A space-based perception constellation multi-dimensional analysis method and system

Abstract

The present application relates to the technical field of constellation performance evaluation, in particular to a space-based sensing constellation multi-dimensional analysis method and system. The method comprises obtaining observation arc segment data; constructing an observation arc segment table based on the observation arc segment, which is a structured organization form of the observation arc segment data; generating a dimension table and establishing an association mapping relationship between the dimension table and the observation arc segment table; based on the association mapping relationship, dynamically selecting dimensions according to the analysis scene and calculating corresponding metric values in real time, and generating structured analysis data for representing the constellation coverage capability according to the calculation results. In the present application, the observation arc segment is taken as the smallest analysis unit of the space-based sensing constellation observation data, solving the problem of insufficient utilization of observation data and the inability to trace fine observation details in the prior art, and realizing a technical breakthrough from summary-level analysis to arc segment-level fine analysis.

Description

Technical Field

[0001] This invention relates to the field of constellation perception effectiveness evaluation technology, specifically to a multi-dimensional analysis method and system for space-based perception constellations. It is particularly suitable for comprehensive, refined, and hierarchical quantitative analysis of the tracking and coverage capabilities of space-based perception constellations for space targets, providing precise data support for constellation design optimization, observation mission planning, constellation perception effectiveness evaluation, and orbital collision risk avoidance. Background Technology

[0002] With the rapid increase in the number of space targets (including spacecraft, rocket debris, and disintegration fragments), the orbital space around Earth is becoming increasingly crowded, significantly increasing the likelihood of collisions between spacecraft and between spacecraft and space debris. Space-based sensing constellations have become the primary means of observing space targets.

[0003] Current technologies and systems related to coverage analysis of space-based sensing constellations do not take the observation arc as the core analysis unit of constellation observation data. They do not make full use of the raw observation data generated by the constellation tracking space targets, lack multi-dimensional in-depth analysis capabilities based on the observation arc, and cannot trace the fine observation details of individual satellites, individual space targets, and specific spatiotemporal regions. They can only achieve simple statistics of summary-level data. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-dimensional analysis method and system for space-based sensing constellations to solve the problems of insufficient utilization of observation data and inability to trace detailed observations.

[0005] Firstly, a multi-dimensional analysis method for space-based sensing constellations is provided, including the following steps:

[0006] Acquire observation arc segment data;

[0007] An observation arc segment table is constructed based on the observation arc segments, which is a structured organization of the observation arc segment data;

[0008] Generate a dimension table and establish a mapping relationship between the dimension table and the observation arc table;

[0009] Based on the aforementioned correlation mapping relationship, dimensions are dynamically selected according to the analysis scenario and corresponding metric values ​​are calculated in real time. Based on the calculation results, structured analysis data for characterizing constellation coverage capability is generated.

[0010] This technical solution supports interactive visualization and export of data in multiple formats. It also supports interactive operations such as chart zooming, filtering, and drill-down, which solves the problems of existing analysis results being monotonous and lacking interactivity, and improves the practicality of the technical solution.

[0011] Optionally, the step of acquiring the observed arc segment data includes:

[0012] Using the observation arc segment as the smallest unit of analysis for observation data from the space-based sensing constellation, one or more complete observation arc segment data corresponding to a single space target are acquired and standardized.

[0013] Optionally, the steps for constructing the observation arc table include:

[0014] Construct an observation arc segment table based on a single observation arc segment;

[0015] A unique observation ID is assigned to each observation arc segment, and the unique observation ID is used as the primary key to construct the observation arc segment table for that single observation arc segment; the observation arc segment table records the core identifier field and the basic attribute field.

[0016] Optionally, the core identification fields include time ID, space ID, target ID, quality ID, and performance ID.

[0017] Optionally, the basic attribute fields include observation start time, observation end time, J2000 spatial coordinates, and observation duration.

[0018] Optionally, the dimension table includes a time dimension table, a space dimension table, a target dimension table, a quality dimension table, and a performance dimension table.

[0019] Optionally, the association mapping relationship includes using the observation arc as a basis, associating quality indicators and performance indicators as computable metrics with the observation arc table, and matching the quality indicators with the corresponding attributes in the quality dimension table, and matching the performance indicators with the corresponding attributes in the performance dimension table.

