Spacecraft general assembly data fusion processing method and system

Abstract

The invention provides a spacecraft general assembly data fusion processing system and method, and the system comprises a dual-core architecture which comprises a general assembly cooperation model module and a knowledge-driven engine module; the hierarchical classification database is connected with the dual-core architecture; the version penetration management module is used for establishing a product version penetration management mechanism based on a preset code; and a rule verification engine for automatically verifying the data based on a preset rule. The method comprises the following steps: carrying out collaborative sharing and intelligent decision making through a dual-core architecture, storing spacecraft general assembly data into a hierarchical classification database for standardized storage, establishing a product version penetration management mechanism, and automatically checking the data based on a preset rule obtained from a knowledge-driven engine module. According to the method, the data normalization and accuracy can be remarkably improved, the full-life-cycle accurate tracing of the data is realized, and the cross-professional collaboration efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of spacecraft manufacturing and testing technology, and more specifically, to a method and system for data fusion processing in spacecraft assembly. Background Technology

[0002] As spacecraft systems become increasingly complex, data management during the satellite AIT (Assembly, Integration, and Test) phase faces significant challenges. This process involves a wide variety of product-related information, design requirements, and experimental test data from diverse sources, leading to severe data fragmentation, inefficient data transmission, and high error rates. These issues not only affect the progress of spacecraft development but may also result in inaccurate test results and risks to product performance.

[0003] Traditional data management methods often rely on a single data storage and management system, which is insufficient to meet the needs of cross-disciplinary collaborative work. Specifically: 1) The diverse sources and inconsistent formats of data make it difficult to transmit information between different departments; due to the lack of smooth data flow, cross-disciplinary collaborative work relies heavily on meetings and email communication, resulting in information synchronization delays and affecting the progress and quality of the entire AIT process.

[0004] 2) The lack of an effective version control mechanism results in poor traceability of design changes and test data. When product status changes or test problems occur, it is difficult to quickly and accurately trace back to the relevant design changes, process adjustments, or historical test data, leading to low efficiency in troubleshooting and decision-making. There is also a lack of a full lifecycle version penetration management mechanism based on the product's unique identifier.

[0005] 3) The lack of standardized verification and integration of cross-disciplinary data leads to insufficient data consistency and reliability. The absence of unified data standards and interfaces between different professional systems results in differences in data definitions, formats, and semantics, making effective data integration difficult. The same parameter may appear inconsistently in different documents, causing a disconnect between the design and the actual physical state.

[0006] 4) The data transmission chain is long, and the process of manual transmission and manual entry is cumbersome, which is not only inefficient, but also very easy to introduce human error, resulting in data distortion or version confusion.

[0007] Patent document CN117061060A discloses a data fusion method and apparatus based on template and frame switching technology, including the following steps: integrating data received by the vehicle-mounted station to obtain a new data queue and a numbered list; adjusting the data in the numbered list according to the signal-to-noise ratio of the vehicle-mounted station to obtain an ordered list; selecting the best template station; sequentially arranging the new data queue according to the order of the vehicle-mounted station numbers in the ordered list to obtain an ordered list; detecting and updating the current theoretical frame count; selecting the data queue with the highest average signal-to-noise ratio in the ordered list as the fusion template; traversing the fusion template to obtain all data continuous with the current theoretical frame count; and performing data fusion. However, this patent document is limited to focusing on "frame-level data continuity assurance in UAV telemetry and control scenarios," only addressing the issues of data loss and synchronization in multi-source data frames, and does not involve data management needs at the business level; while this application addresses the special business scenario of spacecraft assembly and solves the key problems that it cannot cover.

