Spacecraft ground test digital management method and system

Abstract

The application provides a spacecraft ground test digital management method and system, and relates to the field of spacecraft ground test. The method comprises the following steps: obtaining unstructured test planning information; performing structured design on the test planning information, wherein the structured design comprises distinguishing the model-free general content and the model-specific content in the information, and performing structured processing on the two contents respectively to form structured test requirement data; performing structured design on the test scheme based on the structured test requirement data; performing test implementation and automatically collecting test data based on the structured test scheme; performing generalization processing and statistical analysis on the test data; and automatically generating test result output based on the result of the statistical analysis. The application solves the problems of poor timeliness, non-uniform data, low reuse rate and poor information flow in test requirement transmission, realizes full-process digital closed-loop management, improves design efficiency and knowledge reuse rate, and ensures data consistency.

Description

Technical Field

[0001] This application relates to the field of spacecraft ground testing, and in particular to a digital management method and system for the entire process of spacecraft ground testing. Background Technology

[0002] During the development of spacecraft, a series of complex and important large-scale system-level ground tests are required, such as performance testing, docking tests, and various environmental adaptability tests. These tests are typically characterized by high technical difficulty, complex product conditions, and a large amount of data involved.

[0003] In current technological practices, the process management and collaboration of spacecraft ground tests largely rely on an offline document-based transmission model between various units. Specifically, the spacecraft overall design unit first prepares the test outline or test requirements document, then the assembly unit uses this document to develop a detailed test plan, organize the test implementation, and process the test data, and finally the overall design unit summarizes the test results. This collaborative model has several significant drawbacks: First, the information transmission chain is long, with each link relying on document handover, resulting in poor timeliness and difficulty in quickly responding to changes in design status. Second, data formats are often inconsistent between different units and systems, leading to low data quality during transmission and requiring significant manual effort for data format conversion and repeated verification, which is inefficient and prone to errors. Third, test outlines, requirements, and plans exist in unstructured document form, making it difficult to effectively accumulate and reuse requirements, processes, and experience across different spacecraft models. This means that the test design work for each new model needs to start from scratch, resulting in extremely low reuse rates. Finally, data from each link is scattered across documents from different units, forming information silos and severely hindering the unified management and in-depth analysis of test data.

[0004] While some digital systems are currently being used for test process management and data analysis—such as test planning, test process status management, test data aggregation and analysis, and automatic report generation—these systems often focus on digitizing the test implementation and subsequent analysis phases. They fail to address the root causes of the test process, namely the test outline and requirements, by structuring the data flow between design, implementation, and analysis. Consequently, the design, implementation, and analysis phases of the entire test process remain disconnected, failing to form an efficient and seamless business loop. Summary of the Invention

[0005] In view of the deficiencies in the existing technology, the purpose of this invention is to provide a digital management method and system for spacecraft ground tests.

[0006] A digital management method for spacecraft ground testing, provided in this application, is characterized by the following steps: acquiring unstructured test planning information; performing structured design on the test planning information, wherein the structured design includes: distinguishing between de-type-specific general content and type-specific content in the test planning information, and performing structured processing on the general content and specific content respectively to form structured test requirement data; performing structured design of test schemes based on the structured test requirement data; conducting test implementation and automatically collecting test data based on the structured test schemes; performing generalization processing and statistical analysis on the test data; and automatically generating test result output based on the results of the statistical analysis.

[0007] Optionally, the structured design of the test planning information specifically includes: using natural language processing technology to parse the test planning information to identify test entities and distinguish between general and specific content.

[0008] Optionally, the structured design of the test plan includes: designing the test plan into at least one structured form among objects, processes, tables, or pictures to form the structured test plan.

[0009] Optionally, the generalization process includes: performing data alignment or data cleaning on the test data.

[0010] Optionally, the statistical analysis includes at least one of the following: analyzing the matching between the test results of a single project and the indicators; statistically analyzing the differences in test results between different projects; performing single-model performance drift analysis; and performing cross-model association rule mining.

[0011] Optionally, the test results output includes: a structured web-based display supporting online collaborative review, and / or, an automatically generated document report for offline archiving.

[0012] Optionally, the test planning information includes at least one of the following: test objective, product technical status, test content, test items, test procedure, and success criteria.

