A spacecraft electrical system interconnect assembly path planning optimization method and system

Abstract

The present application relates to the technical field of electrical system design, and particularly relates to a spacecraft electrical system interconnection component path planning optimization method and system. The method comprises the following steps: obtaining interconnection component data; constructing adjacency relationship according to the interconnection component data to obtain component adjacency data; selecting a beam splitting point according to the component adjacency data to obtain beam splitting point data; estimating point port cost of the beam splitting point data to obtain point port cost data; generating a control linkage graph according to the component adjacency data to obtain control linkage graph data; generating an interpretation field according to the control linkage graph data to obtain interpretation field data; determining the splitting benefit of the point port cost data and the interpretation field data to obtain splitting benefit data; and evaluating the hard constraint of the splitting benefit data to obtain beam splitting point feasibility evaluation data. The present application realizes the constraint of the beam splitting point position and the control semantics, effectively avoids the cross-stage control reversal and redundancy failure problems.

Description

Technical Field

[0001] This invention relates to the field of electrical system design technology, and in particular to a method and system for optimizing path planning of interconnection components in a spacecraft electrical system. Background Technology

[0002] In spacecraft electrical systems, interconnect components undertake functions such as signal transmission, power distribution, and control logic connectivity. Their path planning and bundler structure design directly affect the system's reliability, maintainability, and mission adaptability. In current engineering practice, interconnect component path planning is primarily based on physical space constraints and geometric deployability. Bundle point locations are often determined by design experience or structural convenience, with the design process mainly focusing on path connectivity and wiring feasibility. However, with the continuous increase in spacecraft system complexity, electrical systems exhibit distinct multi-mission phase operation characteristics, with differences in control logic, signal roles, and redundancy strategies across different mission phases. Against this backdrop, traditional path planning methods based solely on geometric topology or static connections fail to reflect the true role of bundlers in the control link, easily leading to problems such as inconsistent control roles of bundlers at different phases, confusion in primary and backup path logic, and reduced redundancy isolation effectiveness. Summary of the Invention

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

[0004] This application provides a method for optimizing the path planning of interconnection components in a spacecraft electrical system, including the following steps:

[0005] Step S1: Obtain interconnected component data; construct adjacency relationships based on the interconnected component data to obtain component adjacency data;

[0006] Step S2: Select bundle points based on component adjacency data to obtain bundle point data; estimate point port cost based on bundle point data to obtain point port cost data;

[0007] Step S3: Generate a control general meaning linkage diagram based on the component adjacency data to obtain control linkage diagram data; generate interpretation fields from the control linkage diagram data to obtain interpretation field data;

[0008] Step S4: Determine the segmentation benefit of the point port cost data and the interpretation field data to obtain the segmentation benefit data; perform hard constraint evaluation on the segmentation benefit data to obtain the feasibility evaluation data of the split points.

[0009] This invention achieves an optimization transformation of the path structure from traditional geometric wiring to a control semantic-structure collaborative approach. By simultaneously introducing control dependencies and signal linkage semantics during the selection of branch points, the branching locations not only meet the requirements of physical connectivity and deployment feasibility but also maintain semantic consistency with the task control chain, effectively avoiding path role reversal and control conflicts during later task switching or redundant activation. Through a point port cost estimation and splitting benefit comprehensive evaluation mechanism, the cost changes, stage aggregation, and redundancy overlap after path splitting are quantitatively analyzed, reducing the spatial overlap rate of primary and backup paths and the risk of signal interference. Combined with a hard constraint filtering strategy, it ensures that the optimized branching points meet the engineering implementation requirements in terms of structural installation, electromagnetic compatibility, and control stability.

[0010] Preferably, step S1 specifically includes:

[0011] Obtain data from interconnected components;

[0012] Adjacency relationships are extracted from the interconnected component data to obtain adjacency relationship data;

[0013] Control relationships are constructed from the adjacency data to obtain control relationship data;

[0014] Graphs are constructed based on control relationship data to obtain component adjacency data.

[0015] This invention effectively enhances the expressive power of component adjacency relationships at the control level by introducing a modeling approach that abstracts from physical connections to control relationships, building upon the acquisition of interconnected component data. Compared to traditional methods that construct adjacency relationships based solely on geometric connections or port connectivity, this step constructs control relationship data after adjacency relationship extraction. This ensures that adjacency between components no longer merely indicates whether they are connected, but explicitly reflects their actual collaborative relationships in control links, signal driving, and state feedback. By constructing a graph structure based on control relationships, the resulting component adjacency data can simultaneously represent the dual characteristics of physical topology and control dependency.

[0016] Preferably, the selection of the beam splitting point is as follows:

[0017] Physical breakpoints are identified based on the number of adjacencies of components to obtain physical breakpoint data;

[0018] The signal flow graph is constructed based on the component adjacency data to obtain the signal flow graph data;

[0019] Dependency analysis is performed on the signal flow graph data to obtain dependency data;

[0020] The beam splitting points are selected based on the physical beam splitting point data and the dependency data to obtain the beam splitting point data.

[0021] This invention transforms the traditional method of determining bundle splitting locations based solely on structure or experience into a systematic selection driven by both component topology relationships and signal flow behavior. By identifying physical bundle splitting points from component adjacency data, candidate locations with practical deployment conditions can be screened within the global topology, avoiding the introduction of bundle splitting designs in structurally infeasible areas. By constructing a signal flow graph and analyzing its dependencies, the flow and dependencies of control signals, state feedback, and redundant links in the system are explicitly expressed, making bundle splitting point selection no longer limited to geometric connectivity but matching the actual signal transmission paths. By jointly determining physical bundle splitting point data and signal dependencies, unreasonable bundle splitting can be effectively avoided at critical nodes of control links or highly coupled locations, thereby reducing the impact of path splitting on the continuity of control logic and system stability.

[0022] Preferably, the selection of the beam splitting point is as follows:

[0023] Stage control data is obtained by dividing the control process into stages based on dependency data.

[0024] Phase control roles are mapped based on physical bundle splitting point data and phase control data to obtain phase control role data.

[0025] Stability assessment is performed based on the stage control role data to obtain role stability data;

[0026] Based on the character stability data, cross-stage character convergence processing is performed on the physical bundle point data to obtain the bundle point data.

[0027] This invention, based on the screening of physically deployable locations, elevates the determination process of bundle points from a single structural constraint judgment to a decision-making process oriented towards control behavior consistency through control semantic analysis under multi-task stages. By dividing dependency data into stage control segments, the activation range and action boundary of control links in different task stages can be represented, avoiding the misuse of control nodes that are only effective in local stages as long-term bundle points. By mapping stage control data to physical bundle point data for control roles, the control identity of candidate bundle points in each stage can be explicitly expressed. By performing stability evaluation of stage control roles and executing cross-stage role convergence processing, candidate points with control role reversals or semantic conflicts in different stages can be effectively identified, ensuring that selected bundle points maintain control semantic consistency and behavioral stability under multi-stage operation conditions.

[0028] Preferably, the point port cost estimation is as follows:

[0029] Geometric cost and electrical cost are estimated from the bundle splitting point data to obtain geometric cost data and electrical cost data, respectively.

[0030] Based on geometric cost data and electrical cost data, the task cost is estimated to obtain the task cost data;

[0031] Real-time task switching analysis is performed on the task cost data to obtain real-time task cost data;

[0032] Cost optimization is performed on the real-time task cost data to obtain point port cost data.

[0033] This invention extends the cost assessment of beam splitters from traditional static structural measurements to a dynamic, comprehensive analysis oriented towards the task operation process. By separately estimating geometric and electrical costs, the engineering impact of beam splitters on spatial layout complexity, cable routing rationality, signal quality, and electromagnetic compatibility can be quantified, avoiding judgments based solely on experience or a single indicator. Based on geometric and electrical costs, task cost estimation is performed, linking beam splitters to specific task stages, control loads, and signal activity levels, ensuring the cost assessment accurately reflects the actual load-bearing capacity of beam splitters during system operation. Real-time task switching analysis of task cost data identifies potential risks of sudden cost mutations or amplifications during task switching, allowing for the early avoidance of unfavorable beam splitter locations. Through cost optimization, the system obtains point-port cost data that balances structural rationality, electrical reliability, and task adaptability.

[0034] Preferably, the generation of the general meaning linkage diagram is specifically controlled as follows:

[0035] Control function data is obtained by mapping control functions based on component adjacency data;

[0036] Based on the control function data, the action sequence dependency relationship is extracted to obtain the dependency relationship data;

[0037] Map arbitrary stage trigger pairs to dependency data to obtain stage trigger pair data.

[0038] The control general diagram is generated from the stage-triggered data to obtain the control linkage diagram data.

[0039] This invention performs semantic modeling of control functions and behavioral dependencies based on traditional component adjacency relationships, upgrading the structural expression of the electrical system from a static connection description to a dynamic, linkage-oriented expression focused on control behavior. By mapping component adjacency data to control functions, the control responsibilities and functional boundaries of each interconnected component in the system can be clearly defined, avoiding semantic biases caused by inferring control relationships solely based on physical connections. The system extracts action sequence dependencies, explicitly modeling the sequential dependencies between control commands, state feedback, and executed actions, making the evolution of the control chain traceable. The system performs arbitrary stage trigger pair mapping, enabling control relationships to cover trigger conditions and response paths under multi-task stages, solving the problem that traditional control modeling struggles to reflect stage switching behavior. The generated generalized control linkage diagram can represent the control linkage strength and stage adaptability characteristics between components, improving the engineering reliability of the overall system control structure.

