Spacecraft electrical system interconnect assembly beam splitting optimization method and system

Abstract

The present application relates to the technical field of task scheduling management, and particularly relates to a spacecraft electrical system interconnection component beam splitting optimization method and system. The method comprises the following steps: obtaining interconnection component data; extracting stage working condition according to the interconnection component data to obtain stage working condition data; extracting stage beam splitting point according to the stage working condition data to obtain stage beam splitting point data; extracting stage features according to the stage beam splitting point data to obtain stage feature data; judging the function of the beam splitting point according to the stage feature data to obtain beam splitting point function data; constructing stage constraint parameters according to the beam splitting point function data to obtain stage constraint parameter data; and performing stage conflict suppression according to the stage constraint parameter data to obtain interconnection component beam splitting optimization data. The present application realizes the identification and optimal configuration of the beam splitting point of the spacecraft electrical system by constructing stage working condition semantics, extracting dominant feature behaviors and fusing multi-dimensional constraint parameters.

Description

Technical Field

[0001] This invention relates to the field of mission scheduling and management technology, and in particular to a method and system for optimizing beam splitting of interconnection components in a spacecraft electrical system. Background Technology

[0002] As spacecraft systems become increasingly complex, electrical interconnect components, as the core carriers supporting the collaborative operation of various subsystems (such as telemetry, remote control, attitude control, and energy management), exhibit highly coupled, dynamically evolving, and mission-dependent characteristics in their internal path structures and signal transmission relationships. During multi-stage mission execution, the requirements for control logic, signal paths, and load allocation differ significantly between stages, leading to the widespread existence of pluggable, switchable, or reconfigurable interfaces between some components in the system. This makes it difficult for static topology to accurately represent the actual operational paths. Furthermore, traditional electrical connection modeling methods often focus solely on physical connectivity, neglecting changes in control logic and the dominant factors in operational behavior. Under these technical conditions, the setting of beam splitters in electrical systems often relies on manual experience or static analysis, making it difficult to adapt to the stage-specific control behaviors and load requirements. This can easily lead to mismatches between beam splitter positions and actual operational logic, incoordination in structural allocation, and conflicts between multiple source constraints, resulting in engineering risks such as unstable path scheduling, control signal interference, and even uninterpretable system configurations. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method and system for optimizing beam splitting of interconnection components in a spacecraft electrical system, thereby resolving at least one of the aforementioned technical problems.

[0004] This application provides a method for beam splitting optimization of interconnection components in a spacecraft electrical system, including the following steps: Step S1: Obtain interconnect component data; extract stage operating conditions based on interconnect component data to obtain stage operating condition data; Step S2: Extract stage bundle points based on stage operating condition data to obtain stage bundle point data; extract stage features based on stage bundle point data to obtain stage feature data. Step S3: Perform bundle splitting function judgment on the stage feature data to obtain bundle splitting function data; Step S4: Construct stage constraint parameters based on the bundle splitting point function data to obtain stage constraint parameter data; perform stage conflict suppression based on the stage constraint parameter data to obtain interconnect component bundle splitting optimization data.

[0005] This invention extracts stage operating conditions from interconnect component data and uses the differences in operating conditions between the launch and on-orbit phases as constraints for beam splitting optimization, avoiding the hidden failure risks that traditional beam splitting schemes encounter during stage switching. By mapping stage operating conditions to the continuous path of interconnect components and identifying stage beam splitting points, the determination of beam splitting positions no longer relies on geometric inflection points or human experience, but is objectively determined based on the changing characteristics of the path's response behavior at different stages. This allows for accurate identification of key locations that are geometrically continuous but have discontinuous engineering responses. This invention extracts stage features and performs functional judgments on stage beam splitting points, elevating them from simple structural nodes to functional nodes with clearly defined engineering roles. Based on this, stage constraint parameters matching the stage operating conditions are constructed, achieving a refined description of the constraint behavior of beam splitting points. Through a stage conflict suppression mechanism, conflicting constraint requirements at different stages are structurally coordinated, outputting a set of beam splitting optimization results that are consistent and feasible across multiple stage operating conditions.

[0006] Preferably, step S1 specifically includes: Obtain data from interconnected components; Based on the interconnection component data, the phase operation source is extracted to obtain the phase operation source data, which includes launch operation source data and orbital step operation source data. Stage feature data is obtained by extracting stage features from the source data of stage operations using interconnected components. Based on the stage feature data, stage difference features are extracted to obtain stage difference feature data; Based on the stage characteristic data and stage difference characteristic data, the stage operation source data is abstracted to obtain stage operation condition data.

[0007] This invention acquires interconnect component data and extracts phased operation source data, explicitly distinguishing the operational driving factors experienced by the interconnect components during the launch and orbital phases. This avoids the problem of distorted operational condition descriptions caused by simply merging different task phases in traditional methods. The system extracts interconnect component phase features from the phased operation source data, enabling the description of phased operational conditions to go beyond external environmental parameters and reflect the actual response characteristics of the interconnect components at different phases, thereby improving the engineering relevance and accuracy of operational condition modeling. By extracting phase difference features from the phase feature data, the system can identify the response change patterns of the interconnect components between the launch and orbital phases. By using both phase feature data and phase difference feature data for the operational condition abstraction of the phased operation source, the resulting phased operational condition data can simultaneously characterize the operational source's characteristics and its phase differences, giving the operational condition abstraction a unified structural description while retaining phase-specific engineering constraint information.

[0008] Preferably, the working condition abstract is specifically as follows: The task source data for each stage is correlated with the stage characteristic data and the stage difference characteristic data to obtain task source correlation data; Stage morphology identification is performed based on the associated data of the task source to obtain stage morphology data; Differential aggregation is performed on the stage morphological data to obtain differential feature data; Stage semantics are generated from the differential feature data to obtain stage semantic data; Based on the stage semantic data and the difference feature data, the operation source association data is encapsulated to obtain the stage working condition data.

[0009] This invention achieves correlated modeling of stage-specific work source data by jointly analyzing stage-specific feature data and stage-specific difference feature data. This prevents work sources from being isolated input conditions but establishes a clear correspondence with the response characteristics of interconnected components at different stages, thereby improving the consistency between work source descriptions and actual engineering behavior. By performing stage morphology recognition on the work source correlation data, the overall operational form of the work source at different stages can be extracted from multi-dimensional feature changes, avoiding the bias caused by relying solely on a single parameter or instantaneous state to characterize the work condition. Through difference aggregation of stage morphology data, the system can highlight key change features between different stages. The stage semantic data generated by the system converts numerical features into semantic descriptions with engineering meaning, making the expression of stage work conditions more stable and reusable. By encapsulating stage semantic data and difference feature data together to form stage work condition data, the resulting work condition description simultaneously possesses quantitative difference information and qualitative engineering semantics, effectively reducing the reliance on human experience in the work condition abstraction process and improving the uniformity and interpretability of stage work condition modeling.

[0010] Preferably, the stage split point extraction specifically involves: Continuous path data is obtained by unitizing the interconnected component data into continuous path units. Mapping the stage operating condition data to continuous path data yields path mapping data; Discontinuous positions are identified based on the path mapping data to obtain initial bundle data; Based on the initial beam splitting data, the transmission characteristics are determined, and the stage beam splitting point data is obtained.

[0011] This invention utilizes continuous path unitization to process interconnect component data, dividing the originally complex cable layout into continuous path units with clear engineering significance. This provides a clear analytical object and boundary basis for subsequent bundle point identification. By mapping stage operating condition data to continuous path data, a deep integration of operating condition information and path structure is achieved. This allows bundle point determination to no longer rely solely on geometric shape or fixed experience, but rather to reflect the actual response behavior of interconnect components under different stage operating conditions. By identifying discontinuous locations in the path mapping data, key locations with geometric continuity but abrupt changes in engineering response can be effectively discovered, thus avoiding the erroneous bundle division of hidden risk sections that are easily overlooked in traditional methods. By determining the transmission characteristics of the initial bundle data, the system can screen out locations that have a significant transmission effect on constraints or responses under stage operating conditions, ensuring that the determined stage bundle points not only have structural rationality but also play a stable engineering role under multiple stages such as launch vibration and on-orbit operation.

[0012] Preferably, the identification of discontinuous positions specifically involves: Dominant type identification is performed based on path mapping data to obtain dominant type data; Adjacent path comparisons are performed based on dominant type data to obtain adjacent comparison data; Phase misalignment positions are identified in adjacent comparison data to obtain initial beam splitting data.

