A single-satellite sensing fusion self-checking method for low-frequency acceleration noise of a space-oriented gravitational wave detector

Abstract

The application discloses a single-satellite sensing fusion self-checking method for low-frequency acceleration noise of space gravitational wave detection, solves the technical problem of how to directly self-check the acceleration noise of a test mass by using a single satellite under the condition that no additional load hardware is increased, and comprises the following steps: acquiring non-orthogonal geometric relationship parameters of a movable optical machine subassembly under a fixed geometric configuration, and establishing a centroid connecting line direction displacement projection fusion model according to the non-orthogonal geometric relationship parameters; configuring a self-checking control working condition of the movable optical machine subassembly and a matching test mass, and synchronously collecting data under the working condition; based on the model, the geometric relationship parameters and the collected data, solving a corrected differential displacement time sequence, and reconstructing a differential residual acceleration time sequence; performing spectrum estimation on the differential residual acceleration time sequence near a 0.1mHz frequency point, comparing the spectrum estimation with a preset self-checking threshold, and obtaining a self-checking judgment result.

Description

Technical Field

[0001] This invention relates to the field of space gravitational wave detection technology, and in particular to a single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection. Background Technology

[0002] The goal of space-based gravitational wave detection missions is to detect gravitational waves in the mHz frequency band, typically ranging from 0.1 mHz to 1 Hz. These missions require the test mass to exhibit extremely low residual non-gravitational acceleration noise in the low-frequency range, with a typical value needing to reach 10. -15 The magnitude of these indicators is significant. In the ground environment, they are affected by gravity gradients, ground vibrations, temperature drift, and support coupling, making it difficult to directly verify low-frequency performance under "on-orbit free-fall conditions." To verify these indicators in orbit, the LISA Pathfinder (LPF) mission was developed internationally. The LPF deploys two test masses within a single satellite, with the laser interferometry sensitive axis coaxial with the line connecting the two test masses. It achieves differential displacement measurement of the two test masses using a highly stable optical reference and high-precision laser interferometry, and obtains differential residual acceleration noise using drag-free and levitation control methods. However, this approach is a specialized technology verification, inconsistent with the non-orthogonal payload configuration of actual gravitational wave detection satellites, and cannot be directly applied to gravitational wave detection scientific missions. Furthermore, verification missions typically require independent development and launch, resulting in high R&D costs and long implementation cycles.

[0003] Space-based gravitational wave detection satellites or spacecraft, such as LISA, Tianqin, and Taiji, typically employ a payload architecture with an overall telescope pointing orientation, configured with two Movable Opto-Mechanical Assemblies (MOSAs). Each MOSA contains a telescope, an optical platform, and test mass inertial sensors. The satellite achieves overall telescope pointing through attitude control, using the two test masses along their respective sensitive axes as drag-free control references to achieve free fall. To achieve the final detection sensitivity, a complete three-satellite, six-laser link needs to be established, and the six test masses (TMs) need to undergo free fall motion along their sensitive axes. The dominant laser frequency noise is then subtracted by combining time-delay interferometry (TDI) data from the six links. To ensure the three satellites can smoothly enter the final scientific observation mode, it is best to perform on-orbit detection of residual acceleration noise for each TM beforehand.

[0004] However, for the non-orthogonal dual MOSA configuration used in actual space-based gravitational wave detection satellites, the two arms of a single satellite point in different directions, and there is an angle of approximately 60° between the two sensitive axes. Laser interferometers cannot directly provide the relative motion along the line connecting the centroids of the two test masses. Furthermore, factors such as the structural thermal deformation of the two MOSAs, residual acceleration and control crosstalk on the non-sensitive axes, and rotational-translational coupling introduced by attitude rotation further increase the difficulty of on-orbit detection of the single satellite. Therefore, current technology lacks a method for on-orbit self-verification of low-frequency differential residual acceleration noise of the test masses, relying solely on the existing payload hardware and measurement methods of the single satellite without adding additional payload hardware.

[0005] Therefore, this invention proposes a method for self-verification of low-frequency differential residual acceleration noise of inspection quality without adding extra equipment, and which can utilize the existing payload hardware and measurement data of a single satellite. Summary of the Invention

[0006] This invention provides a single-satellite sensor fusion self-testing method for low-frequency acceleration noise detection in space gravitational wave detection. It solves the technical problem of how to directly test the acceleration noise of the test quality using a single satellite near the 0.1 mHz frequency point without adding extra payload hardware.

[0007] The first aspect of this invention provides a single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection, comprising:

[0008] Obtain the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-assembly, and output the centroid connection direction displacement projection fusion model and fixed geometric relationship parameters based on the non-orthogonal configuration geometric relationship parameters;

[0009] Configure the self-inspection control conditions for the movable optomechanical sub-assembly and the inspection quality associated with the movable optomechanical sub-assembly, and determine the self-inspection control conditions.

[0010] Based on the self-test control condition, the satellite payload measurement and control data are synchronously acquired, and the synchronous acquisition dataset is output.

[0011] Using the centroid-connecting direction displacement projection fusion model, the corrected differential displacement time series is calculated based on the fixed geometric parameters and the synchronously acquired dataset.

[0012] Based on the corrected differential displacement time series and the fixed geometric relationship parameters, the differential residual acceleration time series is reconstructed.

[0013] Based on the differential residual acceleration time series, the spectrum near the frequency point is estimated and compared with the preset self-test threshold to generate a self-test judgment result.

[0014] Optionally, the movable optomechanical sub-assembly includes a first movable optomechanical sub-assembly and a second movable optomechanical sub-assembly; the step of outputting the centroid connection direction displacement projection fusion model and fixed geometric parameters according to the non-orthogonal configuration geometric relationship parameters includes:

[0015] With the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly locked in a fixed geometric configuration, the non-orthogonal configuration geometric relationship parameters are used as fixed geometric relationship parameters.

[0016] Based on the fixed geometric relationship parameters, a centroid connection direction displacement projection fusion model for the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly is established respectively.

[0017] Optionally, the configuration of the self-inspection control conditions for the movable optomechanical sub-assembly and the inspection quality associated with the movable optomechanical sub-assembly, and the determination of the self-inspection control conditions, specifically includes:

[0018] The inspection mass associated with the first movable optomechanical sub-assembly is set as a three-degree-of-freedom displacement drag-free reference, and satellite micro-thruster control logic is configured to make the spacecraft follow the inspection mass associated with the first movable optomechanical sub-assembly in three translational degrees of freedom. At the same time, weak suspension, centering and low noise control is applied to the inspection mass associated with the second movable optomechanical sub-assembly, and low noise control is adopted for the non-self-test degrees of freedom of the inspection mass associated with the first movable optomechanical sub-assembly to meet operational safety, thus forming a complete self-test control condition.

[0019] Optionally, the step of synchronously acquiring satellite payload measurement and control data based on the self-test control condition and outputting a synchronously acquired dataset includes:

[0020] Under the self-test control condition, the acquisition time is set, and the laser interferometer displacement data, inertial sensor electrostatic displacement data, and key structure temperature data corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component are acquired and recorded simultaneously, as well as the spacecraft attitude data, alignment angle data, micro-thruster thrust data, and control status data corresponding to the satellite body, and the electrostatic execution voltage data and execution channel status data of the inspection quality of the second movable optomechanical sub-component.

[0021] The system integrates the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, and the temperature data of the key structure corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component, as well as the spacecraft attitude data, alignment angle data, micro-thruster thrust data, and control status data corresponding to the satellite body, and the electrostatic execution voltage data and execution channel status data for quality inspection associated with the second movable optomechanical sub-component, and outputs a synchronously acquired dataset.

[0022] Optionally, the step of employing the displacement projection fusion model along the centroid connection direction, and calculating the corrected differential displacement time series based on the fixed geometric parameters and the synchronously acquired dataset, includes:

[0023] Extract the key structural temperature data of the first movable optomechanical sub-component and the second movable optomechanical sub-component from the synchronously acquired dataset;

[0024] Based on the temperature data of the key structure and the fixed geometric parameters, the temperature fluctuation is converted to obtain the equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure in the direction of the centroid connection.

[0025] Extract the spacecraft attitude data and alignment angle data corresponding to the satellite body from the synchronously acquired dataset, and construct the angle class input vector;

[0026] Based on the angle-type input vector, determine the rotational-translational coupling equivalent displacement time series of the centroid connection direction;

[0027] The equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure along the centroid connection direction and the equivalent displacement time series of rotational-translational coupling along the centroid connection direction are integrated into an equivalent displacement correction term.

[0028] Extract the laser interferometer displacement data and inertial sensor electrostatic displacement data corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component from the synchronously acquired dataset;

[0029] Substitute the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, and the fixed geometric parameters into the displacement projection fusion model along the centroid connection line to calculate the projected displacement of the inspection mass of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid connection line.

[0030] The projection displacement of the inspection mass associated with the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line is calculated using a differential operation to obtain the differential operation result.

[0031] The result of the difference operation is superimposed with the equivalent displacement correction term to obtain the corrected differential displacement time series.