[0040] Preferably, the explanation field generation is specifically as follows:

[0041] Control nodes are extracted from the control linkage diagram data to obtain control node data;

[0042] Control path data is obtained by extracting control paths from control linkage diagram data based on control node data;

[0043] The control path data is mapped to the preset task stage data to obtain the control path stage data;

[0044] The control chain interpretation field is generated based on the control path stage data, resulting in interpretation field data.

[0045] This invention transforms the complex control relationships implicit in control linkage diagrams into structured and understandable semantic descriptions, effectively improving the interpretability and maintainability of control paths in engineering applications. By extracting control nodes from the control linkage diagram data, the key nodes actually involved in control decisions and execution within the system can be clearly identified, avoiding interference from non-critical connections in the analysis. Through control path extraction, the control linkage relationships scattered throughout the graph structure are reorganized into complete control chains, making the logical relationships between control commands, status feedback, and execution actions clearer. The system maps control paths to preset task stages, representing the activation status and scope of different control chains in each operational stage, avoiding ambiguity in control semantics during stage switching. The control chain interpretation fields generated by the system provide intuitive and traceable semantic basis for bundle selection, path splitting benefit evaluation, and abnormal control behavior analysis, reducing reliance on manual experience during system debugging and maintenance.

[0046] Preferably, the profit determination is as follows:

[0047] The path segmentation cost and task phase aggregation are calculated on the point port cost data and the interpretation field data to obtain path segmentation cost data and phase aggregation data, respectively.

[0048] Based on the path segmentation cost data and the stage aggregation data, a path reconstruction simulation of the segmentation point is performed to obtain the path data of the segmentation point.

[0049] Redundancy overlap profile analysis is performed based on the path data of the split points to obtain the splitting revenue data.

[0050] This invention goes beyond traditional path optimization, which focuses solely on the cost of a single path. It incorporates a benefit assessment based on task semantics and redundancy structure, giving path splitting decisions a stronger engineering focus and a holistic system perspective. By calculating path splitting costs and aggregating task stages using point port cost data and explanatory field data, it can simultaneously quantify changes in structural complexity, electrical load, and task stage adaptability before and after path splitting, avoiding cross-stage control chaos caused by local cost reductions. Through path reconstruction simulation at splitting points, the impact of different splitting schemes on path structure and control behavior can be evaluated without affecting actual system operation, thereby identifying potential unfavorable splitting locations in advance. Redundancy overlap profile analysis of splitting point path data effectively assesses the spatial and functional overlap between primary and backup paths, reducing the risk of redundancy failure and interference.

[0051] Preferably, the hard constraint evaluation specifically includes:

[0052] Hard constraint determination is performed on the segmented revenue data to obtain hard constraint determination data, which includes spatial legality determination, electric and steam rationality determination, and control logic verification.

[0053] Based on the hard constraint determination data, compliance point assessment is performed to obtain the feasibility assessment data for the bundle point.

[0054] This invention, based on the evaluation results of beam splitting benefits, performs strong constraint judgments oriented towards engineering implementation conditions to ensure that the optimized beam splitting point results not only have theoretical benefits but also possess feasibility and long-term stability in the actual spacecraft system environment. By performing spatial legality judgment on the beam splitting benefit data, beam splitting points can be effectively prevented from falling into structural interference zones, wiring restricted areas, or uninstallable areas, improving the structural feasibility of the wiring scheme. Through electrical rationality judgment, beam splitting point configuration can be constrained in terms of signal integrity, electromagnetic compatibility, and grounding consistency, reducing the risk of crosstalk, interference, or signal degradation introduced by path splitting. Through control logic verification, beam splitting operations can prevent the integrity of the original control chain from being damaged or control anomalies such as main / backup path logic reversal from being triggered. Only candidate points that simultaneously meet the above hard constraints are retained, ensuring that the obtained optimized beam splitting point data has a high degree of consistency in terms of structure, safety, and control semantics.

[0055] Preferably, this application also provides a spacecraft electrical system interconnection component path planning optimization system for executing the spacecraft electrical system interconnection component path planning optimization method described above. The spacecraft electrical system interconnection component path planning optimization system includes:

[0056] The adjacency relationship construction module is used to obtain interconnected component data; and to construct adjacency relationships based on the interconnected component data to obtain component adjacency data.

[0057] The point port cost estimation module is used to select bundle points based on component adjacency data to obtain bundle point data; and to estimate the point port cost of the bundle point data to obtain point port cost data.

[0058] The interpretation field generation module is used to generate a control generality linkage diagram based on component adjacency data to obtain control linkage diagram data; and to generate interpretation fields from the control linkage diagram data to obtain interpretation field data.

[0059] The bundle splitting optimization module is used to determine the splitting benefit from the point port cost data and the interpretation field data to obtain the splitting benefit data; and to perform hard constraint evaluation on the splitting benefit data to obtain the bundle splitting feasibility evaluation data.

[0060] The beneficial effects of this invention are as follows: By constructing adjacency relationships for interconnected component data, component adjacency data with structural integrity is formed based on traditional physical connectivity relationships. A point port cost estimation mechanism is introduced during the selection of splitting points, incorporating geometric layout complexity, electrical characteristics, and task load factors into the evaluation model, effectively avoiding the hidden dangers of determining splitting positions solely based on experience or a single indicator. By constructing a control semantic linkage diagram and generating corresponding interpretation fields, the implicit control dependencies, action sequence linkages, and multi-stage triggering behaviors between components are explicitly expressed, ensuring that the path structure remains consistent with the control logic. Through splitting benefit determination, the cost changes before and after path splitting, task stage aggregation, and redundant path overlap are evaluated, and combined with hard constraint filtering such as space, electrical, and control logic, ensuring that the final optimized splitting point data is feasible at the engineering implementation level. This invention realizes the transformation of path planning from simple structural optimization to collaborative optimization of "structure-control semantics-task stages," improving the interpretability, reliability, and long-term operational adaptability of spacecraft electrical system path design results. Attached Figure Description

[0061] 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:

[0062] Figure 1 A flowchart illustrating the steps of a method for optimizing path planning of interconnection components in a spacecraft electrical system, according to one embodiment, is shown.

[0063] Figure 2 A flowchart illustrating the steps of an adjacency relationship construction method according to an embodiment is shown.

[0064] Figure 3 A flowchart illustrating the steps of a beam splitting point selection method according to an embodiment is shown;

[0065] Figure 4 A flowchart illustrating the steps of a method for generating a control generality linkage diagram according to an embodiment is shown.

[0066] Figure 5 A flowchart illustrating the steps of a method for determining the profit splitting in one embodiment is shown. Detailed Implementation

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Please see Figures 1 to 5 This application provides a method for optimizing the path planning of interconnection components in a spacecraft electrical system, comprising the following steps:

[0071] Step S1: Obtain interconnected component data; construct adjacency relationships based on the interconnected component data to obtain component adjacency data;

[0072] In one embodiment, the system collects and uniformly encodes data related to interconnected components. The interconnected component data includes at least component identification information, port identification information, port type information, connector specification information, cable segment number, cable segment length, cabling channel or compartment identification, shielding method, twisting method, grounding method, and redundancy group identification. The system manages the identification of components and ports using a unified preset encoding rule, which includes component instance, port attributes, redundant link affiliation, and task stage label. After completing data collection and encoding, the system extracts adjacency information between components based on physical connectivity. For example, the system uses the actual connection path formed by ports, cable segments, and connectors as the basic analysis unit. When two ports are physically connected through the same cable segment or connector, a physical connectivity relationship is established between them. The system aggregates port-level physical connectivity relationships to the component level. As long as there is at least one port-level connection path between two different components, a corresponding adjacency relationship is established between the components, and the physical attribute characteristics corresponding to this adjacency relationship are recorded synchronously, such as cabling length, number of cable segments, and cross-compartment or cross-channel situations. Based on the adjacency relationships, the system constructs controllable adjacency relationships. The system performs control reachability determination on each physical connection path, filtering or marking connections that do not meet control usage conditions based on port type matching, shielding and grounding continuity, and redundant link configuration requirements. For connections that meet control function mapping consistency, redundant link continuity requirements, or availability within the same task phase, the system retains them as control adjacency candidates, thus forming a component adjacency graph. The system outputs the component adjacency graph as component adjacency data, where nodes correspond to interconnected components, and edges represent control adjacency relationships between components. Each adjacency edge is associated with and records its corresponding trace length characteristics, cable jump count characteristics, sensitive area crossing information (sensitive areas include high-heat areas (such as near propulsion devices), strong vibration areas (such as around engine components), strong electromagnetic interference areas (such as near high-power equipment), shielding and grounding status, and redundant link affiliation information).

[0073] Step S2: Select bundle points based on component adjacency data to obtain bundle point data; estimate point port cost based on bundle point data to obtain point port cost data;

[0074] In one embodiment, the system generates a candidate set of branching points in the component adjacency graph. The system analyzes the connection relationships between components to identify node locations with multi-branch convergence or distribution characteristics, and selects locations with a large number of component connections as physical branching point candidates. The system combines cabling channel, compartment, or cable tray information to identify nodes located at the boundaries of different channels or compartments as potential branching locations. For locations in redundant links where the primary and backup links begin to separate or require forced separation, the system also includes them in the candidate branching point range. Based on the foregoing, after generating candidate branching points, the system performs port deployability checks on each candidate point. The system determines whether the location of the candidate point allows for the installation of branch connectors or binding structures based on preset space exclusion zone information; for locations that do not meet the installation conditions, they are directly marked in the candidate set. Simultaneously, the system filters candidate points with insufficient operational clearance based on the spatial distance between the candidate point and the nearest fixed support point, thereby obtaining initial branching point data.