[0013] This invention identifies the dominant type of path mapping data to clarify the main response mechanisms of each path unit under different stage operating conditions of interconnected components, elevating path response analysis from a single numerical judgment to a structured identification of the dominant response relationship. By comparing dominant type data with adjacent paths, the changes in the dominant response mechanism distributed along continuous paths can be effectively revealed, avoiding the one-sidedness of traditional methods that rely solely on geometric continuity or local stress magnitude for bundle division judgment. By identifying the phase misalignment position of adjacent comparison data, the location of abrupt changes in the dominant response mechanism due to stage operating condition switching can be accurately located, enabling the explicit identification of key sections that are geometrically continuous but have discontinuous characteristics at the engineering response level. The system can transform hidden stage response discontinuities into identifiable and manageable engineering nodes, thereby providing an objective basis for determining the initial bundle division point, effectively reducing reliance on manual experience in the bundle division design process, and minimizing fatigue accumulation or port tension risks caused by ignoring stage response abrupt changes.

[0014] Preferably, the stage feature extraction specifically includes: The neighborhood path set is obtained by using the stage bundle point data; Perform stage alignment on the neighborhood path data to obtain stage-aligned data; Stage transfer feature data is obtained by extracting stage transfer features from the stage alignment data. Stage change feature data is obtained by extracting stage change features from neighborhood path data. The stage-transmission characteristic data and the stage-change characteristic data are integrated to obtain stage characteristic data.

[0015] This invention constructs a neighborhood path set based on stage-specific bundle points, limiting the analysis scope of stage features to path segments directly related to the engineering behavior of bundle points. This provides a clear spatial boundary for feature extraction, preventing irrelevant paths from interfering with the analysis results. Stage alignment processing of the neighborhood path data ensures that path responses at different stages are under a unified comparison benchmark, eliminating non-stage-specific influences introduced by differences in path length, fixing methods, or structures, and improving the comparability of response features across different stages. By extracting stage-transfer features from the stage-aligned data, the system can accurately represent the transfer behavior of bundle points to constraints or responses under different stage conditions, providing a basis for identifying the role of bundle points in engineering structures. By extracting stage-change features from the neighborhood path data, the system can reflect the magnitude and trend of response changes in the bundle point's neighborhood during stage switching.

[0016] Preferably, step S3 specifically includes: The dominant features of each stage are identified from the stage feature data to obtain the dominant feature data. Stage behavior data is obtained by constructing stage behavior based on dominant feature data. Functional determination rules are mapped onto the stage behavior data to obtain the bundle point functional data.

[0017] This invention identifies the dominant features of each stage's characteristic data, extracting key features that determine the engineering behavior of the bundle point from multi-dimensional stage features. This avoids interference from secondary or noisy features in subsequent judgments, thereby improving the stability and consistency of bundle point analysis. Based on this, stage behavior data is constructed from the dominant feature data, elevating the response characteristics of the bundle point under different stage conditions from discrete feature descriptions to engineering-meaning behavioral patterns. This provides a unified expression for the comprehensive judgment of the bundle point's function. By mapping the stage behavior data to functional determination rules, the system can transform abstract stage behavior patterns into explicit bundle point function types, achieving an effective transition from feature space to engineering function space. This gives the bundle point function determination process clear logical basis and interpretability, avoiding the uncertainty caused by relying solely on human experience or a single indicator for classification.

[0018] Preferably, the construction of stage constraint parameters is specifically as follows: The stage dimension data is obtained by determining the stage dimension data based on the bundle point function data. The dimensional range is determined based on the stage dimension data to obtain the stage range data. The stage dimension data and stage range data are combined with stage parameters to obtain stage constraint parameter data.

[0019] This invention determines the corresponding stage dimensions based on the functional data of the bundle points, clearly distinguishing the engineering constraint directions that different types of bundle points need to focus on under different stage conditions, thus avoiding the constraint mismatch problem caused by the use of uniform constraint parameters in traditional methods. The scope of each dimension is further determined based on the stage dimension data, ensuring that stage constraints are no longer applied in a single-point or global manner, but are limited to an effective range matching the engineering function of the bundle points, thereby improving the pertinence and rationality of constraint modeling. By combining the stage dimension data with the stage range data to construct stage constraint parameters, the constraint behavior of bundle points under different stages is uniformly described in the form of structured parameters. This invention effectively transforms the functional determination results of bundle points into executable engineering constraints, reducing the subjective influence of human experience in constraint setting, improving the repeatability and consistency of the stage constraint parameter construction process, and contributing to the stable optimization and reliable implementation of interconnected component bundle structures under multi-stage operating conditions.

[0020] Preferably, the stage conflict suppression specifically includes: Stage constraint conflict identification is performed based on stage constraint parameter data to obtain constraint conflict data; Conflict type data is obtained by classifying the constraint conflict data into conflict types. Based on the conflict type data, the bundle points are redistributed to obtain the bundle point redistribution data. Based on the reassignment data of the splitting points, conflict suppression selection is performed to obtain the optimized splitting data of the interconnecting components.

[0021] This invention identifies stage constraint conflicts based on stage constraint parameter data, enabling the early detection of inconsistencies or conflicts between constraint requirements under different stage conditions during bundle optimization, thus preventing structural problems from being exposed in subsequent implementation stages. By classifying the constraint conflict data into conflict types, the sources and manifestations of conflicts are clearly distinguished, providing a basis for targeted handling of different types of stage conflicts. Bundle points are redistributed according to conflict type data, ensuring that bundle structure adjustments no longer rely on manual experience or simple compromises, but rather on an orderly reconstruction of bundle point positions and their functional synergies based on conflict mechanisms. Through conflict suppression selection of the bundle point redistribution results, the system can determine a bundle optimization scheme with consistency across all stage conditions from multiple candidate adjustment schemes, thereby outputting directly implementable interconnect component bundle optimization data.

[0022] Preferably, this application also provides a spacecraft electrical system interconnection component beam splitting optimization system for performing the spacecraft electrical system interconnection component beam splitting optimization method as described above. The spacecraft electrical system interconnection component beam splitting optimization system includes: The stage operating condition extraction module is used to acquire interconnected component data; and to extract stage operating conditions based on the interconnected component data to obtain stage operating condition data. The stage feature extraction module is used to extract stage bundle points based on stage operating condition data to obtain stage bundle point data; and to extract stage features based on stage bundle point data to obtain stage feature data. The bundle splitting point function judgment module is used to judge the bundle splitting point function of the stage feature data and obtain the bundle splitting point function data. The interconnect component bundle optimization module is used to construct stage constraint parameters based on the bundle point function data to obtain stage constraint parameter data; and to suppress stage conflicts based on the stage constraint parameter data to obtain interconnect component bundle optimization data.

[0023] The beneficial effects of this invention are as follows: By extracting stage operating conditions from interconnect component data, the operational characteristics and response features of interconnect components under different mission stages are abstractly modeled, providing a clear stage constraint basis for beam splitting optimization and avoiding design deviations caused by single-condition assumptions. This invention maps stage operating condition data to the interconnect component path structure. Through stage beam splitting point extraction and stage feature extraction, key locations that are geometrically continuous but exhibit discontinuities at the stage response level are identified. This ensures that the determination of beam splitting points no longer relies on geometric experience but is based on objective judgment of multi-stage response characteristics. By judging the function of beam splitting points from stage feature data, beam splitting points are elevated from simple structural locations to functional nodes with clear engineering roles. By constructing stage constraint parameters based on beam splitting point functions and combining them with a stage conflict suppression mechanism, constraint conflicts between the launch stage and the on-orbit stage are structurally coordinated, outputting consistent beam splitting optimization results under multiple stage operating conditions. This invention can effectively reduce the stress concentration and port tension risks generated by interconnect components during launch vibration and on-orbit thermal cycling, improve the reliability and long-term stability of the beam splitting structure, and enhance the repeatability and engineering controllability of the beam splitting optimization process, demonstrating significant engineering application value. Attached Figure Description

[0024] Other features, objects, and advantages of this application will become more apparent from the following detailed description of the non-limiting embodiments, taken with reference to the accompanying drawings: Figure 1 A flowchart illustrating the steps of a beam splitting optimization method for interconnection components of a spacecraft electrical system according to an embodiment is shown. Figure 2A flowchart illustrating the steps of a stage condition extraction method according to an embodiment is shown. Figure 3 A flowchart illustrating the steps of a staged bundle splitting point extraction method according to an embodiment is shown. Figure 4 A flowchart illustrating the steps of a method for determining the function of a beam splitting point according to an embodiment is shown. Figure 5 A flowchart illustrating the steps of a method for constructing stage constraint parameters according to an embodiment is shown. Detailed Implementation