[0008] Therefore, there is an urgent need in the market for a data fusion processing method and system that can achieve data standardization, intelligent management and efficient collaboration in the AIT phase of spacecraft, so as to improve data standardization and system reliability, reduce development costs and shorten the development cycle. Summary of the Invention

[0009] To address the shortcomings of existing technologies, the present invention aims to provide a data fusion processing method and system for spacecraft assembly, which addresses the technical problems of low collaborative efficiency, high data transmission error rate, chaotic version management, and poor data consistency and lifecycle traceability caused by cross-disciplinary data fragmentation, inconsistent data standards, reliance on manual transmission, etc., during the spacecraft assembly, integration, and testing stages.

[0010] A spacecraft assembly data fusion processing system provided by the present invention includes: a dual-core architecture, a hierarchical classification database, a version penetration management module, and a rule verification engine; The dual-core architecture includes an assembly collaboration model module and a knowledge-driven engine module; The hierarchical classification database is connected to the dual-core architecture and is used for standardized, hierarchical, classified storage and visualization of product supporting information, design requirements and test data; The version penetration management module is connected to the hierarchical classification database to establish a product version penetration management mechanism based on preset codes; The rule verification engine is connected to the hierarchical classification database and the knowledge-driven engine module, and is used to automatically verify data based on preset rules.

[0011] Preferably, the assembly collaborative model module is used to realize cross-disciplinary model collaborative design, real-time data sharing, and approval process support; The knowledge-driven engine module is used for intelligent decision-making and optimization based on historical data and business rules.

[0012] Preferably, the hierarchical classification database includes a relational database and a time-series database; The relational database is used to store product matching information, design requirements, and specification data with obvious structured features and complex relationships; The time-series database is used to store test data and result sequence data with timestamps.

[0013] Preferably, the version penetration management module establishes a product version penetration management mechanism based on hierarchical bill of materials (BOM) coding, identifies spacecraft products and components or parts at each level, and realizes full lifecycle traceability of data; When data changes, a new version is generated without overwriting the old version, and the change history is recorded.

[0014] Preferably, it also includes a message queue, which is used for asynchronous data transmission between the hierarchical classification database and the rule verification engine; The rule verification engine is connected to the knowledge-driven engine module to obtain preset rules, and is used to mark the corresponding data items or trigger an alarm when the verification finds that the data does not conform to the preset rules.

[0015] According to the present invention, a spacecraft assembly data fusion processing method is executed by the aforementioned spacecraft assembly data fusion processing system, the method comprising: Step S1: Collaborative sharing is achieved through the final assembly collaborative model module, and intelligent decision-making and optimization are performed based on historical data and business rules through the knowledge-driven engine module; Step S2: Store the spacecraft assembly data in a hierarchical classification database for standardized, hierarchical, and visual storage; Step S3: Establish a product version penetration management mechanism based on preset coding; Step S4: Obtain preset rules from the knowledge-driven engine module; Step S5: The spacecraft assembly data is automatically verified based on the acquired preset rules through the rule verification engine.

[0016] Preferably, the collaborative sharing in step S1 specifically refers to: using the general assembly collaborative model module to realize cross-professional model collaborative design, real-time data sharing, and approval process support.

[0017] Preferably, the data storage in step S2 specifically involves storing product matching information, design requirements and specification data into a relational database, and storing test data and result sequence data into a time-series database.

[0018] Preferably, the preset code in step S3 is a hierarchical Bill of Materials (BOM) code; The established product version penetration management mechanism also includes: generating a new data version when data changes, without overwriting the old version, and recording the change history. Preferably, the method further includes: using a message queue to perform asynchronous data transmission between the hierarchical classification database and the rule verification engine.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves standardized integration of multi-source heterogeneous data by establishing a hierarchical and categorized database and a unified data model. Combined with a rule-based automatic verification mechanism, it can effectively eliminate data inconsistencies and errors. Preliminary application verification shows that data standardization can be improved by more than 85%.

[0020] 2. This invention introduces a version penetration management mechanism based on BOM coding, which strongly links the product entity with its design, process, and test data throughout its entire life cycle, achieving accurate traceability with "one code to the end", and greatly improving the efficiency of problem location and quality attribution.