[0075] The system estimates the geometric cost of each initial bundle splitting point. It analyzes the actual routing paths from the bundle splitting point to each target port, assessing the complexity of the path based on routing length, number of cable segments, and channel transitions. For paths with multiple sharp turns, bends, or abrupt changes in direction, the system introduces corresponding bending penalty factors. For paths crossing hot, vibration-sensitive, or electromagnetically sensitive areas, the system adds corresponding penalty costs based on the area attributes, thus forming a geometric cost assessment result representing the spatial wiring complexity. The system also estimates the electrical cost of the bundle splitting points in conjunction with signal type. For different types of signal links, the system uses electrical constraints for evaluation. For example, for high-speed signals, if the path length exceeds the high-speed limit threshold (e.g., 0.8 meters), it is marked as exceeding the length limit; when the spacing between high-speed signals and other parallel routing paths is less than a set threshold (e.g., 5 centimeters), it is marked as coupling risk; if any segment of the path has an interruption or missing shielding layer, it is marked as shielding discontinuity. For analog signals, the system focuses on determining whether they cross noise source areas; if they cross high-voltage areas, it is marked as high-noise interference risk; if the grounding method changes or is inconsistent in the path, it will also be marked as grounding inconsistency. For power signals, the system assesses their voltage drop and current carrying capacity. If the predicted voltage drop exceeds the allowable range (e.g., >0.5V) or the current carrying capacity is insufficient for the design load, it is marked as voltage drop mismatch and insufficient current margin, respectively. When the link corresponding to the split point triggers unacceptable electrical conditions, the system directly marks the split point as unusable, thus preventing it from entering the subsequent optimization process.

[0076] The system combines mission phase information (spacecraft mission lifecycle segmentation data preset during early modeling or configuration, derived from mission scheduling plans, flight control mode configuration files, or system-level mission execution flowcharts, such as common mission phases including launch, on-orbit deployment, attitude adjustment, scientific payload operation, and emergency response) to analyze the mission cost and real-time switching characteristics of beam splitters. For different mission phases, the system analyzes the set of signals active within that phase and evaluates the coverage of these signals as they pass through beam splitters and their importance in critical control links. Beam splitters located in control closed loops or redundant switching paths are assigned higher mission cost weights. The system compares cost changes between adjacent mission phases, marking beam splitters as mission switching sensitive points when they exhibit significant cost jumps during phase switching. The system processes geometric costs, electrical costs, and mission-related costs to generate point-port cost results for each beam splitter. For bundle points that have been determined to be unacceptable, the system directly excludes them; for the remaining bundle points, the system performs unified dimensional processing on various costs and outputs point port cost data that includes bundle point identifiers, cost values, and characteristics of each cost item.

[0077] Step S3: Generate a control general meaning linkage diagram based on the component adjacency data to obtain control linkage diagram data; generate interpretation fields from the control linkage diagram data to obtain interpretation field data;

[0078] In one embodiment, the system performs function mapping on each control path based on the port type, signal name or code, and subsystem information of the component's adjacent edge / component adjacent data, thereby identifying the control function identifier corresponding to each connection relationship. The system matches the data using a preset function dictionary or control configuration table to determine whether they belong to the same control function, such as attitude control closed loop, propulsion system control, thermal control task scheduling, or data transmission clock synchronization. If there is ambiguity regarding one-to-many function attribution, the system disambiguates the data by combining upstream and downstream device types and port direction information. The system generates control function data to distinguish the attribution of control functions.

[0079] Based on the completed function mapping, the system extracts the sequential dependencies in control actions. These control actions include structured control execution units extracted during task scheduling or function mapping. Each control action includes an action identifier, triggering condition, target component, execution path, and corresponding task stage identifier. Relying on the port attributes of different roles such as controllers, actuators, and sensors, the system identifies the issuing unit, execution response unit, and status feedback unit of the control command based on their directionality and device type, and establishes directed dependencies between command, execution, and feedback. For example, the controller's output is considered the command source, the actuator's input is considered the action target, and the sensor's output is considered the status feedback source. Through the identification of this chain-like relationship, the system establishes a control dependency edge set. The system adds triggering condition information to the control dependency edge set based on task stage data. By analyzing the task stage configuration table or system mode table, the system labels the corresponding active stage set for each dependency, and identifies its corresponding triggering conditions and response paths. For example, stage switching events (such as entering on-orbit mode) and conditional events (such as attitude deviation exceeding a threshold or main link failure) can all serve as triggering criteria for control linkage. By establishing a mapping relationship between triggering conditions and control response paths, stage trigger pair data is formed. Based on the aforementioned control dependency edge set and stage trigger pair data, the system constructs a control linkage graph. This graph uses controllers, actuators, sensors, redundancy switching units, and key relay nodes as graph structure nodes, and dependency edges as graph structure edges, with attributes such as stage activation information, control function identifiers, and triggering conditions appended to the edges. The system sets weight parameters for edges based on factors such as task activation frequency, criticality, and redundancy interference: paths that are activated multiple times simultaneously in the same stage have higher weights; paths located in critical control loops receive weighted boosts; if there is a common source trigger in the primary and backup links and it causes redundant interference, the system marks the path with an interference label. The system generates interpretation fields based on the control linkage graph. The system identifies control nodes in the graph that have the status of a control center or remain active in multiple task stages; it enumerates control paths from the command source to the execution end or feedback end, prioritizing high-weight paths while limiting the maximum path length to avoid path explosion. For each control path, the system generates its activation coverage field in the task stages, including information such as the set of path activation stages, stage entry trigger point, and task switching sensitivity. The system output includes control chain role sequence, control function summary description, explanation fields such as whether master / slave switching and common source interference are involved.

[0080] Step S4: Determine the segmentation benefit of the point port cost data and the interpretation field data to obtain the segmentation benefit data; perform hard constraint evaluation on the segmentation benefit data to obtain the feasibility evaluation data of the split points.

[0081] In one embodiment, the system performs path segmentation simulation analysis for each candidate bundle splitting point / bundling point data. Using the bundle splitting point as the path boundary, the system structurally splits the original connected path into several sub-path segments, and statistically analyzes the cost performance of each sub-path after segmentation to obtain cost decomposition information. By comparing the overall path cost changes before and after segmentation, the system evaluates the impact of the bundle splitting operation on overall cabling complexity and resource consumption. The system combines task stage information to analyze the stage coverage of each sub-path after segmentation, obtaining stage aggregation characteristics. When the task stages carried by each sub-path after segmentation are more concentrated and the cross-stage coupling is reduced, the system determines that it has stage aggregation benefits; conversely, if the mixing degree of links between different task stages increases, a corresponding penalty mark is applied to the bundle splitting point.

[0082] After completing the aforementioned steps, the system performs a path reconstruction simulation at the split points. The system generates corresponding path structure data around each candidate split point, including the main path segment and each branch path segment formed after splitting. It also records the channel sequence and key device node sequence traversed by each path segment. The main path segment includes the main path portion that maintains continuity with the original path direction and function after splitting at the split point, carrying the mainstream data flow of the original control commands. Branch path segments include branch paths generated by the splitting operation that deviate from the mainstream direction of the original path in structure or function. Simultaneously, the system binds the generated interpretation fields to the corresponding path segments, giving each path a control function semantic identifier, such as the type of control loop it belongs to or the task function it belongs to, thus forming path description data.

[0083] The system performs overlap profile analysis on redundant links to obtain redundancy overlap characteristics. The system aligns and compares the spatial paths of the primary and backup links after the split. When two links are in the same channel, the same compartment, and the spatial distance is within a preset threshold range, they are considered to have path overlap. The redundant link is a backup path, logically or physically configured in parallel with the primary control path, used for automatic switching in case of primary path failure. The primary link is the path that undertakes the main control or signal transmission functions in the redundant system and is the default priority signal channel. The backup link is a standby path for redundancy fault tolerance, which does not participate in control transmission under normal circumstances and is only activated when the primary link fails or degrades, to implement the automatic primary / backup switching mechanism. By statistically analyzing the proportion of overlapping paths in the primary link, the system evaluates the impact of the split operation on the degree of redundancy isolation. When the split reduces the spatial overlap between the primary and backup links, it is determined to have redundancy benefits; if the overlap does not improve or even increases, it is marked as a redundancy risk. The system obtains split benefit data, including cost decomposition information, stage aggregation characteristics, and redundancy overlap characteristics.

[0084] After the above benefit analysis is completed, the system performs hard constraint judgment processing on each candidate split point to obtain constraint verification marks. The hard constraint evaluation includes at least three types: first, spatial legality constraint evaluation, used to determine whether the split point is located in an installable area and whether it meets the minimum bending radius and maintenance operation clearance requirements; second, electrical rationality constraint evaluation, used to verify whether the split will cause shield continuity disruption, grounding conflict, or unacceptable crosstalk risks; and third, control logic constraint evaluation, used to analyze whether the split operation will disrupt the original control closed-loop state maintenance relationship, and whether it will introduce problems such as main / backup link logic inversion or common source triggering interference. When any hard constraint is not met, the system will determine that the split point is unusable and record the corresponding reason information. The system performs compliance evaluation and retention processing on the split points that pass the hard constraint verification, and sorts and filters them according to the split benefit performance. The system can select the optimal set of split points according to a preset number range, or extract the set of split points according to the benefit threshold, and output the feasibility evaluation data of the split points, including the split point identifier, benefit split data, and constraint verification marks.