[0032] Optionally, reconstructing the differential residual acceleration time series based on the corrected differential displacement time series and the fixed geometric parameters includes:

[0033] The second-order time derivative of the corrected differential displacement time series is performed to obtain the differential acceleration term;

[0034] Based on the residual acceleration terms of each axis and the fixed geometric relationship parameters, the projection terms of the residual acceleration in the sensitive and non-sensitive axis directions corresponding to the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line are calculated respectively.

[0035] Perform a difference operation on the projection terms of the residual acceleration in the sensitive axis and non-sensitive axis directions corresponding to the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line to obtain the residual acceleration difference projection terms in the sensitive axis and non-sensitive axis directions.

[0036] The differential acceleration term is superimposed with the residual acceleration differential projection terms corresponding to the sensitive axis and non-sensitive axis directions to obtain the differential residual acceleration time series.

[0037] Optionally, the self-test judgment result includes a self-test pass result and a self-test fail result; the step of estimating the spectrum near the frequency point based on the differential residual acceleration time series and comparing it with a preset self-test threshold to generate a self-test judgment result includes:

[0038] The power spectral density of the differential residual acceleration time series is calculated, and the differential residual acceleration noise amplitude spectral density is obtained by conversion.

[0039] The differential residual acceleration noise amplitude spectral density is numerically compared with a preset self-test threshold.

[0040] If the differential residual acceleration noise amplitude spectral density is less than the preset self-test threshold, then the self-test pass result is output.

[0041] If the differential residual acceleration noise amplitude spectral density is greater than or equal to the preset self-test threshold, then the self-test failure result is output.

[0042] The second aspect of this invention provides a single-star sensor fusion self-testing device for low-frequency acceleration noise in space gravitational wave detection, comprising:

[0043] The acquisition module is used to acquire the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-component, and output the centroid connection direction displacement projection fusion model and fixed geometric relationship parameters according to the non-orthogonal configuration geometric relationship parameters;

[0044] The configuration module is used to configure the self-inspection control conditions for the movable optomechanical sub-assembly and the inspection quality associated with the movable optomechanical sub-assembly, and to determine the self-inspection control conditions.

[0045] The acquisition module is used to synchronously acquire satellite payload measurement and control data based on the self-test control conditions and output a synchronous acquisition dataset.

[0046] The calculation module is used to calculate the corrected differential displacement time series using the displacement projection fusion model along the centroid connection direction, based on the fixed geometric relationship parameters and the synchronously acquired dataset.

[0047] The reconstruction module is used to reconstruct the differential residual acceleration time series based on the corrected differential displacement time series and the fixed geometric relationship parameters;

[0048] The comparison module is used to estimate the spectrum near the frequency point based on the differential residual acceleration time series and compare it with a preset self-test threshold to generate a self-test judgment result.

[0049] The third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the single-star sensor fusion self-test method for detecting low-frequency acceleration noise in space gravitational waves as described above.

[0050] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the single-star sensor fusion self-testing method for low-frequency acceleration noise detection of space gravitational waves as described above.

[0051] As can be seen from the above technical solutions, the present invention has the following advantages:

[0052] The above-mentioned technical solution of the present invention provides a single-satellite sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection. It acquires the non-orthogonal configuration geometric parameters of the movable optomechanical sub-component and outputs a centroid-connecting direction displacement projection fusion model and fixed geometric parameters based on these parameters. It configures self-testing control conditions for the movable optomechanical sub-component and its associated inspection quality, determining the self-testing control conditions. Based on the self-testing control conditions, it synchronously acquires satellite payload measurement and control data, outputting a synchronous acquisition dataset. Using the centroid-connecting direction displacement projection fusion model, it calculates the corrected differential displacement time series based on the fixed geometric parameters and the synchronous acquisition dataset. Based on the corrected differential displacement time series and the fixed geometric parameters, it reconstructs the differential residual acceleration time series. Based on the differential residual acceleration time series, it performs frequency spectrum estimation near the frequency point and compares it with a preset self-testing threshold to generate a self-testing judgment result. Based on the above solution, the present invention acquires the non-orthogonal configuration geometric parameters of the movable optomechanical sub-component and establishes a centroid-connecting direction displacement projection model accordingly. By fusing models and defining fixed geometric parameters, a displacement calculation model can be accurately constructed to fit the non-orthogonal configuration characteristics of single-satellite movable optomechanical sub-components, thus adapting to the direction of the centroid connection. Through configuring self-inspection control conditions for the movable optomechanical sub-components and their associated inspection quality, and simultaneously acquiring satellite payload measurement and control data based on these conditions, a synchronously acquired dataset can be obtained, providing comprehensive and time-consistent self-inspection baseline data. Furthermore, by employing a displacement projection fusion model that combines fixed geometric parameters with the synchronously acquired dataset to calculate and correct differential displacement time series, a model adaptable to non-orthogonal configurations can be developed. The displacement calculation requirements along the centroid connection line in the dual MOSA configuration are addressed by combining structural thermal deformation, rotational-translational coupling, and other displacement-related terms for correction, thereby improving the reliability of the differential displacement time series calculation results. Based on this corrected sequence, the differential residual acceleration time series is reconstructed in conjunction with the dynamic relationship of the test mass, which helps to improve the accuracy of the differential residual acceleration reconstruction results. Finally, by estimating the spectrum of this time series near the 0.1 MHz frequency point and comparing it with the preset self-check threshold, on-orbit self-check judgment of low-frequency differential residual acceleration noise of the test mass can be achieved. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of the steps of a single-star sensor fusion self-testing method for detecting low-frequency acceleration noise in space gravitational wave detection, provided in Embodiment 1 of the present invention.

[0055] Figure 2 This is a schematic diagram of a three-satellite formation and inter-satellite laser link provided in Embodiment 1 of the present invention;

[0056] Figure 3 This is a two-dimensional geometric diagram of a single-satellite dual-MOSA nonorthogonal configuration provided in Embodiment 1 of the present invention, used to illustrate the definition and relationship of the satellite coordinate system, the two MOSA coordinate systems, the direction of the line connecting the two TM centroids, and related included angle parameters;

[0057] Figure 4 The TM differential residual acceleration noise budget curve provided in Embodiment 1 of the present invention ( =60°), used to display the residual acceleration noise of TM, electrostatic displacement noise, interferometer displacement noise, MOSA structural thermal deformation noise and their RSS sum, and compare them with the self-test threshold;

[0058] Figure 5 The equivalent displacement noise budget curve provided in Embodiment 1 of the present invention ( =60°), used to display the displacement contribution of each component, the sum of RSS, and the displacement threshold curve obtained by converting the acceleration self-test threshold;

[0059] Figure 6 This is a flowchart of the TM low-frequency differential acceleration noise single-satellite on-orbit self-test method provided in Embodiment 1 of the present invention, which is used to illustrate the processing flow of self-test preparation state, mode start-up, data acquisition, displacement projection fusion, differential residual acceleration reconstruction, power spectral density estimation and self-test judgment.

[0060] Figure 7 This is a structural block diagram of a single-star sensor fusion self-testing device for detecting low-frequency acceleration noise in space gravitational waves, provided in Embodiment 2 of the present invention. Detailed Implementation

[0061] This invention provides a single-satellite sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection. It solves the technical problem of how to directly test the acceleration noise of the test quality using a single satellite near the 0.1 mHz frequency point without adding extra payload hardware.

[0062] This invention also relates to inertial sensing and drag-free control technology, and spacecraft on-orbit performance verification and data processing technology. Specifically, it relates to a method for on-orbit self-testing of differential residual acceleration noise with a test mass near the 0.1 mHz frequency point, using two sets of inertial sensors and existing laser interferometry and electrostatic displacement measurement capabilities on a single space gravitational wave detection satellite, under drag-free and levitation control conditions. This method is limited by the accuracy of electrostatic displacement measurement on the non-sensitive axis of the inertial sensor (1...). This method does not have the capability for high-precision detection across the entire detection frequency band (0.1mHz-1Hz), but it is expected to be able to perform high-precision detection of acceleration noise near the 0.1mHz frequency point.

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0064] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a single-star sensor fusion self-testing method for detecting low-frequency acceleration noise in space gravitational wave detection, as provided in Embodiment 1 of the present invention.

[0065] This invention provides a single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection, comprising:

[0066] Step 101: Obtain the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-component, and output the centroid connection direction displacement projection fusion model and fixed geometric relationship parameters based on the non-orthogonal configuration geometric relationship parameters.

[0067] The movable optomechanical sub-assembly refers to the optomechanical integrated assembly mounted on the satellite, which is used to carry optical and inertial sensing units related to quality measurement. In this embodiment, it includes two sets: a first movable optomechanical sub-assembly and a second movable optomechanical sub-assembly.

[0068] A non-orthogonal configuration refers to a configuration in which, after the two sets of movable optomechanical sub-assemblies are locked in a fixed geometric configuration, the two quality-sensitive axes of the test are not orthogonal, but have an included angle of approximately 60°. Under this configuration, the geometric relationship between the direction of the line connecting the two centroids of the TM and the measurement axis of the laser interferometer and the non-sensitive axis of the inertial sensor can be determined.