[0021] 3. By constructing a unified collaborative model and platform, this invention breaks down professional barriers and data silos, enabling real-time sharing of cross-professional data and process collaboration, reducing communication costs and waiting time, and shortening the development cycle.

[0022] 4. The automated data verification and fusion processing flow of this invention replaces a large number of manual operations, fundamentally reducing the error rate in the data transmission and entry process, ensuring the accuracy of the data used in the AIT process, and providing a solid guarantee for the high-quality delivery of spacecraft. Attached Figure Description

[0023] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the architecture of a spacecraft assembly data fusion processing system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a spacecraft assembly data fusion processing method provided in an embodiment of the present invention; Figure 3 This is a block diagram illustrating the construction of an intelligent collaborative platform provided in an embodiment of the present invention. Detailed Implementation

[0024] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0025] This invention constructs a dual-core architecture of a general assembly collaboration model and data-driven approach; it achieves standardized storage and display of product specifications, design requirements, and test data through a hierarchical and categorized database; it establishes a version penetration management mechanism based on BOM encoding; and it implements automatic cross-disciplinary data verification based on rules. This solves the problems of fragmented satellite AIT data and high transmission error rates. Based on preliminary application verification statistics, data standardization can be improved by more than 85%.

[0026] Example 1 This embodiment provides a data fusion processing system and method for spacecraft assembly. This embodiment aims to address a series of technical problems mentioned in the background art, such as low collaborative efficiency, high data transmission error rates, chaotic version management, and poor data consistency and lifecycle traceability caused by data fragmentation, inconsistent standards, and reliance on manual transmission during the spacecraft assembly, integration, and testing phases. It also comprehensively demonstrates the system architecture, core module functions, and overall workflow.

[0027] According to the present invention, a spacecraft assembly data fusion processing system is provided, such as... Figure 1 As shown, this spacecraft assembly data fusion processing system can be deployed on a server or cloud platform. Its core lies in building a comprehensive data management platform that integrates collaborative work and intelligent decision-making. The system includes a dual-core architecture, a hierarchical and categorized database, a version penetration management module, and a rule verification engine.

[0028] Specifically, the system of this invention connects to multiple heterogeneous data sources through a data acquisition interface. These data sources are the origins of various types of data during the spacecraft assembly, integration, and testing phases, including but not limited to: computer-aided design systems used for 3D model design, which generate structured or semi-structured model files and design parameters; various testing equipment used for mechanical, thermal, and electrical performance testing, which generate a large amount of time-series data or test reports; and other business systems, such as material management systems, process planning systems, and project management systems, which generate product matching information, process documents, and progress data. Due to their different sources, these raw data have significant differences in format, standards, and semantics, which is the root cause of "data silos." The data acquisition interface, through configuring appropriate adapters or using standardized application programming interfaces, enables automated, periodic, or event-triggered data acquisition of these multi-source heterogeneous data.

[0029] The dual-core architecture forms the cornerstone of this invention, comprising an assembly collaboration model module and a knowledge-driven engine module. This design integrates platform capabilities supporting large-scale concurrent collaboration with knowledge capabilities supporting intelligent decision-making, providing robust support for upper-layer applications.

[0030] In one embodiment of the present invention, the assembly collaboration model module constitutes the core of a unified work platform for spacecraft development personnel. This module aims to achieve cross-disciplinary model collaborative design, real-time data sharing, and support for review and approval processes. It also manages data access permissions and organizes online design reviews and approval processes to ensure the standardization and security of data flow. As an optional implementation, the assembly collaboration model module can be built on a microservice architecture. Each core function (such as model rendering, data querying, and process approval) is broken down into independent microservices, which interact with each other through lightweight communication mechanisms (such as RESTful APIs). This architecture features high cohesion and low coupling, facilitating independent development, deployment, and expansion, thereby improving the flexibility, reliability, and maintainability of the entire system. In actual deployment, this module can achieve high performance indicators, such as supporting ≥1000 concurrent users, data throughput ≥1TB / day, and response time <2 seconds. Users interact with the assembly collaboration model module through an intelligent collaboration platform, which can be a web-based application providing rich user interfaces such as data visualization, model browsing, and task management.