[0020] In this technical solution, a structured association between the core table of the observation arc segment and the tables of each dimension is realized through foreign keys. Quality and efficiency indicators are attached to the observation arc segment as calculable metrics, realizing free cross-analysis of multi-dimensional features and solving the problem of single analysis dimension in existing technologies.

[0021] Specifically, the above scheme, based on the observation arc segment as the smallest unit and the association between the arc segment table and the time, space and target dimension tables, further incorporates calculable evaluation indicators such as quality and efficiency into a unified multi-dimensional analysis framework. This allows the observation arc segments, which originally only recorded when, where and who were observed, to also have the quantitative evaluation ability of whether the observation was good or not and whether the efficiency was high, thus fully supporting the refined analysis and traceability of constellation coverage capabilities.

[0022] From a holistic perspective, after associating time, space, and target dimensions, the solution still links quality and performance indicators as calculable metrics to the observation arc table, matching them with the attributes of the quality and performance dimension tables respectively. The advantages are twofold: First, it forms a complete data link encompassing basic observation attributes, multi-dimensional attributes, and calculable evaluation indicators, allowing each observation arc to be interpreted and filtered simultaneously across multiple dimensions such as space, time, target, quality, and performance. This truly enables free cross-analysis across multiple dimensions, rather than simply focusing on basic dimensions like location and time. Second, through explicit matching of indicators and dimension attributes, it ensures unified calculation logic, consistent definitions, reproducibility, and traceability. Quality and performance indicators are no longer scattered values ​​but calculable metrics bound to standardized dimension attributes. This supports dynamic dimension selection and real-time calculation based on scenarios, and allows for reverse tracing of indicator results back to the corresponding dimension rules and original observation arcs. Overall, it solves the problems of insufficient data utilization, single analytical dimensions, and lack of detail traceability, upgrading the entire solution from data storage to a structured analysis system that supports actual business decision-making.

[0023] Optionally, the generation of the dimension table includes:

[0024] Generate the corresponding dimension table based on the core identifier field of the observed arc segment table.

[0025] Foreign keys are used to establish a connection between the core identifier field and the dimension table, thereby creating a mapping relationship between the observation arc table and each dimension table.

[0026] Secondly, a multi-dimensional analysis system for space-based sensing constellations is provided, including:

[0027] The data acquisition module is configured to acquire observation arc segment data;

[0028] The observation arc table construction module is configured to receive observation arc data and construct an observation arc table based on the observation arcs. This observation arc table is a structured organization of the observation arc data.

[0029] A dimension table generation module, connected to the observation arc segment table construction module, is used to generate dimension tables and establish a mapping relationship between the dimension tables and the observation arc segment tables; and...

[0030] The structured analysis module is configured to receive association mapping relationships, dynamically select dimensions according to the analysis scenario based on the association mapping relationships, calculate the corresponding metric values ​​in real time, and generate structured analysis data to characterize constellation coverage capabilities based on the calculation results.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] This invention uses the observation arc segment as the smallest analytical unit for observation data from space-based sensing constellations, solving the problems of insufficient utilization of observation data and inability to trace detailed observation data in existing technologies, and achieving a technological breakthrough from summary-level analysis to arc segment-level refined analysis. Attached Figure Description

[0033] Figure 1 This is a schematic diagram illustrating the steps of the space-based sensing constellation multi-dimensional analysis method of the present invention;

[0034] Figure 2 This is a schematic diagram illustrating the construction steps of the observation arc segment table of the present invention;

[0035] Figure 3 This is a schematic diagram illustrating the mapping relationship between the dimension tables and the observation arc table of this invention;

[0036] Figure 4 This is a schematic diagram of the coverage report of the present invention;

[0037] Figure 5 This is a schematic diagram of the tracking arc segment report of the present invention;

[0038] Figure 6 This is a schematic diagram of the tracking common view report of the present invention;

[0039] Figure 7 This is a schematic diagram of the tracking quantity report of the present invention;

[0040] Figure 8 This is a schematic diagram of the structure of the space-based sensing constellation multi-dimensional analysis system of the present invention. Detailed Implementation

[0041] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0042] In a first aspect, this invention provides a method for multi-dimensional analysis of space-based sensing constellations, comprising the following steps:

[0043] Acquire observation arc segment data;

[0044] An observation arc segment table is constructed based on the observation arc segments, which is a structured organization of the observation arc segment data;

[0045] Generate a dimension table and establish a mapping relationship between the dimension table and the observation arc table;

[0046] Based on the correlation mapping relationship, dimensions are dynamically selected according to the analysis scenario and the corresponding metric values ​​are calculated in real time. The calculation results are used to generate structured analysis data to characterize the constellation coverage capability.