[0069] Geometric relationship parameters refer to parameters used to characterize the geometric relationship between the direction of the line connecting the centroids of the two test masses and the measurement axis of the laser interferometer and the electrostatic displacement measurement axis of the insensitive direction of the inertial sensor. These parameters include at least the angle between the direction of the line connecting the centroids and the measurement axis of the laser interferometer, and the angle between the direction of the line connecting the centroids and the electrostatic displacement measurement axis of the insensitive direction of the inertial sensor. The angles are obtained from the assembly and adjustment data or on-orbit calibration and serve as the basis for establishing the displacement projection model and performing subsequent calculations.

[0070] The centroid-connecting displacement projection fusion model refers to a linear projection model established under two-dimensional approximation conditions based on fixed geometric parameters. It is used to project the displacement data of the sensitive axis of the laser interferometer and the electrostatic displacement data of the non-sensitive direction of the inertial sensor onto the centroid-connecting direction of the two test masses, and then perform fusion calculation.

[0071] Fixed geometric parameters refer to non-orthogonal configuration geometric parameters that remain unchanged after locking the fixed geometric configuration of two sets of movable optomechanical sub-components. They are applied throughout the process of displacement projection, differential displacement calculation, and differential residual acceleration reconstruction, ensuring parameter consistency and calculation stability throughout the entire technical process.

[0072] like Figure 2 As shown, Figure 2 This diagram illustrates the triangular geometric configuration of the three space gravitational wave detection satellites SC1, SC2, and SC3, the arrangement of the dual MOSAs and their corresponding TMs on each satellite, and the representation of the bidirectional laser links along the three inter-satellite arms and their propagation directions. Each space gravitational wave detection satellite includes two test masses, two sets of movable optomechanical subassemblies, a laser interferometer, an electrostatic displacement sensor, micro-thrusters, electrostatic actuators, a temperature sensor, an attitude measurement unit, and a single-satellite on-orbit self-test device. This on-orbit self-test device includes a processor, memory, and a data interface for data exchange with the laser interferometer, inertial sensor, temperature sensor, and attitude or alignment measurement unit.

[0073] It should be noted that the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-assemblies determined during the assembly, adjustment, and on-orbit calibration stages are obtained. These parameters correspond to the spatial relative pose characteristics of the two sets of movable optomechanical sub-assemblies. Subsequently, the fixed geometric configurations of the two sets of movable optomechanical sub-assemblies are locked, and the aforementioned non-orthogonal configuration geometric relationship parameters are set as constant parameters with unchanged values ​​throughout the entire process. Then, a displacement projection fusion model along the centroid connection direction is built based on these constant parameters to achieve directional projection and fusion processing of the mass displacement data of different movable optomechanical sub-assemblies. Finally, the displacement projection fusion model along the centroid connection direction and the fixed geometric relationship parameters are output synchronously.

[0074] Further, step 101 may include the following sub-steps:

[0075] S11. Under the state of locking the fixed geometric configuration of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly, the non-orthogonal configuration geometric relationship parameters are used as fixed geometric relationship parameters.

[0076] S12. Based on fixed geometric parameters, establish displacement projection fusion models of the centroid connection direction for the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly.

[0077] It should be noted that the two MOSAs (i.e., the first movable optomechanical sub-assembly MOSA1 and the second movable optomechanical sub-assembly MOSA2) are locked in a fixed geometric configuration, so that their relative angle in the satellite body coordinate system is... Keep constant (non-orthogonal configuration, e.g., 60°), included angle Obtained from assembly and adjustment data or on-orbit calibration. Considering the main errors and motion near the target frequency, it can be approximately limited to, for example... Figure 3 In the principal plane shown, under a two-dimensional approximation, the following is defined:

[0078] : The direction of the line connecting the two quality centroids;

[0079] : The direction of the interferometer's sensitive axis;

[0080] : The non-sensitive axis direction of electrostatic displacement sensing;

[0081] .

[0082] Subscripts 1 and 2 correspond to TM1 (i.e., the inspection quality associated with the first movable optical-mechanical sub-assembly) and TM2 (i.e., the inspection quality associated with the second movable optical-mechanical sub-assembly). A projection model of the displacement along the line connecting the two TMs is established:

[0083] ;

[0084] in, This is the equivalent displacement term introduced by the thermal deformation of the MOSA structure; For other displacement terms, such as those resulting from rotational-translational coupling; Let the direction of the line connecting the centroids of the two test masses be the unit vector. Unit vector along the measurement axis of the laser interferometer The included angle between them belongs to the category of fixed geometric relationship parameters; The angle between the direction of the line connecting the centroids of the two inspection masses and the direction of the laser interferometer measurement axis corresponding to the inspection mass of the first movable optomechanical sub-assembly is a component of the fixed geometric relationship parameters; The angle between the direction of the line connecting the centroids of the two inspection masses and the direction of the laser interferometer measurement axis corresponding to the inspection mass of the second movable optomechanical sub-assembly is a component of the fixed geometric relationship parameters; The displacement time series of the first movable optomechanical sub-assembly in the measurement axis direction of its laser interferometer is a core component of the synchronous acquisition data of the laser interferometer displacement data. The electrostatic displacement time series of the first movable optomechanical sub-assembly is used to test the quality of its inertial sensor in the direction of its non-sensitive axis. This is a core component of the synchronous acquisition data of the inertial sensor electrostatic displacement data. The displacement time series of the laser interferometer measurement axis direction of the second movable optomechanical sub-assembly is used to test the quality of the second movable optomechanical sub-assembly, which is a core component of the synchronous acquisition data of the laser interferometer displacement data. The electrostatic displacement time series of the second movable optomechanical sub-assembly is used to test the quality of its inertial sensor in the direction of its non-sensitive axis. This is a core component of the synchronous acquisition data of the inertial sensor electrostatic displacement data. The differential displacement time series of the two test masses along the centroid connection (without superimposed equivalent displacement correction terms) is the basic intermediate quantity for solving the corrected differential displacement time series.

[0085] Furthermore, after locking the fixed geometric configuration and determining the fixed geometric relationship parameters for the first and second movable optomechanical sub-assemblies, the spatial orientation of the laser interferometer measurement axis and the insensitive axis of the inertial sensor corresponding to the inspection quality of each set of movable optomechanical sub-assemblies is first determined. Then, the angle between the direction of the line connecting the centroids of the two inspection quality and each measurement axis and insensitive axis is extracted from the fixed geometric relationship parameters. Based on this angle, a displacement projection formula is established. The laser interferometer displacement data and inertial sensor electrostatic displacement data corresponding to each inspection quality are projected onto the direction of the line connecting the centroids of the two inspection quality respectively. Then, the projection results are integrated through a data fusion algorithm, thereby establishing displacement projection fusion models of the direction of the line connecting the centroids of the inspection quality corresponding to the first and second movable optomechanical sub-assemblies. This model can realize the directional transformation and fusion of displacement data of different dimensions of the two sets of inspection quality, providing accurate model support for the subsequent differential displacement time series solution.

[0086] Step 102: Configure the self-inspection control conditions for the movable optomechanical sub-assemblies and the inspection quality of the movable optomechanical sub-assemblies, and determine the self-inspection control conditions.

[0087] Self-test control mode refers to the dedicated working and control state for the unified configuration of the inspection quality of movable optomechanical sub-components and their supporting components in order to achieve self-testing of single-satellite sensor fusion. It is the basic working condition to ensure stable acquisition of satellite payload measurement and control data and accurate calculation of subsequent displacement and acceleration sequences.

[0088] It should be noted that, in conjunction with the established constant geometric relationship parameters and the displacement projection fusion model along the centroid connection line, corresponding self-inspection control modes are configured for the first movable optomechanical sub-component, the second movable optomechanical sub-component, and their respective inspection quality, thereby completing the standardized setting of the self-inspection status of each core component and finally determining the self-inspection control conditions suitable for this single-satellite sensor fusion self-inspection process.

[0089] Furthermore, step 102 can be achieved by performing the following steps:

[0090] The inspection mass associated with the first movable optomechanical sub-assembly is set as a three-degree-of-freedom displacement drag-free reference, and satellite micro-thruster control logic is configured to make the spacecraft follow the inspection mass associated with the first movable optomechanical sub-assembly in three translational degrees of freedom. At the same time, weak suspension, centering and low noise control is applied to the inspection mass associated with the second movable optomechanical sub-assembly, and low noise control is adopted for the non-self-test degrees of freedom of the inspection mass associated with the first movable optomechanical sub-assembly to meet operational safety, thus forming a complete self-test control condition.

[0091] A three-degree-of-freedom displacement drag-free reference refers to the displacement reference state of a test mass in a predetermined three-degree-of-freedom direction, which serves as the reference object for drag-free following control of the spacecraft. In the directions mentioned above, the test mass is kept as free as possible and low-noise, weak control is applied only when necessary, so as to serve as the core reference for the spacecraft to achieve drag-free following control.

[0092] Weak suspension and centering low noise control refers to a control method that uses an electrostatic actuator of an inertial sensor to apply a low-noise, small-amplitude control force to the inspection quality, keeping it near the center of the electrode cage and maintaining a safe gap.