[0031] The knowledge-driven engine module's function is to make intelligent decisions and optimizations based on historical data and business rules. Internally, it builds and maintains a rule knowledge base, which stores a large number of business rules, including expert experience, design specifications, testing standards, and process constraints, summarized from historical assembly, integration, and testing projects. The knowledge-driven engine module not only stores these rules but also has the ability to manage, classify, and reason about them. When the system needs to make decisions or perform verifications, this module can quickly retrieve and provide relevant rules based on the context. In its implementation, the module's rule base capacity is ≥100,000 entries, and the verification accuracy rate is ≥99%.

[0032] A hierarchical and categorized database connected to a dual-core architecture provides the physical foundation for data fusion and standardization. This database is used to standardize, hierarchically categorize, store, and visualize product-related information, design requirements, and test data collected from various data sources. The data standardization process includes analyzing data formats and core fields from different professional systems such as configuration layout, structure, thermal control, and 3D cable networks. Based on this, a unified data model covering the entire stage of spacecraft assembly, integration, and testing is defined. This model clarifies the standard naming, data type, unit, and value range for various types of data. Subsequently, through data cleaning, format conversion, and unit standardization, the collected raw data is transformed into data conforming to this unified data model, and finally, it is categorized and stored according to data attributes (such as professional field, data type, security level, product stage, etc.).

[0033] As an optional implementation, a hierarchical and categorized database can adopt a hybrid database architecture. Specifically, it includes relational databases and time-series databases. Relational databases include databases for product-related information and databases for design requirements and specifications, primarily used to store data with clear structured characteristics and complex relationships, such as product-related information, bills of materials, design specifications, and personnel information. Time-series databases include databases for experimental test data and results, used to store time-stamped sequence data, such as test data collected by sensors during various tests, including temperature, voltage, and vibration frequency. This hybrid architecture can employ optimal storage and query schemes for different types of data, thereby improving overall performance.

[0034] The version penetration management module connects to the hierarchical classification database, and its core function is to establish a product version penetration management mechanism based on preset codes. During spacecraft development, design changes are frequent, making it crucial to ensure that every component uses the correct version of the data. In one embodiment of this invention, the preset code is determined to be the Bill of Materials (BOM) code. The version penetration management module assigns a unique BOM code to the spacecraft product and its components and assemblies at each level, ensuring that all data related to this BOM code (such as design documents, process documents, test reports, inspection records, etc.) is strongly associated with it when stored in the hierarchical classification database. It is understood that when any data changes, this module does not directly overwrite the old data, but generates a new data copy with a new version number, and records detailed historical information about the change, including the modifier, modification time, reason for modification, and associated change order number. In this way, all data forms a clear and tamper-proof version chain, thereby achieving accurate traceability throughout the entire data lifecycle.

[0035] The rule validation engine maintains connections with both the hierarchical classification database and the knowledge-driven engine module. Its function is to automatically validate the consistency and accuracy of data based on preset rules. The rule validation engine is triggered when new data is added to the hierarchical classification database or when existing data is updated. It first connects to the knowledge-driven engine module and retrieves applicable preset rules based on the type and context of the data to be validated. Then, it reads the data to be validated, along with other related cross-disciplinary data, from the hierarchical classification database and executes the rules. For example, a specific validation rule could be: "Rule A: The weight of component A output by the structural engineering discipline must have an error within ±2% of the weight of component A in the overall engineering design specifications." The rule validation engine will read the latest design documents from the structural engineering discipline and the overall engineering design specification documents, extract the weight value of component A, compare and calculate it. If the validation finds that the data does not conform to the preset rules, the rule validation engine will mark the corresponding data item. For example, it will update the status field of the data item in the database to "abnormal" or "pending review" and display it on the intelligent collaboration platform as a highlight or alarm icon. For serious non-compliance, an alarm mechanism can be configured to automatically notify the relevant responsible engineers and managers via system messages, emails, or SMS. The construction block diagram of the intelligent collaborative platform is as follows: Figure 3 As shown.