[0013] This application also provides a digital management system for spacecraft ground tests, characterized by comprising: an information acquisition module for acquiring unstructured test planning information; a test planning information structuring design module for structuring the unstructured test planning information, wherein the structuring design includes: distinguishing between de-type-specific general content and model-specific content in the test planning information, and performing structuring processing on the general content and specific content respectively to form structured test requirement data; a test scheme structuring design module for designing test schemes based on the structured test requirement data; a test implementation and data acquisition module for implementing tests and automatically acquiring test data based on the structured test scheme; a test data processing and analysis module for performing generalization processing and statistical analysis on the test data; and a test result output module for automatically generating test result output based on the results of the statistical analysis.

[0014] Optionally, the experimental planning information structured design module includes at least one of the following: a document extraction submodule; a structured requirements design submodule; and a structured display submodule for previewing during the design process.

[0015] Optionally, the structured design module of the test scheme includes at least one of the following: a structured project design submodule; a structured process design submodule; and a structured display submodule for previewing during the design process.

[0016] Optionally, the test data processing and analysis module includes: a general data processing submodule and a data statistical analysis submodule.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. Improved design efficiency and knowledge reusability. By structuring test planning information such as test outlines or requirements, and particularly by innovatively distinguishing between model-specific and general content, a reusable test knowledge base can be established. This changes the inefficient traditional model that relies on documents and repetitive writing, enabling cross-model tests to quickly call upon and inherit general requirements, significantly improving the efficiency and standardization of test design.

[0019] 2. Ensured data consistency and seamless information flow. Through a unified digital platform and structured data throughout the entire design and implementation process, as well as the automatic collection and generalized processing of experimental data, the problems of low quality and inconsistent information caused by inconsistent data formats and manual transmission were fundamentally solved, achieving seamless information flow throughout the entire process from design to implementation and data analysis.

[0020] 3. Integrated closed-loop management was achieved, improving overall test efficiency. This application integrates previously fragmented processes such as test outline design, scheme design, process implementation, data acquisition and analysis, and test summary into a unified digital workflow. This achieves integrated design, implementation, and analysis, as well as a closed-loop business process, significantly shortening the test cycle and improving the overall efficiency and management quality of spacecraft ground tests.

[0021] 4. Improved automation of summary and analysis. By conducting in-depth analysis of experimental data and automatically generating experimental results output, such as standardized summary reports, analysts are freed from tedious report writing, which not only improves work efficiency but also ensures the standardization, accuracy, and traceability of reports. Attached Figure Description

[0022] 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 flowchart illustrating a digital management method for the entire process of spacecraft ground testing, as provided in this application embodiment.

[0023] Figure 2 This is a functional module structure diagram of a digital management system for spacecraft ground testing provided in an embodiment of this application.

[0024] Figure 3 This is a flowchart of an intelligent structuring method for experimental outlines in one embodiment of this application.

[0025] Figure 4 This is a schematic diagram of the interface for collaborative review and dual-modal output functions in one embodiment of this application.

[0026] Figure 5 This is a timing diagram showing the signaling interactions between the participating parties in the embodiments of this application. Detailed Implementation

[0027] 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.

[0028] Example 1 This embodiment provides a basic implementation scheme for the digital management of the entire process of spacecraft ground testing. The scheme aims to effectively connect previously independent stages such as test planning, program design, test implementation, data analysis, and summary reporting through an integrated digital management system, thereby constructing a complete business closed loop from unstructured information input to structured result output.

[0029] Please see Figure 1 and Figure 2 ,in, Figure 1 This application provides an overall flowchart of a digital management method for the entire process of spacecraft ground testing. Figure 2 The functional module structure diagram of the spacecraft ground test digital management system 100 for implementing this method is shown below. In one embodiment of this application, the working process of the method and system will be described in detail using a typical thermal vacuum test of a certain type of communication satellite as an example.