[0085] Preferably, step S1 specifically includes:

[0086] Step S11: Obtain interconnect component data;

[0087] In one embodiment, the system extracts interconnect component data from various spacecraft electrical system design documents, including electrical system design databases, cable lists, interface control tables, and 3D wiring models. The system standardizes and organizes the extracted interconnect component information. The interconnect component data includes at least the component's unique identifier, the port number and direction attribute (including input, output, or bidirectional), and the signal type information carried by each port, such as control signals, power signals, analog signals, digital signals, or high-speed communication signals. The system records the number of each cable segment and its connection relationship between ports, and indicates the redundancy link attribute (such as the specific number of the main link, backup link, or redundant channel) for each port or cable, as well as the functional system or subsystem identifier to which the component belongs. The system performs unified encoding processing on the component and port information. The encoding is constructed using a combination of component number, port number, and redundancy attribute to ensure that each port entity has a unique index identifier in the system.

[0088] Step S12: Extract adjacency relationships based on the interconnected component data to obtain adjacency relationship data;

[0089] In one embodiment, the system determines the physical connectivity between components based on port-level connectivity. When a port of one component is directly connected to a port of another component via the same cable segment or connector, a port-level adjacency relationship is determined to exist between the two ports. This determination rule covers all types of physical interconnection paths, regardless of signal type or connection form; as long as the ports have direct connectivity, they are included in the adjacency category. During adjacency relationship generation, the system analyzes the port information connected to the starting and ending ends of each cable segment, generating adjacency relationship records in a port-cable segment-port structure, forming a port-level adjacency set. Based on the generated port-level adjacency relationships, the system performs component-level adjacency mapping. When there is at least one port-level connectivity path between two different components, the system establishes a corresponding component adjacency relationship at the component level. The system records all port pairs associated with the component adjacency relationship as its port set, counts the number of connecting cable segments, and extracts redundant attribute information involved in the connection relationship (such as whether they belong to the same main chain, backup chain, or redundant group). The system outputs adjacency relationship data, which includes information such as each pair of components with physical connections, the corresponding set of port connections, the number of cable segments that the connection depends on, and whether they belong to the same redundancy group.

[0090] Step S13: Construct control relationships from the adjacency relationship data to obtain control relationship data;

[0091] In one embodiment, the system performs validity screening on the original adjacency relationship data to ensure that only connections with control significance are retained, including but not limited to the following situations: First, the signal type carried by the adjacent port is a control signal or a state feedback signal; second, the two ports belong to the same functional system, or a control or state dependency relationship has been clearly defined in the design configuration; third, the connection relationship is active in at least one task execution phase, that is, it actually participates in the implementation of the control function in the task flow. Only when any of the above conditions are met will the system retain the adjacency relationship as a control relationship and process it further. The system determines the direction of the control relationship based on the port's direction attribute and device type information. The output port of the controller and the input port of the actuator constitute a "command issuance" type control path; while the output port of the sensor transmits state data to the input port of the controller, which constitutes a "state feedback" type path. Based on this determination logic, the system clarifies the functional role and signal flow of each node in the control relationship. During the construction of the control relationship, the system simultaneously marks the redundancy characteristics. When a control path involves redundant configuration of the main chain and backup chain, the system records its redundancy attribute information and identifies whether a common-source control triggering mechanism exists, or whether signal transmission is achieved through a shared relay node. The system outputs control relationship data, including the starting and target components of each control relationship, control direction, control function identification information, relevant redundant link attributes, and task phase activation tags.

[0092] Step S14: Construct a graph based on the control relationship data to obtain component adjacency data.

[0093] In one embodiment, the system defines each interconnected component as a node in a graph, containing the component's unique identifier and its associated functional system or subsystem attributes. The system constructs the edge structure of the graph based on control relationship data. Each control relationship is mapped to a directed edge in the graph, and the direction of this edge aligns with the signal transmission direction in the control relationship to accurately reflect the flow path of control commands or feedback signals. The system adds control function identifiers, redundancy attribute information, and task phase activation tags, among other control semantic content, to the graph edges. Regarding graph structure generation, the system constructs a directed graph or a hybrid graph structure with directional attributes based on the directional attributes and redundancy features contained in the control relationships. This graph structure clearly displays the control dependency network between components while retaining information on multi-link and primary / backup relationships. The system outputs component adjacency data, which is structured as a graph model, containing a set of component nodes, a set of control relationship edges, and control function information, redundancy attributes, and task phase attributes bound to each edge.

[0094] Preferably, the selection of the beam splitting point is as follows:

[0095] Step S21: Identify physical breakpoints for the number of adjacencies of components to obtain physical breakpoint data;

[0096] In one embodiment, the system performs adjacency statistics on each component node in the component adjacency graph. Adjacency refers to the number of other components with which the component has a control connection. By traversing all nodes in the graph and calculating the number of control connections for each node, the system generates component adjacency count data. Based on the adjacency count and structural characteristics, the system determines whether a component node has the potential for physical splitting. A component node that meets any of the following conditions can be considered to have physical splitting potential and is included in the physical splitting point candidate set: First, the component's adjacency count is greater than or equal to a preset threshold (e.g., greater than or equal to 3), indicating that the component has a convergence or distribution relationship of multiple signals in the cabling structure; second, the component simultaneously connects to multiple functional systems or has a primary / backup link interleaving situation, indicating that it is located in the boundary area of ​​different functional logics; third, the component is located at the intersection node of a compartment, cable tray, or cable channel in the spatial structure. After forming the preliminary candidate set, the system verifies the actual port layout conditions of the candidate component nodes. The system evaluates whether the spatial environment where the component port is located allows the installation of branch connectors, cable fixing structures, or other necessary cabling support structures. If the system determines that the area has spatial constraints or structural obstacles that prevent it from meeting the actual beam splitting installation requirements, it will mark the node from the candidate set. The system outputs physical beam splitting point data, including the identifier of each component that meets the conditions, its associated port set, the corresponding adjacency value, and related redundancy attribute information.

[0097] Step S22: Construct a signal flow graph based on the component adjacency data to obtain signal flow graph data;

[0098] In one embodiment, the system uses component ports as basic nodes in the signal flow graph. Each node is bound to its corresponding signal type, port direction attribute (e.g., input, output, or bidirectional), and the functional system identifier information to which the port belongs. Regarding graph edge construction, the system determines whether there is a valid control transmission path between ports based on the control relationship information in the component adjacency data. If a control connection exists between two ports, the system establishes a directed edge in the signal flow graph, with the edge's direction set according to the port's direction attribute—output port to input port—thus clarifying the signal transmission path and direction of action. The system adds redundancy information and task phase attributes to the edge structure of the signal flow graph. For example, if a control relationship belongs to a redundant link (e.g., main chain and backup chain), the system appends its redundancy identifier to the graph edge. Simultaneously, the system marks the activation phase information corresponding to this control relationship during task execution (e.g., for launch phase, on-orbit phase, or attitude control switching phase) in the graph structure. The system completes the generation of the entire signal flow graph. This graph structure is organized in the form of a directed graph, containing a set of port nodes, a set of control edges, and the redundancy attributes and task activation labels bound to them.

[0099] Step S23: Perform dependency analysis on the signal flow graph data to obtain dependency data;

[0100] In one embodiment, the system first determines the control dependencies between nodes in the signal flow graph. A control dependency is identified when the output signal of one node functions as a control trigger condition, action execution prerequisite, or state criterion for another node. After determining the dependency, the system distinguishes and labels the dependency types. Based on the node device attributes and control roles, the system classifies dependencies into several types, including instruction dependencies formed by the controller transmitting instructions to the actuator, state feedback dependencies formed by the sensor feeding back state information to the controller, and redundancy switching dependencies formed by the primary link state change triggering the backup link switch in a redundant architecture. By refining the identification of dependency types, the system can represent the functional positioning and risk characteristics of different control links within the system. Based on node-level dependency identification, the system performs path-level dependency extraction analysis. The system traverses the dependencies along the directed paths in the signal flow graph, extracts the complete control links formed by multiple dependent edges, and records the dependency order, key relay nodes, and functional role distribution in each link. The system outputs dependency data, which includes at least dependency node pair information, dependency direction attributes, dependency type identifiers, and corresponding task stage tags.

[0101] Step S24: Select bundle splitting points based on physical bundle splitting point data and dependency relationship data to obtain bundle splitting point data.

[0102] In one embodiment, the system maps and matches physical bundle points with control-dependent paths. The system identifies the structural location of each physical bundle point in the signal dependency graph and determines whether the bundle point is located in the convergence region or bifurcation node of a control-dependent path. The system filters the validity of physical bundle points based on multiple decision rules. If a physical bundle point simultaneously carries multiple control-dependent paths (i.e., multiple control signals converge or are distributed from this point), and its location is before a logical bifurcation or after a convergence of dependent paths, does not disrupt the control sequence structure in the path, and is not located in the middle section of a single control loop with loop-keeping functionality, then this bundle point is considered feasible for splitting and marked as a valid candidate point. The system performs conflict verification on redundant path characteristics. If a physical bundle point is located at a common source control node of the main chain and the backup chain, and the bundle splitting operation causes the linkage between the main and backup paths to fail, the redundant structure to be destroyed, or the control switching logic to be disordered, then this physical bundle point will be marked from the candidate set. The system outputs the filtered bundle point data. This data includes the identification information of each valid bundle point, its associated set of control dependency paths, and the verification results during the primary and backup link adaptation process.

[0103] Preferably, the selection of the beam splitting point is as follows:

[0104] Stage control data is obtained by dividing the control process into stages based on dependency data.

