleaned satellite signal source signal; perform signal denoising on the cleaned satellite signal source signal to generate a denoised satellite signal source signal; perform outlier processing on the denoised satellite signal source signal to generate a cleaned signal matrix; Step S13: Extract the spatiotemporal features of the cleaned signal matrix to obtain signal time feature data and signal space feature data; perform multi-layer signal fusion on the cleaned signal matrix based on the signal time feature data and signal space feature data to generate a multi-layer fused signal matrix; Step S14: Optimize the multi-layer fused signal matrix to generate a comprehensive satellite observation signal.

[0008] This invention utilizes a distributed edge computing network to acquire initial acquisition signals, significantly improving the efficiency and real-time performance of signal acquisition in step S11. Edge computing enables data processing close to the data source, reducing data transmission latency and improving the overall system response speed. In step S12, the signal cleaning and denoising process lays the foundation for signal purification. This process effectively removes impurities and noise from the signal, generating a purer signal and ensuring the accuracy of subsequent processing. In particular, signal outlier processing further ensures the integrity and reliability of the signal data. Through signal outlier processing, step S12 can identify and correct outliers in the signal data, thereby avoiding the impact of outlier data on the final result. The generation of the purified signal matrix provides a high-quality data foundation for the extraction of spatiotemporal features of the signal. In step S13, the extraction of spatiotemporal features of the signal allows for a deeper understanding of the signal data in both time and space dimensions. This process improves the multidimensional analysis capabilities of the data. Subsequent multi-layer signal fusion further integrates and optimizes these feature data, generating a multi-layer fused signal matrix containing richer and more accurate information. The signal optimization process in step S14 ensures that the final generated satellite observation composite signal reaches its optimal state. By optimizing the fused signal matrix, the system can eliminate potential signal distortion and improve the overall signal quality, thus laying a solid foundation for subsequent signal analysis and processing. Through a series of cleaning, denoising, and optimization processes, the system can generate highly purified and stable satellite observation signals. This high-quality signal not only improves the accuracy of satellite observations but also provides a reliable foundation for subsequent signal transmission and applications.

[0009] Preferably, step S2 includes the following steps: Step S21: Acquire observation spatial geographic information data; Step S22: Extract elevation data from the observation spatial geographic information data to generate observation spatial elevation information data; convert the observation spatial geographic information data into point cloud data to generate observation spatial point cloud data; Step S23: Perform three-dimensional meshing on the observation spatial point cloud data to generate observation spatial mesh data; perform three-dimensional reconstruction of the observation spatial elevation information data and observation spatial mesh data to generate a three-dimensional terrain model of the observation spatial; Step S24: Perform dynamic spatiotemporal meshing on the three-dimensional terrain model of the observation spatial based on the satellite observation integrated signal to generate a spatial signal mesh division area.

[0010] This invention provides a solid foundation for subsequent spatial modeling and signal analysis by acquiring spatial geographic information data. This data includes detailed geographic features of the observation area, ensuring geographic accuracy and realism throughout the process. In step S22, elevation information data of the observation space is extracted, allowing the system to accurately capture the terrain's undulations. This high-precision elevation data provides a crucial height reference for subsequent 3D reconstruction, making the model more consistent with the actual terrain. After the spatial geographic information data is converted into point cloud data, the system can describe each point in space in detail using 3D coordinates. This comprehensive spatial data description allows the subsequent 3D meshing process to more accurately reflect terrain features. In step S23, the point cloud data is converted into structured observation space grid data through 3D meshing processing. This process not only effectively organizes the spatial data but also improves its operability, making the 3D reconstruction process smoother and more efficient. Combining the elevation information data with the grid data, the 3D terrain model of the observation space generated in step S23 can realistically reproduce the topography of the observation area. This detailed 3D model provides an accurate geographic reference for subsequent signal grid division, making signal analysis more scientific and practical. The dynamic spatiotemporal grid division based on satellite observation signals in step S24 ensures that the signal division area can be adjusted according to changes in time and space. This dynamic division improves the accuracy and adaptability of signal analysis, enabling the system to better cope with complex environmental conditions. By combining satellite observation signals with a three-dimensional spatial model, step S24 achieves a deep integration of signal analysis and spatial modeling. This combination not only improves the accuracy of signal coverage but also provides three-dimensional spatial support for signal optimization. The signal grid division based on the three-dimensional terrain model can better match terrain features and avoid the generation of signal dead zones. This optimized grid division improves the spatial coverage and effectiveness of the signal, laying the foundation for subsequent signal observation and site selection.

[0011] Preferably, step S24 includes the following steps: Step S241: Perform initial spatiotemporal partitioning on the three-dimensional terrain model of the observation space based on the satellite observation integrated signal to generate initial spatial partitioning data for satellite observation; Step S242: Extract regional signal spectrum features from the satellite observation integrated signal using the initial spatial partitioning data for satellite observation to obtain regional integrated signal spectrum feature data; perform spectrum continuity analysis on the regional integrated signal spectrum feature data to generate continuous regional signal spectrum data; Step S243: Perform dynamic detection on the continuous regional signal spectrum data to generate dynamic continuous regional signal data; refine the initial spatial partitioning data for satellite observation based on the dynamic continuous regional signal data to generate optimized spatial partitioning data for satellite observation; Step S244: Perform dynamic spatiotemporal grid division on the initial spatial partitioning data for satellite observation based on the optimized spatial partitioning data for satellite observation to generate spatial signal grid division regions.

[0012] This invention generates initial spatial partitioning data for satellite observations by performing initial spatiotemporal partitioning on a three-dimensional terrain model based on integrated satellite observation signals. This process divides the observation area into spatial units suitable for signal analysis and processing, providing clear regional boundaries for subsequent signal feature extraction and optimization. By extracting regional signal spectral features from the initial spatial partitioning data, regional integrated signal spectral feature data is obtained. This process allows for detailed analysis of signal characteristics in different regions, providing strong data support for signal quality and characteristic analysis. Spectral continuity analysis is performed on the regional integrated signal spectral feature data, generating continuous regional signal spectral data. This analysis can identify continuous changes in the signal spectrum, helping to detect signal stability and continuity, and ensuring signal consistency and reliability in different regions. The dynamic detection process in step S243 generates dynamic continuous regional signal data. This process can monitor the dynamic changes of the signal in real time, helping to identify and process trends in signal changes. Based on this data, the initial spatial partitioning is refined, generating optimized satellite observation spatial partitioning data, making the partitioning more precise and adaptable to actual signal changes. Finally, by performing dynamic spatiotemporal grid division based on the optimized spatial partitioning data, the final spatial signal grid division region is generated. This dynamic partitioning not only considers changes in the actual signal but also adjusts based on the optimized partition data, ensuring the signal grid effectively covers the observation area and reflects the actual signal characteristics. Step S24's processing flow, through dynamic detection and optimization, enables the final generated spatial signal grid partitioning region to more comprehensively cover the observation area. This comprehensive coverage improves the accuracy of signal monitoring, reduces signal blind spots, and enhances the overall system performance. The dynamic spatiotemporal partitioning and grid division process allows the system to adjust according to actual signal changes, enhancing its adaptability and flexibility. This flexibility enables the system to cope with various complex environmental conditions, improving its application effectiveness in different scenarios.

[0013] Preferably, step S3 includes the following steps: Step S31: Perform regional signal coherence analysis on the spatial signal grid division area to generate a regional signal coherence atlas; perform signal fluctuation detection on the spatial signal grid division area based on the regional signal coherence atlas to generate regional signal fluctuation data; Step S32: Select signal observation station locations in the spatial signal grid division area using the regional signal fluctuation data to generate spatial location data for signal observation stations; Step S33: Perform location environment perception on the spatial location data for signal observation stations to generate spatial environment perception data for signal observation stations; Step S34: Confirm the construction type of the signal observation station using the spatial environment perception data for the spatial location data for signal observation stations to obtain signal observation station construction type data, wherein the signal observation station construction type data includes ground observation station construction type data and rooftop observation station construction type data.

[0014] This invention accurately assesses signal consistency across spatial signal grid divisions through regional signal coherence analysis in step S31. The resulting regional signal coherence atlas provides spatial distribution information on signal phase and intensity, helping to identify consistency and potential interference during signal propagation, thereby optimizing signal quality. Signal fluctuation detection based on the regional signal coherence atlas provides in-depth understanding of signal fluctuations in different regions. The generated regional signal fluctuation data reveals the temporal and spatial fluctuation characteristics of the signal, helping to identify areas with poor signal stability and providing a scientific basis for subsequent site selection. Step S32 uses regional signal fluctuation data analysis for regional site selection of signal observation stations. This process ensures that the observation station site selection prioritizes coverage of areas with smaller and more stable signal fluctuations, thereby improving observation effectiveness and reducing blind spots in signal monitoring. In step S33, environmental perception is performed on the spatial region of the signal observation station. The generated environmental perception data provides information on environmental conditions of the selected site area, such as building distribution and meteorological conditions. This comprehensive environmental perception data ensures that the site selection process takes into account actual environmental factors, thereby improving the adaptability of the observation station construction. By analyzing the spatial environment perception data of the signal observation station, step S34 can accurately determine the construction type of the observation station. The generated construction type data for ground observation stations and rooftop observation stations provide specific construction plans, ensuring that the observation station design can meet the requirements of different environments and needs. The entire process of step S3 achieves comprehensive optimization from signal coherence analysis to site selection environment perception, and then to construction type confirmation. This optimization not only improves the scientific nature of the observation station site selection, but also ensures the suitability of the construction plan, enabling the finally constructed observation station to operate efficiently in the actual environment.