[0030] First, in step S10, the test outline or requirements are compiled and obtained. Specifically, engineers from the spacecraft overall design unit use their client equipment, namely... Figure 5 The overall design terminal 300 shown logs into the spacecraft ground test digital management system 100. The engineer uploads a preliminary, unstructured test planning document for this communication satellite thermal vacuum test to the designated interface of the spacecraft ground test digital management system 100. This document could be a Word document titled "Outline of Thermal Vacuum Test for XX-01 Communication Satellite." It should be noted that this document is typically a product of traditional work methods, consisting of a mix of natural language text, tables, and a few diagrams, containing a preliminary test plan. The test planning information may include at least one of the following: test objective, product technical status, test content, test items, test procedure, and success criteria. For example, the test objective section describes it as "verifying the correctness of the satellite's thermal control system design and the reliability of each device's operation under the simulated high vacuum, cold black background, and alternating solar radiation thermal environment of outer space"; the product technical status section lists the satellite units, software version numbers, and ground test equipment involved in the test in tabular form; the test items section specifies multiple operating conditions to be performed, such as "high temperature stable operating condition," "low temperature stable operating condition," and "high and low temperature alternating operating condition"; the success criteria section defines specific indicators such as "all telemetry parameters are within the allowable range" and "the power consumption fluctuation of key units does not exceed 5%." After receiving this unstructured document, the information acquisition module of the spacecraft ground test digital management system 100 stores it and uses it as the starting point for the entire digital process.

[0031] Subsequently, the process proceeds to step S20, where the acquired test planning information is structured. This step can be executed automatically or semi-automatically by the test outline / requirement structured design module 10 in the spacecraft ground test digital management system 100. This module aims to transform unstructured document content into machine-readable, processable, and reusable structured data. Specifically, the document extraction submodule 11 within the test outline / requirement structured design module 10 is first activated, employing advanced document parsing technology to automatically read the uploaded Word document and identify and initially separate text paragraphs, tables, images, and other elements.

[0032] Based on this, the Test Outline / Requirements Structured Design Module 10 performs a crucial operation: distinguishing between "de-model-specific general content" and "model-specific content" in the test planning information. De-model-specific general content refers to content that is not dependent on a specific satellite model and is generally applicable in similar tests (such as all thermal vacuum tests). Examples include sections such as "Preface," "References," "General Test Environment Requirements" (e.g., vacuum level better than 1.33 x 10^-3 Pa, test chamber inner wall temperature below 100 K), "Safety Requirements" (e.g., personnel operating procedures, equipment grounding requirements), and "Division of Labor and Job Requirements." Correspondingly, model-specific content refers to specific information directly related to this "XX-01" satellite, such as the aforementioned "Product Technical Status" list, specific temperature ranges in the "Test Items" (e.g., high-temperature stable operating condition requirement +75℃, low-temperature stable operating condition requirement -65℃), and specific "Success Criteria."

[0033] After differentiation, the system performs differentiated structured processing. For general content that is not model-specific, the system calls upon a pre-built general knowledge base containing a large number of standardized test clauses for structured design, such as automatically matching and loading a standard "thermal vacuum test safety requirements" structured template. For model-specific content, the system guides the overall design engineer to perform structured detailed design through interactive forms or editors provided by the structured project design submodule 12. For example, the system generates a structured form for "test items," where the engineer needs to fill in the "high temperature stable operating conditions" described in the document into the "item name" field, "+75℃" into the "temperature target value" field, and "lasting 48 hours" into the "duration" field. Through this series of operations, the unstructured information that was originally scattered throughout the document is transformed into clearly defined, structured test requirement data and stored in the system database.

[0034] Further, the process proceeds to step S30, where, based on the structured test requirement data generated in the previous step, a structured design of the test plan is carried out. Please continue reading... Figure 5 After completing the structured design, the digital management system 400 sends a notification to the assembly and testing terminal 500 used by the engineers at the assembly and testing unit. The assembly and testing engineers log into the spacecraft ground test digital management system 100 and enter the test plan structured design module 20 to create a new test plan. At this time, the system automatically loads and displays all the structured test requirement data previously determined by the overall design unit, ensuring the accurate and lossless transmission of design requirements. Based on this, the assembly and testing engineers can use various design tools provided by the system, such as the structured process design submodule, to draw detailed test flowcharts by dragging and dropping components, clearly defining the sequence and dependencies of each step (such as "vacuuming," "cooling," "powering on," "telemetry interpretation," etc.). Engineers can also use the object editor to define the specific equipment used in the test, personnel assignments, interface connections, etc. In this way, the entire test plan is designed as a collection of structured objects, processes, tables, and other elements, rather than a static document. This greatly improves the standardization and accuracy of the plan design and lays a solid foundation for subsequent automated implementation.