[0105] In one embodiment, the system retrieves the mission phase set / mission phase data from the spacecraft's pre-stored mission planning file or control mode configuration table, either locally or in a database. This phase set includes the launch phase, on-orbit initialization phase, normal on-orbit operation phase, and emergency response phase, with each mission phase assigned a unique phase identifier. The system performs phase-based decomposition of control dependencies. For each control dependency edge in the dependency data, the system determines which specific mission phases it is active in based on its activation condition, mission configuration label, or control mode identifier. If a dependency is triggered in multiple mission phases, the system copies it to the corresponding mission phases. After labeling the dependency phases, the system clusters the labeled dependencies using each mission phase as an index, constructing a phase control subgraph for each mission phase. Each subgraph contains only the valid control nodes and control dependency edges within that mission phase, forming a phase-oriented control structure representation. The system outputs stage control data, including the identification information of each task stage, the set of control nodes activated in that stage, and the set of control dependencies associated with it, which constitute the stage control diagram structure.

[0106] Phase control roles are mapped based on physical bundle splitting point data and phase control data to obtain phase control role data.

[0107] In one embodiment, the system maps physical bundle points to a stage control subgraph structure. For each identified physical bundle point, the system determines whether its corresponding component or port appears in the control node set of the current task stage. If the bundle point participates in the control dependency path within the stage, it is considered to have a control role attribute in that stage. After confirming the matching relationship between the bundle point and the stage control node, the system determines its control function role in the current stage based on its topological position in the control subgraph. If a bundle point is located at the starting point of multiple control dependency paths or at the starting convergence node of multiple paths, it indicates that it has instruction scheduling function in task control, and the system marks it as a main control convergence role; if a bundle point is located in the middle of the control closed-loop path and undertakes the logical role of state feedback or control condition transmission, it is marked as a state loop role; if a bundle point is located at a fork node of the control path, and the fork involves the separation and distribution behavior of the main chain and the backup chain, it is marked as a redundant distribution role. The system independently records the control role of each physical bundle point in different task stages, establishing a cross-stage role mapping relationship. The system generates data in the form of triplets: bundle point identifier, task phase identifier, and control role type, recording the evolution trajectory of control functions at each bundle point during task execution. The system outputs phase control role data including the bundle point identifier, task phase identifier, and the corresponding control role type information within that phase.

[0108] Stability assessment is performed based on the stage control role data to obtain role stability data;

[0109] In one embodiment, the system performs a consistency comparison of the control role type of each physical bundle point in each task phase. The system analyzes whether the role function exhibits continuity and logical consistency across different phases by statistically analyzing the changes in the role type undertaken by the bundle point across multiple phases. After completing the role comparison, the system calculates a role stability evaluation index based on the role change characteristics. When a bundle point consistently assumes the same type of control role throughout all task phases, such as continuously serving as a master control convergence role or a state loop role, its control semantic continuity is considered good, and its stability score is the highest. If the bundle point undergoes role changes in some phases but no role function conflict occurs, such as switching between master control convergence and redundant distribution, the system assigns a medium stability score. If a bundle point alternates between semantically conflicting control roles in multiple phases, such as being a control output node in some phases and a state feedback node in others, it is determined that there is a risk of role semantic inversion, and its stability is rated at the lowest level. The system also explicitly marks role semantic inversion scenarios. If the same bundle point assumes both the role of issuing control commands (e.g., master control convergence) and the role of providing status feedback (e.g., state loop) in different stages, it is considered to have a potential risk of control logic reversal. Based on this, the system generates a risk flag field and binds it to the stability data. The system outputs role stability data, which includes the bundle point identifier, the corresponding stability score level, and information indicating whether there is a semantic inversion risk.

[0110] Based on the character stability data, cross-stage character convergence processing is performed on the physical bundle point data to obtain the bundle point data.

[0111] In one embodiment, the system pre-sets a role stability threshold, which defines the acceptability boundary of physical bundle points during the evolution of control logic. The system uses this threshold to determine whether the control role of each bundle point remains sufficiently consistent across different stages. For bundle points with stability scores below the set threshold, or those marked as having semantic inversion risk during stability assessment, the system considers them to cause control path disorder or role conflict in multi-stage control scenarios and therefore marks them from the bundle point candidate set. For bundle points with stability scores meeting the threshold requirements but exhibiting slight differences in control roles in some task stages, the system performs role convergence processing. The system statistically analyzes the control role type that appears most frequently at each bundle point across all task stages and uses this as the control role for that bundle point. Through this role unification mechanism, the system achieves maximum convergence of control semantics across multiple task stages, avoiding inconsistencies in control strategies caused by role drift. The system outputs bundle point data after cross-stage role convergence processing, including a unique identifier for each bundle point, the unified converged control role type, and the applicable task stage range for that role.

[0112] Preferably, the point port cost estimation is specifically as follows:

[0113] Geometric cost and electrical cost are estimated from the bundle splitting point data to obtain geometric cost data and electrical cost data, respectively.

[0114] In one embodiment, the system extracts all feasible candidate cabling paths to the target component port based on the established control connection relationships in the component adjacency data, focusing on the port corresponding to each bundle point. This set of paths is used to evaluate the spatial cabling path cost from the current bundle point to each target. After path extraction, the system accumulates the physical length of the cable segments in each candidate path to obtain the base length cost, which serves as the initial value for geometric cost evaluation. The system introduces spatial complexity penalties based on the complexity of the area traversed by the path and the actual difficulty of cabling. For example, in the path, a fixed structural crossing penalty value is added to the base cost for each crossing of a structural compartment or cable tray. If the path involves multiple sharp turns (i.e., the angle of directional change exceeds a preset threshold), the total cost is calculated by multiplying the number of sharp turns by a predefined bending penalty coefficient. When the path traverses complex spatial environments such as heat-sensitive areas, vibration-sensitive areas, or cabling-restricted areas, the system also introduces corresponding area penalties to reflect the additional cost of cabling in high-risk environments. The system accumulates the base length and the aforementioned penalty values ​​to form the geometric cost data for each bundle point on the spatial path. At the same time, the system retains the specific composition information of each penalty item.

[0115] The system identifies and classifies the electrical characteristics of the signals carried at each branch point, and performs electrical cost calculations using differentiated evaluation methods based on different signal types. Signal types can be categorized into three main types: control signals, power signals, and high-speed data signals. For control signals, the system evaluates factors affecting signal quality, such as grounding consistency, reference potential stability, and the number of intermediate connectors along the path. For example, the system checks whether the grounding methods of each segment in the path are consistent (e.g., single-point grounding or multi-point grounding). If there is a switching of grounding methods, it is marked as grounding inconsistency. If power or high-current signal lines are laid in parallel with control signals in the path, the system marks it as having a susceptible reference potential. If the control signal has no clear reference ground, it is also marked as having a floating ground risk. When the number of connectors in the signal path exceeds a set threshold (e.g., ≥3), it is assessed as having too many connections, as more connectors increase contact resistance and signal integrity risks. For power signals, the system focuses on the current margin, voltage drop, and overload risk caused by load concentration at the branch point. For example, the system calculates the current carrying capacity based on the cross-sectional area of ​​the power signal path and the design maximum current. If the current utilization rate is >9... If the current margin is 0%, it is marked as insufficient. Based on the path length and wire resistance, the voltage drop is estimated. If the predicted value is >0.5V (or exceeds the device tolerance), it is marked as abnormal voltage drop. The system counts the total load power of the power output path associated with the split point. If the concentration exceeds the rated design threshold (e.g., >80%), it is marked as overload risk. For high-speed signals, the system detects whether the path length exceeds the transmission timing allowable range, whether parallel wiring constitutes poor coupling, and whether the shielding structure is complete and continuous, etc., which are high-frequency specific constraints. For example, the total length of the signal path is calculated. If it is greater than the maximum wiring length allowed by the high-speed protocol (e.g., 0.8 meters), it is marked as length exceeding the limit. If the parallel wiring length with other high-speed / high-current paths is >10cm and the spacing is <5cm, it is marked as high coupling risk. The system checks whether the shielding layer continuously covers the entire path and has no breaks or poor transition grounding. If it does not meet the requirements, it is marked as shielding damage. During the electrical assessment, if a branch point is found to cause any of the following unacceptable electrical conditions, the system will immediately mark the electrical cost of that branch point as unusable or assign it an extremely high penalty value for elimination in subsequent selections. Unacceptable conditions include, but are not limited to, disruption of shielding continuity, redundancy failure due to the primary and backup links coexisting in a high-interference area, and voltage drop or timing margin exceeding design tolerances. The system summarizes various electrical assessment indicators to form electrical cost data for each branch point and adds risk marking information to branch points with potential electrical risks (current utilization rate between 80% and 90%; voltage drop between 0.3V and 0.5V; high-speed path length approaching the critical range (e.g., >0.7m); presence of minor grounding switching or single-point shielding interruption).

[0116] In one embodiment, geometric cost estimation includes: (expression:) , For geometric cost data, This is the total path length. For the number of compartment crossings, For the cost of a single segment crossing, For the number of cable tray switching times, For the cost of switching cable trays, For the number of sharp turns, This is the bending penalty coefficient. Assign values ​​to environmental area penalty items (based on area type).

[0117] Electrical cost estimation includes: power supply signals: , The electrical value of power supply signals. This is the predicted value of the path voltage drop. To allow for a maximum voltage drop, This represents the actual operating current of the path. For the maximum allowable current of the path, Associate the total load power at the split point. The rated power carrying capacity of the corresponding power supply branch; control signals: , For the electrical value of control signals, For the number of connectors, This represents the number of times the grounding method changes. Interference flag (0, 1, or number of times); High-speed signal: , The electrical value of high-speed signals. For path length, The maximum allowable path length for high-speed signals. For parallel coupling length, To allow the maximum parallel coupling length, To mask the number of interrupts. , For the cost of electricity, The electrical value of power supply signals. For the electrical value of control signals, The electrical cost of high-speed signals.