[0036] The spacecraft assembly data fusion processing method is executed by the aforementioned spacecraft assembly data fusion processing system, such as... Figure 2 As shown, it includes the following steps. Step S1: Multi-source heterogeneous data acquisition. The system acquires raw data from data sources such as computer-aided design systems and testing equipment through a data acquisition interface.

[0037] Step S2: Data Standardization and Classification Storage. The collected data is sent to a hierarchical classification database, where it is cleaned, formatted, and stored in a relational database or time-series database according to a pre-defined classification system.

[0038] Step S3: Version Association Based on Bill of Materials Code. The version penetration management module tags the data stored in the database with version tags and associates them with the corresponding product bill of materials codes to establish a traceability link.

[0039] Step S4: Automatic cross-disciplinary rule validation. As one of the core steps, the rule validation engine obtains rules from the knowledge-driven engine module and performs consistency and compliance checks on the data in the database.

[0040] Step S5: Data Sharing and Visualization. After verification, the processed and verified data, along with its version information and verification status, are presented to authorized users on the intelligent collaboration platform through the assembly collaboration model module. Users can query, browse, and analyze this highly integrated and reliable data for efficient collaborative work.

[0041] To further illustrate the working process and beneficial effects of this embodiment, it is assumed that this system was applied in the assembly, integration, and testing project of a certain type of communication satellite. This project involves approximately 1000 key data items, covering multiple fields such as design, testing, and manufacturing processes. For example, in a specific application, a structural engineer submitted the latest main load-bearing structure design document (version v2.1) through the intelligent collaborative platform. After receiving the document, the version penetration management module immediately associated it with the corresponding bill of materials code "SAT-COM-01-STR-001" and recorded the submitter and time. This operation triggered the rule verification engine. The engine matched a rule from the knowledge-driven engine module: "The allowable error between the main load-bearing structure weight (105kg) and the overall design index (100kg) is ±2%". The engine automatically performed the verification and found that the actual weight of 105kg exceeded the allowable upper limit of 102kg, and the verification failed. The system then executes preset response actions, including: marking the status of design file v2.1 as "verification failed - overweight" in the database; sending an early warning notification to the structural engineer and the overall professional manager via the system's built-in instant messaging tool, stating "The weight of the main load-bearing structure (SAT-COM-01-STR-001) design file v2.1 is out of tolerance, please verify"; and automatically preventing this version of the design file from entering the downstream process planning flow until the data problem is corrected and passes verification. Through the application of this system in this final assembly, integration, and testing project, statistical analysis shows that data standardization (such as unified naming and correct format) has improved by up to 86.7%. Simultaneously, the data error rate caused by manual transmission (such as version confusion and numerical entry errors) has significantly decreased from approximately 15% before application to below 2%, greatly ensuring development quality and improving collaborative efficiency.

[0042] This invention aims to solve the data fragmentation problem in the satellite AIT (Assembly, Integration, and Test) phase, achieving efficient collaborative management and standardized application of cross-disciplinary data, particularly suitable for data integration and optimization of complex spacecraft systems. Addressing the problems of low data management efficiency, insufficient cross-disciplinary collaboration, and poor data consistency and reliability in existing technologies for the spacecraft AIT phase, this invention proposes a spacecraft assembly data fusion processing method based on hierarchical classification databases and version penetration management. This method achieves efficient integration, verification, and traceability of cross-disciplinary data by establishing unified data specifications and standardized interfaces, and combines a rule engine to dynamically verify and optimize the data, ensuring data consistency and reliability.