[0035] Subsequently, step S40 is initiated for experimental implementation and automatic data acquisition. At the test site, the experimental implementation and data acquisition module 30 automatically or semi-automatically configures the relevant testing equipment and software based on the structured test plan designed in the previous step. For example, the system can automatically issue a "heat to +75℃" command to the temperature control equipment and, according to the process definition, automatically trigger the data acquisition task after reaching stable conditions. Throughout the entire test, the experimental implementation and data acquisition module 30 automatically and in real-time collects massive amounts of test data through integrated hardware and software interfaces with various sensors, telemetry devices, and ground testing equipment. This includes telemetry values ​​of temperature at various parts of the satellite, telemetry values ​​of voltage and current at each individual unit, and pressure values ​​in the vacuum chamber. All collected data is accurately timestamped and stored in real-time or in batches in the central database of the spacecraft ground test digital management system 100, effectively avoiding errors and delays that may be caused by manual recording and transcription.

[0036] After the test is completed, the process proceeds to step S50, where the collected test data undergoes generalization processing and statistical analysis. This step is executed by the test data processing and analysis module 40. First, the generalized data processing submodule 41 performs generalization processing on the raw data. Given the potential differences in sampling rates and data formats among different sensors or devices, this submodule converts these heterogeneous data into a unified, standardized data format, such as a standardized time series data format, and stores it in the database. This process is a crucial prerequisite for achieving cross-system and cross-model data analysis. Subsequently, the data statistical analysis submodule 42 is activated, automatically performing a matching analysis between the test results and preset indicators based on the structured success criteria defined in the test outline. For example, the system automatically extracts all temperature telemetry data during the "high-temperature stable operating condition," calculates its mean and fluctuation range, and compares it with the requirement of "+75℃±2℃" in the criteria, thereby automatically determining whether the test has passed.

[0037] Finally, in step S60, based on the statistical analysis results, the test results output, such as a test summary report, is automatically generated. The test summary report synthesis module 50 automatically extracts the various analysis results, key data graphs, and qualification judgment conclusions generated in step S50 according to a preset report template, and fills these contents into the corresponding sections of the template, for example, visually displaying them in the form of tables, images, etc. Ultimately, the system automatically synthesizes a complete and formatted draft of the "XX-01 Communication Satellite Thermal Vacuum Test Summary Report." Engineers from the overall design and assembly testing units can preview, modify, and finally confirm the report online.

[0038] Through the above steps, this embodiment fully demonstrates how to utilize a unified spacecraft ground test digital management system 100 to achieve end-to-end digital closed-loop management of spacecraft ground tests, from outline preparation to summary report. The entire process is driven by structured data, with information flowing efficiently and accurately between the overall design terminal 300, the digital management system 400, and the final assembly and testing terminal 500, thereby significantly improving test efficiency and data quality.

[0039] Example 2 This embodiment is a detailed description of a preferred implementation of step S20, "Structured Design of Experiment Outline / Requirements," in Embodiment 1. As an optional implementation, its core lies in introducing natural language processing and machine learning technologies to achieve intelligent and automated parsing and classification of unstructured experiment planning information (such as experiment outline documents), thereby minimizing manual intervention and improving the efficiency and accuracy of structured design.

[0040] Please see Figure 3The figure shows a detailed flowchart of the intelligent structured test outline method in this embodiment. This process is also executed by the test outline / requirement structured design module 10 in the spacecraft ground test digital management system 100.

[0041] The process begins at step S210, where an unstructured document is input. Similar to Example 1, the overall design engineer uploads a test outline document in Word or PDF format.

[0042] Next, the process moves to the core processing stage of this embodiment. In step S220, the system performs named entity recognition processing. The test outline / requirement structured design module 10 integrates a named entity recognition engine based on a pre-trained language model (such as BERT or a similar model). This engine has been trained on a large corpus of aerospace test documents and can accurately identify test entities with specific meanings in the text. For example, when the engine processes the statement "Under high-temperature stable operating conditions, the temperature of unit A is required not to exceed +85℃", it can automatically identify and label "high-temperature stable operating conditions" as a "test project" entity, "unit A" as a "product component" entity, "temperature" as a "test parameter" entity, and "not exceeding +85℃" as a "constraint condition" entity. By scanning the entire document, the system can extract all key test entities and their attributes.