[0093] Electrostatic actuation refers to the execution method that uses electrostatic forces to control the motion posture and position of the inspection quality, and it is the core control means of inertial sensors for inspection quality.

[0094] Micro-thruster control logic refers to the control rules that regulate the thrust output and action timing of satellite micro-thrusters, used to achieve the translational following motion of the spacecraft to the benchmark inspection quality.

[0095] It should be noted that the inspection mass TM1 is set as a three-degree-of-freedom displacement drag-free reference: the spacecraft follows TM1 in the three translational degrees of freedom via micro-thrusters, keeping TM1 in a free-floating state as much as possible in the relevant directions. Weak levitation control is applied to the inspection mass TM2 to maintain TM2 at the center of the electrode cage and a safe clearance from the cage, preventing prolonged drift from causing contact with the electrodes. This process does not require TM1 to be completely uncontrolled in all degrees of freedom, but rather to minimize electrostatic discharge in the degrees of freedom related to self-testing, while other degrees of freedom can employ necessary low-noise control to ensure operational safety. Specifically, weak levitation control is applied to TM2 to maintain its position at the center of the electrode cage with minimal noise, and electrostatic discharge on TM1 is minimized in the degrees of freedom related to self-testing, while other degrees of freedom use low-noise control to ensure operational safety.

[0096] In this embodiment, the inspection mass associated with the first movable optomechanical sub-assembly is used as the motion reference and is set to a three-degree-of-freedom displacement drag-free reference state. Based on this, the control logic of the satellite micro-thruster is matched and configured so that the spacecraft as a whole accurately follows the motion of the inspection mass in the three translational degrees of freedom, isolating the spacecraft's own motion from interfering with the reference inspection mass. At the same time, weak suspension, centering, and low-noise control is applied to the inspection mass associated with the second movable optomechanical sub-assembly to keep it in a stable, centered, and low-disturbance motion state. Low-noise control mode is adopted for the non-self-testing degrees of freedom of the inspection mass associated with the first movable optomechanical sub-assembly to ensure the safety of the satellite's on-orbit operation, thereby forming a complete self-testing control condition and providing stable operating conditions for subsequent synchronous data acquisition, differential displacement calculation, and differential residual acceleration reconstruction.

[0097] Step 103: Based on the self-test control condition, synchronously collect satellite payload measurement and control data and output the synchronous collection dataset.

[0098] Satellite payload measurement and control data refers to the measurement data collected by various payload devices on the satellite and the control status data output by related control devices. It covers displacement and temperature data of movable optomechanical sub-components, satellite attitude and thruster data, and inspection quality control data, etc. It is the basic data for various calculations and analyses during the self-test process.

[0099] Synchronous acquisition datasets refer to the collection of various satellite payload measurement and control data obtained synchronously under self-test control conditions, after time alignment and integration. The data has a unified time sequence and complete content, and is used for subsequent steps such as constructing equivalent displacement correction terms and solving the differential displacement time series of corrections.

[0100] It should be noted that after the self-test control condition is running stably, a reasonable acquisition duration is set according to the frequency point estimation requirements. Various measurement devices related to the movable optomechanical sub-components, the satellite body, and the inspection quality are triggered synchronously to acquire the measurement data and control data corresponding to the satellite payload. The acquired data is then time-aligned and preliminarily integrated to ensure the synchronization and integrity of the data, and finally, a synchronous acquisition dataset is output.

[0101] Furthermore, step 103 may include the following sub-steps:

[0102] S31. Under self-test control mode, set the acquisition duration and simultaneously acquire and record the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, the temperature data of the key structure corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component, the spacecraft attitude data, alignment angle data, micro-thruster thrust data, control status data corresponding to the satellite body, as well as the electrostatic execution voltage data and execution channel status data of the inspection quality of the second movable optomechanical sub-component.

[0103] S32. Integrate the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, and the temperature data of the key structure corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component, and the spacecraft attitude data, alignment angle data, micro-thruster thrust data, and control status data corresponding to the satellite body, as well as the electrostatic execution voltage data and execution channel status data of the second movable optomechanical sub-component for quality inspection, and output the synchronously acquired dataset.

[0104] Control status data refers to the timing data output by various satellite control devices that characterize the execution status of control logic. It is used to monitor the stability of self-test control conditions and ensure the validity of the collected data.

[0105] Electrostatic discharge (ESD) voltage data refers to the timing data of the voltage applied to the ESD actuator used for quality inspection of the second movable optomechanical sub-assembly. It is used to reflect the intensity and status of ESD and to provide data support for analyzing ESD interference.

[0106] The execution channel status data refers to the working status timing data of the electrostatic execution channel that is matched with the second movable optomechanical sub-assembly for quality inspection. It is used to monitor the operational stability of the electrostatic execution channel and ensure the reliability of electrostatic control.

[0107] It should be noted that the synchronously acquired and recorded data includes micro-thruster thrust data and control status, TM2 electrostatic discharge voltage and discharge channel status, spacecraft attitude angle data, and alignment angle or equivalent angle information of the interferometric measurement link. Simultaneously, the acquisition duration of the synchronous acquisition meets the frequency estimation requirements near 0.1 MHz, with a selectable continuous stable duration of approximately 28 hours, or at least approximately 2.8 hours, combined with multiple repeated measurements to improve statistical results.

[0108] Specifically, under self-test conditions, at least the following measurement and control data shall be collected and recorded simultaneously:

[0109] a) Laser interferometer displacement data: , ;

[0110] b) Electrostatic displacement sensing data: , ;

[0111] c) Temperature data: It needs to cover the key structural locations of two MOSA systems. The temperature data is provided by the satellite's existing payload temperature sensors, without adding any new hardware.

[0112] d) Other auxiliary data: micro-thruster thrust data and control status; TM2 electrostatic actuator voltage and actuator channel status; spacecraft attitude angle, angular velocity or angular acceleration data; alignment angle or equivalent angle information of the interferometric measurement link, etc.

[0113] The acquisition duration should meet the frequency estimation requirement of 0.1 MHz: a continuous stable duration T can be selected. obs ≥10 5 s (approximately 28 hours, approximately 10 0.1 MHz cycles), or at least satisfy T obs ≥10 4 The statistic is improved by combining multiple repeated measures (approximately 2.8 hours, one cycle) with s (approximately 2.8 hours).

[0114] In this embodiment, after the self-test control condition is running stably and all equipment is in normal working condition, an appropriate acquisition duration is set according to the requirement of 0.1 MHz frequency point estimation to ensure that the acquired data can cover the target frequency domain and meet the requirements of subsequent spectrum analysis. Then, the synchronous acquisition trigger mechanism is activated, causing the laser interferometer and inertial sensor on the first and second movable optomechanical sub-components to start synchronously, and to acquire and record the corresponding laser interferometer displacement data and inertial sensor electrostatic displacement data in real time. At the same time, the key structural temperature data of the two sets of components are acquired synchronously, and the attitude measurement, alignment detection, and micro-thruster monitoring equipment of the satellite body are triggered synchronously to acquire and record the spacecraft attitude data, alignment angle data, micro-thruster thrust data and control status data. The second acquisition trigger mechanism is activated synchronously. The movable optomechanical sub-assembly is equipped with electrostatic discharge voltage data and execution channel status data for quality inspection, ensuring that the acquisition timing of all data is completely consistent and without time deviation. After acquisition, all recorded data undergoes time sequence verification, and abnormal or distorted data is removed. Then, according to preset data integration rules, data from different sources and of different types are classified and summarized, and missing synchronization timing identifiers are supplemented. Finally, a synchronized acquisition dataset is output. By realizing the synchronized acquisition and standardized integration of data from multiple devices and of multiple types, the integrity, timing consistency, and accuracy of the data are ensured. This reduces subsequent calculation deviations caused by data asynchrony, incompleteness, or abnormality, providing reliable data support for the subsequent construction and correction of equivalent displacement correction terms and the differential displacement time series calculation, laying a data foundation for improving self-inspection accuracy.

[0115] Step 104: Using the centroid-connecting direction displacement projection fusion model, the corrected differential displacement time series is calculated based on fixed geometric parameters and synchronously acquired datasets.

[0116] It should be noted that, firstly, the key structural temperature data of two sets of movable optomechanical sub-components and the satellite's attitude and alignment angle data are extracted from the synchronously acquired dataset. These are then converted into equivalent displacement time series of thermal deformation and equivalent displacement time series of rotational-translational coupling of the movable optomechanical sub-component structure along the centroid connection line, respectively, and integrated into an equivalent displacement correction term. Next, the displacement data of the laser interferometer and the electrostatic displacement data of the inertial sensor in the dataset are extracted, substituted into the displacement projection fusion model along the centroid connection line, and combined with fixed geometric relationship parameters, the projected displacement of the two sets of test masses along the centroid connection line is calculated. The projected displacement is differentially calculated to obtain the basic differential displacement result. Finally, the basic differential displacement result is superimposed with the equivalent displacement correction term to complete the solution of the corrected differential displacement time series.