[0015] Preferably, step S31 includes the following steps: Step S311: Filtering grid cell signals in the spatial signal grid division region to obtain grid cell signals; calculating local coherence of the grid cell signals to obtain local coherence matrices; Step S312: Aggregating several local coherence matrices for regional coherence to generate a regional coherence map; performing multi-scale analysis on the regional coherence map to generate regional multi-scale coherence analysis data, wherein the multi-scale analysis includes time-scale analysis, spatial-scale analysis, frequency-scale analysis, and scale-invariance analysis; Step S313: Performing comprehensive regional signal coherence analysis on the regional coherence map based on the regional multi-scale coherence analysis data to generate a regional signal coherence map set; extracting signal frequency components from the regional signal coherence map set to obtain regional signal frequency data; Step S314: Calculating the frequency fluctuation amplitude of the regional signal frequency data to obtain regional signal fluctuation data.

[0016] This invention ensures high-quality and representative signal data by dividing the spatial signal area into grids and filtering the grid cells. The obtained grid cell signals provide accurate foundational data for subsequent coherence calculations, thereby improving the accuracy of the analysis. Local coherence calculations on the grid cell signals generate local coherence matrices that detail the signal coherence within each grid cell. This meticulous calculation helps identify signal consistency and interference in local regions, providing crucial information for the overall analysis of regional signals. Aggregating several local coherence matrices for regional coherence generates a regional coherence map that provides signal coherence information over a large area. This comprehensive aggregation analysis helps understand the overall consistency of the signal over a larger region and identify potential signal problems and interference sources. Multi-scale analysis of the regional coherence map, including temporal, spatial, frequency, and scale-invariant analysis, provides a comprehensive perspective of the signal at different scales. This analysis reveals the signal's variation characteristics across different time, space, and frequency ranges, improving the overall understanding of signal coherence. The comprehensive regional signal coherence analysis based on regional multi-scale coherence analysis data generates a regional signal coherence atlas that provides accurate data support for the overall evaluation of the signal. This process systematically summarizes the coherence characteristics of the signal and provides a reliable basis for subsequent frequency component extraction. Frequency component extraction from regional signal coherence atlases yields regional signal frequency data that details the signal's frequency characteristics. This data extraction helps analyze the signal's frequency distribution and variations, providing a foundation for further research on signal fluctuations. Calculating the frequency fluctuation amplitude of the regional signal frequency data reveals the amplitude of signal fluctuations at different frequencies. This calculation process identifies the signal's frequency fluctuation characteristics and helps detect changes in signal stability and consistency.

[0017] Preferably, step S34 includes the following steps: Step S341: Perform environmental electromagnetic radiation analysis on the environmental sensing data of the spatial area of ​​the signal observation station to generate environmental electromagnetic radiation data; Step S342: Calculate the building density index on the environmental sensing data of the spatial area of ​​the signal observation station to obtain environmental building density data; Step S343: Calculate the construction suitability index using the environmental electromagnetic radiation data and the environmental building density data to obtain a comprehensive construction suitability index; wherein the formula for calculating the construction suitability index is as follows: In the formula, This is expressed as a comprehensive construction suitability index. It is expressed as the environmental electromagnetic radiation index. This is expressed as the environmental building density index. This represents the weighting coefficient used to adjust the influence of the environmental electromagnetic radiation index on the comprehensive building suitability index. This is represented as a weighting coefficient used to adjust the influence of the building density index on the comprehensive construction suitability index; Step S344: Compare the comprehensive construction suitability index with the preset construction suitability threshold. When the comprehensive construction suitability index is greater than or equal to the preset construction suitability threshold, the construction type of the ground observation station is confirmed based on the spatial area site selection data of the signal observation station, thereby obtaining the ground observation station construction type data; when the comprehensive construction suitability index is less than the preset construction suitability threshold, the construction type of the roof observation station is confirmed based on the spatial area site selection data of the signal observation station, thereby obtaining the roof observation station construction type data; Step S345: Merge the ground observation station construction type data and the roof observation station construction type data to generate the signal observation station construction type data.

[0018] This invention ensures high-quality and representative signal data by dividing the spatial signal area into grids and filtering the grid cells. The obtained grid cell signals provide accurate foundational data for subsequent coherence calculations, thereby improving the accuracy of the analysis. Local coherence calculations on the grid cell signals generate local coherence matrices that detail the signal coherence within each grid cell. This meticulous calculation helps identify signal consistency and interference in local regions, providing crucial information for the overall analysis of regional signals. Aggregating several local coherence matrices for regional coherence generates a regional coherence map that provides signal coherence information over a large area. This comprehensive aggregation analysis helps understand the overall consistency of the signal over a larger region and identify potential signal problems and interference sources. Multi-scale analysis of the regional coherence map, including temporal, spatial, frequency, and scale-invariant analysis, provides a comprehensive perspective of the signal at different scales. This analysis reveals the signal's variation characteristics across different time, space, and frequency ranges, improving the overall understanding of signal coherence. The comprehensive regional signal coherence analysis based on regional multi-scale coherence analysis data generates a regional signal coherence atlas that provides accurate data support for the overall evaluation of the signal. This process systematically summarizes the coherence characteristics of signals and provides a reliable basis for subsequent frequency component extraction. Frequency component extraction from regional signal coherence atlases yields regional signal frequency data that details the signal's frequency characteristics. This data extraction helps analyze the signal's frequency distribution and variations, providing a foundation for further research on signal fluctuations. Calculating the frequency fluctuation amplitude of regional signal frequency data reveals the amplitude of signal fluctuations at different frequencies. This calculation process identifies the signal's frequency fluctuation characteristics, aiding in detecting changes in signal stability and consistency. Through systematic signal screening, local and regional coherence calculation, multi-scale analysis, frequency data extraction, and fluctuation amplitude calculation, comprehensive and in-depth signal analysis is achieved. This systematic analysis accurately captures the complex characteristics of signals, improving the precision and reliability of signal processing.

[0019] Preferably, step S4 includes the following steps: Step S41: Perform conventional ground observation construction simulation on the ground observation station construction type data to generate conventional ground observation station construction simulation data; Step S42: Perform lightweight roof observation construction simulation on the roof observation station construction type data to generate lightweight roof observation station construction simulation data; Step S43: Based on the conventional ground observation station construction simulation data and the lightweight roof observation station construction simulation data, fuse the observation station three-dimensional model of the observation space terrain three-dimensional model to generate a BeiDou navigation satellite three-dimensional continuous observation station model; Step S44: Based on the BeiDou navigation satellite three-dimensional continuous observation station model, perform engineering drawing conversion to generate BeiDou navigation satellite continuous observation station construction view drawings to execute the modular-based lightweight design operation of BeiDou navigation satellite continuous observation station.

[0020] This invention generates conventional ground observation station construction simulation data through conventional ground observation construction simulation of ground observation station construction type data. This simulation can predict the performance of ground observation stations in actual construction in detail, ensuring that the construction plan meets actual needs and improving the reliability of the construction plan. Lightweight rooftop observation construction simulation generates lightweight rooftop observation station construction simulation data. This process takes into account the special needs and design constraints of rooftop observation stations, ensuring the lightweight and adaptable nature of the construction plan, increasing the flexibility and feasibility of the construction plan. The conventional ground observation station construction simulation data and the lightweight rooftop observation station construction simulation data are fused into a three-dimensional model of the observation station, generating a three-dimensional continuous observation station model for the BeiDou navigation satellite. This fusion process integrates the advantages of the two construction schemes into a comprehensive model, providing a comprehensive three-dimensional view for actual construction and improving the comprehensiveness and coordination of the construction plan. The engineering drawing process transforms the three-dimensional continuous observation station model of the BeiDou navigation satellite into detailed construction view drawings. This process provides precise construction details and implementation steps, ensuring clear guidance for each stage of the construction process, improving the accuracy and practicality of the construction drawings. The generated construction drawings for the BeiDou navigation satellite continuous observation station support lightweight construction operations. This lightweight design can reduce the weight of construction materials and structures, lower construction costs and time, while ensuring the functionality and performance of the observation station. Attached Figure Description