[0043] Next, in step S230, the system performs text classification processing to automatically distinguish between "de-modeling general content" and "model-specific content." To this end, the system employs an intelligent method combining text classification algorithms. The system traverses each paragraph or logical unit (such as a chapter) of the document and calculates its textual features. One feasible technique is to combine a pre-built database containing all historical model test outlines to calculate the term frequency-inverse document frequency score for each paragraph. If the content of a paragraph (e.g., the "Safety Precautions" chapter) appears frequently in multiple different model test outlines in the database and has a high textual similarity, the system automatically classifies it as "de-modeling general content." Conversely, if a paragraph contains a code specific to the current model (e.g., "XX-01"), a unique parameter range, or new requirements not found in historical documents, the system classifies it as "model-specific content."

[0044] After completing entity recognition and content classification, the process proceeds to step S240, where knowledge graph mapping is performed. The system doesn't simply list the identified entities and content; instead, it maps them to a predefined spacecraft ground test knowledge graph. This knowledge graph is stored in the form of a graph database, where nodes represent various entities (such as test items, product components, test parameters, constraints, etc.), and edges represent the relationships between them. For example, the system might identify "high-temperature stable operating condition" as a "test item" node and connect related entities such as "Unit A," "temperature," and "not exceeding +85℃" as attributes or associated nodes to this project node, forming semantic relationships such as "(high-temperature stable operating condition) - contains -> (test object: Unit A) - has -> (parameter: temperature) - satisfies -> (constraint: not exceeding +85℃)". In this way, scattered information in the document is organized into a knowledge network with rich semantic connections.

[0045] Finally, in step S250, the system outputs structured experimental requirements data based on the constructed knowledge graph. This data is provided to the downstream experimental scheme structured design module 20 in a standard format (such as JSON or XML). Simultaneously, the system can also visualize the results of intelligent parsing and classification on an interactive user interface for final confirmation and fine-tuning by the overall design engineer. For example, the system can display all experimental items and their associated parameters and constraints in a tree or graphical structure, allowing engineers to easily check, modify, or supplement information.

[0046] Compared to the method in Example 1, which relied on manual form filling, this example significantly improves the automation and accuracy of experimental outline structuring by introducing natural language processing technology. This not only frees manual labor from tedious copy-pasting and information extraction, reducing processing time by over 80% according to calculations, but also allows for the discovery and solidification of implicit relationships from unstructured text through knowledge graph construction. This continuously enriches and optimizes the experimental knowledge base, laying the foundation for higher-level knowledge reuse and intelligent decision-making.

[0047] Example 3 This embodiment is a deepening and expansion of step S50 "Experimental Data Analysis" in Embodiment 1, aiming to introduce more advanced data processing and multidimensional analysis models to achieve in-depth mining and predictive analysis of experimental data. In other words, it enables the system not only to determine the success or failure of a single experiment, but also to provide data insights to support product improvement and experimental plan optimization.

[0048] The functions of this embodiment are mainly implemented by the test data processing and analysis module 40 in the spacecraft ground test digital management system 100. This module includes a general data processing submodule 41 and a data statistical analysis submodule 42.

[0049] First, in the data generalization processing stage, the data generalization processing submodule 41 performs more refined operations than in Example 1. Besides unifying the data format, its focus is on data alignment and data cleaning. Data alignment is key to resolving inconsistencies in sampling rates between data from different sources. For example, in a thermal vacuum experiment, a satellite temperature sensor might report data at a frequency of 1 Hz, while the vacuum gauge pressure data might be reported every 10 seconds. To perform correlation analysis, the data generalization processing submodule 41 uses appropriate interpolation algorithms (such as linear interpolation or spline interpolation) to resample the low-frequency data on the time axis, aligning it with the time points of the high-frequency data, thereby generating a time-synchronized multidimensional dataset. Data cleaning aims to ensure the quality of the data used for analysis. This submodule integrates a series of anomaly detection algorithms, such as isolated forest or the statistically based 3-sigma criterion. The system automatically scans all acquired time-series data, identifies obvious noise points, data spikes, or flat lines caused by sensor failure, and automatically marks or removes these anomalous data to avoid interfering with subsequent analysis results.

[0050] Next, in the statistical analysis phase, the data statistical analysis submodule 42 adds at least two advanced analysis functions based on Example 1: single-model performance drift analysis and cross-model association rule mining.