[0117] Furthermore, step 104 may include the following sub-steps:

[0118] S41. Extract the key structural temperature data of the first movable optomechanical sub-component and the second movable optomechanical sub-component from the synchronously acquired dataset;

[0119] S42. Based on the key structural temperature data and fixed geometric parameters, temperature fluctuation is transformed to obtain the equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure along the centroid connection direction.

[0120] S43. Extract the spacecraft attitude data and alignment angle data corresponding to the satellite body from the synchronously acquired dataset, and construct the angle class input vector;

[0121] S44. Based on the angle-type input vector, determine the rotational-translational coupling equivalent displacement time series of the centroid connection direction;

[0122] S45. Integrate the equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure in the direction of the centroid connection and the equivalent displacement time series of rotational-translational coupling in the direction of the centroid connection into an equivalent displacement correction term.

[0123] S46. Extract the laser interferometer displacement data and inertial sensor electrostatic displacement data corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component from the synchronously acquired dataset;

[0124] S47. Substitute the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, and the fixed geometric parameters into the displacement projection fusion model along the centroid connection line, and calculate the projected displacement of the inspection mass of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid connection line, respectively.

[0125] S48. Perform differential calculation on the projected displacement of the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line to obtain the differential calculation result.

[0126] S49. The difference operation result is superimposed with the equivalent displacement correction term to obtain the corrected difference displacement time series.

[0127] It should be noted that the modeling and correction of the equivalent displacement term introduced by the thermal deformation of the MOSA structure includes: collecting temperature data (i.e., key structure temperature data) by temperature sensors deployed at key structural locations of the MOSA, and based on the relatively slow temperature change near the 0.1 mHz frequency point, using a quasi-static linear coupling model to transform temperature fluctuations into a time series of equivalent displacements of the movable optomechanical sub-component structure along the centroid connection line. The coupling coefficient from temperature to equivalent displacement is calculated from the material's thermal expansion coefficient and the equivalent structural length, or obtained by regression identification of displacement and temperature data within the on-orbit stable observation segment.

[0128] Furthermore, the estimation and correction of the rotational-translational coupled displacement term includes: constructing an angle-type input vector based on the satellite's attitude data and alignment angle data, and using a linear coupling model to obtain the rotational-translational coupled equivalent displacement time series in the direction of the centroid connection line. The coupling coefficient vector is obtained through on-orbit calibration or regression identification in the on-orbit stable observation segment, and is updated as needed according to the self-checking conditions.

[0129] Among these, acquiring temperature sensor data at key structural locations of the MOSA (such as near the optical reference, near the inertial sensor housing, at the mounting interface, and at the support structure) is crucial. Since temperature changes are relatively slow near 0.1 mHz, a quasi-static linear coupling model can be used to convert temperature fluctuations into equivalent displacements along the connecting lines.

[0130] ;

[0131] In the formula, The equivalent displacement time series in the direction of the line connecting the two centroids of the test mass is introduced by the thermal deformation of the two sets of movable optomechanical component structures, i.e., the equivalent displacement time series of the thermal deformation of the movable optomechanical component structure. This is the time-series data of temperature fluctuations collected at the location of the i-th temperature sensor, which is the difference between the current temperature and the reference temperature. It belongs to the key structure temperature data in the synchronously acquired dataset. This refers to the total number of temperature sensors deployed at key structural locations of the MOSA, corresponding to multiple temperature acquisition points such as near the optical reference, near the inertial sensor housing, at the mounting interface, and at the support structure. The coupling coefficient from temperature to equivalent displacement, expressed in m / K, can be calculated from the material's thermal expansion coefficient, equivalent structural length, and geometric projection relationship, or obtained through regression identification of displacement and temperature data within the on-orbit stable observation segment. When thermal inertia needs to be considered, it can be generalized to a first-order dynamic coupling. ,in (t) is the thermal-structural transfer function, which can be obtained through on-orbit calibration.

[0132] Taking the rotational-translational coupling term as an example, under non-orthogonal configuration and high-precision readout conditions, the combined effects of spacecraft attitude rotation, alignment changes, and geometric deviations introduce rotational-translational coupling errors into the displacement reconstruction along the connecting lines. To improve the reliability of low-frequency self-test results, an equivalent displacement term for rotational-translational coupling is constructed based on the acquired angle-type data. .

[0133] in This can be represented using a linear coupling model:

[0134] ;

[0135] In the formula, The rotational-translational coupling equivalent displacement time series is the one along the line connecting the centers of mass. It is an angle-type input vector, which can be composed of alignment angle signals or attitude angle information; The coupling coefficient vector (unit: m / rad) can be obtained through on-orbit calibration or regression identification during stable observation periods, and can be updated as needed based on operating conditions.

[0136] It is worth mentioning that the quasi-static linear coupling model refers to a coupling model that establishes an instantaneous linear mapping relationship between temperature fluctuations and equivalent displacements along the centroid connection direction in scenarios where temperature changes are relatively slow (such as near the 0.1 MHz frequency point) and dynamic hysteresis effects such as thermal inertia can be ignored. This model assumes that temperature fluctuations and equivalent displacements satisfy a linear response relationship, does not need to consider time delays or dynamic transition processes, and can complete the conversion of temperature fluctuations to equivalent displacements along the centroid connection direction simply by fixing the coupling coefficient. It is the core calculation model in this invention used to convert temperature fluctuations of the MOSA key structure into a time series of equivalent displacements of thermal deformation of the movable optomechanical sub-component structure.

[0137] In this embodiment, the key structural temperature data of the first and second movable optomechanical sub-components are first accurately extracted from the synchronously acquired dataset. Taking into account the relatively slow temperature change near the 0.1 MHz frequency point, a quasi-static linear coupling model is used. Based on the geometric projection relationship in the fixed geometric parameters, the temperature fluctuations collected by each temperature sensor are converted into an equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure along the centroid connection direction. Then, the spacecraft attitude data and alignment angle data corresponding to the satellite body are extracted from the synchronously acquired dataset. The two types of angle data are concatenated into an angle-type input vector according to the time dimension. A linear coupling model is used to obtain the rotational-translational coupling equivalent displacement time series along the centroid connection direction. The above two types of equivalent displacement time series are then combined... The columns are aligned by timestamp and integrated into an equivalent displacement correction term. Then, the displacement data of the laser interferometer and the electrostatic displacement data of the inertial sensor corresponding to the first and second movable optomechanical sub-assemblies are extracted. These data, along with fixed geometric parameters, are substituted into the displacement projection fusion model along the centroid connection line. The projected displacement of the two sets of movable optomechanical sub-assemblies along the centroid connection line is calculated. The two types of projected displacements are subjected to time-series difference operations to obtain the basic difference operation results. Finally, the basic difference operation results are superimposed with the equivalent displacement correction term time-by-time to obtain the corrected differential displacement time series. By correcting the displacement error introduced by the thermal deformation and rotational translational coupling of the MOSA structure, the accuracy of the differential displacement time series is improved, and the impact of error interference on subsequent self-test calculations is reduced.

[0138] Step 105: Based on the corrected differential displacement time series and fixed geometric relationship parameters, reconstruct the differential residual acceleration time series.

[0139] It should be noted that the differential displacement time series is first processed by second-order time derivative to obtain the differential acceleration term. Then, combined with fixed geometric parameters, the projection terms of the residual acceleration along the line connecting the centroids of the two sets of movable optomechanical sub-assemblies in the sensitive and non-sensitive axes of the test mass are calculated respectively. The two types of projection terms are differentially processed to obtain the residual acceleration differential projection term. Finally, the differential acceleration term and the residual acceleration differential projection term are superimposed to obtain the differential residual acceleration time series.

[0140] Furthermore, step 105 may include the following sub-steps:

[0141] S51. Perform second-order time derivative processing on the corrected differential displacement time series to obtain the differential acceleration term;

[0142] S52. Based on the residual acceleration terms of each axis and the fixed geometric relationship parameters, calculate the projection terms of the residual acceleration in the sensitive and non-sensitive axis directions corresponding to the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the line connecting the centroids, respectively.

[0143] S53. Perform a differential operation on the projection terms of the residual acceleration in the sensitive axis and non-sensitive axis directions corresponding to the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the line connecting the centroids to obtain the residual acceleration differential projection terms in the sensitive axis and non-sensitive axis directions.

[0144] S54. The differential acceleration term is superimposed with the residual acceleration differential projection terms corresponding to the sensitive axis and non-sensitive axis directions to obtain the differential residual acceleration time series.

[0145] The residual acceleration terms for each axis refer to the time-series data of the residual acceleration of the inspection quality associated with the first movable optomechanical assembly and the second movable optomechanical assembly in the sensitive and non-sensitive axis directions.

[0146] It should be noted that the differential residual acceleration reconstruction includes: taking the second-order time derivative of the corrected differential displacement time series to form a differential acceleration term, and combining the projection terms of the residual acceleration in the directions of the TM sensitive axis and the non-sensitive axis to obtain the differential residual acceleration time series.