[0021] Figure 1 is a flowchart illustrating the steps of a modular design method for a lightweight continuous observation station for BeiDou navigation satellites; Figure 2 is a comparison of the construction schemes of the rooftop lightweight continuous observation station WH13 and the ground-based continuous observation station WH10; Figure 3 is a structural diagram of the lightweight continuous observation station for BeiDou navigation satellites; Figure 4 is a schematic diagram of signal processing for the integrated satellite observation signals in Figure 1; The realization of the purpose, functional characteristics, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0023] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0024] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] To achieve the above objectives, please refer to Figures 1 to 3. A lightweight design method for a modular BeiDou navigation satellite continuous observation station is described. The method includes the following steps: Step S1: Acquire the initial acquisition signal from the satellite signal source; perform signal preprocessing on the initial acquisition signal to generate a purified signal matrix; extract the spatiotemporal features of the purified signal matrix to obtain signal time feature data and signal spatial feature data; perform signal fusion optimization on the purified signal matrix based on the signal time feature data and signal spatial feature data to generate a comprehensive satellite observation signal; Step S2: Acquire observation spatial geographic information data; perform three-dimensional reconstruction of the observation spatial geographic information data to generate a three-dimensional terrain model of the observation space; perform dynamic spatiotemporal grid division on the three-dimensional terrain model of the observation space based on the comprehensive satellite observation signal to generate a spatial signal grid division region; Step S3: Perform regional signal coherence analysis on the spatial signal grid division region to generate a regional signal coherence atlas; perform spatial signal coherence analysis on the spatial signal grid division region based on the regional signal coherence atlas; perform spatial signal coherence analysis on the spatial signal grid division region to generate a spatial signal grid division region ... The signal grid is divided into regions for signal observation station site selection, generating spatial region site selection data for signal observation stations; environmental perception is applied to the spatial region site selection data for signal observation stations, generating spatial region environmental perception data for signal observation stations; the construction type of the observation station is confirmed based on the spatial region environmental perception data for signal observation stations, thereby obtaining ground observation station construction type data and rooftop observation station construction type data; Step S4: Conventional ground observation construction simulation is performed on the ground observation station construction type data to generate conventional ground observation station construction simulation data; lightweight construction modularization is performed on the rooftop observation station construction type data to generate lightweight rooftop observation station construction simulation data; based on the conventional ground observation station construction simulation data and the lightweight rooftop observation station construction simulation data, the three-dimensional model of the observation space terrain is converted into engineering drawings, thereby generating construction view drawings for BeiDou navigation satellite continuous observation stations, to execute the modular lightweight design operation of BeiDou navigation satellite continuous observation stations.

[0026] This invention purifies the initial acquired signals from satellite signal sources, eliminating environmental noise and interference, thereby improving signal purity. Subsequent spatiotemporal feature extraction further enhances the signal's temporal and spatial resolution, ensuring the accuracy and precision of the integrated satellite observation signals. This processing significantly improves the overall monitoring capability of the system and reduces signal distortion. Using geographic information data from the observation space, a high-precision 3D terrain model is created through three-dimensional reconstruction. This model provides a precise geographic basis for subsequent signal grid division, ensuring a high degree of alignment between the divided signal areas and the actual terrain. This not only improves the scientific accuracy of signal distribution but also optimizes resource allocation efficiency. Coherence analysis of the spatial signal grid division areas identifies areas of phase consistency or inconsistency during signal propagation. This analysis helps optimize the site selection of signal observation stations, ensuring optimal signal transmission quality within the selected areas, thereby improving the overall system's signal coverage and stability. The site selection process for signal observation stations incorporates environmental perception data, fully considering the environmental conditions of the selected area, such as building height and meteorological conditions. This measure ensures that the construction types of observation stations can adapt to different geographical and environmental conditions, improving the efficiency of station construction and operation. Furthermore, the construction simulation of ground and rooftop observation stations further optimizes the flexibility of the construction scheme, enabling it to better meet the needs of different scenarios. While ensuring functionality, the system adopts a lightweight design, especially in the construction of rooftop observation stations, reducing the impact on the building structure and lowering construction and maintenance costs. This design allows the continuous observation stations of the BeiDou Navigation Satellite System to be deployed in a wider range of environments, improving the system's adaptability and accessibility. By generating engineering drawings that meet actual operational needs, the system provides detailed construction guidance for the actual construction process. These drawings cover not only the construction of conventional ground observation stations but also the design of lightweight rooftop observation stations, ensuring smooth construction and reducing errors during construction. Integrating multiple data types, from satellite signals to geographic information, from coherence atlases to environmental perception data, each step relies on precise data processing and analysis. This data-driven approach ensures the scientific and rational construction of continuous observation stations, effectively improving the overall service capabilities of the BeiDou Navigation Satellite System. Therefore, this invention improves the construction efficiency and adaptability of the observation station through precise signal processing, dynamic spatial model construction, accurate site selection, and flexible construction schemes.

[0027] In this embodiment of the invention, referring to Figure 1, which is a flowchart illustrating the steps of a modular BeiDou navigation satellite continuous observation station lightweight design method, the method includes the following steps: Step S1: Acquire the initial acquisition signal from the satellite signal source; perform signal preprocessing on the initial acquisition signal to generate a purified signal matrix; extract the temporal and spatial features of the purified signal matrix to obtain signal time feature data and signal spatial feature data; perform signal fusion optimization on the purified signal matrix based on the signal time feature data and signal spatial feature data to generate a comprehensive satellite observation signal; In this embodiment of the invention, raw signal data is acquired from the satellite receiving device. This data is typically an unprocessed signal containing noise and other interference. The raw signal data is converted into an analyzable digital format. Filtering algorithms, such as Gaussian filtering or Kalman filtering, are used to remove high-frequency noise and low-frequency interference from the signal to improve the signal-to-noise ratio. Wavelet transform and other methods are applied to remove random noise. Outliers (such as sudden interference) in the signal are identified and corrected to ensure data stability. The processed data is organized into a multi-dimensional signal matrix, with dimensions including time, space, and frequency. Temporal characteristics of the signal, such as periodicity and frequency variations, are extracted using methods like Short-Time Fourier Transform (STFT). Spatial domain analysis techniques, such as multi-channel signal processing or beamforming, are used to obtain spatial characteristics of the signal, such as the azimuth and elevation angles of the signal source. Temporal feature data can be represented as a spectrogram or time series, while spatial feature data can be represented as a signal radiation pattern or array response map. The temporal and spatial feature data are then fused using multi-dimensional features to form a comprehensive feature vector, considering the temporal and spatial dependencies. Deep learning models (such as convolutional neural networks or LSTM) are applied to optimize the fused feature vector, removing redundant information and enhancing the representation of useful signals. The optimized feature vector is then remapped back into the signal matrix, ultimately yielding an enhanced version of the satellite observation composite signal.

[0028] Step S2: Acquire spatial geographic information data; perform 3D reconstruction of the spatial geographic information data to generate a 3D terrain model; dynamically divide the 3D terrain model into spatiotemporal grids based on satellite observation signals to generate spatial signal grid division areas; In this embodiment of the invention, spatial geographic information data is acquired from multiple data sources such as Global Navigation Satellite System (GNSS), Geographic Information System (GIS), remote sensing data, and Digital Elevation Model (DEM). This data typically includes information such as terrain height, land cover type, and geographic coordinates. The acquired geographic information data is converted into a standard data format suitable for 3D reconstruction, such as point cloud data or raster data. The geographic information data is preprocessed, including removing outliers, filling missing data points, and data interpolation, to ensure data integrity and accuracy. Based on the processed geographic information data, 3D point cloud data of the terrain is generated using triangulation or stereo matching techniques. Surface reconstruction algorithms (such as Poisson surface reconstruction or Delaunay triangulation) are applied to convert the point cloud data into a 3D terrain model. This model can accurately reflect the terrain undulations and surface features of the observation space. The reconstructed terrain data is integrated into a complete 3D terrain model, which can be a triangular mesh, voxel representation, or surface representation. The satellite observation composite signal generated in step S1 is correlated with the 3D terrain model in the observation space to understand the signal propagation characteristics within the terrain. Ray tracing or ground wave propagation models can be used to simulate the signal propagation path. Based on the spatial and temporal characteristics of the satellite observation composite signal, the 3D terrain model is dynamically meshed. Commonly used algorithms include quadtree and octree segmentation algorithms, as well as voxel-based segmentation techniques. Dynamic spatiotemporal meshing not only considers spatial terrain changes but also adjusts the mesh density according to temporal signal intensity fluctuations. This dynamic adjustment can be achieved through time series analysis or spatiotemporal interpolation methods. Finally, a spatiotemporally dynamic signal meshing model is output, which divides different regions of signal transmission in the observation space, suitable for subsequent signal analysis and optimization.

[0029] Step S3: Perform regional signal coherence analysis on the spatial signal grid division area to generate a regional signal coherence atlas; select signal observation station locations based on the regional signal coherence atlas within the spatial signal grid division area to generate spatial location data for signal observation stations; perform environmental sensing on the spatial location data for signal observation stations to generate environmental sensing data for signal observation stations; confirm the construction type of the observation stations based on the environmental sensing data for signal observation stations, thereby obtaining construction type data for ground observation stations and construction type data for rooftop observation stations; In this embodiment of the invention, coherence analysis is performed using signal data from the regional signal grid division area. Phase difference-based coherence measurement methods, such as short-time Fourier transform (STFT) or phase spectral density analysis, can be used to calculate the degree of signal coherence in different grids. Coherence characteristics of different regions are extracted, such as signal phase consistency, coherence time, and coherence bandwidth. The analysis results are visualized to generate multiple regional signal coherence maps, including coherence strength distribution maps and phase consistency maps. These atlases reflect the signal coherence between different grid areas, helping to identify regions with low signal interference and high signal quality. Based on the regional signal coherence atlases, grid areas with high signal coherence and low interference are identified as potential candidate sites for signal observation stations. Taking into account other factors such as geographical location, terrain features, and signal coverage, the candidate areas are evaluated and scored using a multi-factor approach. The optimized and filtered signal observation station site selection data is output, including the geographic coordinates, grid identifier, and comprehensive evaluation score for each candidate area. Environmental perception data is collected from the selected signal observation station site areas using sensors, UAV remote sensing, or environmental monitoring systems. The acquired data may include climatic conditions (such as wind speed, temperature, and humidity), surface characteristics (such as vegetation cover and soil type), and the impact of human activities (such as buildings and roads). The collected environmental data is processed and analyzed to assess the suitability of the site selection, including the potential impact of natural conditions on the observation station equipment. The processed environmental data is then organized into a structured perception dataset for further decision support. Based on environmental sensing data analysis of the environmental characteristics of different site selection areas, and combined with the functional requirements of the signal observation station (such as observation accuracy and weather resistance requirements), the most suitable observation station construction type is determined. The observation station types are divided into two categories: ground-based observation stations and rooftop observation stations. Specific classifications can be based on environmental factors such as terrain stability, wind load, and surface space utilization. The final output is construction type data for each site selection area, including suggested structural types, material selections, and infrastructure requirements.