[0051] Single-model performance drift analysis is primarily applied to tests requiring long-term operation, such as life tests or long-term stability assessments. For such tests, simply determining whether the final result is within the specified range is insufficient; monitoring the stability of key performance parameters throughout the entire process is more important. Therefore, the data statistical analysis submodule 42 introduces a statistical process control (SPC) approach. For example, the system continuously collects data on the output power parameter of a key RF unit during the test and automatically plots its mean-range graph. By analyzing this control graph, the system can automatically detect non-random fluctuation patterns, such as a "trend" pattern where the parameter mean continuously rises or falls, or a "cyclic" pattern of periodic fluctuations. Once early signs of such performance drift are detected, even if the parameter value has not yet exceeded the final specification limits, the system will immediately issue an alert to the analysis engineer to promptly investigate the root cause of the problem and avoid serious issues later in the test or after the product is on track.

[0052] Cross-model association rule mining is a more in-depth data mining function, aiming to discover potential and unknown patterns from the historical test data of all models. The data statistical analysis submodule 42 gathers all standardized test databases that have undergone generalization processing and applies association rule mining algorithms (such as Apriori or FP-Growth algorithms) to discover strong correlations between data items across different dimensions. For example, the system might analyze vibration test data from all models to discover a rule: "When component A comes from a specific supplier batch and the test environment temperature is above 30℃, the probability that the vibration response spectrum of this component exceeds the standard in the Z-axis direction is 85%." Such analysis results are no longer simple "pass / fail" judgments, but reveal complex potential relationships between product performance and factors such as suppliers, environmental conditions, and manufacturing batches. These discovered patterns and insights have extremely important guiding value for locating common quality problems, improving supply chain management, and optimizing the design and testing plans for future models.

[0053] In addition, the data statistical analysis submodule 42 can also support the analysis of differences in test results between different projects. For example, in a single test, it can compare the performance differences of the same parameter under "high temperature conditions" and "low temperature conditions"; or in similar tests of different models, it can compare the distribution differences of key performance indicators to evaluate the effects of design improvements.

[0054] This embodiment, by introducing sophisticated data preprocessing techniques and advanced statistical and machine learning analysis methods, achieves an upgrade from "data statistics" to "data insights." The system can not only automatically evaluate test results, but also proactively identify performance drift trends, warn of potential risks, and uncover common patterns across different models, providing unprecedented and powerful data support for improving the quality of spacecraft products and continuously optimizing the development process.

[0055] Example 4 This embodiment focuses on collaborative work models and diverse delivery methods in areas such as experiment planning, scheme design, and results summarization. By providing a web-based collaborative review platform and supporting dual-modal output functions that include online structured display and offline document reports, this embodiment aims to solve the problems of low efficiency and version confusion in traditional offline review and approval processes, and to meet the diverse needs of different users during digital transformation.

[0056] Please see Figure 4 and Figure 5 ,in, Figure 4 The collaborative review and bimodal output interface in this embodiment is illustrated schematically. Figure 5 This demonstrates the information exchange between the participating parties.

[0057] In this embodiment, the spacecraft ground test digital management system 100 provides a collaborative platform based on Web technology. After the overall design unit completes the structured design of the test outline (step S20), or the assembly unit completes the structured design of the test plan (step S30), this structured content can be visualized on a dedicated collaborative review interface 200, instead of just being stored as data in the background.

[0058] like Figure 4 As shown, the core of the collaborative review interface 200 is the structured content display area 210. Within this area, each structured item of the test outline or plan, such as a specific technical requirement, a process step, or a test item, is displayed clearly and independently. Users with different roles, such as the overall engineer from the overall design terminal 300, the test engineer from the final assembly test terminal 500, and representatives from the quality management department, can log in to the platform using their respective accounts and perform online operations on any specific structured item. For example, if a quality engineer, while reviewing a success criterion, believes its definition is not rigorous enough, they can directly enter a modification suggestion in the review comment box 211 next to the item, such as "Suggest adding an assessment requirement for data stability time." The system will automatically record who made the comment, when, and for which item. Other relevant personnel can immediately see this comment and respond or adopt it. For stages requiring formal approval, users can also use the electronic signature function to approve items or the entire document online.

[0059] Understandably, this online collaborative review model has significant advantages over traditional methods such as sending documents via email or holding in-person review meetings. First, all review comments and revision records are precisely linked to specific structured items and are permanently saved by the system, forming a complete, clear, and traceable review history, thus completely eliminating problems caused by inconsistent document versions. Second, the review process is parallel and asynchronous, allowing experts to log in at their convenience to conduct reviews, which greatly improves review efficiency; statistics show that it can shorten the review cycle by more than 50%.