[0118] Task cost data is obtained by estimating the task cost based on geometric cost data and electrical cost data;

[0119] In one embodiment, the system matches the control path associated with each bundle point with the task stage data. By analyzing the activation information of control dependencies in the task stages, the system identifies the set of signals transmitted through the bundle point in each task stage, including ordinary control signals, critical closed-loop signals, and primary / backup redundant path signals, thereby clarifying the stage-specific load of the bundle point during task execution. After completing the task stage and path mapping, the system evaluates the path load of the bundle points in each task stage. The evaluation includes the number of signals transmitted through the bundle point in the current stage, the number of signals belonging to the control link, and whether redundant path switching or backup link transmission is involved. Based on the prior geometric and electrical costs, the system combines task stage attributes and sets corresponding weights through a preset set of weight parameters to form task cost calculation parameters. For bundle points in critical mission phases (such as launch or emergency response phases), the critical control links will be given higher evaluation weights in their mission cost calculations. If a bundle point is located in the middle of a control closed-loop path, the system will also introduce a control logic preservation penalty term to increase its mission cost value, thereby preventing path splitting from disrupting the closed-loop structure. The system generates mission cost data for each bundle point in each mission phase. This data not only includes the cost value in a specific mission phase but also records the differences in mission load and fluctuations in control importance between phases.

[0120] In one embodiment, task cost estimation includes: the system reading the control path and task stage activation table corresponding to the split point, and extracting the set of activation signals transmitted through the split point for each task stage k. The system counts the total number of signals during this phase. Number of control link signals and the number of redundant link signals .according to Electrical cost during the signal type determination phase When only a single type of signal is present, directly take the corresponding value. , or When multiple types of signals are involved simultaneously, the maximum value is taken as the dominant electrical constraint term for that stage. The system then determines the appropriate term based on the allowed upper limit of path complexity for that stage. Normalize the geometric cost to obtain the stage geometric burden value. If the breakpoint is located in the middle of the control closed loop, then record the closed loop hold flag. Otherwise, it is 0; if the stage involves a primary / standby switchover, then a switchover flag is recorded: Otherwise, it is 0.

[0121] Task cost data: .in, Used to characterize the beam splitting point at the 1st Task burden at each task stage. The system performs semantic mapping and burden transformation based on the task stage, converting the geometric cost into a stage geometric burden value. Set up electrical cost Converted to stage electrical value By combining the signal load and control logic characteristics under the task phase, task cost representation data of the bundle point under the task phase is constructed, thereby completing the task cost estimation.

[0122] Real-time task switching analysis is performed on the task cost data to obtain real-time task cost data;

[0123] In one embodiment, the system extracts all adjacent phase transition pairs from the mission phase sequence, such as transitioning from "launch phase" to "on-orbit phase," or from "on-orbit phase" to "emergency phase." Each pair of consecutive mission phases is used as the transition boundary for analysis. For each bundle point, the system calculates its mission cost value before and after the phase transition, and calculates the cost change magnitude, i.e., the cost transition index. After completing the cost transition calculation, the system performs analysis based on preset criteria. If the mission cost value of a bundle point jumps in any pair of phase transitions and exceeds a preset threshold, the system determines that the bundle point is a mission transition sensitive point. For such nodes, the system adds an additional preset transition risk penalty term in subsequent path selection and bundle optimization. The system fuses the above cost transition information with the original mission cost data (either by direct addition or by adjusting the cost transition information based on the transition risk penalty term before addition) to form real-time mission cost data containing mission phase transition response characteristics.

[0124] Cost optimization is performed on the real-time task cost data to obtain point port cost data.

[0125] In one embodiment, the system optimizes real-time task cost data. Based on task importance and scheduling urgency (system preset parameters defined in a preset task configuration table or scheduling strategy rules), the system categorizes tasks into high, medium, and low criticality levels, comparing costs only within tasks of the same level. The system performs extreme value marking, identifying and eliminating outliers in individual indicators such as response latency, communication bandwidth, and path length to prevent them from interfering with the overall evaluation results. Instead of prioritizing bottlenecks, the system selects the worst-performing individual indicator in the task cost as the representative cost for that path. The system retains task stage information, recording the cost performance of each point port at different task stages. The point port cost data output by the system includes the dominant cost item, corresponding indicator value, anomaly marker, and task level identifier.

[0126] Preferably, the generation of the general meaning linkage diagram is specifically controlled as follows:

[0127] Step S31: Map control functions based on component adjacency data to obtain control function data;

[0128] In one embodiment, the system pre-constructs a control function dictionary based on expert knowledge or engineering experience. This dictionary abstracts common control function scenarios into standardized function identifiers and configures corresponding signal names, port type combinations, and device role attributes for each function identifier. Control function types include, but are not limited to, attitude control closed loop, propulsion valve actuation, thermal control loop regulation, data transmission link start / stop control, and redundant path switching control. During function identification, the system iterates through the connection relationships in the component adjacency data based on the control function dictionary, matching the adjacency information between each pair of ports with control functions. If the signal name, signal type, and functional subsystem to which adjacent ports belong match a certain function mode in the control function dictionary, the system maps the adjacency relationship to the corresponding control function. If an adjacency relationship satisfies some features of multiple function modes, the system performs function disambiguation processing by combining port direction attributes (such as input or output) and the type of connected device (such as controller, actuator, or sensor). For adjacency relationships that cannot be matched to any control function identifier, the system marks them as non-control general relationships. The system outputs control function data, which includes adjacency relationship identifiers, mapped control function identifiers, information on components and ports involved in the connection, related redundant link attributes, and preliminary labeling of applicable task stages.

[0129] Step S32: Extract the action sequence dependency based on the control function data to obtain dependency data;

[0130] In one embodiment, the system identifies the action roles of participating components within each control function. This identification is based on component type and port direction: when a component is a controller and its port is output type, the system identifies it as the starting point role of "instruction issuance action"; when a component is an actuator and its port is input type, the system considers it as the target role of "action execution"; and when a component is a sensor and its port is output type, it indicates that the component undertakes the functional role of "state feedback action". Based on the action role identification results, the system constructs a directed dependency path within each control function according to the control logic sequence of "instruction issuance → action execution → state feedback". This path reflects the control process from the controller issuing a command, through the actuator responding and executing, to the sensor transmitting the state back, forming an action sequence dependency chain. During the dependency construction process, the system identifies special control structures, such as closed-loop paths and parallel actions. If the system detects that the feedback action is ultimately transmitted back to the input port of the original controller, thus forming a signal feedback loop, the system marks it as a "control closed loop." If the same control command is connected to multiple actuators, the system marks the path as a "parallel action branch." This structure is often used in scenarios involving multi-component collaborative execution or redundant deployment. The system outputs structured dependency data, including the identifiers of the dependency start and end components, dependency direction attributes, dependency type (such as instruction dependency, execution dependency, feedback dependency), and whether it belongs to a closed-loop structure or a parallel action branch.

[0131] Step S33: Map arbitrary stage trigger pairs to the dependency data to obtain stage trigger pair data;

[0132] In one embodiment, the system obtains a set of mission phases from the spacecraft's mission planning document or control mode configuration file, and configures / extracts / acquires corresponding triggering conditions for each phase. These triggering conditions may include phase transition events (such as entering the launch phase or on-orbit mode), threshold out-of-bounds events for state variables (such as temperature or attitude error exceeding set ranges), and fault detection events (such as main chain failure or sensor disconnection). These triggering conditions constitute the pre-condition logic for activating dependency chains in different mission phases of the control system. The system performs phase adaptation determination on each action sequence dependency path in the dependency relationship data. Based on the control functions, signal types, and activation conditions involved in the dependency path, the system determines which specific mission phases have the pre-conditions for control. If a dependency relationship is only invoked in a specific mission phase, the system only maps the path to that phase; if the dependency path has the potential to be triggered in multiple phases, the system copies it, generating a mapping instance for each corresponding phase. After completing the adaptation determination, the system constructs phase trigger pairs based on the mapping results. The system establishes a mapping relationship in the form of "trigger condition - dependency path," where the trigger condition serves as the input signal for the control logic, and the associated action sequence dependency chain serves as the response path after the condition is triggered. The system outputs stage trigger pair data. The output includes the identification information of each trigger condition, the task stage to which it belongs, and the identifier of the action sequence dependency path associated with it.

[0133] Step S34: Generate a control general diagram from the stage trigger data to obtain control linkage diagram data.

[0134] In one embodiment, the system extracts all components involved in control functions from the stage trigger pairs and defines them as nodes in the control generality graph. Each node represents a functional component, and also includes the component type (e.g., controller, actuator, sensor), its functional role in the control path (e.g., command source, execution end, or feedback source), and identification information regarding whether the component participates in redundant links. The system constructs directed edges in the graph structure based on the action sequence dependency chains contained in the stage trigger pairs. Each dependency chain represents a path from the issuance of a set of control commands to execution and feedback; the system inserts it as a directed edge in the control generality graph, with the edge's direction consistent with the action sequence in the dependency chain. Each graph edge is bound to its corresponding task stage information and triggering condition, used to characterize the task conditions under which the control path is activated. The system assigns a linkage strength weight to each edge. Specifically, if a control path appears repeatedly in multiple task stages, the system assigns a basic weight based on the number of occurrences (e.g., 1 point for each repetition); if the path forms a complete closed loop (including instructions, execution, and feedback), it is assigned a higher weight (e.g., 5 points); if the path involves actions such as master / slave switching or fault tolerance arbitration, it is assigned a medium weight (e.g., 3 points). The linkage strength weight is the sum of these weightings. The system outputs control linkage graph data. This graph data includes a set of graph nodes and a set of graph edges, and records the associated task stage, triggering conditions, and linkage strength attributes for each edge.