[0147] Among them, in obtaining Then, construct the differential residual acceleration estimate along the line connecting the centroids of the mass:

[0148] ;

[0149] in:

[0150] The second derivative of the differential displacement time series of the two TMs along n, i.e., the differential residual acceleration time series;

[0151] :The projection of the residual acceleration along the n-direction in the sensitive axis direction of two TM laser interferometry measurements, i.e., the differential projection term of the residual acceleration corresponding to the sensitive axis direction;

[0152] :The projection of the residual acceleration along the n-direction in the non-sensitive axis direction of the two TM electrostatic displacement measurements, that is, the differential projection term of the residual acceleration in the non-sensitive axis direction;

[0153] In this embodiment, the second-order time derivative of the corrected differential displacement time series is first performed according to the fixed time step corresponding to the self-test control condition to ensure the continuity and accuracy of the time domain calculation, thereby obtaining the differential acceleration term characterizing the relative acceleration change along the line connecting the centroids of the two test masses. Subsequently, based on the residual acceleration terms of each axis and the geometric projection relationship between the line connecting the centroids of the two test masses and the sensitive and non-sensitive axes in the fixed geometric relationship parameters, the residual accelerations of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly in the sensitive and non-sensitive axis directions of the test masses are projected onto the centroid connection direction, respectively, to complete the calculation of the two types of residual acceleration projection terms. Then, the two sets of projection terms are subjected to differential operation after time alignment to reduce the influence of the approximate common-mode disturbance component in the two measurements, thereby obtaining the residual acceleration differential projection terms corresponding to the sensitive and non-sensitive axis directions. Finally, the differential acceleration terms and the residual acceleration differential projection terms are superimposed point by point at the same timestamp to complete the reconstruction of the differential residual acceleration time series. By performing second-order time derivatives on the corrected differential displacement sequence and combining the projection relationship between the residual accelerations of the sensitive and non-sensitive axes along the centroid line for joint reconstruction, the reconstruction accuracy and self-test reliability of the differential residual acceleration time sequence can be improved, providing a basis for subsequent spectrum estimation and self-test judgment near the 0.1 mHz frequency point.

[0154] Step 106: Based on the differential residual acceleration time series, perform spectrum estimation near the frequency point and compare it with the preset self-test threshold to generate a self-test judgment result.

[0155] It should be noted that the target frequency for spectrum estimation is first defined as 0.1 mHz. A preset power spectrum estimation method is used to calculate the power spectral density of the differential residual acceleration time series, focusing on extracting the spectral information near the 0.1 mHz frequency point. The spectrum near the frequency point is estimated and converted to obtain the differential residual acceleration noise amplitude spectral density. Then, the noise amplitude spectral density is precisely compared with a preset self-test threshold. If the noise amplitude spectral density is less than the preset self-test threshold, a self-test pass result is output; if it is greater than or equal to the preset self-test threshold, a self-test fail result is output. Finally, a complete self-test judgment result is generated. By accurately estimating the spectrum and comparing it with the threshold, the reliability and accuracy of the self-test judgment are ensured.

[0156] Furthermore, step 106 may include the following sub-steps:

[0157] S61. Calculate the power spectral density of the differential residual acceleration time series and convert it to obtain the differential residual acceleration noise amplitude spectral density.

[0158] S62. Compare the differential residual acceleration noise amplitude spectral density with the preset self-test threshold numerically;

[0159] S63. If the differential residual acceleration noise amplitude spectral density is less than the preset self-test threshold, then output the self-test pass result.

[0160] S64. If the differential residual acceleration noise amplitude spectral density is greater than or equal to the preset self-test threshold, then output a self-test failure result.

[0161] It should be noted that this step is used to complete the calculation and evaluation of differential residual acceleration noise along the centroid connection between the two TMs, verify the feasibility of the method, and determine the on-orbit self-test indicators. This includes two implementation paths: on-orbit measured data spectrum analysis and theoretical noise budget analysis, corresponding to the on-orbit engineering application and performance verification scenarios of this method, respectively. For the on-orbit measured data spectrum, the Welch power spectrum estimation method or the Discrete Fourier Transform (DFT) single-frequency point estimation method can be used. For performance verification, the theoretical frequency domain noise budget method can be used. This is achieved through differential residual acceleration time series... Calculate the power spectral density, convert and extract the amplitude spectral density near the 0.1 mHz frequency point:

[0162] ;

[0163] Compare this value with a self-test threshold (e.g., 1×10). -14 The comparison is performed, and the self-test judgment result is output.

[0164] It is worth mentioning that the single-frequency point spectrum estimation in this step can use the Welch power spectrum estimation method, the discrete Fourier single-frequency point estimation method, or the theoretical frequency domain noise budget analysis method to calculate the power spectral density of the differential residual acceleration time series, and then convert it into amplitude spectral density by square root and compare it with the preset self-test threshold.

[0165] For example, the two MOSAs on a single satellite are locked into a fixed geometric configuration, so that their relative angle in the satellite's body coordinate system remains constant, denoted as .like Figure 3 As shown, The direction of the line connecting the centroids of TM1 and TM2. The direction of the interferometer's sensitive axis. Let the non-sensitive axis direction of the inertial sensor be defined. . and Obtained from assembly and adjustment data or on-orbit calibration, it is considered a constant during the self-test observation period. In this embodiment, it is taken as... =60°, =60°, =120°.

[0166] Under self-test conditions, interferometer displacement data are acquired synchronously. , and electrostatic displacement sensing data , Based on the geometric projection relationship, the two sets of readings are fused and reconstructed to obtain the differential displacement time series along the n-direction. :

[0167] ;

[0168] Among them, the thermally induced deformation equivalent displacement term introduced by the stability of the MOSA structure As an important correction term in differential displacement reconstruction, it cannot be ignored. As a non-essential correction item for self-checking and reconstruction, it will be ignored for the time being.

[0169] Considering that the self-test frequency in this embodiment is located near 0.1 MHz, and the temperature change is relatively slow, a quasi-static linear coupling model is used to convert the temperature fluctuation into an equivalent displacement in the direction of the connection:

[0170] ;

[0171] in, The coupling coefficient between temperature and equivalent displacement is expressed in m / K. It can be calculated from the linear expansion coefficient of the material, the equivalent structural length, and the geometric projection relationship, or obtained by regression identification of the displacement-temperature data within the on-orbit stable observation section.

[0172] TM1 is set as the drag-free reference: the spacecraft follows TM1 in translational freedom via micro-thrusters, allowing TM1 to float as freely as possible in the XY plane. Weak hovering and centering control is applied to TM2 to keep its displacement within a safe range and minimize noise. Data, including interferometer displacement, is collected during self-test mode. , Electrostatic displacement , The data collection duration can be selected as T. obs ≥10 5 s to meet the frequency estimation requirement of 0.1mHz.

[0173] Construct the differential residual acceleration of the two test masses along the n-direction:

[0174] ;

[0175] In the spectrum estimation, this embodiment employs a theoretical noise budget analysis method to verify the rationality and achievable accuracy of the noise allocation in the self-testing method of this invention, rather than for processing on-orbit measured time series data. Therefore, the amplitude spectral density of each noise source is directly modeled and geometrically projected in the frequency domain. Displacement-acceleration frequency domain conversion is used to convert displacement-type noise into acceleration-type noise. Finally, the root sum of squares (RSS) of each noise term is taken to complete the total noise synthesis, thereby clarifying the contribution magnitude of each noise term and the noise characteristics across the entire frequency band under this self-testing method system. For the spectrum analysis and self-testing of the differential residual acceleration time series data actually acquired in orbit, it is still necessary to perform the Welch method or the discrete Fourier single-frequency point estimation method described in this invention.

[0176] The following will calculate the 0.1 mHz frequency point. , and detection threshold The comparison outputs the self-test results. The assumption that each component of the noise is independent is applied, and the total noise is calculated using RSS. Considering that the two TM noises are independent, additional multiplication is required during the synthesis. f is the target frequency, i.e., 0.1mHz, which is the core target frequency for this self-test spectrum estimation and is used to calculate the frequency-dependent noise correction term.

[0177] Specifically, regarding the displacement readout noise of the laser interferometer:

[0178] Single TM: ;

[0179] Differential projection: ;

[0180] For inertial sensor electrostatic displacement readout noise:

[0181] Single TM: ;

[0182] Differential projection: ;

[0183] For thermal deformation displacement noise of MOSA structure:

[0184] The effective length of the titanium alloy support in the two MOSAs is approximately L = 0.50 m, and the coefficient of thermal expansion of the titanium alloy is approximately... Its temperature stability is approximately .

[0185] MOSA: ;

[0186] For other equivalent displacement noise, the contribution of this noise is ignored in this embodiment.