[0047] Secondly, it provides a multi-dimensional analysis system for space-based sensing constellations. Figure 8 The structure of the space-based sensing constellation multi-dimensional analysis system is shown, including:

[0048] The data acquisition module is configured to acquire observation arc segment data;

[0049] The observation arc table construction module is configured to receive observation arc data and construct an observation arc table based on the observation arcs. This observation arc table is a structured organization of the observation arc data.

[0050] The dimension table generation module is connected to the observation arc segment table construction module. The dimension table generation module is used to generate dimension tables and establish the association mapping relationship between the dimension tables and the observation arc segment tables; and...

[0051] The structured analysis module is configured to receive association mapping relationships, dynamically select dimensions according to the analysis scenario based on the association mapping relationships, calculate the corresponding metric values ​​in real time, and generate structured analysis data to characterize constellation coverage capabilities based on the calculation results.

[0052] This invention uses the observation arc segment as the smallest analytical unit for observation data from a space-based sensing constellation. It first acquires and standardizes one or more complete observation arc segment data of a single space target from the constellation. Based on the observation arc segment data, it extracts features in five dimensions: time, space, target, mass, and efficiency. It then constructs a standardized two-level three-dimensional spatial grid system to improve the accuracy of spatial analysis. Subsequently, it builds a five-dimensional analysis model and generates structured analysis data in real time by dynamically combining analysis dimensions and computable metrics. This enables comprehensive, refined, and hierarchical analysis of constellation coverage capabilities and supports interactive display and data export of analysis results.

[0053] The specific implementation methods of the above solutions are disclosed and described through the following examples:

[0054] Some possible embodiments, in which Figure 1 The steps of the multi-dimensional analysis method for space-based sensing constellations are shown, including:

[0055] Step S100: Obtain the observed arc segment data.

[0056] First, observational data is acquired through satellites in the space-based sensing constellation. This includes raw data collected by satellite payloads, preprocessed data after ground decoding and correction, and standardized observational data. Then, the observational data is analyzed using observation arcs as the smallest unit. Each observation arc corresponds to a set of observational data. The acquired observation arc data can be data corresponding to a single observation arc or a set of data corresponding to multiple observation arcs, including but not limited to the following scenarios:

[0057] Scenario 1: Single satellite observation data of a single target, that is, a satellite completes a continuous tracking observation of a space target, forming an observation arc segment, corresponding to a set of observation arc segment data;

[0058] Scenario 2: Multiple observation data of a single satellite on a single target, that is, the same satellite observes the same target multiple times at different times of transit, forming multiple observation arcs, corresponding to multiple sets of observation arc data;

[0059] Scenario 3: Collaborative observation data of multiple satellites on a single target, where multiple satellites in a constellation observe the same target at different times and in different orbits, forming multiple observation arcs and corresponding to multiple sets of observation arc data.

[0060] In other words, the steps for acquiring observation arc data include: using the observation arc as the smallest unit of analysis for observation data from the space-based sensing constellation, acquiring and standardizing one or more complete observation arc data corresponding to a single space target.

[0061] The above observation arc is the smallest unit of analysis. The definition of the observation arc is: the continuous observation record of the optical camera of a single satellite in the space-based sensing constellation on the same space target from the first successful observation to the last observation.

[0062] Step S200: Construct an observation arc segment table based on the observation arc segments. This observation arc segment table is a structured organization of the observation arc segment data.

[0063] First, the collected observation data is standardized. The purpose of standardization is to eliminate differences between observation data from different sources and in different formats, ensuring that the data format is uniform, the units are consistent, and the semantics are standardized, laying the foundation for subsequent table construction and data association. Specific implementation methods include unifying the time base to UTC time, unifying the spatial coordinates to the J2000 coordinate system, unifying the data format to a parsable structured format (such as CSV, JSON, or database table format), unifying field naming rules, and removing invalid data (such as data missing core information or data with obvious errors) to ensure that all observation data are comparable and correlateable.