[0043] Example 2 This embodiment focuses on an optimized data interaction process, designed to support the efficient, stable, and scalable operation of the system described in Embodiment 1. When processing massive amounts of high-concurrency assembly, integration, and testing data, decoupling and asynchronous communication between system modules are crucial.

[0044] like Figure 1 As shown, in this embodiment, the spacecraft assembly data fusion processing system further includes a message queue. This message queue (which can be implemented using mature open-source technologies such as RabbitMQ or Kafka) acts as a data bus and buffer between the data acquisition end and the core processing module. This design achieves loose coupling between modules and is key to building a high-performance, event-driven architecture.

[0045] The specific data interaction process is as follows: Step 1: Data Acquisition and Publishing. The data acquisition interface obtains raw data from various data sources (such as CAD software, simulation software databases, and test equipment) through an adapter. After preliminary formatting (e.g., adding message headers containing metadata such as data type, source, and timestamp), the acquired data is published as messages to designated topics or exchanges in message queues.

[0046] Step 2: Message Queue Buffering. The message queue receives and temporarily stores these data messages. Its existence acts as a buffer, enabling traffic smoothing and trough filling. Even if a large amount of data floods in within a short period (such as at the end of a large-scale simulation or test), it will not directly impact the backend database and processing modules, but will be smoothly cached, awaiting subsequent processing.

[0047] Step 3: Database Subscription and Storage. The database service subscribes to data in the message queue in a hierarchical and categorized manner. When a new message arrives, the database service retrieves it and routes it to the appropriate processing logic based on the category identifier in the message header. For example, data identified as product accessory information will be parsed and stored in a relational database; data identified as test data will be parsed and stored in a time-series database.

[0048] Step 4: Trigger Version Management. After data is successfully imported into the database, the database service will issue a data import success event notification. This event can be republished to another topic in the message queue or broadcast through other internal event bus mechanisms. The version penetration management module subscribes to such events.

[0049] Step 5: Version Tagging and Re-triggering. Upon receiving the event notification, the version penetration management module adds a version tag and BOM code association to the newly entered data based on the event content (such as the data's unique identifier and associated BOM information). After versioning is complete, the version penetration management module will also issue a version tagging completion event notification, triggering the subsequent processes again.

[0050] Step 6: Verification and Result Push. The rule verification engine subscribes to the version labeling completion event. Upon receiving the event, it initiates an automatic verification process for the corresponding data. After verification, the rule verification engine writes the verification result (e.g., updating the data status to "verified" or "abnormal") back to the hierarchical classification database via a reverse data update request or event. Simultaneously, the updated data status is reflected in real-time on the intelligent collaboration platform through the final assembly collaboration model module for user viewing.

[0051] Taking a specific work process as an example: A thermal control engineer completed a 72-hour thermal vacuum test on a satellite, generating several gigabytes of temperature sensor data. This data was published to a message queue in real time and in batches through a data acquisition interface. The time-series database service continuously consumed and stored this data from the queue. When the test ended, a complete test report in PDF format was submitted and also published to the message queue. The hierarchical classification database service received the report file, stored it in the file storage system, and created a metadata record in the relational database. This operation triggered the version penetration management module, which tagged the report with "TVAC-Test-Report-v1.0" and associated it with the satellite's bill of materials code. Subsequently, the rule verification engine was triggered, reading the report content (possibly requiring optical character recognition technology) and checking key parameters in the report, such as whether the peak sensor temperature at extreme temperatures exceeded the threshold defined in the design specifications. After successful verification, the status of the test report on the intelligent collaboration platform was updated from "verified" to "verified," and all engineers in relevant fields could see this credible report.

[0052] By adopting this asynchronous processing mechanism based on message queues and event-driven mechanisms, loose coupling between system modules is achieved. For example, even if the rule validation engine processes slowly due to rule complexity, it will not block the front-end data collection and storage process, thus ensuring the overall data throughput and high availability of the system. At the same time, this architecture also makes the system more scalable. If new data processing steps (such as data mining or machine learning model training) need to be added in the future, only new services need to be developed to subscribe to the relevant events, without modifying existing modules.