[0187] For residual acceleration noise projection, in this embodiment, the residual acceleration in the TM sensitive axis direction and the residual acceleration in the non-sensitive axis direction are respectively set as and Projection of single TM residual acceleration along the n-direction (f=0.1mHz):

[0188]

[0189] ;

[0190] Two TM differential synthesis: ;

[0191] To convert displacement ASD to acceleration ASD, multiply by (2πf). 2 Therefore, the total noise (RSS) can be written as:

[0192]

[0193] Furthermore, for the 0.1mHz component contribution table (ASD, unit) As shown in Table 1:

[0194]

[0195] Based on the above noise component budget results at the 0.1 MHz frequency point, the following 10 plots were obtained. -5 Hz-10 -2 The TM differential residual acceleration amplitude spectral density budget curve and the equivalent displacement amplitude spectral density budget curve in the Hz frequency band are respectively as follows: Figure 4 , Figure 5 As shown, the full-band acceleration noise budget curve visually presents the frequency distribution characteristics of each noise source under the self-testing system of this method. In the target self-testing frequency band near 0.1 mHz, the TM residual acceleration is the dominant noise term. The contributions of interferometer displacement noise, electrostatic displacement noise, and MOSA structural thermal deformation noise are all far below the target threshold. The total differential residual acceleration noise RSS synthesis result at the 0.1 mHz frequency point is 9.7 × 10⁻⁶. -15 The self-test judgment can be completed by directly comparing with the self-test threshold; the equivalent displacement noise budget curve gives the full-band equivalent displacement threshold converted from the acceleration threshold, which can intuitively reflect the noise margin of this method in the target self-test frequency band and the boundary of the full-band self-test capability, providing quantitative support for the selection of frequency band and identification of error sources for on-orbit self-test.

[0196] In this embodiment, the target frequency for this self-test is first defined as 0.1 mHz. A preset power spectrum estimation method (such as Welch's method or Discrete Fourier Transform) is used to calculate the power spectral density of the differential residual acceleration time series. Spectral information is extracted near the 0.1 mHz frequency. Simultaneously, based on the assumption that the individual noise components are uncorrelated, the total noise is calculated using RSS. Considering that the two TM noises are independent, an additional multiplication factor is applied during the synthesis. After conversion, the differential residual acceleration noise amplitude spectral density is obtained. This noise amplitude spectral density is then compared precisely at each frequency point with a preset self-test threshold. If the noise amplitude spectral density at 0.1 MHz is less than the preset self-test threshold, a self-test pass result is output; if the noise amplitude spectral density at that frequency point is greater than or equal to the preset self-test threshold, a self-test fail result is output, ultimately generating a complete self-test judgment result. For feasibility verification and performance evaluation, a theoretical frequency domain noise budget analysis method can be used. This involves modeling and geometrically projecting each noise source in the frequency domain, and synthesizing the total noise using RSS under the assumption that the individual noise components are uncorrelated. This evaluates the rationality of noise distribution near the target frequency point, achievable accuracy, and self-test capability boundaries of the proposed method.

[0197] For comparison of technical effectiveness, existing technologies can be referenced. Space gravitational wave detection satellites or spacecraft, such as LISA, Tianqin, and Taiji, typically employ a payload architecture with an overall telescope pointing, configured with two Movable Optomechanical Subassemblies (MOSAs). Each MOSA contains a telescope, an optical platform, and a test mass inertial sensor. The satellite achieves overall telescope pointing through attitude control, using the two test masses in their respective sensitive axis directions as drag-free control references to achieve free fall. To achieve the final detection sensitivity, a complete three-star, six-laser link needs to be established, and the six telescopes (TMs) need to achieve free fall motion along their sensitive axis directions. The influence of dominant laser frequency noise is then subtracted by combining the time-delayed interferometry (TDI) data from the six-link interferometry. To ensure the three-star system can smoothly enter the final scientific observation mode, it is best to perform on-orbit detection of residual acceleration noise in each TM beforehand.

[0198] Therefore, this invention proposes a method for self-verification of low-frequency differential residual acceleration noise in inspection quality without adding additional equipment, utilizing existing payload hardware and measurement data from a single satellite. Without adding additional payload hardware, it utilizes two inspection quality systems on the single satellite and existing measurement methods (laser interferometer sensitive axis displacement measurement and inertial sensor non-sensitive axis electrostatic displacement measurement), combined with drag-free and weak levitation control. Through geometric projection fusion, thermally induced structural drift correction, control application term conversion and subtraction, attitude non-inertial term and rotational translation coupling term correction, and differential residual acceleration reconstruction, the self-verification of differential residual acceleration noise in inspection quality is completed near the 0.1 mHz frequency point.

[0199] Specifically, such as Figure 6 As shown, this method is applied to a single space gravitational wave detection satellite. The satellite includes two test masses, TM1 and TM2, and two sets of movable optomechanical subassemblies, MOSA1 and MOSA2. The sensitive axes of the two test masses are non-orthogonal. The method includes the following steps:

[0200] S1. Lock MOSA1 and MOSA2 into a fixed geometric configuration and obtain the geometric relationship parameters between the direction of the line connecting the two centroids of TM and the measurement axis of the laser interferometer and the non-sensitive axis of the inertial sensor;

[0201] S2. Based on the geometric relationship parameters, establish a projection fusion model of the displacement along the line connecting the centroids of the two TMs. Project the displacement data of the sensitive axis of the laser interferometer and the electrostatic displacement data of the non-sensitive axis of the inertial sensor corresponding to TM1 and TM2 onto the line connecting the centroids of the two TMs and perform fusion calculation. Then, perform differential calculation on the displacement of the two TMs along the direction to obtain the differential displacement time series along the line connecting the centroids.

[0202] S3. Configure self-test control mode, set TM1 as a three-degree-of-freedom displacement drag-free reference and use micro-thrusters to make the spacecraft follow TM1 in three translational degrees of freedom, while applying weak suspension centering control to TM2 to maintain a safe gap between TM2 and the electrode cage.

[0203] S4. Under self-test control conditions, synchronously collect and record measurement and control data including at least laser interferometer displacement data, inertial sensor electrostatic displacement data, temperature data, and attitude or alignment angle data.

[0204] S5. Based on temperature data, the equivalent displacement term introduced by the thermal deformation of the MOSA structure is modeled and corrected. Based on attitude or alignment angle data, other displacement terms such as rotational-translational coupling are estimated and corrected to obtain the corrected differential displacement time series.

[0205] S6. Based on the verification mass dynamics relationship, the corrected differential displacement time series is reconstructed by differential residual acceleration. Combined with the residual acceleration of TM along the sensitive axis and non-sensitive axis, the differential residual acceleration time series along the centroid connection direction is obtained.

[0206] S7. Perform spectrum estimation on the differential residual acceleration time series near the 0.1 mHz frequency point, obtain the differential residual acceleration noise amplitude spectral density near the 0.1 mHz frequency point, and compare it with the self-test threshold to output the self-test judgment result.

[0207] Compared with the prior art, the present invention has the following beneficial effects:

[0208] 1. Without changing the existing scientific payload architecture: Self-testing can be completed by relying solely on the existing two TMs, laser interferometer, inertial sensor electrostatic displacement measurement, micro-thrusters and electrostatic actuators, attitude and temperature data of a single satellite;

[0209] 2. Adaptation to non-orthogonal dual MOSA configuration: Through geometric projection fusion, the sensitive axis of the laser interferometer and the non-sensitive axis of the inertial sensor are fused into a centroid line direction observation.

[0210] 3. Self-checking of indicators near 0.1mHz: Based on the frequency domain relationship of displacement-acceleration and differential residual acceleration reconstruction, residual acceleration noise can be evaluated and threshold determined near 0.1mHz;

[0211] 4. Structural thermal deformation and other displacement-related terms can be corrected: Structural thermal fluctuations can be corrected by temperature identification, and rotational and translational coupling terms can be estimated and corrected to improve the reliability of self-inspection results.

[0212] In this embodiment of the invention, the above-mentioned technical solution provides a single-satellite sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection. Specifically, it is a single-satellite on-orbit self-testing method for low-frequency (around 0.1mHz, i.e., near the lower limit of the 0.1mHz-1Hz gravitational wave detection frequency band) differential acceleration noise of inertial sensor test quality for space gravitational wave detection. It is applicable to scientific payload architectures in which a single satellite contains two test qualities (TMs) and two sets of movable optomechanical subassemblies (MOSAs), and the TM sensing axes are non-orthogonal (with an included angle of about 60°). This method, without adding additional hardware, first locks two MOSAs into a fixed geometric configuration; second, it establishes the geometric projection relationship between the direction of the line connecting the centers of mass of the two telescopes (TMs) and the measurement axes of the laser interferometer and the electrostatic displacement measurements of the non-sensitive axes of the inertial sensor; third, it sets a drag-free attitude control mode so that the satellite follows one TM in a three-degree-of-freedom displacement without drag, and applies weak suspension control to the other TM to maintain its position at the center of the polarimeter cage; then, it synchronously collects data such as interferometric displacement measurement data, electrostatic displacement measurement data, satellite attitude, and MOSA temperature, and fuses and calculates the relative acceleration change along the line connecting the centers of mass of the two TMs; during data processing, it can correct for the effects of thermal deformation terms and rotation-to-length (TTL) terms of the MOSA structure; finally, it obtains the differential residual acceleration noise amplitude spectral density near the 0.1 mHz frequency point and completes self-test judgment. This invention can achieve direct detection and evaluation of the key low-frequency acceleration noise performance of inertial sensors at low cost and in orbit without changing the existing scientific payload architecture of space gravitational wave detection satellites.

[0213] Please see Figure 7 , Figure 7This is a structural block diagram of a single-star sensor fusion self-testing device for detecting low-frequency acceleration noise in space gravitational waves, provided in Embodiment 2 of the present invention.