[0064] Optionally, the observation arc segment table adopts a conventional structured format in this field, such as a database table, an Excel spreadsheet, or structured text. Each row of the table corresponds to one observation arc segment, and each column corresponds to a core identifier field or a basic attribute field.

[0065] Figure 2 The steps for constructing the observation arc segment table are shown, including:

[0066] Step S210: Construct an observation arc segment table based on the observation arc segments;

[0067] Create a blank structured table. Optionally, you can create a database table using SQL statements, a table object using a programming language (such as Python or Java), or an Excel spreadsheet using regular office software. The table must reserve column space for core identifier fields and basic attribute fields.

[0068] Subsequently, for each observation arc segment, a row of data space is allocated to it in the table. If data corresponding to multiple observation arc segments is obtained, the above operation is repeated to fill the data of all observation arc segments into the same observation arc segment table in sequence. That is, a single observation arc segment table can hold the data of multiple observation arc segments without the need to create a separate table for each observation arc segment.

[0069] Step S220: Assign a unique observation ID to the observation arc segment, and use the unique observation ID as the primary key of the observation arc segment table for that single observation arc segment; the observation arc segment table records the core identifier field and the basic attribute field.

[0070] The unique observation ID refers to the unique identifier code assigned to each observation arc segment in the entire observation arc segment table (which can be in the form of numbers, letters, or a combination of numbers and letters, such as "Arc_20260306_001"). Its core function is to distinguish different observation arc segments, ensuring that each observation arc segment has a unique and identifiable identifier in the table, and avoiding data confusion.

[0071] A primary key is a field in a relational table used to uniquely identify each row of data. In this embodiment, the unique observation ID is used as the primary key of the observation arc table. Its core principle is to achieve fast querying, modification, and association of a single observation arc data through the uniqueness of the primary key. It is the basis for establishing a mapping relationship between the observation arc table and each dimension table.

[0072] The core identifier field is used to establish associations with subsequent dimension tables. Its content must be consistent with the primary key in the corresponding dimension table to ensure the accuracy of the association mapping. Optionally, the core identifier field includes time ID, space ID, target ID, quality ID, and performance ID.

[0073] Basic attribute fields refer to fields used to directly describe the core characteristics of the observed arc segment itself. They do not need to be associated with other dimension tables and can be directly extracted from the observation data to intuitively reflect the basic information of the observed arc segment. Optionally, basic attribute fields include observation start time, observation end time, J2000 spatial coordinates, and observation duration.

[0074] Step S220 includes:

[0075] A unique observation ID is assigned to each observation arc segment using a conventional method for generating unique identifiers in this field. The generation rules can be customized (e.g., “Arc_year_month_date_serial number”, such as “Arc_20260306_001” and “Arc_20260306_002”). After generation, the observation ID is filled into the “Unique Observation ID” column (primary key column) of the corresponding row in the observation arc segment table.

[0076] From the standardized observation arc data, core information related to time, space, target, quality, and effectiveness is extracted and filled into the corresponding columns of the observation arc table according to the preset field format. For example, the observation start time "2026-03-06 08:00:00" is extracted from the observation arc data, converted into the time ID "20260306080000", and then filled into the "Time ID" column. The grid number "Space_005" of the observation area is extracted and filled into the "Space ID" column as the space ID. The target number "Target_001" is extracted and filled into the "Target ID" column as the target ID. Based on the signal-to-noise ratio and data integrity of the observation data, the quality level code "Q01" (quality ID) and the effectiveness level code "E02" (effectiveness ID) are determined and filled into the corresponding columns respectively.

[0077] From the standardized observation arc data, the observation start time, observation end time, J2000 spatial coordinates, and observation duration are directly extracted and filled into the corresponding columns of the observation arc table according to a unified format. For example, the observation start time is filled as "2026-03-06 08:00:00", the observation end time is filled as "2026-03-06 08:15:00", the J2000 spatial coordinates are filled as "right ascension 120°, declination 30°, distance 500km", and the observation duration is calculated as "15 minutes" and then filled into the corresponding column. The observation duration can be automatically calculated by "observation end time - observation start time", and the J2000 spatial coordinates can be directly extracted from the satellite observation data (which has been standardized and formatted uniformly).