[0053] Example 3 Example 3 This embodiment details how the version penetration management module achieves precise data lifecycle traceability based on bill of materials (BOM) coding. This is one of the core technical means of this invention to solve the difficulty of data traceability and ensure product quality consistency. Specifically, the establishment of the version penetration management mechanism relies first on a hierarchical BOM coding system. This coding system can accurately describe the physical composition and structure of spacecraft products. In this embodiment, hierarchical coding is used to uniquely identify spacecraft products and their components or parts at each level. For example, the BOM code for an entire satellite can be defined as SAT-01. A power subsystem, as a sub-level of the entire satellite, can have its BOM code defined as SAT-01-PWR. A specific battery unit component within the power subsystem can be further defined as SAT-01-PWR-BAT-001. This hierarchical coding method not only uniquely identifies each physical object but also explains the assembly relationships between them.

[0054] Based on this, the version penetration management module mandates that all data entering the hierarchical classification database must be associated with its corresponding bill of materials (BOM) code. All data related to the battery cell assembly SAT-01-PWR-BAT-001, whether it's design drawings v1.0 from the design phase, test outlines v1.2 from the testing phase, or acceptance reports v1.0 from the production acceptance phase, must include the BOM code SAT-01-PWR-BAT-001 in their metadata when the data is entered into the database.

[0055] Based on the aforementioned bill of materials (BOM) coding system and data association mechanism, the system possesses penetrating traceability capabilities. The intelligent collaborative platform provides a traceability query interface. When a user needs to query the complete data history of a product or component, they simply enter its BOM code. For example, when a user enters SAT-01-PWR-BAT-001, the version penetration management module will perform a penetration query. Using this BOM code as an index, it retrieves all associated data items in the hierarchical classification database. The query results will be presented to the user in a clear and visual manner. For example, it can display all relevant documents for the battery unit from the initial design drawings (which may include multiple versions such as v1.0, v1.1, v2.0, etc.) to the final acceptance report in a timeline, or organize the data according to logical relationships (design -> manufacturing -> testing -> acceptance). Users can easily view any historical version of any file, as well as detailed records of each version change (modifier, time, reason, etc.).

[0056] To further illustrate its application value, consider a scenario of reviewing an on-orbit fault. An anomaly occurs in the power subsystem of a SAT-01 satellite in orbit, and the fault analysis team needs to quickly pinpoint the root cause. In traditional workflows, this process could take days or even weeks of cross-departmental document collection, organization, and verification. In contrast, with this system, analysts only need to input the power subsystem's bill of materials code, SAT-01-PWR, into the traceability interface of the intelligent collaborative platform. Within seconds, the system returns a complete set of data covering the entire lifecycle of the subsystem, including top-level design, detailed designs of subordinate components, procurement lists, assembly processes, and test reports at each stage. The analysis team can easily compare different versions of test reports. For example, they might quickly discover that in a certain ground test, the acceptance test data curve for a batch of battery cells (such as SAT-01-PWR-BAT-001 to SAT-01-PWR-BAT-010) exhibits a slight abnormal fluctuation, which was overlooked during the initial review. By further querying information such as the manufacturer and raw material batches of this batch of battery cells, the problem can be quickly located to that specific batch of battery cells. This rapid and accurate data traceability capability provides unprecedented and powerful technical support for product quality control, design iteration optimization, and fault-free troubleshooting.

[0057] Finally, the internal working logic of the cross-professional data automatic verification workflow provided in this embodiment of the invention, namely the rule verification engine, is described in detail below: Step 1: Receive the data to be verified. This step is usually triggered by an event.

[0058] Step 2: The engine matches applicable verification rules from the rule base provided by the knowledge-driven engine module based on the type and attributes of the data to be verified.

[0059] Step 3: The engine executes each rule to check the data.