[0214] This invention provides a single-star sensor fusion self-testing device for low-frequency acceleration noise in space gravitational wave detection, comprising:

[0215] The acquisition module 701 is used to acquire the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-component, and output the centroid connection direction displacement projection fusion model and fixed geometric relationship parameters based on the non-orthogonal configuration geometric relationship parameters;

[0216] Configuration module 702 is used to configure the self-inspection control conditions for the movable optomechanical sub-component and the inspection quality associated with the movable optomechanical sub-component, and to determine the self-inspection control conditions.

[0217] The acquisition module 703 is used to synchronously acquire satellite payload measurement and control data based on the self-test control condition and output the synchronous acquisition dataset.

[0218] The solution module 704 is used to solve the corrected differential displacement time series based on the centroid line displacement projection fusion model and fixed geometric parameters and synchronously acquired dataset.

[0219] Reconstruction module 705 is used to reconstruct the differential residual acceleration time series based on the corrected differential displacement time series and fixed geometric relationship parameters;

[0220] The comparison module 706 is used to estimate the spectrum near the frequency point based on the differential residual acceleration time series and compare it with the preset self-test threshold to generate a self-test judgment result.

[0221] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0222] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the single-star sensor fusion self-test method for low-frequency acceleration noise detection of space gravitational waves as described in the above embodiments.

[0223] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the single-star sensor fusion self-testing method for low-frequency acceleration noise detection of space gravitational waves as described in the above embodiments.

[0224] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0225] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0226] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0227] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0228] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection, characterized in that, include: Obtain the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-assembly, and output the centroid connection direction displacement projection fusion model and fixed geometric relationship parameters based on the non-orthogonal configuration geometric relationship parameters; Configure the self-inspection control conditions for the movable optomechanical sub-assembly and the inspection quality associated with the movable optomechanical sub-assembly, and determine the self-inspection control conditions. Based on the self-test control condition, the satellite payload measurement and control data are synchronously acquired, and the synchronous acquisition dataset is output. Using the centroid-connecting direction displacement projection fusion model, the corrected differential displacement time series is calculated based on the fixed geometric parameters and the synchronously acquired dataset. Based on the corrected differential displacement time series and the fixed geometric relationship parameters, the differential residual acceleration time series is reconstructed. Based on the differential residual acceleration time series, the spectrum near the frequency point is estimated and compared with the preset self-test threshold to generate a self-test judgment result.

2. The single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection according to claim 1, characterized in that, The movable optomechanical sub-assembly includes a first movable optomechanical sub-assembly and a second movable optomechanical sub-assembly; The step of outputting the centroid connection direction displacement projection fusion model and fixed geometric parameters based on the non-orthogonal configuration geometric relationship parameters includes: With the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly locked in a fixed geometric configuration, the non-orthogonal configuration geometric relationship parameters are used as fixed geometric relationship parameters. Based on the fixed geometric relationship parameters, a centroid connection direction displacement projection fusion model for the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly is established respectively.

3. The single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection according to claim 2, characterized in that, The self-inspection control configuration for the movable optomechanical sub-assembly and the associated inspection quality is determined as follows: The inspection mass associated with the first movable optomechanical sub-assembly is set as a three-degree-of-freedom displacement drag-free reference, and satellite micro-thruster control logic is configured to make the spacecraft follow the inspection mass associated with the first movable optomechanical sub-assembly in three translational degrees of freedom. At the same time, weak suspension, centering and low noise control is applied to the inspection mass associated with the second movable optomechanical sub-assembly, and low noise control is adopted for the non-self-test degrees of freedom of the inspection mass associated with the first movable optomechanical sub-assembly to meet operational safety, thus forming a complete self-test control condition.

4. The single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection according to claim 2, characterized in that, Based on the self-test control condition, the satellite payload measurement and control data are synchronously acquired, and a synchronous acquisition dataset is output, including: Under the self-test control condition, the acquisition time is set, and the laser interferometer displacement data, inertial sensor electrostatic displacement data, and key structure temperature data corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component are acquired and recorded simultaneously, as well as the spacecraft attitude data, alignment angle data, micro-thruster thrust data, and control status data corresponding to the satellite body, and the electrostatic execution voltage data and execution channel status data of the inspection quality of the second movable optomechanical sub-component. The system integrates the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, and the temperature data of the key structure corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component, as well as the spacecraft attitude data, alignment angle data, micro-thruster thrust data, and control status data corresponding to the satellite body, and the electrostatic execution voltage data and execution channel status data for quality inspection associated with the second movable optomechanical sub-component, and outputs a synchronously acquired dataset.

5. The single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection according to claim 2, characterized in that, The method employs the displacement projection fusion model along the centroid connection direction, and calculates the corrected differential displacement time series based on the fixed geometric parameters and the synchronously acquired dataset, including: Extract the key structural temperature data of the first movable optomechanical sub-component and the second movable optomechanical sub-component from the synchronously acquired dataset; Based on the temperature data of the key structure and the fixed geometric parameters, the temperature fluctuation is transformed to obtain the equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure in the direction of the centroid connection line. Extract the spacecraft attitude data and alignment angle data corresponding to the satellite body from the synchronously acquired dataset, and construct the angle class input vector; Based on the angle-type input vector, determine the rotational-translational coupling equivalent displacement time series of the centroid connection direction; The equivalent displacement time series of thermal deformation of the movable optomechanical sub-component structure along the centroid connection direction and the equivalent displacement time series of rotational-translational coupling along the centroid connection direction are integrated into an equivalent displacement correction term. Extract the laser interferometer displacement data and inertial sensor electrostatic displacement data corresponding to the first movable optomechanical sub-component and the second movable optomechanical sub-component from the synchronously acquired dataset; Substitute the displacement data of the laser interferometer, the electrostatic displacement data of the inertial sensor, and the fixed geometric parameters into the displacement projection fusion model along the centroid connection line to calculate the projected displacement of the inspection mass of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid connection line. The projection displacement of the inspection mass associated with the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line is calculated by differential operation to obtain the differential operation result; The difference operation result is superimposed with the equivalent displacement correction term to obtain the corrected differential displacement time series.

6. The single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection according to claim 2, characterized in that, The reconstruction of the differential residual acceleration time series based on the corrected differential displacement time series and the fixed geometric parameters includes: The second-order time derivative of the corrected differential displacement time series is performed to obtain the differential acceleration term; Based on the residual acceleration terms of each axis and the fixed geometric relationship parameters, the projection terms of the residual acceleration in the sensitive and non-sensitive axis directions corresponding to the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line are calculated respectively. Perform a difference operation on the projection terms of the residual acceleration in the sensitive axis and non-sensitive axis directions corresponding to the inspection quality of the first movable optomechanical sub-assembly and the second movable optomechanical sub-assembly along the centroid line to obtain the residual acceleration difference projection terms in the sensitive axis and non-sensitive axis directions. The differential acceleration term is superimposed with the residual acceleration differential projection terms corresponding to the sensitive axis and non-sensitive axis directions to obtain the differential residual acceleration time series.

7. The single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection according to claim 1, characterized in that, The self-test judgment result includes a self-test pass result and a self-test fail result; the step of estimating the spectrum near the frequency point based on the differential residual acceleration time series and comparing it with a preset self-test threshold to generate a self-test judgment result includes: The power spectral density of the differential residual acceleration time series is calculated, and the differential residual acceleration noise amplitude spectral density is obtained by conversion. The differential residual acceleration noise amplitude spectral density is numerically compared with a preset self-test threshold. If the differential residual acceleration noise amplitude spectral density is less than the preset self-test threshold, then the self-test pass result is output. If the differential residual acceleration noise amplitude spectral density is greater than or equal to the preset self-test threshold, then the self-test failure result is output.

8. A single-star sensor fusion self-testing device for low-frequency acceleration noise detection in space gravitational wave detection, characterized in that, include: The acquisition module is used to acquire the non-orthogonal configuration geometric relationship parameters of the movable optomechanical sub-component, and output the centroid connection direction displacement projection fusion model and fixed geometric relationship parameters according to the non-orthogonal configuration geometric relationship parameters; The configuration module is used to configure the self-inspection control conditions for the movable optomechanical sub-assembly and the inspection quality associated with the movable optomechanical sub-assembly, and to determine the self-inspection control conditions. The acquisition module is used to synchronously acquire satellite payload measurement and control data based on the self-test control conditions and output a synchronous acquisition dataset. The calculation module is used to calculate the corrected differential displacement time series using the displacement projection fusion model along the centroid connection direction, based on the fixed geometric relationship parameters and the synchronously acquired dataset. The reconstruction module is used to reconstruct the differential residual acceleration time series based on the corrected differential displacement time series and the fixed geometric relationship parameters; The comparison module is used to estimate the spectrum near the frequency point based on the differential residual acceleration time series and compare it with a preset self-test threshold to generate a self-test judgment result.

9. An electronic device, characterized in that, The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the single-star sensor fusion self-test method for detecting low-frequency acceleration noise in space gravitational waves as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the single-star sensor fusion self-testing method for low-frequency acceleration noise in space gravitational wave detection as described in any one of claims 1-7.

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