[0135] Preferably, the explanation field generation is specifically as follows:

[0136] Control nodes are extracted from the control linkage diagram data to obtain control node data;

[0137] In one embodiment, the system defines screening rules for control nodes and traverses all nodes in the control linkage graph, making judgments based on preset conditions. If a node plays a role such as issuing control commands, triggering action execution, or providing status feedback in any directed control dependency path, it is directly identified as a control node. If the sum of the number of incoming and outgoing edges of a node exceeds a set threshold, such as reaching or exceeding 2, it indicates that the node plays a pivotal role in the control path and is also included in the control node set. If a node participates in critical control behaviors such as primary / backup redundancy path switching or cross-stage triggering, it can also be considered an important node with control functions. After the control nodes are screened, the system labels each control node with a role type based on the edge connection direction of the node in the control linkage graph and the device and functional attributes carried by the node itself. If a node is located at the beginning of the control path, it is usually a controller output port and is labeled as a "control command node." If a node is in the middle of the path, it is usually connected to an actuator input port and is labeled as an "action execution node." If a node serves as the end of a control loop or a feedback source, it is usually a sensor output port and is labeled as a "status feedback node." If a node performs primary / backup path selection, switching, or arbitration functions, it is labeled as a "redundant arbitration node." The system outputs control node data. This data includes a unique identifier for each control node, its control function, control role type, and the set of task stages associated with the node.

[0138] Control path data is obtained by extracting control paths from control linkage diagram data based on control node data;

[0139] In one embodiment, the system determines the start and end nodes of the path based on the role type of the control node. Components marked as control command nodes are used as the starting point of the control path, and action execution nodes or status feedback nodes with direct control relationships with them are used as the end point, thus defining the logical boundaries of the path search. During the path search process, the system employs a graph-based directed path traversal method, sequentially searching for reachable paths from the starting node until the end node is reached. To avoid generating redundant or unanalyzable paths, the system applies the following constraints during traversal: first, the path length is limited to a preset maximum number of hops to avoid information generalization or computational overhead caused by abnormally long links; second, when duplicate nodes appear in the path, the system determines that the path forms a closed-loop structure and performs closed-loop marking; third, paths with linkage strength higher than a set threshold are prioritized. After path extraction, the system cleans up and merges duplicate or similar paths. If multiple control paths have completely identical node sequences, or only minor differences at non-critical nodes, the system merges them into standardized paths and assigns them unified path identifiers. The system outputs control path data. This data includes a unique identifier for each control path, a sequential list of constituent nodes, the path type (such as a linear path, closed-loop path, or parallel action branch path), and the control function identifier information to which it belongs.

[0140] The control path data is mapped to the preset task stage data to obtain the control path stage data;

[0141] In one embodiment, the system extracts a set of task stages from the task configuration file or control mode document and sets activation conditions and time intervals for each task stage. These task stages may include launch preparation, attitude initialization, on-orbit cruise, attitude control adjustment, redundancy switching response, etc., and each stage has an independent control objective and execution sequence. The system performs task stage matching and determination for each control path. By traversing all the linkage graph edges involved in the control path, the system determines the validity of the entire path in each stage based on the task stage information marked in its edge attributes. If all control dependency edges contained in the path are active in a certain task stage, the system determines that the control path is executable in that stage and records it as a "stage-valid path"; if only some dependency edges in the path are activated in that stage, the path is marked as a "stage-restricted path," indicating that there is a logical or structural missing element in its control chain under that stage. For paths with cross-stage validity, the system records their persistence characteristics. If a control path remains valid across multiple consecutive task phases, the system marks it as a "cross-phase persistence path"; while for paths valid only within a specific task phase, the system records their "phase specificity." The system outputs control path phase data, including a unique identifier for each path, the set of task phases to which it applies, whether it possesses cross-phase persistence characteristics, and explanatory information regarding phase limitations.

[0142] The control chain interpretation field is generated based on the control path stage data, resulting in interpretation field data.

[0143] In one embodiment, the system pre-defines a template structure for control chain interpretation fields. This template is used to uniformly describe the semantic content of each control path. The template includes at least the following fields: control chain identifier, control function summary, path role sequence, applicable task stage set, redundancy characteristic description, and stage sensitivity description. The template structure is derived by the system abstracting general description items based on the functional execution logic and stage dependencies of control paths corresponding to preset / predefined / engineering experience when constructing the control chain interpretation field template. By summarizing the common characteristics of various control chains at the structural, functional, and behavioral levels, a field item framework is formed. The determination of template fields references common control path attribute expression forms in historical engineering cases and combines task stage management requirements to form the template structure. During the field filling process, the system extracts and generates interpretation field content for each control path. The system constructs the role sequence information corresponding to the path based on the node order and node role type in the path, as the functional execution structure of the control chain. For example, the system can arrange the instruction issuing node, action execution node, and status feedback node in sequence to form a control chain role description. The system also summarizes the control function information associated with the path and generates a control function summary. If the control path contains primary / standby switching nodes or redundant arbitration nodes, the system automatically generates a corresponding redundancy structure description field, indicating that the path has redundancy and fault tolerance characteristics. When the path experiences changes in activation status, role function changes, or start / stop behavior in different task stages, the system generates a stage sensitivity description field to indicate the control impact or path stability issues caused by stage switching. The system outputs the generated explanation field data, using key-value pair mapping or tabular data organization.

[0144] Preferably, the profit determination is as follows:

[0145] Step S41: Perform path segmentation cost calculation and task phase aggregation processing on the point port cost data and explanation field data to obtain path segmentation cost data and phase aggregation data respectively;

[0146] In one embodiment, the system determines the original path structure in its unsegmented state for each candidate bundle splitting point, serving as a baseline path. The baseline path consists of the control paths from the source component to the target component identified in the component adjacency data, with the shortest path or the path conforming to a predetermined control strategy selected as a reference. The system simulates and generates a virtual splitting scheme at the candidate bundle splitting point, dividing the original path into multiple sub-paths at that point. Each sub-path connects the bundle splitting point to its corresponding upstream and downstream component ports, thus forming a new path structure unit. The system calls existing cost evaluations for each sub-path, sequentially calculating its geometric cost, electrical cost, and real-time task cost. The sum of the costs of each sub-path is used as the total cost corresponding to the current splitting scheme. The system compares this total cost with the point-port cost of the original baseline path to obtain the change in path splitting cost.

[0147] Beyond structural cost calculation, the system evaluates the task stage aggregation of a path based on explanatory field data. It counts the total number of task stages involved in the path before splitting and records whether the path is active in each stage. For each sub-path after splitting, the system calculates its applicable task stage set, identifying whether each sub-path primarily serves a single task stage or adjacent stages, or whether it spans multiple functionally unrelated stages. If the split sub-paths are more concentrated in task stage distribution and the path structure successfully achieves functional isolation of task stages, aggregation is considered improved; conversely, if splitting causes the path structure to overlap across multiple task stages or breaks the control encapsulation of the original stage boundaries, aggregation is considered reduced. The system outputs stage aggregation data.

[0148] Step S42: Simulate the path reconstruction of the split points based on the path splitting cost data and the stage aggregation data to obtain the path data of the split points;

[0149] In one embodiment, the system restructures the original path in the component adjacency graph based on the generated segmentation scheme. The system replaces the portion of the original path located at candidate branch points with multiple sub-paths. These sub-paths connect the branch points to their upstream or downstream component ports, forming a new set of control paths. During the reconstruction, the order of control dependencies in the original path remains unchanged. After path reconstruction, the system performs a consistency check on the newly generated set of sub-paths to determine if it meets the following basic structural constraints: first, the execution order of all control dependencies has not been reversed; second, each sub-path maintains effective connectivity in the component adjacency graph; and third, no isolated nodes or broken chain structures or other connection anomalies have been introduced. If any condition is not met, the segmentation scheme is deemed an invalid reconstruction. The set of paths that pass the consistency check will be used as new candidate paths for attribute labeling. The system re-labels each sub-path with its geometric structure, electrical connections, and other feature information, and updates its control semantics and task stage adaptation information in conjunction with the aforementioned explanation field data. The system outputs split point path data, which includes the set of sub-paths corresponding to each split scheme, the attribute characteristics of each sub-path, and the applicability evaluation results under different task stages.

[0150] Step S43: Perform redundancy overlap profile analysis based on the split point path data to obtain splitting revenue data.