[0025] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. Functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] Please see Figures 1 to 5 This application provides a method for beam splitting optimization of interconnection components in a spacecraft electrical system, comprising the following steps: Step S1: Obtain interconnect component data; extract stage operating conditions based on interconnect component data to obtain stage operating condition data; In one embodiment, the system collects interconnect component data from the spacecraft's electrical system. This data includes cable routing, port connections, component structural fixing methods, material properties, and installation location information. Based on the control requirements and load conditions of different mission phases (mission phases are defined according to spacecraft mission procedures, including launch, orbit insertion, and on-orbit operation phases), the system categorizes the interconnect component data into operational source types corresponding to the launch and on-orbit phases. Combining the component's structural installation status and load transfer path, the system models and analyzes the main response mechanisms of the interconnect components in each phase. For example, in the launch phase, the focus is on vibration response modeling, while in the on-orbit phase, the focus is on thermally induced deformation response modeling. The modeling inputs include component structural fixing information, material parameters, path topology, and load boundary conditions (such as launch load and heat flux distribution). Based on finite element simulation or parametric models, the system outputs data such as nodal stress values, thermal expansion, and displacement response, forming the dominant response characteristics of the path and nodes in each phase, such as maximum vibration acceleration or thermal strain distribution. The system compares the response results of each path and node at different stages with the simulation or calculation results of the above modeling analysis, identifies the dominant changes in response, and extracts the features that constitute the differences in stage response. For example, the system compares and analyzes the dominant response types (such as vibration and thermal deformation) of each path / node in two stages. If the dominant type changes, or the response amplitude significantly increases under a certain mechanism (such as thermal stress increasing from 0.1 MPa to 0.8 MPa), it is determined that a dominant change exists. The response feature data are then categorized and integrated to generate stage operating condition data.

[0029] Step S2: Extract stage bundle points based on stage operating condition data to obtain stage bundle point data; extract stage features based on stage bundle point data to obtain stage feature data. In one embodiment, the system processes the path information of interconnected components (cable path layout, port connection relationships, and component structure fixing methods), dividing the overall path into several consistent path units with structural continuity and connection logic. The system maps stage operating condition data to each path unit, identifying the dominant response type of each unit under different task stages. For example, it identifies that a certain path segment exhibits vibration response as dominant during the launch stage, while thermal response is dominant during the on-orbit stage. The system compares the dominant response mechanisms of adjacent path units. If it identifies phenomena such as switching of response mechanisms, obvious reversal of response direction (the trend of change of response variables (such as stress, temperature, displacement) in the time dimension), or abrupt change in sensitivity (whether the rate of change of the path response intensity to external inputs (such as load, heat flow, signal changes) changes significantly within the path segment), then the location of the change is considered as a potential candidate for a bundle splitting point. The system assesses the response transfer capability of candidate bundle split points, determining whether there are changes in structural characteristics at that location, such as abrupt changes in constraint stiffness (the force required for unit deformation of the path structure under specific boundary conditions) or response amplification and transfer characteristics between paths (the behavior where the response output between path nodes is greater than the input, such as a small vibration at one end causing a strong displacement at the other). If the above conditions are met, it is confirmed as a stage bundle split point. After completing the bundle split point identification, the system constructs a set of neighboring paths for each stage bundle split point, uniformly aligning and normalizing the response data of non-stage factors in the path (such as path length and material differences). The system extracts the stage response transfer characteristics (such as the gradual increase or decrease trend of response intensity) and stage switching characteristics (such as the change in response slope, the relative displacement change of path nodes, etc.) corresponding to each bundle split point, and integrates the above information to form stage feature data for subsequent structural configuration and response control analysis.

[0030] Step S3: Perform bundle splitting function judgment on the stage feature data to obtain bundle splitting function data; In one embodiment, the system analyzes the dominant response behavior of each beam splitter under different mission stages based on extracted stage feature data. For example, a beam splitter may exhibit a sudden change in structural stiffness during the launch stage, possessing strong load transfer capabilities, while during the on-orbit stage, it may act as a transitional adjustment node for thermal expansion. For these behaviors, the system constructs corresponding stage behavior patterns and categorizes them into structural role types based on various behavioral characteristics, including "stable load-bearing type," "buffer absorption type," and "transitional adjustment type." The stable load-bearing type indicates that the beam splitter exhibits strong structural rigidity and stability in all stages, with small response variation amplitudes, and is part of the main load path, undertaking long-term stable transmission. The buffer absorption type indicates that the beam splitter exhibits large response amplitudes and frequent fluctuations during stage transitions, possessing the ability to absorb or dissipate system disturbances, acting as a buffer for stress or heat flow. The transitional adjustment type indicates that the beam splitter is at a critical position of response intensity transition in certain stages, possessing the function of adjusting deformation, response speed, or signal flow direction, and serving as an interface point for multi-stage function switching. The system matches the identified stage behavior patterns with preset function mapping rules to determine the functional positioning of the beam splitter within the system structure. Functional determination relies not only on the performance of a single stage but also on the consistency or complementarity of response mechanisms across multiple stages. For example, the system will only classify a node as a "transitional adjustment type" beam splitter if it exhibits significant stress concentration characteristics during the launch stage and deformation adjustment capabilities during the on-orbit stage. The system outputs beam splitter functional data including functional type identification information and a description of the behavioral distribution across each stage.

[0031] Step S4: Construct stage constraint parameters based on the bundle splitting point function data to obtain stage constraint parameter data; perform stage conflict suppression based on the stage constraint parameter data to obtain interconnect component bundle splitting optimization data.

[0032] In one embodiment, the system determines the main constraint dimensions involved in different task stages based on the functional type of each bundle point. For example, if a bundle point is determined to be "stable bearing type," its constraint parameters focus on the structural strength dimension; if it is "buffer absorption type," the focus is on the displacement release dimension; and for "transition adjustment type" bundle points, the coupling relationship between multiple response dimensions is comprehensively considered. The system, combined with the actual layout of the component path and the installation structure conditions, clarifies the constraint range of each dimension, such as determining the constraint start and end positions and the coverage length of the action segment. After combining each constraint dimension with its corresponding range, the system constructs a stage constraint parameter object to describe the response boundary and engineering tolerance range of the bundle point in each stage. The system performs conflict identification analysis among the constraint parameters of all stages to determine whether there are contradictions caused by different requirements in multiple stages. For example, if the same location requires high stiffness characteristics in one stage but flexible buffering capability in another stage, this constitutes a conflict in the constraint dimension; similarly, overlapping or breakage of the action areas between multiple bundle points also constitutes a conflict in the constraint range. The system categorizes identified conflicts by type, such as strength conflicts (the same structural node needs to meet opposite strength characteristics at different stages, such as requiring both high stiffness support and elastic energy absorption, leading to an inability to balance material design), range coupling conflicts (multiple constraint dimensions act on the same area, with overlapping areas but inconsistent boundary definitions, resulting in response interference or parameter redundancy), and functional overlap conflicts (the functional areas of multiple bundle points intersect, and their functional types are inconsistent (e.g., one is a rigid connection point, and the other is a dynamic adjustment point), causing role conflicts during task execution). For bundle points with high conflict levels, the system calls a preset optimization strategy library to perform adjustment and optimization operations, including repositioning the bundle point, changing its functional type, or adding collaborative response nodes to share the conflict pressure. After optimization and adjustment, the system evaluates the engineering consistency and response coordination of each scheme, selects the optimal bundle configuration scheme, and generates interconnected component bundle optimization data based on it.

[0033] Preferably, step S1 specifically includes: Step S11: Obtain interconnect component data; In one embodiment, the system extracts interconnect component data from the spacecraft electrical system design database or related engineering platform. This data includes cable path data, such as the connection sequence between ports, the actual length of the cables, and their wiring paths; structural installation data, such as the type of bracket to which the cables are attached, the fixing method, and the location of key tension points; connector and port information, including the installation direction of the ports, the number of pins corresponding to each port, and the electrical category to which they belong; material property parameters, such as the thermal expansion performance, structural stiffness, and fatigue life of the materials constituting the components; and the spatial layout relationship of the components within the spacecraft, such as the location of their respective compartments, the distance between them and adjacent components, and the number of redundant cable layers. The system organizes the above data uniformly and labels it with a unique component number and mission phase identifier to obtain the interconnect component data.