[0030] Step S4: Perform conventional ground observation construction simulation on the ground observation station construction type data to generate conventional ground observation station construction simulation data; perform lightweight construction modularization on the roof observation station construction type data to generate lightweight roof observation station construction simulation data; based on the conventional ground observation station construction simulation data and the lightweight roof observation station construction simulation data, convert the three-dimensional model of the observation space terrain into engineering drawings to generate construction view drawings of BeiDou navigation satellite continuous observation stations, so as to perform the modular lightweight design operation of BeiDou navigation satellite continuous observation stations.

[0031] In this embodiment of the invention, key parameters for construction simulation are set based on ground-based observation station construction type data, including geological conditions, foundation structure design, material usage, and equipment configuration. Engineering simulation software or Building Information Modeling (BIM) technology is used to simulate the construction process of a conventional ground-based observation station. The simulation includes foundation excavation and pouring, structural installation, and equipment layout. During the simulation, the impact of the construction process on the surrounding environment is assessed, including soil disturbance, water consumption, noise, and vibration. Adverse effects are reduced by adjusting design or construction methods, and simulated construction process data, including construction procedures, timelines, and resource consumption, is output to provide a reference for actual construction. Based on rooftop observation station construction type data, a lightweight observation station structure is designed, considering factors such as material weight reduction, modular design, and ease of installation to reduce the load on the building roof. Using a lightweight design scheme, the construction process of the rooftop observation station is simulated using simulation tools. This includes roof structure reinforcement, application of lightweight materials, and installation of observation equipment. The simulation process assesses the roof structure's load-bearing capacity, wind load impact, and seismic performance to ensure the reliability and stability of the lightweight design. It outputs simulated construction process data, including design details, construction steps, and timelines, providing a basis for actual construction. The simulation data of the two types of observation stations are combined with a 3D model of the observation space terrain to generate detailed engineering drawings. These drawings should include building floor plans, elevations, sections, and detailed drawings of key nodes. The engineering drawings should clearly indicate the observation station's foundation structure, support system, equipment layout, wiring diagrams, material specifications, and construction methods to ensure completeness and operability. BIM technology is used to convert the engineering drawings into a 3D visualization model, facilitating the construction team's intuitive understanding of the design intent and construction steps. Combining the engineering drawings of the ground and rooftop observation stations, a comprehensive construction view of the BeiDou navigation satellite continuous observation station is generated. This drawing should include the overall layout of the observation station, equipment arrangement, and pipeline layout. The lightweight design and construction methods are clearly indicated in the drawings to ensure the effective use of materials and resources during the observation station construction process, reducing construction difficulty and costs. The generated drawings will be provided to the construction team as a specific guideline for building the BeiDou navigation satellite continuous observation station. During construction, precise operations can be performed according to the drawings, ensuring the smooth progress and quality of the construction work.

[0032] Preferably, step S1 includes the following steps: Step S11: Obtain the initial acquisition signal of the satellite signal source using a distributed edge computing network, wherein the initial acquisition signal of the satellite signal source includes BeiDou navigation satellite signals and radiation source interference signals, and the initial acquisition signal of the satellite signal source is represented as: ;in The first Beidou receiving antenna for the Beidou navigation satellite observation station The combined signal vector received by each receiving unit This refers to the number of elements in the BeiDou receiving antenna array. This is a vector of the BeiDou satellite signal. This refers to the BeiDou satellite signal vector received by the BeiDou navigation satellite observation station. For the first Signals from other radiation sources For signals from other radiation sources, To follow the steady-state distribution of background noise, This refers to the number of BeiDou satellites received by a single BeiDou satellite observation station. For the number of other sources of interfering radiation, Step S12: Perform signal cleaning on the initial acquired signals of the satellite signal sources to generate a cleaned satellite signal source signal; perform signal denoising on the cleaned satellite signal source signal to generate a denoised satellite signal source signal; perform outlier processing on the denoised satellite signal source signal to generate a cleaned signal matrix; Step S13: Extract the spatiotemporal features of the cleaned signal matrix to obtain signal time feature data and signal space feature data; perform multi-layer signal fusion on the cleaned signal matrix based on the signal time feature data and signal space feature data to generate a multi-layer fused signal matrix; Step S14: Optimize the multi-layer fused signal matrix to generate a comprehensive satellite observation signal.

[0033] In this embodiment of the invention, initial satellite signal acquisition signals are obtained by deploying distributed edge computing nodes around the world. Each edge node is equipped with a satellite receiving device, enabling real-time acquisition of satellite signals. Utilizing the computing power of the edge computing nodes, the acquired satellite signals undergo preliminary processing, including time delay correction and data format conversion, to reduce the load on the back-end central processing. Filters (such as bandpass filters) are applied to remove frequency interference and irrelevant noise from the signal, generating a cleaned signal. Signals acquired from multiple sources are aligned in time and space to ensure consistency in subsequent processing. Wavelet transform or empirical mode decomposition methods are used to remove random noise from the cleaned signal, generating a purer denoised signal. Outliers in the signal, such as signal mutations or missing data, are detected through statistical analysis or machine learning models. Detected outliers are corrected or interpolated to ensure signal integrity, ultimately generating a cleaned signal matrix. Short-time Fourier transform (STFT) or Hilbert-Huang transform (HHT) is used to extract the temporal features of the signal, including the spectrum and temporal fluctuations. Spatial features of the signal, such as the angle of arrival (AOA) or signal spatial distribution, are extracted through spatial filtering or beamforming techniques. Based on the extracted temporal and spatial feature data, multi-layer signal fusion is performed, integrating features from different dimensions into a multi-layer fused signal matrix. Deep learning models (such as multilayer perceptrons or convolutional neural networks) can be used to achieve multi-layer feature fusion. Optimization algorithms (such as particle swarm optimization and genetic algorithms) are applied to optimize the multi-layer fused signal matrix, enhancing its robustness and accuracy. The optimized multi-layer fused signal matrix is ​​then processed into the final satellite observation composite signal. This signal contains rich spatiotemporal information, providing high-quality input for subsequent signal analysis and applications.

[0034] Preferably, step S2 includes the following steps: Step S21: Acquire observation spatial geographic information data; Step S22: Extract elevation data from the observation spatial geographic information data to generate observation spatial elevation information data; convert the observation spatial geographic information data into point cloud data to generate observation spatial point cloud data; Step S23: Perform three-dimensional meshing on the observation spatial point cloud data to generate observation spatial mesh data; perform three-dimensional reconstruction of the observation spatial elevation information data and observation spatial mesh data to generate a three-dimensional terrain model of the observation spatial; Step S24: Perform dynamic spatiotemporal meshing on the three-dimensional terrain model of the observation spatial based on the satellite observation integrated signal to generate a spatial signal mesh division area.

[0035] In this embodiment of the invention, high-resolution image data of the observation area is acquired via remote sensing satellites, serving as the primary source of geographic information data. Precise geographic coordinate data, including topographic feature points and boundary lines, is obtained using Global Navigation Satellite System (GNSS) and ground mapping instruments. Relevant geographic information data, such as administrative divisions, road networks, and water body distribution, is acquired from GIS databases. Elevation data of the observation space is extracted from remote sensing imagery or LiDAR data to generate a Digital Elevation Model (DEM). The extracted elevation data is converted to a standard format (such as GeoTIFF or ESRIASCII) for subsequent processing, outputting processed elevation data containing precise altitude information for each point within the observation area. Using LiDAR data or stereo vision technology, the two-dimensional image data of the observation space is converted into three-dimensional point cloud data. The generated point cloud data is denoised and outlier removed to improve data accuracy and clarity, outputting cleaned point cloud data containing the three-dimensional coordinates of each point on the surface within the observation area. Algorithms such as Delaunay triangulation or octree segmentation are used to perform three-dimensional meshing of the observation space point cloud data, generating three-dimensional mesh data of the observation space. The generated grid data is optimized, such as by reducing the number of grids and smoothing grid boundaries, to improve the model's accuracy and computational efficiency. The resulting 3D gridded data includes geometric information about the observation space. The elevation data and grid data of the observation space are fused, incorporating topographic relief and spatial structure, to generate a complete 3D terrain model. Using 3D modeling software or a self-developed algorithm, the fused data is reconstructed to generate a 3D model of the observation space terrain, reflecting the true topography. The reconstructed 3D model, containing detailed terrain and spatial structure of the observation area, is output. Based on the spatiotemporal characteristics of the satellite observation signals, a dynamic segmentation algorithm (such as spatiotemporal convolutional networks or adaptive mesh generation) is used to dynamically mesh the 3D terrain model of the observation space. The size and shape of the grid are set according to the spatiotemporal distribution density and spatial frequency characteristics of the signal to ensure the rationality and accuracy of the mesh division. The dynamic spatiotemporal mesh division results are output, forming a series of spatial signal mesh division regions, each representing an independent signal monitoring unit.