[0151] In one embodiment, the system identifies redundant control paths in the segmented path structure, specifically including the primary path and its corresponding backup path. During the identification process, the system determines whether they belong to the same redundancy group based on path attributes, control function identifiers, and task phase coverage, and extracts the set of sub-paths contained in each group as the analysis object. The system performs structural comparison on the above primary and backup sub-paths to identify their overlapping segments in the spatial layout. The judgment criteria include whether the sub-paths are located in the same compartment, whether they follow the same wiring path, or whether they are in adjacent spatial intervals. Any path segment that meets any of the conditions is marked as an overlapping segment to indicate the spatial overlap of the primary and backup channels in the actual deployment. After completing the identification of overlapping segments, the system calculates the degree of overlap for each group of redundant paths. For example, the system counts the total length or number of all overlapping segments and compares it with the total length of the primary path to form a redundancy overlap index, which is used to represent the overlap ratio of redundant paths in the physical wiring structure. Based on the above overlap index, the system combines the change trend before and after the segmentation to determine the profitability of the segmentation behavior. If the splitting operation reduces the physical overlap between the primary and backup paths, it indicates that the scheme helps improve the system's fault tolerance and isolation, and is therefore considered to have positive redundancy benefits. Conversely, if the overlap does not change significantly, or even increases, it indicates that the splitting contributes little to redundancy optimization or has a negative impact. The system jointly analyzes the changes in path splitting costs, the aggregation evaluation results of task stages, and the changes in redundancy overlap, and outputs splitting benefit data.

[0152] Preferably, the hard constraint evaluation specifically includes:

[0153] Hard constraint determination is performed on the segmented revenue data to obtain hard constraint determination data, which includes spatial legality determination, electric and steam rationality determination, and control logic verification.

[0154] In one embodiment, regarding spatial legality determination, the system matches the spatial coordinates of candidate branch points in the 3D cabling model with a preset list of deployable areas. If the point falls into a structural interference zone, a restricted area for equipment maintenance, or an installation restriction zone, it is directly marked as spatially invalid. The system verifies the minimum installation gap between the branch point and surrounding structural components. If this gap is less than a preset maintenance and safety distance threshold, the location is also marked as non-compliant. For each sub-path after splitting, the system also calculates the bending angle and corresponding bending radius formed at the branch point. If there are cases where the cable bending radius requirements are not met, the deployment is considered infeasible. The results of the above three verifications are combined to generate a spatial legality determination result, along with a detailed explanation of the reasons for non-compliance.

[0155] Regarding electrical rationality assessment, the system categorizes the signals involved at the split point. If different types of signals are found to be forcibly co-located in a no-mixing area, it is directly considered an electrical conflict. The system searches for the parallel relationships of primary / backup paths or functional signal paths in the physical layout. If their parallel length exceeds the preset electromagnetic compatibility limit, it will be judged as having an electromagnetic interference risk. The system also checks whether the grounding method and shielding structure maintain continuity after the split. If there are problems such as shielding interruption or grounding domain disorder, and these cannot be resolved through the conversion node, it will also be considered as not meeting the electrical rationality requirements. The output of the electrical rationality assessment includes compliance status and specific electrical risk type identification.

[0156] In the control logic verification phase, the system confirms the integrity of the control dependency chain based on the interpretation fields and path data. If path splitting leads to an interruption of a control chain, a broken feedback path, or a missing critical control path, it is considered a logical violation. The system detects whether the control role of the splitting point undergoes semantic reversal in each task stage, such as changing from a master node to a subordinate response node. Such changes will affect the stability of the control logic and are considered a serious mismatch. The system also performs consistency verification on the redundant triggering and logical interlocking mechanisms of the master and backup paths. If there are problems such as merging of master and backup trigger sources or failure of interlocking strategies, these will also be classified as logical errors. All results will be output as control logic verification data, along with corresponding failure type labels. The system summarizes the judgment results from the three dimensions of space, electrical, and control logic to form hard constraint judgment data. Each candidate split point / split point data will receive three judgment tags, and the system will simultaneously record the reasons for each violation.

[0157] Based on the hard constraint determination data, compliance point assessment is performed to obtain the feasibility assessment data for the bundle point.

[0158] In one embodiment, the system sets a compliance criterion, requiring candidate bundle points to simultaneously meet three hard evaluation conditions: legal spatial layout, reasonable electrical characteristics, and complete control logic, in order to be considered a compliant point. If any criterion is found to be non-compliant, the bundle point will be marked with a corresponding non-compliant tag, including a detailed explanation of the non-compliance, the type of violation, and a corresponding scenario description. During the compliance point screening and evaluation process, the system dynamically aggregates all candidate points that pass the screening, generates bundle point feasibility evaluation data, and synchronously inherits the splitting benefit data and explanation fields corresponding to each compliant point. The bundle point feasibility evaluation data output by the system includes: the unique identifier of the compliant point, the corresponding path splitting benefit index, the tag status of passing the hard constraint criterion / hard constraint evaluation, and the set of task stages to which the bundle point is applicable.

[0159] Preferably, this application also provides a spacecraft electrical system interconnection component path planning optimization system for executing the spacecraft electrical system interconnection component path planning optimization method described above. The spacecraft electrical system interconnection component path planning optimization system includes:

[0160] The adjacency relationship construction module is used to obtain interconnected component data; and to construct adjacency relationships based on the interconnected component data to obtain component adjacency data.

[0161] The point port cost estimation module is used to select bundle points based on component adjacency data to obtain bundle point data; and to estimate the point port cost of the bundle point data to obtain point port cost data.

[0162] The interpretation field generation module is used to generate a control generality linkage diagram based on component adjacency data to obtain control linkage diagram data; and to generate interpretation fields from the control linkage diagram data to obtain interpretation field data.

[0163] The bundle splitting optimization module is used to determine the splitting benefit from the point port cost data and the interpretation field data to obtain the splitting benefit data; and to perform hard constraint evaluation on the splitting benefit data to obtain the bundle splitting feasibility evaluation data.

Claims

1. A method for optimizing path planning of interconnection components in a spacecraft electrical system, characterized in that, Includes the following steps: Step S1: Obtain interconnect component data; Adjacency relationships are constructed based on interconnected component data to obtain component adjacency data; Step S2: Identify physical bundle points for the component adjacency count to obtain physical bundle point data; construct a signal flow graph based on the component adjacency data to obtain signal flow graph data; perform dependency analysis on the signal flow graph data to obtain dependency data. Based on the physical bundle splitting point data and dependency data, bundle splitting points are selected to obtain bundle splitting point data; point port cost is estimated from the bundle splitting point data to obtain point port cost data. Step S3: Generate a control general meaning linkage diagram based on the component adjacency data to obtain control linkage diagram data; generate interpretation fields from the control linkage diagram data to obtain interpretation field data; Step S4: Perform path segmentation cost calculation and task phase aggregation processing on the point port cost data and explanation field data to obtain path segmentation cost data and phase aggregation data respectively; Based on the path segmentation cost data and stage aggregation data, a path reconstruction simulation of the segmentation point is performed to obtain the segmentation point path data; based on the segmentation point path data, a redundancy overlap profile analysis is performed to obtain the segmentation benefit data; hard constraint determination is performed on the segmentation benefit data to obtain hard constraint determination data, which includes spatial legality determination, electric and steam rationality determination, and control logic verification; based on the hard constraint determination data, compliance point assessment is performed to obtain the feasibility assessment data of the branch point. The specific selection of the beam splitter point is as follows: Stage control data is obtained by dividing the control process into stages based on dependency data. Phase control roles are mapped based on physical bundle splitting point data and phase control data to obtain phase control role data. Stability assessment is performed based on the stage control role data to obtain role stability data; Based on the character stability data, cross-stage character convergence processing is performed on the physical bundle point data to obtain the bundle point data.

2. The method according to claim 1, characterized in that, Step S1 is as follows: Obtain data from interconnected components; Adjacency relationships are extracted from the interconnected component data to obtain adjacency relationship data; Control relationships are constructed from the adjacency data to obtain control relationship data; Graphs are constructed based on control relationship data to obtain component adjacency data.

3. The method according to claim 1, characterized in that, The point port cost estimation is as follows: Geometric cost and electrical cost are estimated from the bundle splitting point data to obtain geometric cost data and electrical cost data, respectively. Based on geometric cost data and electrical cost data, the task cost is estimated to obtain the task cost data; Real-time task switching analysis is performed on the task cost data to obtain real-time task cost data; Cost optimization is performed on the real-time task cost data to obtain point port cost data.

4. The method according to claim 1, characterized in that, The specific steps for generating the control general linkage diagram are as follows: Control function data is obtained by mapping control functions based on component adjacency data; Based on the control function data, the action sequence dependency relationship is extracted to obtain the dependency relationship data; Map arbitrary stage trigger pairs to dependency data to obtain stage trigger pair data. The control general diagram is generated from the stage-triggered data to obtain the control linkage diagram data.

5. The method according to claim 1, characterized in that, The specific steps for generating the explanation field are as follows: Control nodes are extracted from the control linkage diagram data to obtain control node data; Control path data is obtained by extracting control paths from control linkage diagram data based on control node data; The control path data is mapped to the preset task stage data to obtain the control path stage data; The control chain interpretation field is generated based on the control path stage data, resulting in interpretation field data.

6. A path planning and optimization system for interconnection components of a spacecraft electrical system, characterized in that, For executing the spacecraft electrical system interconnection component path planning optimization method as described in claim 1, the spacecraft electrical system interconnection component path planning optimization system includes: The adjacency relationship construction module is used to obtain interconnected component data; and to construct adjacency relationships based on the interconnected component data to obtain component adjacency data. The point port cost estimation module is used to select bundle points based on component adjacency data to obtain bundle point data; and to estimate the point port cost of the bundle point data to obtain point port cost data. The interpretation field generation module is used to generate a control generality linkage diagram based on component adjacency data to obtain control linkage diagram data; and to generate interpretation fields from the control linkage diagram data to obtain interpretation field data. The bundle splitting optimization module is used to determine the splitting benefit from the point port cost data and the interpretation field data to obtain the splitting benefit data; and to perform hard constraint evaluation on the splitting benefit data to obtain the bundle splitting feasibility evaluation data.

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