[0078] Step S200 assigns a unique observation ID as the primary key to each observation arc segment, while recording and associating the time ID, spatial ID, target ID, quality ID, and effectiveness ID, as well as the observation start time, observation end time, J2000 spatial coordinates, and observation duration, to form a standardized observation arc segment table, providing complete and standardized basic data for subsequent multi-dimensional feature extraction.

[0079] The core function of the observation arc table is to organize and store observation arc data in a standardized, structured tabular format. Essentially, it maps observation arcs to structured data, providing a unified, standardized, and traceable foundational data carrier for subsequent dimension table generation, relationship establishment, and multi-dimensional feature extraction. Using a single observation arc as the data organization unit, a unique observation ID (primary key) ensures independent differentiation of observation arc data. A core identifier field enables association and adaptation with subsequent dimension tables, and basic attribute fields fully preserve the core characteristics of the observation arcs, ensuring data standardization, integrity, and operability.

[0080] Step S300: Generate a dimension table and establish a mapping relationship between the dimension table and the observation arc table. Figure 3 The mapping relationship between each dimension table and the observation arc table is shown. The generated dimension tables include:

[0081] Generate the corresponding dimension table based on the core identifier field of the observed arc segment table.

[0082] Foreign keys are used to establish associations between the core identifier fields and the dimension tables, thereby creating a mapping relationship between the observation arc table and each dimension table. The dimension tables include:

[0083] The time dimension table is generated based on the "time ID" of the observation arc table. The primary key is the time ID, and the supplementary attributes include year, month, day, hour, minute, second, time slice, and observation period type (day / night).

[0084] The spatial dimension table is generated based on the "spatial ID" of the observation arc table. The primary key is the spatial ID, and the supplementary attributes include grid name, region, J2000 coordinate system coverage, and latitude and longitude interval.

[0085] The target dimension table is generated based on the "target ID" of the observation arc table. The primary key is the target ID, and the supplementary attributes include target name, target type (such as satellite / space debris), orbital altitude, and orbital inclination.

[0086] The quality dimension table is generated based on the "quality ID" of the observation arc table. The primary key is the quality ID, and the supplementary attributes include the quality level name (such as excellent / good / qualified), signal-to-noise ratio threshold, data integrity requirements, and quality judgment rules.

[0087] The performance dimension table is generated based on the "performance ID" of the observation arc table. The primary key is the performance ID, and the supplementary attributes include the performance level name (such as excellent / good), the observation duration threshold, the coverage number requirement, and the performance scoring criteria.

[0088] The association mapping relationship refers to establishing a row-level correspondence between the observation arc table and each dimension table, with the primary key (dimension table) and foreign key (observation arc table) as the link. Essentially, it realizes the traceable and interactive mapping of the core identifier field of the observation arc table → the primary key of the dimension table → the detailed attributes of the dimension table, so that the observation arc data can be associated with the detailed attributes of each dimension, supporting subsequent multi-dimensional analysis.

[0089] Optionally, the association mapping relationship includes associating the observation arc table with the time dimension table, the spatial dimension table, and the target dimension table based on the observation arc.

[0090] Optionally, the association mapping relationship includes using the observation arc as a basis, associating quality indicators and performance indicators as computable metrics with the observation arc table, and matching the quality indicators with the corresponding attributes in the quality dimension table, and matching the performance indicators with the corresponding attributes in the performance dimension table.

[0091] Taking the observation arc table and time dimension table as examples (the spatial and target dimension tables are associated in the same way, only the associated fields are replaced):

[0092] Confirm that the associated fields match. Ensure that the "Time ID" (foreign key) in the observation arc table and the "Time ID" (primary key) in the time dimension table have completely identical formats and values ​​(e.g., both are in the format "YYYYMMDDHHMMSS" and both have values ​​"20260306080000" and "20260306093000"). This is a prerequisite for association.

[0093] To establish a relationship, taking MySQL as an example, execute the following SQL statement to create a foreign key constraint, directly implementing database-level relationship mapping and ensuring data consistency (which can be directly reused):

[0094] ALTER TABLE Observation Arc Table ADD CONSTRAINT fk_arc_time FOREIGN KEY (Time ID) REFERENCES Time Dimension Table (Time ID); where fk_arc_time is the foreign key constraint name, which can be customized; "Observation Arc Table (Time ID)" is the foreign key field, and "Time Dimension Table (Time ID)" is the primary key field.