[0036] Preferably, step S24 includes the following steps: Step S241: Perform initial spatiotemporal partitioning on the three-dimensional terrain model of the observation space based on the satellite observation integrated signal to generate initial spatial partitioning data for satellite observation; Step S242: Extract regional signal spectrum features from the satellite observation integrated signal using the initial spatial partitioning data for satellite observation to obtain regional integrated signal spectrum feature data; perform spectrum continuity analysis on the regional integrated signal spectrum feature data to generate continuous regional signal spectrum data; Step S243: Perform dynamic detection on the continuous regional signal spectrum data to generate dynamic continuous regional signal data; refine the initial spatial partitioning data for satellite observation based on the dynamic continuous regional signal data to generate optimized spatial partitioning data for satellite observation; Step S244: Perform dynamic spatiotemporal grid division on the initial spatial partitioning data for satellite observation based on the optimized spatial partitioning data for satellite observation to generate spatial signal grid division regions.

[0037] In this embodiment of the invention, a density clustering algorithm (such as DBSCAN) is applied to perform preliminary spatiotemporal partitioning of the terrain 3D model based on the distribution of satellite observation composite signals in the observation space. Combining terrain features and signal strength, the size and shape of the initial partitions are set to ensure that the partitions reflect both the spatial distribution of the signals and match the terrain characteristics. The initial partitioning results are output, with each partition representing a spatial unit for subsequent signal feature extraction and grid division. Spectral analysis is performed on the satellite observation composite signals within each initial spatial partition, using Fourier transform or wavelet transform to extract spectral feature data within the region. The extracted spectral feature data is normalized and structured to generate regional composite signal spectral feature data, reflecting the signal spectral characteristics of each partition. Based on the spectral feature data, the spectral continuity of the signals in each partition is analyzed to evaluate the stability and consistency of the signals in time and space. The analysis results are output, generating regional signal spectral continuity data and marking regions with good spectral continuity and regions with discontinuities. Time-frequency analysis techniques (such as Short-Time Fourier Transform (STFT) or Time-Frequency Distribution (TFR)) are used to dynamically detect continuous regional signal spectrum data, capturing the spatiotemporal dynamic changes of the signal. The results of this dynamic detection are output, generating continuous dynamic data of the regional signal that reflects the signal's changing trends over different time periods. Based on this continuous dynamic data, the boundaries of the initial partitions are adjusted, refining the partition structure. Refinement rules may include spatiotemporal consistency of the partitions and the distribution patterns of signal characteristics. Optimized partition data is output, providing more accurate spatial partitioning information and ensuring the accuracy and coverage of signal monitoring. Adaptive grid partitioning techniques (such as Voronoi diagrams or quadtree partitioning) are used to dynamically divide the optimized spatial partitions into spatiotemporal grids, ensuring consistency of signal characteristics within each grid cell. The grid partitioning results are output, forming a series of spatial signal grid partitioning regions, each of which can be used for more detailed signal monitoring and analysis.

[0038] Preferably, step S3 includes the following steps: Step S31: Perform regional signal coherence analysis on the spatial signal grid division area to generate a regional signal coherence atlas; perform signal fluctuation detection on the spatial signal grid division area based on the regional signal coherence atlas to generate regional signal fluctuation data; Step S32: Select signal observation station locations in the spatial signal grid division area using the regional signal fluctuation data to generate spatial location data for signal observation stations; Step S33: Perform location environment perception on the spatial location data for signal observation stations to generate spatial environment perception data for signal observation stations; Step S34: Confirm the construction type of the signal observation station using the spatial environment perception data for the spatial location data for signal observation stations to obtain signal observation station construction type data, wherein the signal observation station construction type data includes ground observation station construction type data and rooftop observation station construction type data.

[0039] In this embodiment of the invention, coherence analysis techniques (such as phase synchronization analysis and cross-spectral analysis) are used to evaluate the coherence of signals within a spatial signal grid division area, determining the correlation and consistency of signals between different grid cells. The coherence analysis results are visualized to generate a regional signal coherence atlas, displaying the degree of signal coherence between different grid areas. Based on the regional signal coherence atlas, time-series analysis or fluctuation detection techniques (such as GARCH models or dynamic time warping) are used to detect the signal fluctuations in each grid area, assessing signal stability, outputting signal fluctuation detection results, generating regional signal fluctuation data, and identifying areas with large or unstable signal fluctuations. Based on the regional signal fluctuation data, grid areas with smaller fluctuations and higher signal coherence are selected as candidate areas for signal observation stations. Combining geographical factors such as terrain, transportation, and environment, the candidate areas are optimized to ensure that the construction of the observation station meets actual needs, outputting site selection results, generating spatial regional site selection data for signal observation stations, and marking the precise location of the observation station. Multiple environmental sensors are deployed in the candidate site areas to collect environmental data in real time, including meteorological conditions, air quality, electromagnetic interference, and noise levels. The collected environmental data is processed and analyzed using machine learning or statistical analysis techniques to assess the environmental suitability of each candidate area. The analysis results are output, generating spatial area environmental perception data for signal observation stations, reflecting the environmental characteristics of each site and its impact on station construction. By comprehensively analyzing the spatial area site selection data and environmental perception data, the optimal construction type for each site is determined. If the site has a stable environment, open space, and is easy to construct, it is confirmed as a ground-based observation station construction type, suitable for large-scale, long-term observation tasks. If the site is located in an urban or densely built-up area with a suitable environment, it is confirmed as a rooftop observation station construction type, suitable for small-scale, flexibly deployed observation tasks. The construction type confirmation results are output, generating signal observation station construction type data, specifically including ground-based observation station construction type data and rooftop observation station construction type data.

[0040] Preferably, step S31 includes the following steps: Step S311: Filtering grid cell signals in the spatial signal grid division region to obtain grid cell signals; calculating local coherence of the grid cell signals to obtain local coherence matrices; Step S312: Aggregating several local coherence matrices for regional coherence to generate a regional coherence map; performing multi-scale analysis on the regional coherence map to generate regional multi-scale coherence analysis data, wherein the multi-scale analysis includes time-scale analysis, spatial-scale analysis, frequency-scale analysis, and scale-invariance analysis; Step S313: Performing comprehensive regional signal coherence analysis on the regional coherence map based on the regional multi-scale coherence analysis data to generate a regional signal coherence map set; extracting signal frequency components from the regional signal coherence map set to obtain regional signal frequency data; Step S314: Calculating the frequency fluctuation amplitude of the regional signal frequency data to obtain regional signal fluctuation data.

[0041] In this embodiment of the invention, signals within a spatial signal grid division region are filtered to extract signal data from each grid cell. The filtering criteria include signal strength, quality, and correlation, forming a signal dataset for each grid cell to prepare for local coherence calculation. Coherence calculation methods (such as phase synchronization metrics and mutual information) are used to perform local coherence analysis on the signal in each grid cell, outputting a local coherence matrix for each grid cell. This matrix contains the coherence information of the signal within that grid cell. Multiple local coherence matrices are aggregated, and regional coherence maps are generated using methods such as weighted averaging and principal component analysis (PCA), forming an image displaying the overall signal coherence within the region. The coherence changes of the signal at different time scales are analyzed to identify time-varying characteristics. The consistency of the signal at spatial scales is evaluated to discover spatial distribution patterns. The frequency components of the signal are analyzed to understand spectral characteristics and their changes at different scales. The stability and consistency of signal characteristics at different scales are checked to ensure the general applicability of the analysis results, outputting multi-scale analysis results, including data on time, space, frequency, and scale invariance analysis. Based on regional multi-scale coherence analysis data, a comprehensive analysis of regional coherence maps is performed, integrating coherence information at various scales to form a detailed regional signal coherence atlas, displaying the signal coherence and its overall performance in different regions. Frequency components of the signal are extracted from the regional signal coherence atlas, and spectral analysis techniques (such as Fourier transform or wavelet transform) are used to obtain regional signal frequency data. The extracted frequency data is output, showcasing the signal spectral characteristics of different regions. Fluctuation amplitude is calculated on the regional signal frequency data to evaluate the signal fluctuation across different frequency ranges. Statistical methods (such as standard deviation and root mean square value) can be used to measure the frequency fluctuation amplitude, outputting the calculation results to generate regional signal fluctuation data, displaying the fluctuation amplitude and stability of signal frequencies in each region.