[0060] After all reviews and modifications are completed and the final version is confirmed, this embodiment provides a flexible bimodal output function, which is reflected in: Figure 4 The output options are in section 220. Users can choose different output methods according to different application scenarios.

[0061] The first method is "online access." Team members can continue to view and reference the final version of the structured data through a web interface (i.e., the collaborative review interface 200). For example, during the trial implementation phase, on-site testers can directly view the structured trial steps on their terminals, checking off each completed step, and the system automatically records the execution status. This mode fully leverages the advantages of digital, structured data, enabling dynamic interaction and real-time updates.

[0062] The second method is "offline report generation." Considering that standardized traditional documents are still needed in many situations, especially for formal archiving, reporting to superiors, or communicating with external units, users can click the "Generate Offline Report" button 221 in the output options area 220. After clicking, the system's test summary report synthesis module 50 (or a similar module, also applicable to generating outlines and program documents) is activated. This module reads the final version of structured data and automatically fills the data into the corresponding positions in the preset template selected by the user (e.g., a report template conforming to national military standards or internal unit specifications). The system can automatically handle formatting, arrange headers and footers, generate a table of contents, and finally export a PDF or Word document that is completely identical in content to the online version but with a highly standardized format.

[0063] This dual-modal output mechanism of "design once, release twice" cleverly balances the efficiency of digital interaction with the seriousness and standardization requirements of traditional document delivery. It not only greatly enhances the system's usability and user acceptance, but also provides a smooth transition solution for aerospace research and development units undergoing digital transformation, ensuring that they can enjoy the convenience brought by digitalization while meeting existing management and archiving systems.

[0064] 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.

[0065] 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 digital management method for spacecraft ground tests, characterized in that, Includes the following steps: Obtain unstructured experimental planning information; The test planning information is structured, which includes: distinguishing between general content and model-specific content in the test planning information, and performing structured processing on the general content and model-specific content respectively to form structured test requirement data; Based on the structured test requirement data, a structured design of the test plan is carried out; Based on the structured test plan, the test is carried out and the test data is automatically collected; The experimental data were then subjected to generalization processing and statistical analysis. Based on the results of the statistical analysis, the experimental results are automatically generated.

2. The method according to claim 1, characterized in that, The structured design of the experimental planning information specifically includes: Natural language processing technology is used to parse the test planning information in order to identify test entities and distinguish between general and specific content.

3. The method according to claim 1, characterized in that, The structured design of the experimental scheme includes: The test plan is designed in at least one structured form, such as objects, processes, tables, or pictures, to form the structured test plan.

4. The method according to claim 1, characterized in that, The generalization process includes: The experimental data were aligned or cleaned.

5. The method according to claim 1, characterized in that, The statistical analysis includes at least one of the following: Analyze the matching between the test results of individual projects and the indicators; The differences in experimental results between different projects were statistically analyzed; Perform single-model performance drift analysis; Perform cross-model association rule mining.

6. The method according to claim 1, characterized in that, The test results output includes: Supports structured web-based presentation for online collaborative review, and / or automatically generated document reports for offline archiving.

7. The method according to claim 1, characterized in that, The trial planning information includes: The test objective, product technical status, test content, test items, test procedure, and success criteria must include at least one of the following: test objective, product technical status, test content, test items, test procedure, and success criteria.

8. A digital management system for spacecraft ground testing, characterized in that, include: The information acquisition module is used to acquire unstructured experiment planning information; The test planning information structured design module is used to structure the unstructured test planning information. The structured design includes: distinguishing between de-modeling general content and model-specific content in the test planning information, and performing structured processing on the general content and specific content respectively to form structured test requirement data. The test scheme structured design module is used to design test schemes based on the structured test requirement data. The test implementation and data acquisition module is used to implement the test and automatically collect test data based on the structured test plan. The test data processing and analysis module is used to perform generalized processing and statistical analysis on the test data; The test result output module is used to automatically generate test result output based on the results of the statistical analysis.

9. The system according to claim 8, characterized in that, The experimental planning information structured design module includes at least one of the following: Document extraction submodule; Structured design requires the design of sub-modules; A structured presentation submodule used for previewing during the design process.

10. The system according to claim 8, characterized in that, The structured design module for the test plan includes at least one of the following: Structured project design submodule; Structured process design submodule; A structured presentation submodule used for previewing during the design process.

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