[0060] Step 4: Determine the verification results: If all rules pass verification, the process proceeds to Step 5, updating the data status to "verified." Otherwise, if any verification fails, the process jumps to Step 6 to mark the abnormal data, and then triggers an alarm or notification in Step 7, sending warning information to relevant personnel. This automated closed-loop verification process constitutes a reliable barrier to ensure the quality of data flowing into the collaborative environment.

[0061] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0062] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A spacecraft assembly data fusion processing system, characterized in that, include: Dual-core architecture, hierarchical and categorized database, version penetration management module, and rule verification engine; The dual-core architecture includes an assembly collaboration model module and a knowledge-driven engine module; The hierarchical classification database is connected to the dual-core architecture and is used for standardized, hierarchical, classified storage and visualization of product supporting information, design requirements and test data; The version penetration management module is connected to the hierarchical classification database to establish a product version penetration management mechanism based on preset codes; The rule verification engine is connected to the hierarchical classification database and the knowledge-driven engine module, and is used to automatically verify data based on preset rules.

2. The spacecraft assembly data fusion processing system according to claim 1, characterized in that, The assembly collaboration model module is used to realize cross-disciplinary collaborative design, real-time data sharing, and support for the approval process; The knowledge-driven engine module is used for intelligent decision-making and optimization based on historical data and business rules.

3. The spacecraft assembly data fusion processing system according to claim 1, characterized in that, The hierarchical classification database includes relational databases and time-series databases; The relational database is used to store product matching information, design requirements, and specification data with obvious structured features and complex relationships; The time-series database is used to store test data and result sequence data with timestamps.

4. The spacecraft assembly data fusion processing system according to claim 1, characterized in that, The version penetration management module establishes a product version penetration management mechanism based on hierarchical bill of materials (BOM) coding, identifies spacecraft products and components or parts at each level, and realizes full lifecycle data traceability. When data changes, a new version is generated without overwriting the old version, and the change history is recorded.

5. The spacecraft assembly data fusion processing system according to claim 1, characterized in that, It also includes a message queue, which is used for asynchronous data transmission between the hierarchical classification database and the rule verification engine; The rule verification engine is connected to the knowledge-driven engine module to obtain preset rules, and is used to mark the corresponding data items or trigger an alarm when the verification finds that the data does not conform to the preset rules.

6. A method for data fusion processing in spacecraft assembly, characterized in that, The method, performed by the spacecraft assembly data fusion processing system according to any one of claims 1 to 5, comprises: Step S1: Collaborative sharing is achieved through the final assembly collaborative model module, and intelligent decision-making and optimization are performed based on historical data and business rules through the knowledge-driven engine module; Step S2: Store the spacecraft assembly data in a hierarchical classification database for standardized, hierarchical, and visual storage; Step S3: Establish a product version penetration management mechanism based on preset coding; Step S4: Obtain preset rules from the knowledge-driven engine module; Step S5: The spacecraft assembly data is automatically verified based on the acquired preset rules through the rule verification engine.

7. The spacecraft assembly data fusion processing method according to claim 6, characterized in that, The collaborative sharing mentioned in step S1 specifically refers to: using the general assembly collaborative model module to realize cross-professional model collaborative design, real-time data sharing, and approval process support.

8. The spacecraft assembly data fusion processing method according to claim 6, characterized in that, The data storage in step S2 specifically involves storing product matching information, design requirements and specification data into a relational database, and storing test data and result sequence data into a time-series database.

9. The spacecraft assembly data fusion processing method according to claim 6, characterized in that, The preset code mentioned in step S3 is a hierarchical Bill of Materials (BOM) code; The established product version penetration management mechanism also includes: generating a new data version when data changes, without overwriting the old version, and recording the change history.

10. The spacecraft assembly data fusion processing method according to claim 6, characterized in that, The method also includes: using a message queue to perform asynchronous data transmission between the hierarchical classification database and the rule verification engine.

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