[0042] Preferably, step S34 includes the following steps: Step S341: Perform environmental electromagnetic radiation analysis on the environmental sensing data of the spatial area of ​​the signal observation station to generate environmental electromagnetic radiation data; Step S342: Calculate the building density index on the environmental sensing data of the spatial area of ​​the signal observation station to obtain environmental building density data; Step S343: Calculate the construction suitability index using the environmental electromagnetic radiation data and the environmental building density data to obtain a comprehensive construction suitability index; wherein the formula for calculating the construction suitability index is as follows: In the formula, This is expressed as a comprehensive construction suitability index. It is expressed as the environmental electromagnetic radiation index. This is expressed as the environmental building density index. This represents the weighting coefficient used to adjust the influence of the environmental electromagnetic radiation index on the comprehensive building suitability index. This is represented as a weighting coefficient used to adjust the influence of the building density index on the comprehensive construction suitability index; Step S344: Compare the comprehensive construction suitability index with the preset construction suitability threshold. When the comprehensive construction suitability index is greater than or equal to the preset construction suitability threshold, the construction type of the ground observation station is confirmed based on the spatial area site selection data of the signal observation station, thereby obtaining the ground observation station construction type data; when the comprehensive construction suitability index is less than the preset construction suitability threshold, the construction type of the roof observation station is confirmed based on the spatial area site selection data of the signal observation station, thereby obtaining the roof observation station construction type data; Step S345: Merge the ground observation station construction type data and the roof observation station construction type data to generate the signal observation station construction type data.

[0043] In this embodiment of the invention, electromagnetic radiation sensors are deployed in the selected site area to collect electromagnetic radiation data in the environment. The collected electromagnetic radiation data is analyzed to calculate the environmental electromagnetic radiation index, which is then output for subsequent building suitability assessment. Geographic Information System (GIS) data or remote sensing imagery is used to identify the distribution of buildings within the selected site area. The building density index within the selected site area is calculated, reflecting the concentration of buildings within the area, and the environmental building density index data is output, providing a basis for building suitability analysis. A comprehensive building suitability index is calculated for each selected site area according to a formula. A preset building suitability threshold is set, and the calculated comprehensive building suitability index is compared with the preset threshold. If the comprehensive building suitability index is greater than or equal to the preset threshold, it is confirmed as a ground observation station construction type, and the ground observation station construction type data is output. If the comprehensive building suitability index is less than the preset threshold, it is confirmed as a rooftop observation station construction type, and the rooftop observation station construction type data is output. The construction type data of ground observation stations and rooftop observation stations are merged to generate complete construction type data of signal observation stations. The comprehensive construction type data of signal observation stations is output to provide the final construction type information for the construction plan.

[0044] Preferably, step S4 includes the following steps: Step S41: Perform conventional ground observation construction simulation on the ground observation station construction type data to generate conventional ground observation station construction simulation data; Step S42: Perform lightweight roof observation construction simulation on the roof observation station construction type data to generate lightweight roof observation station construction simulation data; Step S43: Based on the conventional ground observation station construction simulation data and the lightweight roof observation station construction simulation data, fuse the observation station three-dimensional model of the observation space terrain three-dimensional model to generate a BeiDou navigation satellite three-dimensional continuous observation station model; Step S44: Based on the BeiDou navigation satellite three-dimensional continuous observation station model, perform engineering drawing conversion to generate BeiDou navigation satellite continuous observation station construction view drawings to execute the modular-based lightweight design operation of BeiDou navigation satellite continuous observation station.

[0045] In this embodiment of the invention, relevant architectural design and construction specifications are collected based on ground-based observation station construction type data. Building simulation software (such as Revit and SketchUp) is used for design and modeling. The construction requirements of the ground-based observation stations, including structural layout, material usage, and construction techniques, are input into the simulation software. Construction simulations of the ground-based observation stations are performed, generating conventional ground-based observation station construction simulation data, including construction progress, material requirements, and cost estimates, thus forming ground-based observation station construction simulation data to demonstrate the construction process and expected effects of conventional ground-based observation stations. Based on rooftop observation station construction type data, design specifications and requirements for lightweight rooftop observation stations are collected, and lightweight design and simulation software (such as AutoCAD and 3DMax) is used for modeling and optimization. The construction requirements of the rooftop observation stations are input into the simulation software, including lightweight structure, material selection, and installation methods. Construction simulations of the rooftop observation stations are performed, generating lightweight rooftop observation station construction simulation data, including structural stability, load calculation, and material usage, thus forming rooftop observation station construction simulation data to demonstrate the construction effects and design optimizations of lightweight rooftop observation stations. We collected simulation data on the construction of conventional ground-based observation stations and lightweight rooftop observation stations, and used 3D modeling and fusion software (such as Blender and 3ds Max) to synthesize the models. We then integrated the conventional ground-based observation station models and the lightweight rooftop observation station models to create a comprehensive 3D model of the observation station. We adjusted and optimized the model details to ensure seamless integration of the ground-based and rooftop observation stations, generating a 3D continuous observation station model for the BeiDou navigation satellite system, showcasing the final layout and structural design. We then organized the data and design information from the 3D continuous observation station model for the BeiDou navigation satellite system and used engineering drawing software (such as AutoCAD and MicroStation) to create drawings. Based on the 3D model, we generated detailed construction view drawings, including floor plans, elevations, and sections, optimizing the drawing details to ensure the construction team could accurately understand the construction requirements and design. These construction view drawings for the BeiDou navigation satellite continuous observation station serve as the foundational documents for executing the lightweight construction work.

[0046] Preferably, step S41 includes the following steps: Step S411: Perform low-density area virtual site selection on the ground observation station construction type data and the dense environmental building data to generate low-density area site selection data for ground observation stations; Step S412: Calculate the observation field of view on the low-density area site selection data for ground observation stations to obtain the observation field of view distance data for station site selection; Step S413: Perform optimal virtual site selection simulation on the low-density area site selection data for ground observation stations using the site site of view distance data to generate optimal ground observation station site selection data; Step S414: Simulate the construction process on the optimal ground observation station site selection data to generate conventional ground observation station construction simulation data.

[0047] In this embodiment of the invention, low-density areas are identified using environmental building density index data. A site selection range is defined to ensure the selected area meets low-density standards. Geographic Information System (GIS) software (such as ArcGIS or QGIS) is used for site selection analysis. Virtual site selection is performed in the low-density areas to generate low-density area site selection data for ground observation stations. This data, containing information about the ground observation stations, is used for subsequent field-of-view calculations and site selection optimization. The low-density area site selection data for the ground observation stations is acquired, and the field-of-view parameters of the observation equipment are set, including field of view range and distance. Field-of-view calculation software or algorithms are used to calculate the observation field-of-view distance data for each selected site (such as line-of-sight calculation and occlusion analysis). The calculation results are processed to obtain the observation field-of-sight distance data for each selected site, providing a basis for site selection optimization. Based on the observation field-of-sight distance data, site selection optimization standards are set, such as maximum field-of-sight coverage and minimum interference. Optimal site selection simulations are conducted using simulation and optimization tools (such as Matlab and optimization algorithms). Site selection is optimized based on field-of-view distance data to generate optimal ground observation site selection data, providing optimized solutions for actual construction. This involves acquiring optimal ground observation site selection data and collecting relevant construction codes and standards. Construction process simulations are then performed using building simulation software (such as Revit and SketchUp). The simulation includes construction steps, resource requirements, and timelines, generating conventional ground observation site construction simulation data to demonstrate the construction process and expected results.

[0048] Preferably, step S42, which involves modularizing the construction type data of the rooftop observation station, includes: classifying the construction type data of the rooftop observation station into structural styles to generate standard construction structure data; splitting the standard construction structure data into component dimensions to generate component granularity list data; encoding the component granularity list data into connection interfaces to generate modular interface identifier data; checking the power and signal channel compatibility of the modular interface identifier data to generate channel compatibility confirmation data; performing high-frequency interference fault-tolerant encapsulation processing on the channel compatibility confirmation data to generate electromagnetic safety module data; performing structural stress tolerance analysis on the electromagnetic safety module data to generate wind and earthquake-resistant structural assessment data; adapting the wind and earthquake-resistant structural assessment data to roof load constraints to generate structural load compatibility data; binding sensor deployment data to the structural load compatibility data to generate functional component encapsulation map data; rearranging the module encapsulation sequence of the functional component encapsulation map data to generate assembly process priority data; and performing spatial layout compression encapsulation on the assembly process priority data to execute the modular construction encapsulation operation.