[0034] Step S12: Extract the phase operation source based on the interconnection component data to obtain phase operation source data, which includes launch operation source data and orbital step operation source data. In one embodiment, the system, based on the overall operational process model of the spacecraft mission, divides the mission process into multiple functional stages and extracts the operating conditions of the launch and orbital stages. For each stage's specific working environment and load characteristics, the system identifies the types of operational sources related to interconnected components and constructs stage operational source data. During the launch stage, the system identifies the following operational sources: first, high-frequency vibration excitation, manifested as continuous input of frequency spectral density, causing periodic disturbances to cable paths and connection points; second, impact acceleration loads, characterized by high-intensity impacts within a short period, placing high demands on structural strength and fixing devices; and third, launch direction constraint response, which requires identifying key stress surfaces in a defined coordinate system and capturing structural constraints in the main load-bearing directions. During the orbital phase, the system identifies the following operational sources: First, thermal cycling loads; second, microgravity displacement disturbances (under orbital flight conditions, due to the spacecraft's microgravity environment, structural components may still experience minor positional disturbances caused by inertia, temperature control systems, or attitude adjustments even without significant external loads), manifesting as slow but continuous micro-displacement effects that challenge connection stability; and third, structural breathing effects (components undergoing long-term thermal expansion and contraction cycles, resulting in periodic micro-displacements between interfaces or connections, forming an "opening-closing" dynamic response state, similar to a "breathing" process), i.e., long-term thermal expansion and contraction causing relative displacement between component ports, thus affecting signal transmission or structural collaborative stability. Each of these operational sources is bound to its respective mission phase and labeled with its type of action (e.g., structural, thermal, electrical) and its impact priority in the component path, thus forming phase operational source data.

[0035] Step S13: Extract stage features from the source data of the stage operations using interconnected components to obtain stage feature data; In one embodiment, the system calculates the physical response characteristics of the interconnect component path for each identified task source at each stage. The system combines the path topology and material parameters of the interconnect components to perform response simulations on each path unit, analyzing its dynamic behavior under the influence of specific task sources. During the launch phase, the system extracts the response characteristics of each path segment under high-frequency vibration and impact loads, including but not limited to vibration transmission capability, peak acceleration response, and connection stability fluctuation indicators, such as identifying its vibration transmission capability and the occurrence of peak acceleration response. During the orbital stage, the system mainly extracts the response performance of the path segment under changes in the thermal environment, including the rate of change of thermally induced displacement, long-term offset, and interface response stability factor, such as the rate of change of thermally induced displacement and stable offset caused by temperature cycling. The system organizes the response results of each path segment at different stages into a stage response vector, including multiple physical quantity dimensions such as mechanical response, thermal response, and displacement change, and encapsulates this data to form stage feature data.

[0036] Step S14: Extract stage difference features based on stage feature data to obtain stage difference feature data; In one embodiment, given the differences in the types of operational sources caused by different mission phases, the system adopts a multi-dimensional processing strategy for the response data of each path segment: for the mechanical response and displacement change dimensions, since they can be obtained in both the launch and orbital stage phases, the system performs direct comparative analysis; while for stage-specific dimensions such as thermal response, normalization is used to characterize and steady-state offset and disturbance sensitivity assessment is performed within a single stage, thereby achieving a reasonable comparison of response characteristics between stages. For example, in the launch phase, the system extracts the response stability window of the path unit based on the response time series data under impact and high-frequency vibration, calculates its mean drift rate and post-disturbance recovery time within the window, and uses them as initial indicators of steady-state offset and disturbance sensitivity. In the orbital stage, the system identifies the periodic trend of thermally induced displacement change based on the path displacement curve under thermal cycling disturbance, and calculates its periodic offset amplitude and temperature disturbance response gradient, which are used as initial indicators of steady-state offset and disturbance sensitivity. All the obtained indicators are uniformly mapped to a normalized influence factor / part of the response result. Based on the stage response vector generated in the previous step, the system compares and analyzes the response results of each path segment under different mission stages, extracting the inter-stage difference characteristics. The system calculates the difference or relative ratio of the response values ​​of the same path unit under the launch stage and the orbital stage, thereby quantifying the magnitude of its response change. The system determines whether the dominant response mechanism of the path unit changes between different stages, such as transitioning from vibration-dominant to thermal deformation-dominant response, as one of the criteria for structural functional state changes. The system performs difference measurement processing, including but not limited to the intensity ratio coefficient of response change and the change in the dominant characteristic direction of the stage. The latter can be identified by performing feature decomposition analysis on the response data to determine the degree of offset of the principal axis direction in different stages. If the system detects that the response difference of a path segment between two stages exceeds a preset threshold, such as the response change exceeding a specific ratio or a large angle between the response characteristic directions, the path segment is marked as a "stage difference sensitive segment". The system outputs stage difference characteristic data, including the path segment number, the identified difference type, the difference intensity value, and a flag indicating whether the dominant response mechanism has switched.

[0037] Step S15: Based on the stage feature data and stage difference feature data, perform work condition abstraction on the stage operation source data to obtain stage work condition data.

[0038] In one embodiment, the system comprehensively analyzes stage characteristic data and stage difference characteristic data to abstractly model the work sources under different task stages, forming a work condition description. The system establishes a response relationship mapping between work sources and path segments to represent the response intensity and impact range of each work source on different path segments. Based on the response characteristics exhibited by path segments under the influence of various work sources, the system clusters and classifies them into several response forms, such as "rigid conduction type" exhibiting obvious stress transmission characteristics, "flexible transition type" with buffer absorption capabilities, or "thermal buffer type" dominated by temperature influence. For each response form, the system assigns a corresponding work condition semantic label to describe its structural response characteristics and dominant physical mechanism, such as "vibration-dominant - rigid segment" or "thermal-dominant - drift accumulation segment." The system combines stage difference characteristic information to determine the degree of response consistency of path segments during stage switching. If abnormal changes such as abrupt changes in the dominant mechanism or reversal of the response direction are identified, the corresponding work condition is marked as high-risk. The system abstracts each stage of the work source into an independent work condition description object, which includes the following fields: work condition number, dominant work source type, set of affected path segments, response semantic label, and stage difference significance level.

[0039] Preferably, the working condition abstract is specifically as follows: The task source data for each stage is correlated with the stage characteristic data and the stage difference characteristic data to obtain task source correlation data; In one embodiment, the system performs multi-dimensional correlation analysis on stage-specific task sources based on stage-specific characteristic data and stage-specific difference characteristic data to construct task source correlation data. The system pre-sets a set of correlation criteria, which may include structural matching rules, functional response matching rules (determining whether the effects (such as vibration, heat, and shock) generated by a task source have responsive adaptability on the target path unit, i.e., whether the path has absorption, conduction, buffering, and other functional characteristics that match the task source), and temporal adaptability rules (identifying whether the activation time or cycle of the task source matches the working sequence, activation window, or response delay capability of the module where the target path is located), etc. These rules are jointly formulated based on industry engineering experience, statistical patterns of historical task data, and expert knowledge. Among them, the structural matching rules (whether the task source meets the response requirements defined in the stage-specific characteristics in terms of structural attributes) mainly rely on the functional attributes of the task source, the category of the component module to which it belongs, and its positional relationship in the interconnection topology to screen whether it meets the constraints defined in the stage-specific characteristic data. For example, if the module to which a task source belongs is in a high-stiffness requirement area, it must have structural response characteristics that match it. The system identifies behavioral changes by comparing the action parameters (such as load intensity, frequency variation, and thermal disturbance amplitude) of the same work source at different stages to assess its behavioral stability during stage switching. If the magnitude of the change exceeds the threshold standard set in the difference characteristics, the system marks the work source as a changed work source. Based on the above association judgment results, the system constructs a work source association matrix to represent the matching strength between each work source unit and structural features. Each row in the matrix corresponds to a work source unit, and each column represents its association degree in a specific dimension, which can be represented by Boolean labels (such as whether it is associated) or weighted numerical labels (representing the association strength).

[0040] Stage morphology identification is performed based on the associated data of the task source to obtain stage morphology data; In one embodiment, the system analyzes and categorizes the structural response state of a task phase based on the behavioral trajectories of various task sources recorded in the task source association data, in order to identify the corresponding working condition of the phase. The system extracts representative indicators from three dimensions: First, the system statistically analyzes the activity distribution of task sources within a phase, including the number of high-frequency changing task sources (task sources whose action parameters (such as load intensity, frequency, disturbance amplitude, etc.) change multiple times per unit time within a certain phase, identified by setting a threshold for the number of changes or a threshold for the disturbance gradient) and their distribution area in the path; Second, the system analyzes the behavioral patterns of the dominant task source (the task source that has the most significant impact on the component response, the longest duration, or the widest range of action in the current phase), identifies its operating frequency, the direction of change of the main control parameters, and the persistence, thereby determining the control main line and core load distribution of the phase; Third, the system performs convergence analysis on the key parameters of the task sources in the phase to determine whether they tend to stabilize within a certain period of time or path, such as whether they fluctuate around a certain value range. Combining the above indicators, the system establishes a set of phase morphology classification rules for judging the phase state. For example, if most work sources exhibit small variations within a phase and the dominant behavior shows a consistently stable trend, the system identifies this phase as a "steady-state state." If multiple work sources exhibit abrupt changes and their parameter changes are chaotic, it is classified as a "disturbance state." If the structural attributes or control objectives of the dominant work source switch during the phase, the system marks it as a "transitional state." The system outputs phase morphology data, including the phase number, the morphology classification label obtained (e.g., steady-state, disturbance, or transition), and the corresponding judgment criteria.