[0095] Link quality / performance metrics (calculable values) to the observation arc table, and then match the dimension table using primary key-foreign key matching. Complete practical steps (the linking methods for quality and performance dimensions are exactly the same):

[0096] Quality and performance indicators are used as calculable metrics and linked to the observation arc table. Directly calculable and quantifiable quality indicators (such as data integrity rate and signal-to-noise ratio) and performance indicators (such as observation duration and coverage count) are extracted from the observation arc data and added as new fields to the observation arc table (e.g., adding "data integrity rate" and "observation duration" columns to the observation arc table), thus achieving the association between "observation arc" and "metric value".

[0097] Match Quality ID / Performance ID (foreign key). Based on the newly added quality / performance index values ​​in the observation arc table, match the corresponding Quality ID / Performance ID (foreign key) according to the attribute judgment criteria in the quality dimension table and performance dimension table, and populate the Quality ID / Performance ID field in the observation arc table;

[0098] (Example: Observe the quality indicator "data completeness rate 98%" of a certain data in the observation arc table, compare it with the attribute "Q01=high quality, data completeness rate ≥95%" in the quality dimension table, match the quality ID "Q01" and populate it into the quality ID field of the data.)

[0099] Establish a primary key-foreign key relationship. Confirm that the quality ID / performance ID (foreign key) of the observed arc table is completely consistent with the quality ID / performance ID (primary key) of the quality dimension table / performance dimension table in terms of format and value. Execute the same foreign key constraint SQL as in Scenario 1 (taking the quality dimension table as an example):

[0100] ALTER TABLE Observation Arc Table ADD CONSTRAINT fk_arc_quality FOREIGN KEY (quality ID) REFERENCES Quality Dimension Table (quality ID).

[0101] Regardless of the specific association scenario, the core principle is that the core identifier field (foreign key) of the observation arc table points to the core identifier field (primary key) of each dimension table. A unique correspondence is established through database foreign key constraints, ultimately enabling the linkage between the observation arc table and all dimension tables.

[0102] Step S400: Based on the correlation mapping relationship, dynamically select dimensions according to the analysis scenario and calculate the corresponding metric values ​​in real time. Generate structured analysis data to characterize constellation coverage capability based on the calculation results.

[0103] Based on the association mapping relationship established in step S300, one or more dimensions can be flexibly selected according to the needs of the actual analysis scenario. The computable metric values ​​in the observation arc table and the detailed attributes in the dimension table are called to calculate the corresponding metric indicators in real time. Finally, the calculation results are organized into a structured form to intuitively represent the coverage capability of the space-based sensing constellation.

[0104] Specifically, it supports the arbitrary selection of multi-dimensional combinations such as "constellation as a whole - individual satellite", "time - space - target", and "quality - efficiency" according to actual needs, to achieve diverse cross-analysis requirements; it supports hierarchical data decomposition and in-depth analysis from overall constellation coverage capability → coverage contribution of individual satellites → coverage of specific orbital areas → tracking details of individual space targets, to achieve a comprehensive evaluation from macro to micro; it supports refined statistical analysis by time granularity, space granularity, and target granularity, improving the accuracy of analysis results; and it supports comparative analysis of coverage capabilities of different time periods, different satellites, different target types, and different space regions, providing quantitative comparative basis for constellation mission planning and optimization.

[0105] In some possible implementations, based on the observation arc table, core features are extracted from five dimensions: time, space, target, quality, and effectiveness. The feature extraction rules, calculation methods, and implementation details for each dimension are as follows:

[0106] (1) Time dimension feature extraction

[0107] Using the time ID of the observed arc segment table as the association identifier, fine-grained feature calculation is achieved at multiple time granularities such as seconds, minutes, hours, and days. The core features extracted are coverage, revisit period, and continuous tracking duration.

[0108] Coverage: The number of valid observed targets within each time granularity is counted, and the constellation coverage within that time period is calculated by dividing the number of observed targets by the total number of targets.

[0109] Revisit period: For a single space target, all its associated observation arcs are sorted in ascending order by start time, and the statistical value of the difference between the start times of adjacent observation arcs is calculated as the constellation revisit period of the target.