[0049] In this embodiment of the invention, the construction type data of rooftop observation stations are categorized by structural style. This process is based on the observation station design drawings or structural prototype data, and uses a structural template comparison algorithm for categorization. Based on building structural characteristics (such as installation location, dimensions, materials used, etc.), different standard construction structure types are defined, such as light steel frame type, concrete foundation type, and combined support type, generating standard construction structure data and encoding it as a structural type identifier (e.g., "ST-RS-01" represents light steel roof type). Subsequently, the standard construction structure data is split into component dimensions. The splitting follows unified structural disassembly rules, classifying the main components in the structural model into hierarchical parts, including base units, main frames, support beams, instrument platforms, cable troughs, etc. Each component unit needs to have its three-dimensional geometric parameters (length, width, height), mass, interface type, installation angle, etc., extracted to form component granularity list data, which is then expressed in a structured manner in CSV or XML format. Next, connection interface encoding is implemented for each component in the component granularity list data. The coding process must adhere to the definitions of mechanical and electrical connection interfaces in national or industry standards (such as GB / T 17420-2019), determining the connection method (bolts, snap-fit, welding, cable heads, etc.) for each component and assigning it a unique interface number and interface type label, generating modular interface identification data (e.g., interface number "IF-CC-004", interface type "snap-fit ​​connection"). After obtaining the modular interface identification data, power and signal channel compatibility checks are performed. These checks include: power line voltage level matching, power margin, electrical signal type consistency (analog / digital), and data bandwidth requirements. Based on the interface electrical parameter table and module electrical standard specifications, a parameter matching algorithm is used to determine whether the connection conditions are met, and channel compatibility confirmation data is generated for each module connection point, including compatibility status flags, interface numbers, and power and signal interface configuration tables. Furthermore, high-frequency interference-tolerant encapsulation processing is performed on the channel compatibility confirmation data. Electromagnetic compatibility (EMC) simulation software (such as ANSYS HFSS) is used to analyze the high-frequency interference sources in the signal transmission path of the inter-module communication channel, detecting potential electromagnetic leakage, common-mode interference, and cable coupling interference. Based on the simulation results, fault-tolerant encapsulation methods such as deploying metal shielding layers, filters, or using twisted-pair cabling are implemented, and relevant processing configurations are recorded to generate EMC module data. Next, structural stress tolerance analysis is performed on the EMC module data. This step uses structural finite element analysis tools (such as ABAQUS or ANSYS Mechanical) to analyze and simulate the stress-strain response of the module structure under wind and seismic loads. Analysis inputs include parameters such as material elastic modulus, component connection stiffness, maximum wind speed in the roof area (e.g., 50 m / s), and peak seismic acceleration (e.g., 0.2g). The analysis results are used to determine whether the structural displacement and stress are within acceptable ranges, outputting wind and seismic resistance structural evaluation data.Next, roof load constraint adaptation processing is performed on the wind and earthquake resistance structural assessment data. Based on roof load limits in building design codes (e.g., maximum static load of 1.2 kN / m²), the total mass of the entire modular structure is constrained, and the structural layout and material selection are optimized based on the mass distribution to generate structural load compatibility data. Then, sensor deployment and binding operations are performed on the structural load compatibility data. According to functional requirements and spatial layout rules, corresponding sensor units (such as temperature and humidity, wind speed, electromagnetic radiation, GPS timing, etc.) are bound to each functional area, and sensor installation interface numbers and sampling frequency parameters are defined (e.g., "TS-GPS-002", sampling frequency 1Hz). Functional component encapsulation map data is generated, graphically indicating the sensor placement positions and interface binding relationships. Afterwards, the functional component encapsulation map data is rearranged into module encapsulation sequences. A topology sorting algorithm based on task priority is used to analyze the component installation dependency graph, determine the assembly sequence (e.g., the base must be installed before the frame), and generate assembly process priority data. The data format includes process number, component number, dependency node list, and priority value. Finally, spatial layout compression and encapsulation processing is applied to the priority data of the assembly process. This processing is based on a three-dimensional space utilization optimization model and uses a compact layout algorithm (such as a three-dimensional binning algorithm) to complete the module compression arrangement within the limited roof area, forming the final spatial layout compression configuration file, thereby completing the modular encapsulation operation of the entire rooftop observation station. More specifically, the construction of the rooftop observation station must adhere to the principles of scientific site selection and structural safety to ensure stable equipment operation and accurate signal reception. First, when selecting a construction location, interference sources should be avoided. The site should be more than 200 meters away from facilities such as microwave stations, radio transmitters, and high-voltage power lines, and the distance from microwave channels and high-voltage lines should be greater than 100 meters. It must also meet the requirements of unobstructed overhead views and good visibility. Second, a horizontal roof surface above the load-bearing beams and columns is selected for foundation reinforcement. A 0.5m × 0.5m × 3cm groove is excavated to expose the reinforcing steel, and at least four 12mm diameter bolts are welded onto it. Simultaneously, a main reinforcing bar is selected and welded to the building's existing lightning protection strip, using synthetic polyester material for fixation to ensure the integrity of the grounding and lightning protection system. Next, the main structure of the observation pier will be prefabricated. The main structure adopts a coaxial three-tube design. The innermost layer is a steel data cable protection tube with a diameter of 5 cm and a wall thickness of 1-2 mm. The middle layer is the main load-bearing cylinder—an indium steel load-bearing cylinder with a diameter of 20 cm and a wall thickness of 3-5 mm. Its material is composed of 60% iron, 30-40% nickel, and trace amounts of manganese and carbon. It has high strength, strong corrosion resistance, and low expansion coefficient, which can ensure that the phase center of the Beidou navigation satellite observation antenna maintains spatial stability under temperature changes. The outermost layer is a stainless steel outer sleeve with a diameter of 30 cm and a wall thickness of 1-2 mm. It is used for structural protection and aesthetics. Its top is equipped with a flexible connection mechanism to connect with the antenna mounting chassis, which plays a role in sealing and buffering.During structural assembly, a lightweight thermal insulation material primarily composed of polyethylene is filled between the indium steel load-bearing cylinder and the outer stainless steel outer sleeve to prevent heat conduction. The upper end of the observation pier connects to the mounting base of the BeiDou navigation satellite observation antenna, while the lower end is welded with a flange and fixed to the roof slab with expansion bolts. To further enhance stability, a lightweight reinforced concrete base is added at the bottom, and the joint with the roof is waterproofed. The exterior is decorated with fluorocarbon paint, and its structural construction standards follow the specifications for surveying piers. Next, the prefabricated observation pier is installed on the roof using the fixing flange. The BeiDou navigation satellite observation antenna is mounted on the upper end, and data and power cables are run through protective conduits, one end connected to the antenna and the other end connected to the BeiDou receiver indoors. The overall length of the observation pier can be adjusted according to the roof space conditions, ranging from a minimum of 0.8 meters to a maximum of 4 meters. Even in the highest configuration, the total weight is controlled within 200 kilograms, reducing weight by more than 80% compared to traditional concrete markers. Furthermore, due to the extensive use of prefabricated components, on-site installation only takes 1-2 days, shortening the construction cycle by more than 60% compared to traditional methods. Finally, the BeiDou navigation satellite observation antenna is covered by a fiberglass radome to protect the antenna itself. This radome can transmit the initial signal from the BeiDou navigation satellite signal source and surrounding radiated interference signals. The combined signal from both is received by the observation antenna for subsequent processing and analysis.

[0050] Of particular importance, step S42, which involves performing a lightweight rooftop observation construction simulation based on the rooftop observation station construction type data, includes: performing virtual site selection for high-density areas based on the rooftop observation station construction type data and dense building data to generate high-density area site selection data for rooftop observation stations; conducting a preliminary assessment of the building roof structure based on the high-density area site selection data for rooftop observation stations to generate rooftop assessment data for rooftop observation stations, where the preliminary assessment of the building roof structure includes load-bearing capacity assessment, structural material assessment, and roof area assessment; using the rooftop assessment data for rooftop observation stations to perform lightweight equipment selection and virtual layout on the high-density area site selection data for rooftop observation stations to generate lightweight equipment layout data; using the lightweight equipment layout data to perform roof material and protection design to generate rooftop observation station protection material data; performing dynamic load distribution analysis based on the rooftop observation station protection material data to generate rooftop dynamic load analysis data; and performing a lightweight rooftop observation construction simulation based on the rooftop dynamic load analysis data to generate lightweight rooftop observation station construction simulation data.

[0051] In this embodiment of the invention, building density data of the target area is acquired, including building distribution, density, and height information. The building density data is analyzed to identify high-density areas. These areas should have good signal reception conditions. Virtual site selection is performed within the high-density areas to generate high-density area site selection data for rooftop observation stations. Areas with sufficient space and suitable for construction are selected as candidate locations. The load-bearing capacity of the roof is measured to ensure it can support the observation equipment and ancillary facilities. The strength and durability of the roof materials are analyzed, including whether reinforcement is needed. The roof area is measured to ensure it is sufficient to accommodate all planned equipment. Lightweight observation equipment that meets the roof's load-bearing capacity and space requirements is selected. Computer-aided design (CAD) tools are used for equipment layout to generate lightweight equipment layout data. Based on the layout data, suitable roof materials and protective measures are designed to generate rooftop observation station protective material data. The impact of the equipment layout on the dynamic load distribution of the roof is analyzed to generate rooftop dynamic load analysis data. Based on the dynamic load analysis data, simulation tools are used to simulate the construction of the lightweight rooftop observation station, generating lightweight rooftop observation station construction simulation data. The design scheme is optimized based on the simulation results to ensure safety and stability during actual construction.

[0052] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0053] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A lightweight design method for a modular BeiDou navigation satellite continuous observation station, characterized in that, Includes the following steps: Step S1: Acquire the initial signal from the satellite signal source; The initial acquired signals from the satellite signal source are preprocessed to generate a clean signal matrix; the spatiotemporal features of the clean signal matrix are extracted to obtain signal time feature data and signal spatial feature data; based on the signal time feature data and signal spatial feature data, the clean signal matrix is ​​fused and optimized to generate a comprehensive satellite observation signal; Step S2: Obtain observation spatial geographic information data; Step S3: Perform 3D reconstruction of the observation space geographic information data to generate a 3D terrain model of the observation space; dynamically divide the 3D terrain model of the observation space into spatiotemporal grids based on the integrated satellite observation signals to generate spatial signal grid division regions; Step S3: Perform regional signal coherence analysis on the spatial signal grid division regions to generate regional signal coherence atlases; select signal observation station locations in the spatial signal grid division regions based on the regional signal coherence atlases to generate spatial location data for signal observation stations; perform environmental sensing on the spatial location data for signal observation stations to generate spatial environmental sensing data for signal observation stations; and use the spatial environmental sensing data for signal observation stations to perform spatial environmental sensing on the signal observation station locations. The regional site selection data is used to confirm the construction type of the observation station, thereby obtaining the construction type data of the ground observation station and the construction type data of the roof observation station; Step S4: Perform conventional ground observation construction simulation on the construction type data of the ground observation station to generate conventional ground observation station construction simulation data; Perform lightweight construction modularization on the construction type data of the roof observation station to generate lightweight roof observation station construction simulation data; Based on the conventional ground observation station construction simulation data and the lightweight roof observation station construction simulation data, the three-dimensional model of the observation space terrain is converted into engineering drawings to generate the construction view drawings of the Beidou navigation satellite continuous observation station, so as to carry out the modular lightweight design operation of the Beidou navigation satellite continuous observation station.

2. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the initial acquisition signal from the satellite signal source using a distributed edge computing network, wherein the initial acquisition signal from the satellite signal source includes BeiDou navigation satellite signals and interference signals from radiation sources, and the initial acquisition signal from the satellite signal source is represented as: ;in The first Beidou receiving antenna for the Beidou navigation satellite observation station The combined signal vector received by each receiving unit This refers to the number of elements in the BeiDou receiving antenna array. This is a vector of the BeiDou satellite signal. This refers to the BeiDou satellite signal vector received by the BeiDou navigation satellite observation station. For the first Signals from other radiation sources For signals from other radiation sources, To follow the steady-state distribution of background noise, This refers to the number of BeiDou satellites received by a single BeiDou satellite observation station. For the number of other sources of interfering radiation, Step S12: Perform signal cleaning on the initial acquired signals of the satellite signal sources to generate a cleaned satellite signal source signal; perform signal denoising on the cleaned satellite signal source signal to generate a denoised satellite signal source signal; perform outlier processing on the denoised satellite signal source signal to generate a cleaned signal matrix; Step S13: Extract the spatiotemporal features of the cleaned signal matrix to obtain signal time feature data and signal space feature data; perform multi-layer signal fusion on the cleaned signal matrix based on the signal time feature data and signal space feature data to generate a multi-layer fused signal matrix; Step S14: Optimize the multi-layer fused signal matrix to generate a comprehensive satellite observation signal.

3. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Acquire observation spatial geographic information data; Step S22: Extract elevation data from the observation spatial geographic information data to generate observation spatial elevation information data; convert the observation spatial geographic information data into point cloud data to generate observation spatial point cloud data; Step S23: Perform three-dimensional meshing on the observation spatial point cloud data to generate observation spatial mesh data; perform three-dimensional reconstruction of the observation spatial elevation information data and observation spatial mesh data to generate a three-dimensional terrain model of the observation space; Step S24: Perform dynamic spatiotemporal meshing on the three-dimensional terrain model of the observation space based on the satellite observation integrated signal to generate a spatial signal mesh division area.

4. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Perform initial spatiotemporal partitioning on the three-dimensional terrain model of the observation space based on the satellite observation integrated signal to generate initial spatial partitioning data for satellite observation; Step S242: Extract regional signal spectrum features from the satellite observation integrated signal using the initial spatial partitioning data for satellite observation to obtain regional integrated signal spectrum feature data; perform spectrum continuity analysis on the regional integrated signal spectrum feature data to generate continuous regional signal spectrum data; Step S243: Perform dynamic detection on the continuous regional signal spectrum data to generate dynamic continuous regional signal data; refine the initial spatial partitioning data for satellite observation based on the dynamic continuous regional signal data to generate optimized spatial partitioning data for satellite observation; Step S244: Perform dynamic spatiotemporal grid division on the initial spatial partitioning data for satellite observation based on the optimized spatial partitioning data for satellite observation to generate spatial signal grid division regions.

5. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform regional signal coherence analysis on the spatial signal grid division area to generate a regional signal coherence atlas; perform signal fluctuation detection on the spatial signal grid division area based on the regional signal coherence atlas to generate regional signal fluctuation data; Step S32: Select signal observation station locations in the spatial signal grid division area using the regional signal fluctuation data to generate spatial location data for signal observation stations; Step S33: Perform location environment perception on the spatial location data for signal observation stations to generate spatial environment perception data for signal observation stations; Step S34: Confirm the construction type of the signal observation station using the spatial environment perception data for the spatial location data for signal observation stations to obtain signal observation station construction type data, which includes ground observation station construction type data and rooftop observation station construction type data.

6. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 5, characterized in that, Step S31 includes the following steps: Step S311: Divide the spatial signal grid into regions and filter the grid cell signals to obtain grid cell signals; perform local coherence calculation on the grid cell signals to obtain local coherence matrices; Step S312: Aggregate several local coherence matrices for regional coherence to generate a regional coherence map; perform multi-scale analysis on the regional coherence map to generate regional multi-scale coherence analysis data, wherein the multi-scale analysis includes time scale analysis, spatial scale analysis, frequency scale analysis, and scale invariance analysis; Step S313: Perform comprehensive regional signal coherence analysis on the regional coherence map based on the regional multi-scale coherence analysis data to generate a regional signal coherence map set; extract signal frequency components from the regional signal coherence map set to obtain regional signal frequency data; Step S314: Calculate the frequency fluctuation amplitude of the regional signal frequency data to obtain regional signal fluctuation data.

7. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 5, characterized in that, Step S34 includes the following steps: Step S341: Perform environmental electromagnetic radiation analysis on the spatial area environmental sensing data of the signal observation station to generate environmental electromagnetic radiation data; Step S342: Calculate the building density index on the spatial area environmental sensing data of the signal observation station to obtain environmental building density data; Step S343: Calculate the construction suitability index using the environmental electromagnetic radiation data and the environmental building density data to obtain a comprehensive construction suitability index; The formula for calculating the construction suitability index is as follows: In the formula, This is expressed as a comprehensive construction suitability index. It is expressed as the environmental electromagnetic radiation index. This is expressed as the environmental building density index. This represents the weighting coefficient used to adjust the influence of the environmental electromagnetic radiation index on the comprehensive building suitability index. This is represented as a weighting coefficient used to adjust the influence of the building density index on the comprehensive construction suitability index; Step S344: Compare the comprehensive construction suitability index with the preset construction suitability threshold. When the comprehensive construction suitability index is greater than or equal to the preset construction suitability threshold, the construction type of the ground observation station is confirmed based on the spatial area site selection data of the signal observation station, thereby obtaining the ground observation station construction type data; when the comprehensive construction suitability index is less than the preset construction suitability threshold, the construction type of the roof observation station is confirmed based on the spatial area site selection data of the signal observation station, thereby obtaining the roof observation station construction type data; Step S345: Merge the ground observation station construction type data and the roof observation station construction type data to generate the signal observation station construction type data.

8. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform conventional ground observation construction simulation on the ground observation station construction type data to generate conventional ground observation station construction simulation data; Step S42: Perform modular construction encapsulation and lightweight roof observation construction simulation on the roof observation station construction type data to generate lightweight roof observation station construction simulation data; Step S43: Based on the conventional ground observation station construction simulation data and the lightweight roof observation station construction simulation data, perform observation station 3D model fusion on the observation space terrain 3D model to generate a BeiDou navigation satellite 3D continuous observation station model; Step S44: Based on the BeiDou navigation satellite 3D continuous observation station model, perform engineering drawing conversion to generate BeiDou navigation satellite continuous observation station construction view drawings to execute the modular BeiDou navigation satellite continuous observation station lightweight design operation.

9. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 8, characterized in that, Step S41 includes the following steps: Step S411: Perform low-density area virtual site selection on the ground observation station construction type data and the dense environmental building data to generate low-density area site selection data for ground observation stations; Step S412: Calculate the observation field of view on the low-density area site selection data for ground observation stations to obtain the observation field of view distance data for station site selection; Step S413: Perform optimal virtual site selection simulation on the low-density area site selection data for ground observation stations using the site site of view distance data to generate optimal ground observation station site selection data; Step S414: Simulate the construction process on the optimal ground observation station site selection data to generate conventional ground observation station construction simulation data.

10. The lightweight design method for a modular BeiDou navigation satellite continuous observation station according to claim 8, characterized in that, Step S42, which involves modularizing the construction type data of the rooftop observation station, includes: classifying the structural style of the rooftop observation station construction type data to generate standard construction structure data; splitting the standard construction structure data into component dimensions to generate component granularity list data; encoding the connection interfaces of the component granularity list data to generate modular interface identifier data; checking the power and signal channel compatibility of the modular interface identifier data to generate channel compatibility confirmation data; performing high-frequency interference fault-tolerant encapsulation processing on the channel compatibility confirmation data to generate electromagnetic safety module data; performing structural stress tolerance analysis on the electromagnetic safety module data to generate wind and earthquake-resistant structural assessment data; adapting the wind and earthquake-resistant structural assessment data to roof load constraints to generate structural load compatibility data; binding sensor deployment data to the structural load compatibility data to generate functional component encapsulation map data; rearranging the module encapsulation sequence of the functional component encapsulation map data to generate assembly process priority data; and performing spatial layout compression encapsulation on the assembly process priority data to execute the modular construction encapsulation operation.

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