[0041] Differential aggregation is performed on the stage morphological data to obtain differential feature data; In one embodiment, the system summarizes and integrates the response difference characteristics of each task source under different time periods and conditions based on stage morphology data, thereby extracting difference feature data. The system performs a unified time window normalization operation to align the difference data of all task sources according to a unified time axis. The system pre-assigns corresponding weights to the difference features of each task source according to its dominance in the stage morphology. For example, task sources located at the core of structural control or frequently triggering response changes will have higher weights in the overall aggregation, while peripheral task sources will be assigned lower weights. The system selects the difference aggregation method according to the specific morphology type of the stage: for the "steady state" stage, the median aggregation method is used; for the "disturbed state" stage, the maximum value aggregation method is used; and for the "transitional state" stage, the weighted moving average method is used to smooth the response fluctuations during the stage transition process and capture gradual characteristics. The difference feature data output by the system includes the overall change magnitude index within the stage, a list of dominant difference feature items (e.g., the main affected response variables), and their influence range in the component path.

[0042] Stage semantics are generated from the differential feature data to obtain stage semantic data; In one embodiment, the system, based on difference feature data and stage morphology data, performs semantic processing on the differences in each stage by calling a built-in semantic mapping rule library, thereby generating stage semantic data. The system identifies semantic trigger conditions, extracts the dominant change items in the difference feature data, and determines whether the trigger conditions of a specific semantic label are met. For example, when it is identified that the parameter change amplitude in a certain stage exceeds a set threshold and the dominant job source is switched, the system will automatically trigger the "run refactoring" semantic label; or, when the difference change items are concentrated but the amplitude is in a medium range, the "load migration" semantic label is triggered accordingly, which is used to characterize the stage migration characteristics of system functions or paths. The system then enters the semantic content filling stage. Based on the morphological type and difference index results of the corresponding stage, semantic tag content is constructed. This content includes, but is not limited to, the stage name, main response features, the determined difference type, and key influencing factors. The main response features are physical response indicators that have a significant impact on the interconnected component path in the current stage or stage comparison. They are usually selected from the stage response vectors based on the dimensions with the largest change or the highest structural correlation, such as strain amplitude, nodal displacement change, and thermal drift rate. The extraction criteria can be based on the magnitude of the comparative response amplitude (the top-ranking indicators in terms of change). One scenario involves responses with a wide propagation range within the structural path and response dimensions that have a significant correspondence with the work source. Key influencing factors are fundamental parameters that cause stage differences. These typically manifest as work source characteristics, system configuration parameters, or path environmental variables that cause changes in the dominant response. Examples include control parameters of the dominant work source (such as thermal load cycle, impact intensity), changes in path structural parameters (such as modulus, fixing method), and variations in environmental boundary conditions (such as temperature range or microgravity condition changes). These are reflected in the difference feature data as high-weighted difference items or as the main controlling factors triggering morphological switching. The stage semantic data output by the system includes semantic labels and judgment criteria.

[0043] Based on the stage semantic data and the difference feature data, the operation source association data is encapsulated to obtain the stage working condition data.

[0044] In one embodiment, the system standardizes various input data fields. For example, all job source identifiers are generated using hash fingerprinting, semantic tags are uniformly labeled using a predefined enumeration format, and difference items and feature fields use a common naming convention. The system performs association integrity verification to check whether the dominant difference items involved in the stage semantic data have corresponding entities in the job source association data. In the data encapsulation stage, the system constructs a nested data package, building an independent working condition data object for each stage. This object contains: stage identifier, semantic tag, main difference feature items and their degree of change description, and the association mapping relationship of job sources (including active status and weight values, etc., with weight values ​​initially assigned in the aforementioned semantic mapping rule base). For example, if a certain stage exhibits high-frequency control unit switching, the system will generate a running reconstruction semantic tag and record the changes and impact range in the difference part, while also indicating the active status and response weight of key units in the job source mapping.

[0045] Preferably, the stage split point extraction specifically involves: Step S21: Perform continuous path unitization based on the interconnection component data to obtain continuous path data; In one embodiment, the system performs structural analysis on the collected interconnected component data to construct a connection topology diagram between components. By identifying the connection relationships between electrical nodes, the system decomposes the overall interconnection structure into a set of logically connected paths. During path construction, the system defines each path as a continuous combination of electrical component units, which typically include components such as connecting cables, plugs, and terminals. The starting node of a path is generally a signal source module, such as a main power supply or central control unit, while the ending node is a signal receiving end, such as a subsystem module or load execution unit. The system sets the following unitization rules: if there is a connection discontinuity in the path, such as a connector or pluggable module interface, this location is considered the boundary of the path; if there is a multi-hop branch structure in the path (such as a "T-shaped" fork), the system takes the branch point as the starting point of a new path and divides it into independent path units. The system uses a depth-first traversal algorithm to extract paths from the topology diagram. The continuous path data generated by the system is output in list form, and each path data record includes a path number, starting and ending component identifiers, path length information, and channel type classification.

[0046] Step S22: Map the stage working condition data to the continuous path data to obtain path mapping data; In one embodiment, the system sets mapping conditions. If a job source exhibits high activity in a specific stage—for example, its control parameters fluctuate drastically or its state changes frequently—and the component node associated with the job source belongs to the structural range of a continuous path, the system determines that the job source has a valid impact on the path unit. If multiple such highly active job sources exist in a path, the system marks the path as a high-mapping-density path for that stage. The system constructs a mapping information list for each continuous path, recording in detail the job source mapping situation of the path in each stage. Specifically, this includes: stage identifier, job source number, parameter fluctuation value of the job source in the path, and the duration of the impact. If a job source cannot find a structural correspondence in any path, the system ignores its mapping relationship in that stage.

[0047] Step S23: Identify discontinuous positions based on path mapping data to obtain initial bundle data; In one embodiment, the system traverses the mapping behavior sequence of job sources at each stage along a continuous path to determine whether there are structural connection anomalies between adjacent job sources. Specifically, this includes situations where two adjacent job sources are not directly connected in the path topology, or where significant abrupt changes occur during parameter variations, and where the path segment exhibits structural interruptions, loose connections, or signal path shifts. Path segments meeting these conditions are marked as structurally discontinuous regions by the system. The system analyzes whether there are significant shifts in the active time periods of adjacent job sources in the path. If it finds that although these job sources are structurally connected, their parameter active time windows are inconsistent, and the location happens to be at a structural breakpoint or weak connection region, the system will strengthen the discontinuity marking of this segment as an auxiliary basis for bundle division judgment. The system merges multiple spatially close location segments that meet the above structural or temporal discontinuity conditions to form a centrally distributed initial bundle division data set. The initial bundle division data output by the system.

[0048] Step S24: Determine the transmission characteristics based on the initial beam splitting data to obtain the stage beam splitting point data.

[0049] In one embodiment, for information blocking, the system determines whether the signal type changes before and after the path location, such as switching from a power signal to a control signal, or from an analog signal to a digital signal. If such a type change exists, it indicates that the point is a signal logic break and has structural bundle significance. An initial score is given using a preset index score table. For parameter abruptness, the system analyzes whether the physical parameters of the connecting components before and after the point show significant jumps, such as whether key indicators like resistance, capacitance, or current density change significantly within a short distance. If a set threshold is exceeded, it indicates that the point has physical separation characteristics, and an initial score is given using a preset index score table. For functional boundary, the system determines whether the location is at the junction of control logic from two different operating sources, such as the location where the main control module issues control commands to the subsystem. With signal flow and responsibility switching, an initial score is given using a preset index score table. Regarding state isolation, the system assesses whether the point before and after belongs to different system state partitions or electrical isolation areas, such as whether it crosses compartments, switches power supply modules, or jumps to logical function levels. An initial score is given using a preset index scoring table. After completing the above determination, the system performs multi-factor scoring on each initial bundle point, weighting the scores based on the preset importance of each index and the initial score. For example, the weights can be set as follows: information blocking 40%, parameter mutability 30%, functional boundary 20%, and state isolation 10%. When the score of a bundle point exceeds a preset threshold (e.g., 70 points), the system confirms it as a formal stage bundle point.