[0110] Continuous tracking duration: Extract the time difference between the end time and the start time of a single observation arc segment to obtain the actual continuous tracking duration of that arc segment. It also supports the summary statistics of the tracking duration of a single target.

[0111] (2) Spatial dimension feature extraction

[0112] Using the Earth's center as a reference frame, a standardized two-level three-dimensional spatial grid system is constructed. Combined with J2000 spatial coordinate data of the observed arc segment, precise extraction of spatial features is achieved. The core steps are as follows:

[0113] Two-level three-dimensional grid system construction: A global grid system covering specific orbital altitude ranges such as LEO, MEO, and GEO is constructed. The grid system consists of two levels of grids. The first level grid is divided into spherical sectors according to latitude, longitude, right ascension, declination, and radial distance for rapid location of high-risk areas in space. The second level grid further constructs a more refined local grid within the high-risk areas identified by the first level grid for accurate calculation of space coverage. Each grid is assigned a unique grid ID.

[0114] Precise spatial positioning of targets: Calculate the real-time spatial coordinates of space targets in the J2000 coordinate system in each observation arc segment to achieve precise spatial positioning of targets;

[0115] Spatial feature calculation: The target trajectory points are mapped to their respective 3D grids according to coordinates. The total observation time and number of observation arcs of each grid are counted to generate the spatial distribution characteristics of the coverage area. At the grid level, the grids that have been observed by two or more satellite optical cameras in the same time period are counted and marked as overlapping coverage areas. Grids that have never been observed in the analysis period are identified and marked as observation blind spots. At the same time, the quantitative calculation of the overlapping coverage ratio and the blind spot ratio is realized.

[0116] (3) Target dimension feature extraction

[0117] Using the target ID as the unique primary key, core attribute features of the target are extracted from the observation arc data and the space target catalog library, thus binding target features to the observation arc. The extracted core attributes include: target type (failed satellite, in-orbit spacecraft, space debris, manned spacecraft, etc.), orbit type (LEO / MEO / GEO / elliptical orbit, etc.), target size, and core orbital parameters. This feature dimension supports multi-dimensional filtering and analysis based on target category, orbit type, target priority, and other conditions.

[0118] (4) Quality dimension feature extraction

[0119] Raw observation quality parameters at the arc segment level are extracted to achieve refined quantification of observation data quality. Key extracted parameters include: imaging signal-to-noise ratio, imaging blur, satellite attitude stability, and target signal reception strength. Threshold standards for each parameter are also set to comprehensively rate the quality of individual observation arc segments, providing a quantitative basis for subsequent observation data selection.

[0120] (5) Performance dimension feature extraction

[0121] Based on the characteristic data of observation arcs and time, space, target, and quality dimensions, the constellation observation performance is quantitatively analyzed. The core performance characteristics extracted include: coverage completion of a single target / specific spatial region, utilization efficiency of constellation observation resources per unit time, deviation of indicators such as revisit cycle / tracking duration / regional coverage from the preset mission requirements, coverage contribution ratio of a single satellite, and overall observation data quality rate of the constellation.

[0122] Foreign keys are used to establish fixed relationships with each dimension table, as shown in Table 1:

[0123] Observation arc table Observation ID (primary key) The core table stores basic information for each observation. Time dimension table Time ID (primary key) The arc table is linked via a foreign key using a time ID to retrieve time attributes such as time period and date. Spatial Dimension Table Grid ID (Primary Key) The arc segment table is linked via a grid ID foreign key to obtain spatial location and coverage information. Target Dimension Table Target ID (primary key) The arc segment table is linked via a foreign key using the target ID to retrieve attributes such as target type and track. Quality Dimension Table Quality ID (Primary Key) The arc segment table is linked via a quality ID foreign key to obtain quality indicators such as signal-to-noise ratio, ambiguity, and attitude stability. Performance Dimension Table Performance ID (Primary Key) The arc table is linked via a performance ID foreign key to obtain performance metrics such as coverage and revisit cycle.

[0124] Table 1

[0125] The arc segment table in the table refers to the table of observed arc segments.

[0126] Taking the multi-dimensional analysis of the coverage capability of the StarEye space-based sensing constellation for space targets at altitudes of 500km-2000km as an example:

[0127] Observation constellation: StarEye Space-based Sensing Constellation, consisting of 2 observation satellites (Satellite ID: XY-01XY-02).