[0050] Preferably, the identification of discontinuous positions specifically involves: Dominant type identification is performed based on path mapping data to obtain dominant type data; In one embodiment, during the feature extraction stage, the system classifies and statistically analyzes the work sources associated with each path segment, including their types (such as power modules, control units, execution units, etc.), and quantitatively analyzes the behavioral characteristics of each type of work source, extracting key indicators such as parameter change rate, activity frequency, and control priority. The system combines these indicators to assign corresponding weights to different types of work sources, comprehensively forming a behavioral dominance degree describing the behavior of the path segment. During the dominance type determination stage, the system categorizes each path segment according to preset rules: if the behavioral intensity of power-type work sources is the highest in a path segment, the path segment is marked as "power supply type," where the behavioral intensity percentage is the proportion of the response intensity triggered by a certain type of work source (such as power source) in the path segment to the total response intensity triggered by all work sources in that path segment; if controller-type work sources are dominant, it is marked as "control type"; if the execution mechanism is the dominant response source, it is marked as "execution type"; if the weight distribution of various work sources is balanced and there is no obvious dominant behavior, it is marked as "mixed type" or "non-dominant."

[0051] Adjacent path comparisons are performed based on dominant type data to obtain adjacent comparison data; In one embodiment, the system constructs a path adjacency graph using continuous path data to identify structural adjacency relationships between paths. If two paths have directly connected nodes, shared components, or physically adjacent wiring relationships, the system determines them as structurally adjacent paths and includes them in the comparative analysis. In the dominant type comparison stage, the system extracts the dominant type label for each pair of adjacent paths and makes a judgment based on preset rules. For example, if one path is control type and the other is power supply type, the pair of paths is determined to constitute a "control-power supply gap"; if one is control type and the other is execution type, it is considered a "control-execution switch"; if both paths are control type but have different dominant operation sources, it is considered a "control source switch"; if there is an inconsistency in dominant types and one path is marked as "non-dominant," it is classified as a "semantic weak connection," indicating that there is uncertainty or fuzzy boundary in its functional connection. The system records the comparison results in a structured manner to form adjacent comparison data. This data includes path number pairs, dominant type difference labels, behavioral difference intensity scores (calculated based on the path behavioral indicator difference measurement results), and Boolean flags indicating whether there is potential discontinuity risk.

[0052] Phase misalignment positions are identified in adjacent comparison data to obtain initial beam splitting data.

[0053] In one embodiment, the system combines the temporal behavior information between path segments with differences in the dominant structural type to identify location regions where signal triggering timing is inconsistent or logical response is interrupted, i.e., phase misalignment. The system extracts the behavioral time sequence of the dominant operation source for each path segment at each stage, specifically including the start and end times of parameter changes, behavior triggering cycles, and the time points of state switching, to reconstruct the behavioral rhythm of the path segment during actual operation. The system performs temporal comparison analysis on adjacent path segments based on preset misalignment judgment rules. If any of the following conditions exist, it can be determined as phase misalignment: 1) the control path segment issues a control signal first, while the execution path segment's response is significantly delayed, exceeding the set time tolerance window; 2) the parameter changes between path segments are inconsistent in direction, such as one trending upwards while the other is declining; 3) there is a significant mismatch in behavior, such as one end switching states while the other remains stationary. If the above misalignment phenomenon is accompanied by a significant change in the dominant type, such as switching from "control type" to "execution type," the system will assign a higher misalignment importance score. The scoring process is as follows: if the control path segment issues a control signal before the execution path segment, and the response delay exceeds a preset time tolerance window, it is recorded as a valid misalignment event and awarded 30 points. If the parameter changes in opposite directions between path segments, such as one segment showing an upward trend while the other shows a downward trend, 20 points are awarded. If a state switch occurs in one segment while the adjacent path segment remains stationary at a certain moment, another 20 points are awarded. If any of the above misalignment behaviors are accompanied by a change in the dominant type (such as from control type to execution type), the system will award an additional 30 weighted points. The system focuses on the identified misalignment locations, marking the connecting nodes of the path segments with misalignment behavior as initial bundle candidate points. The system performs cluster analysis on multiple adjacent misalignment points, merging them into continuous candidate regions. If the misalignment score at a certain location exceeds the system's set judgment threshold (e.g., 80 points), it is officially recorded as initial bundle data, indicating that the location has discontinuities at both the structural and behavioral levels, possessing obvious bundle indicators.

[0054] Preferably, the stage feature extraction specifically includes: The neighborhood path set is obtained by using the stage bundle point data; In one embodiment, the system constructs a path neighborhood set centered on each bundle point based on the key structural locations identified by the bundle point data. During the neighborhood range setting phase, for each bundle point, the system determines its continuous path segment as the core path and expands it in the forward and backward directions by a fixed number of hops (e.g., extending by two segments forward and backward). Simultaneously, an expansion limit can be set based on the physical length of the path, for example, the total path length cannot exceed 20 meters, or the number of topology hops can be limited to a set value. Based on the topology graph of interconnected components, the system identifies other path segments directly connected to or sharing components with the path segment and includes them in the neighborhood set. If a path segment is simultaneously covered by multiple bundle point neighborhoods, the system marks it as a high-overlap path. The system represents data in the form of "bundle point ID → neighborhood path segment set," with each neighborhood set recording the path segment number, the component identification information connected to the path, and the depth level in the overall topology graph.

[0055] Perform stage alignment on the neighborhood path data to obtain stage-aligned data; In one embodiment, the system determines the standard time window for each stage based on the stage time boundaries recorded in the stage operating condition data. If the start or end times of certain path segments deviate slightly from the stage time boundary (e.g., within ±10 milliseconds), the system considers them to be within the same stage and forcibly aligns and merges them. For cases where the behavioral data of some path segments lacks sufficient time resolution, the system uses linear interpolation to fill in the time series data. The system outputs the aligned data in a unified structure format. Each path segment data includes a path number, an aligned time vector, and the corresponding behavioral change curve (e.g., the trajectory of current and voltage changes over time).

[0056] Stage transfer feature data is obtained by extracting stage transfer features from the stage alignment data. In one embodiment, the system constructs a path transmission chain based on the topological connection order of interconnected components and the time-series information of each path segment in the stage alignment data. This involves connecting multiple path segments according to structural dependencies to form a path response chain within a stage. For example, if the output of path A connects to path B, and then further connects to path C, a stage transmission chain is formed. The system extracts transmission characteristic indicators for each pair of adjacent path segments. These include: 1) transmission delay, where the system calculates the time interval between changes in the behavior of the upstream path segment and the response of the downstream path segment; 2) response amplitude similarity, analyzing whether the parameter fluctuation amplitudes between path segments are highly correlated (parameter fluctuation amplitudes are calculated through similarity); and 3) response direction consistency, used to determine whether the change trends between path segments maintain the same direction, such as both showing an upward or downward trend. The system outputs these characteristics. Each path segment records parameters such as the numbers of its upstream and downstream paths, transmission delay values, response similarity scores, and direction consistency indicators.

[0057] Stage change feature data is obtained by extracting stage change features from neighborhood path data. In one embodiment, the system statistically analyzes the internal behavioral change indicators of a path segment during a phase, including: (1) parameter fluctuation amplitude, representing the extreme value of parameter (such as voltage, current) change in the path segment per unit time; (2) change frequency, recording the number of behavioral switching events that occur within the phase, such as the frequency of switching between on / off states or transitioning from steady state to disturbance; (3) duration, identifying the duration of high-amplitude behaviors (parameter data exceeding a threshold) within the phase; and (4) fluctuation pattern, analyzing whether the behavioral changes exhibit characteristics such as periodicity, gradualism, or abruptness. The system will extract and output the results, including path number, behavioral fluctuation amplitude, behavioral switching frequency, duration of key states, and fluctuation pattern classification.

[0058] The stage-transmission characteristic data and the stage-change characteristic data are integrated to obtain stage characteristic data.

[0059] Preferably, step S3 specifically includes: Step S31: Identify the dominant features of the stage feature data to obtain the dominant feature data; In one embodiment, the system sets multiple feature importance scoring indicators to represent the importance of each path segment within a stage. These include: first, a variation amplitude score (calculating the difference between the maximum and minimum values ​​of path segment parameters (such as current, voltage, temperature, etc.), or statistical indicators such as standard deviation and sliding window volatility), used to assess the intensity of parameter fluctuations in the path segment; second, a transit centrality score (based on the path segment's connectivity in the network topology, the frequency of information flow or energy flow passing through it, using graph theory methods such as betweenness centrality and degree centrality), used to determine whether the path is located at the core of multiple signal links or energy channels; third, a response coupling score (using indicators such as temporal similarity of parameter changes between paths, correlation coefficient, and dynamic time warping (DTW), used to measure the behavioral synchronicity and temporal consistency between the path and other paths; and fourth, an abnormal fluctuation index (by detecting signal characteristics such as short-term abrupt changes, frequency of state switching, and abnormal fluctuation frequency), used to identify whether the path segment exhibits highly sensitive behaviors such as sudden changes or frequent switching. The system assigns corresponding weights to each path segment based on the aforementioned scoring dimensions and calculates a score. For example, a combination of weights such as 0.4 for variation amplitude, 0.3 for transmission centrality, 0.2 for coupling, and 0.1 for anomaly indicators can be used. If the comprehensive score of a path segment exceeds a preset threshold (e.g., 0.75), it is marked as the "dominant path" for that stage, and its corresponding dominant feature items are extracted. The system outputs the identification results, recording the path number, dominant score value, and representative feature labels for each dominant path, such as "high amplitude fluctuation," "transmission centrality," and "high frequency of anomalies." High amplitude fluctuation refers to parameters exhibiting significant up-and-down fluctuations within the stage, with large numerical changes. Transmission centrality refers to the path being a key node in multiple control or energy paths, frequently undertaking transmission tasks. High frequency of anomalies refers to the path segment frequently experiencing sudden changes, abnormal jumps, or switching events within the stage.

[0060] Step S32: Construct stage behaviors based on dominant feature data to obtain stage behavior data; In one embodiment, during the behavior tag generation stage, the system matches the characteristic combinations corresponding to the dominant path and its dynamic characteristics (such as high amplitude fluctuations, transmission hubs, and frequent anomalies) with a preset behavior tag library to generate behavior tags. Tags include: "Power transfer behavior," corresponding to a dominant power supply path accompanied by significant changes in the transmission structure; "Control switching behavior," characterized by sudden changes in control path parameters and inconsistent timing of upstream and downstream responses; "Execution activation behavior," often triggered by short-term high fluctuations in execution-type paths; and "Cooperative interference behavior," representing situations where multiple paths experience abnormal fluctuations simultaneously but lack a unified response mechanism. During the behavior pattern modeling stage, the system models and abstracts the temporal behavior of the dominant path segment, constructing a behavior graph reflecting the internal state transition logic of the stage using state diagrams, behavior change sequences, or event trigger pairs. In this graph, nodes represent different behavioral states (such as "stable," "mutation," and "reconstruction"), and edges represent the switching relationships between states or the coupling triggering mechanisms between paths. This is used to demonstrate the flow patterns and structural coupling relationships of behavior within a stage. "Stable" refers to the path or system behavior parameters remaining within a normal fluctuation range over a period of time. "Mutation" refers to a significant jump or abnormal fluctuation in behavior parameters within a short period. "Reconstruction" refers to a structural adjustment in the system's internal control relationships, path-dominant structure, or response mechanism, such as a change in dominant path, a change in functional division of labor, or a reorganization of the transmission structure. The system outputs stage behavior data. Each data record includes a stage number, the identified behavior label, a list of dominant path numbers that triggered the behavior, and the evolution sequence of the behavior within the stage (such as "stable → mutation → reconstruction").

[0061] Step S33: Map the stage behavior data to functional judgment rules to obtain the bundle point functional data.

[0062] In one embodiment, the system invokes a preset function determination rule base. The rules use "behavior label + dominant path feature combination" as input conditions and output the corresponding branch point function type. Function types include: "signal isolation point," used to block different signal flows; "control reconfiguration point," representing a node for control logic reorganization; "functional boundary point," used to delineate the boundaries of system functional modules; and "current scheduling point," indicating the location of power path scheduling and load allocation. The system matches the dominant path and its associated branch points in the stage behavior data one by one, searching for rule items that match the behavior feature combination. If multiple rules match simultaneously, the system selects the rule with the highest weight based on preset rule priority and outputs it. The system outputs the function determination result. Each branch point data includes its unique identifier, the matched behavior type, and the determined function type. For example, if a branch point is identified as being in the "control switching behavior" path and determined as a "control reconfiguration point" according to the rule base, the system outputs its function as "control reconfiguration point."

[0063] Preferably, the construction of stage constraint parameters is specifically as follows: Step S41: Determine the stage dimension based on the bundle splitting point function data to obtain the stage dimension data; In one embodiment, the system calls a built-in function type and dimension mapping table to determine the corresponding dimension category for each bundle point based on its function type. For example, if the function of a bundle point is "signal isolation point," the system labels its corresponding control dimension as "control signal dimension"; if the function is "current dispatch point," it corresponds to "load current dimension"; if it is "functional boundary point," it is mapped to "component structure dimension" based on the component structure differences in the connection path of the bundle point; and for function types involving multiple changes, such as "control reconfiguration point," the system simultaneously labels its associated "control signal dimension" and "transmission logic dimension," forming a dual-dimensional identification. When there are multiple bundle points in a stage, the system merges their respective dimensions to form a stage dimension set. Duplicate dimensions are deduplicated, and for dimensions with hierarchical inclusion relationships (such as "subsystem control dimension" being a subset of "system control dimension"), the system prioritizes retaining the upper-level dimensions. The system outputs stage dimension data, including the stage number and its corresponding control / behavior dimension set.

[0064] Step S42: Determine the dimension range based on the stage dimension data to obtain the stage range data; In one embodiment, the system selects the corresponding parameter source rules based on the dimension type. If the dimension belongs to the electrical quantity category (such as load current, voltage, etc.), the system will refer to the stage statistics recorded in the historical operation data, such as average value, maximum fluctuation range, etc., to comprehensively determine the upper and lower limits of the parameter. If the dimension belongs to the logic control category (such as control signal switching path), the system reads the predefined state transition sequence or logic mode from the control strategy configuration file to determine the acceptable range of control path changes. For structural dimensions (such as component connection relationships), the system, based on the physical topology information of the interconnected components, marks the set of operable connection paths or the allowed pluggable node boundaries within the current stage. The system outputs the processing results, including the dimension name, constraint type (e.g., range type, logic type, or structural type), upper and lower limits of values ​​or allowed state combinations, a list of action paths, the stage time window in which the constraint takes effect, the minimum value, maximum value, and unit of measurement for each dimension. For example, for the "load current dimension," the system sets its effective range to 4.2A to 7.5A.

[0065] Step S43: Combine the stage dimension data and stage range data with stage parameters to obtain stage constraint parameter data.

[0066] In one embodiment, the system standardizes the structure of constraint parameter objects. Each constraint parameter object should include the following: the corresponding dimension name, constraint type (e.g., range-based, logical, or structural), upper and lower limits of values ​​or allowed state combinations, a list of action paths, and the time window in which the constraint takes effect. The system selects parameter combination rules based on dimension attributes. For example, if a dimension is a "control signal dimension" with a logical constraint type, the system will list the allowed control command path combinations for that stage using a state transition matrix; if it is an electrical dimension such as a "load current dimension," a range-based constraint type is used, specifying the allowed upper and lower current limits for load allocation and dynamic scheduling within that range; when multiple dimensions act on the same path segment simultaneously, the system will perform merging based on path overlap to prevent the same path from being covered by multiple mutually exclusive constraints in the same stage. The system outputs stage constraint parameter data. Each data object indicates the constraint number, dimension type, specific constraint content, action path, and its effective time interval. For example, a control logic constraint object can explicitly allow a state transition operation from "State_A" to "State_C" to be performed between paths P011 and P012 during phase T1 to T2.

[0067] Preferably, the stage conflict suppression specifically includes: Stage constraint conflict identification is performed based on stage constraint parameter data to obtain constraint conflict data; In one embodiment, the system pre-sets multiple conflict identification rules to compare and analyze constraint parameters one by one. Conflict types include: Multiple constraint conflicts on the same path: When the same path segment is covered by two or more constraint parameters simultaneously, and their constraint ranges conflict (e.g., one constraint has a current limit of 6.0A, and another has 5.0A), the system will determine it as a "range conflict," indicating a physical scheduling inconsistency problem. Multi-dimensional mutual exclusion conflicts: If constraints from different dimensions (e.g., control logic and load scheduling) propose mutually exclusive control operations on the same object (e.g., a branch point), for example, one constraint requires disconnection while another requires maintaining connection, it will be determined as an "operation conflict," indicating a conflict in functional execution logic. Time window conflicts: When a path or behavioral unit has overlapping constraint effective time windows in multiple stages, but the allowed states or operation contents are inconsistent, the system will determine it as a "timing conflict," used to indicate execution transition anomalies during stage switching. The system will output the identified conflict results in a structured manner. Each conflict entry includes a conflict number, conflict type, involved path segment number, relevant constraint parameter identifier, and conflict details.