[0128] Observation targets: Starlink satellites (target noradID: 44716, 44720, orbital altitude 500km-2000km);

[0129] Analysis scope: The time range is from 2026-02-01 00:00:00.000 to 2026-02-02 00:00:00.000, and the spatial range is the orbital altitude area of ​​120°-130° east longitude, 30°-40° north latitude, and 200km-4500km altitude under the J2000 coordinate system;

[0130] Analysis requirements: To implement multi-dimensional coverage analysis of targets in this region by this constellation, and obtain... Figure 4 The coverage report shown Figure 5 The tracking arc report shown Figure 6 The tracking co-view report shown and Figure 7 The report showing the number of tracks is displayed.

[0131] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A multi-dimensional analysis method for space-based sensing constellations, characterized in that, Includes the following steps: Acquire observation arc segment data; An observation arc segment table is constructed based on the observation arc segments, which is a structured organization of the observation arc segment data; Generate a dimension table and establish a mapping relationship between the dimension table and the observation arc table; Based on the aforementioned correlation mapping relationship, dimensions are dynamically selected according to the analysis scenario and corresponding metric values ​​are calculated in real time. Based on the calculation results, structured analysis data for characterizing constellation coverage capability is generated.

2. The multi-dimensional analysis method for space-based sensing constellations according to claim 1, characterized in that, The steps for acquiring the observed arc segment data include: Using the observation arc segment as the smallest unit of analysis for observation data from the space-based sensing constellation, one or more complete observation arc segment data corresponding to a single space target are acquired and standardized.

3. The multi-dimensional analysis method for space-based sensing constellations according to claim 1, characterized in that, The steps for constructing the observation arc segment table include: Construct an observation arc segment table based on the observation arc segments; A unique observation ID is assigned to each observation arc segment, and the unique observation ID is used as the primary key to construct the observation arc segment table for that single observation arc segment; the observation arc segment table records the core identifier field and the basic attribute field.

4. The multi-dimensional analysis method for space-based sensing constellations according to claim 3, characterized in that, The core identification fields include Time ID, Space ID, Target ID, Quality ID, and Performance ID.

5. The multi-dimensional analysis method for space-based sensing constellations according to claim 3, characterized in that, The basic attribute fields include observation start time, observation end time, J2000 spatial coordinates, and observation duration.

6. The multi-dimensional analysis method for space-based sensing constellations according to claim 4, characterized in that, The dimension tables include a time dimension table, a space dimension table, a target dimension table, a quality dimension table, and an effectiveness dimension table.

7. The multi-dimensional analysis method for space-based sensing constellations according to claim 6, characterized in that, The association mapping relationship includes associating the measured arc segment table with the time dimension table, the spatial dimension table, and the target dimension table based on the observed arc segment.

8. The multi-dimensional analysis method for space-based sensing constellations according to claim 7, characterized in that, The association mapping relationship includes using the observation arc as a basis, associating quality indicators and performance indicators as computable metrics with the observation arc table, and matching the quality indicators with the corresponding attributes in the quality dimension table, and matching the performance indicators with the corresponding attributes in the performance dimension table.

9. The multi-dimensional analysis method for space-based sensing constellations according to any one of claims 3 to 8, characterized in that, The generated dimension table includes: Generate the corresponding dimension table based on the core identifier field of the observed arc segment table. Foreign keys are used to establish a connection between the core identifier field and the dimension table, thereby creating a mapping relationship between the observation arc table and each dimension table.

10. A space-based sensing constellation multi-dimensional analysis system, characterized in that, include: The data acquisition module is configured to acquire observation arc segment data; The observation arc table construction module is configured to receive observation arc data and construct an observation arc table based on the observation arcs. This observation arc table is a structured organization of the observation arc data. A dimension table generation module is connected to an observation arc table construction module. The dimension table generation module is used to generate dimension tables and establish an association mapping relationship between the dimension tables and the observation arc table. as well as, The structured analysis module is configured to receive association mapping relationships, dynamically select dimensions according to the analysis scenario based on the association mapping relationships, calculate the corresponding metric values ​​in real time, and generate structured analysis data to characterize constellation coverage capabilities based on the calculation results.

Images