[0068] Conflict type data is obtained by classifying the constraint conflict data into conflict types. In one embodiment, the system pre-defines a standardized conflict type tag library to systematically identify different conflict scenarios. Tags include: RANGE_CONFLICT / Interval Conflict: Indicates that the numerical ranges of constraint parameters overlap but their boundaries are inconsistent, leading to execution conflicts; LOGIC_CONFLICT / Logical Conflict: Indicates that different constraints issue logically mutually exclusive control commands for the same objective, such as one requiring closure and another requiring opening; SEQUENCE_CONFLICT / Sequence Conflict: Indicates that the effective time intervals of constraints overlap, but the operation order or content is incompatible; STRUCTURE_CONFLICT / Structural Conflict: Indicates that different constraints have inconsistent requirements in terms of physical topology, such as a node requiring both connection and disconnection; REDUNDANT_CONSTRAINT / Redundant Constraint: Indicates that the constraint content is completely identical but originates from different sources, resulting in redundancy, which can be merged. In the classification process, the system matches each piece of conflict data against the above rule library to determine its conflict type. If multiple conflict type determination conditions are met simultaneously, the system will uniquely classify and label it according to priority, with the priority order being: structural conflict > logical conflict > parameter range conflict. Each classification result is accompanied by a detailed explanation of the classification rationale and a system-recommended handling strategy. The system outputs the conflict type, with each record including the conflict number, the corresponding category label, and a suggested handling strategy.

[0069] Based on the conflict type data, the bundle points are redistributed to obtain the bundle point redistribution data. In one embodiment, the system formulates corresponding redistribution strategies based on different conflict types. For example, when the conflict is a range conflict, the system attempts to adjust the position of the bundle point on the path, such as moving it forward or backward by one path hop, to change the scope of the constraint and thus avoid the conflict area; if it is a logical conflict, the system changes the control affiliation of the bundle point, allowing it to be independently scheduled in a specific control dimension to avoid logical mutual exclusion; if it is a structural conflict, the system adjusts the path topology so that related path segments bypass the conflicting node or no longer share the same components, reducing physical coupling. The system evaluates all available redistribution candidate nodes or path segments, focusing on analyzing the integrity of their connection topology, compatibility with existing constraints, and whether they meet the requirements of load balancing and path continuity. If the system cannot find an alternative solution that meets the above conditions in the current structure, the conflict is marked as an "unsolvable conflict," retaining the possibility of subsequent manual intervention or solution optimization. The system outputs the redistribution results. Each redistribution record includes the original bundle point number, the adjusted position number, a Boolean flag indicating whether the conflict was successfully resolved, and a system-generated explanation of the adjustment reason.

[0070] Based on the reassignment data of the splitting points, conflict suppression selection is performed to obtain the optimized splitting data of the interconnecting components.

[0071] In one embodiment, based on the results of the reassignment of bundle points and combined with the previous conflict type analysis, the system selects an appropriate suppression strategy for each conflict instance to output the optimized bundle configuration of interconnect components. In specific implementations, the system has built-in multiple conflict suppression strategies for selection, including but not limited to adjusting the position of bundle points to avoid conflict areas (marked as "ADJUST_SPLIT"), merging multiple constraints with duplicate content or consistent goals into one (marked as "MERGE_CONSTRAINT"), disabling constraints with less impact on the system or lower priority (marked as "DISABLE_CONSTRAINT"), or reconstructing part of the path topology to achieve physical conflict avoidance (marked as "RESTRUCTURE_PATH"). Before execution, the system prioritizes strategies that do not affect existing control logic to ensure that the main functional links are not disrupted. If multiple feasible suppression schemes exist for a certain conflict, the system will sort and filter them according to the following selection principles: control logic integrity first, system energy distribution balance second, and lowest implementation cost third, and record the adopted strategy. For each processed bundle point, the system outputs its optimization status, the specific strategy adopted, and whether the conflict has been completely resolved.

[0072] Preferably, this application also provides a spacecraft electrical system interconnection component beam splitting optimization system for performing the spacecraft electrical system interconnection component beam splitting optimization method as described above. The spacecraft electrical system interconnection component beam splitting optimization system includes: The stage operating condition extraction module is used to acquire interconnected component data; and to extract stage operating conditions based on the interconnected component data to obtain stage operating condition data. The stage feature extraction module is used to extract stage bundle points based on stage operating condition data to obtain stage bundle point data; and to extract stage features based on stage bundle point data to obtain stage feature data. The bundle splitting point function judgment module is used to judge the bundle splitting point function of the stage feature data and obtain the bundle splitting point function data. The interconnect component bundle optimization module is used to construct stage constraint parameters based on the bundle point function data to obtain stage constraint parameter data; and to suppress stage conflicts based on the stage constraint parameter data to obtain interconnect component bundle optimization data.

Claims

1. A method for optimizing beam splitting of interconnection components in a spacecraft electrical system, characterized in that, Includes the following steps: Step S1: Obtain interconnect component data; extract stage operating conditions based on interconnect component data to obtain stage operating condition data; Step S2: Perform continuous path unitization based on interconnected component data to obtain continuous path data; map stage working condition data to continuous path data to obtain path mapping data; identify discontinuous positions based on path mapping data to obtain initial bundle data; Based on the initial bundle splitting data, the transmission characteristics are determined to obtain the stage bundle splitting point data; based on the stage bundle splitting point data, stage features are extracted to obtain the stage feature data. Step S3: Identify the dominant features of the stage feature data to obtain the dominant feature data; Stage behavior data is obtained by constructing stage behavior data based on dominant feature data; functional judgment rules are then mapped onto the stage behavior data to obtain bundle point functional data. Step S4: Construct stage constraint parameters based on the bundle point function data to obtain stage constraint parameter data; identify stage constraint conflicts based on the stage constraint parameter data to obtain constraint conflict data; classify conflict types based on the constraint conflict data to obtain conflict type data; redistribute bundle points based on the conflict type data to obtain bundle point redistribution data; select conflict suppression based on the bundle point redistribution data to obtain interconnect component bundle optimization data.

2. The method according to claim 1, characterized in that, Step S1 is as follows: Obtain data from interconnected components; Based on the interconnection component data, the phase operation source is extracted to obtain the phase operation source data, which includes launch operation source data and orbital step operation source data. Stage feature data is obtained by extracting stage features from the source data of stage operations using interconnected components. Stage difference features are extracted from stage feature data to obtain stage difference feature data; Based on the stage characteristic data and stage difference characteristic data, the stage operation source data is abstracted to obtain stage operation condition data.

3. The method according to claim 2, characterized in that, The abstract working condition is specifically defined as follows: The task source data for each stage is correlated with the stage characteristic data and the stage difference characteristic data to obtain task source correlation data; Stage morphology identification is performed based on the associated data of the task source to obtain stage morphology data; Differential aggregation is performed on the stage morphological data to obtain differential feature data; Stage semantics are generated from the differential feature data to obtain stage semantic data; Based on the stage semantic data and the difference feature data, the operation source association data is encapsulated to obtain the stage working condition data.

4. The method according to claim 1, characterized in that, Discontinuous position identification specifically involves: Dominant type identification is performed based on path mapping data to obtain dominant type data; Adjacent path comparisons are performed based on dominant type data to obtain adjacent comparison data; Phase misalignment positions are identified in adjacent comparison data to obtain initial beam splitting data.

5. The method according to claim 1, characterized in that, The specific steps of feature extraction are as follows: The neighborhood path set is obtained by using the stage bundle point data; Perform stage alignment on the neighborhood path data to obtain stage-aligned data; Stage transfer feature data is obtained by extracting stage transfer features from the stage alignment data. Stage change feature data is obtained by extracting stage change features from neighborhood path data. The stage-transmission characteristic data and the stage-change characteristic data are integrated to obtain stage characteristic data.

6. The method according to claim 1, characterized in that, The specific construction of stage constraint parameters is as follows: The stage dimension data is obtained by determining the stage dimension data based on the bundle point function data. The dimensional range is determined based on the stage dimension data to obtain the stage range data. The stage dimension data and stage range data are combined with stage parameters to obtain stage constraint parameter data.

7. A beam splitting optimization system for interconnection components of a spacecraft electrical system, characterized in that, For executing the spacecraft electrical system interconnect component beam splitting optimization method as described in claim 1, the spacecraft electrical system interconnect component beam splitting optimization system comprises: The stage operating condition extraction module is used to acquire interconnected component data; and to extract stage operating conditions based on the interconnected component data to obtain stage operating condition data. The stage feature extraction module is used to extract stage bundle points based on stage operating condition data to obtain stage bundle point data; and to extract stage features based on stage bundle point data to obtain stage feature data. The bundle splitting point function judgment module is used to judge the bundle splitting point function of the stage feature data and obtain the bundle splitting point function data. The interconnect component bundle optimization module is used to construct stage constraint parameters based on the bundle point function data to obtain stage constraint parameter data; and to suppress stage conflicts based on the stage constraint parameter data to obtain interconnect component bundle optimization data.

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