Problem analysis and evaluation method and system for on-orbit flight task

Abstract

The invention provides a problem analysis and evaluation method and system for an on-orbit flight task. The method comprises the following steps: leading on-orbit data; carrying out normalization processing and storage on the on-orbit data according to a time dimension; according to the time of the on-orbit data, carrying out sub-library storage according to a first set time interval, carrying out sub-table storage on the data in one library according to a second set time interval, and carrying out partition processing on the data in one table according to a third set time interval; and processing the on-orbit data according to a set analysis and evaluation criterion to obtain the problem of the on-orbit task. The method has the advantages that the source of poor in-orbit flight effect can be directly positioned, including factors such as aircraft performance, load performance and human decision.

Description

Technical Field

[0001] This application belongs to the field of aerospace engineering, and specifically relates to a method and system for problem analysis and evaluation for on-orbit flight missions. Background Art

[0002] An on-orbit flight mission refers to the specific behaviors of a space vehicle to explore, develop, and utilize space and celestial bodies. It has a special status in aspects such as international politics, national economy, and social impact, and is an important indicator to evaluate a country's comprehensive national strength. It usually includes multiple stages such as spacecraft orbit transfer, rendezvous and docking, separation and evacuation, and payload power on / off, involving multiple components such as the spacecraft platform and payload. And the problem analysis and evaluation of on-orbit flight missions, as the basis of the space mission control and command and decision-making system, is the guarantee for the successful operation of space missions. The results of the problem analysis and evaluation of on-orbit flight missions are directly related to whether the next stage of the mission can proceed, indicating the important position of the problem analysis and evaluation of on-orbit flight missions in the process of space operation.

[0003] Due to the unpredictability of the space environment, the limitations of human understanding, and the large size, numerous functions, complex systems of the satellite, and tens of thousands of components on the satellite, the operating state of the spacecraft has risks and challenges. The existing related theoretical methods cannot cover the entire on-orbit flight process, and it is impossible to classify and analyze the impacts of factors such as the performance of the spacecraft and payload and human decision-making on on-orbit flight. Therefore, it is still a very challenging research to model, detect, and diagnose on-orbit flight missions; on the other hand, to meet the increasingly complex construction of spacecraft and payloads, it is urgent to solve the problem analysis theory and methods for on-orbit flight missions under the background of dense flight missions, networked equipment composition, and multi-source data correlation. Therefore, carrying out research on the problem analysis method for on-orbit flight missions has great theoretical value and practical significance. Summary of the Invention

[0004] The purpose of this application is to overcome the defect that it is difficult to analyze and evaluate on-orbit flight problems in the prior art.

[0005] To achieve the above purpose, this application proposes a method for problem analysis and evaluation for on-orbit flight missions, including:

[0006] Step S1: Access on-orbit data;

[0007] Step S2: Normalize and store the on-orbit data according to the time dimension; store the on-orbit data in sub-libraries at a first set time interval according to the time of the on-orbit data, store the data in a library in sub-tables at a second set time interval, and partition the data in a table at a third set time interval;

[0008] Step S3: Process the on-orbit data according to the set analysis and evaluation criteria to obtain the problems of on-orbit missions.

[0009] As an improvement of the above method, the construction process of the analysis and evaluation criteria includes:

[0010] Step A1: Construct a calculation unit for the analysis operator; the calculation unit includes calculation functions for basic geometric operations;

[0011] Step A2: Construct an index system for problem analysis and evaluation; the problem analysis index system is a multi-layer structure; the bottom layer of the multi-layer structure is the analysis criteria; the analysis criteria include an algorithm model for data processing using the analysis operator;

[0012] Step A3: Store the index system for problem analysis and evaluation.

[0013] As an improvement of the above method, the index system for problem analysis and evaluation is an index system for task completion degree analysis;

[0014] The index system for task completion degree analysis is divided into three layers. The first layer is the completion degree of major experiments, the second layer is the completion degree of experimental items, and the third layer is the completion degree of experimental stages.

[0015] As an improvement of the above method, the index system for problem analysis and evaluation is an index system for key technical ability analysis;

[0016] The index system for key technical ability analysis is divided into three layers. The first layer is the ability for in-orbit flight test tasks, the second layer is the ability for in-orbit flight test stages, and the third layer is the key technical ability for each test stage.

[0017] As an improvement of the above method, the index system for problem analysis and evaluation is an index system for overall performance analysis;

[0018] The index system for overall performance analysis is divided into three layers. The first layer is the comprehensive performance of the aircraft platform and the performance of each payload, the second layer is the subsystem performance of the aircraft platform and each payload, and the third layer is the component performance in the subsystem.

[0019] As an improvement of the above method, step S2 further includes:

[0020] When storing on-orbit data, associate the time range corresponding to each layer of the index system for problem analysis and evaluation with the time point of the on-orbit data.

[0021] As an improvement of the above method, the first set time interval is in months; the second set time interval is in days; the third set time interval is in hours.

[0022] As an improvement of the above method, step S2 further includes:

[0023] Establish the index relationship between weekly data and daily data.

[0024] As an improvement of the above method, the processing of on-orbit data to obtain the problems of on-orbit missions includes:

[0025] Split the mission into key actions, and analyze the completion of each key action using the weight analysis method, expert scoring method, analytic hierarchy process, chain ratio coefficient method or fuzzy comprehensive evaluation method to find the key action with the lowest score, which is the problem of the on-orbit mission.

[0026] This application also includes a problem analysis and evaluation system for on-orbit flight missions, which is implemented based on the above method. The system includes:

[0027] Data access module, used to access on-orbit data;

[0028] Data storage module, used to normalize and store on-orbit data according to the time dimension; store the data in different databases according to the first set time interval according to the time of on-orbit data, store the data in different tables in one database according to the second set time interval, and partition the data in one table according to the third set time interval;

[0029] Problem analysis module, used to process on-orbit data according to the set analysis criteria to obtain the problems of on-orbit missions;

[0030] Calculation unit construction module, used to construct the calculation unit of the analysis operator; the calculation unit includes calculation functions for basic geometric operations;

[0031] Index system construction module, used to construct a problem analysis index system; the problem analysis index system is a multi-layer structure; the bottom layer of the multi-layer structure is the analysis criteria; the analysis criteria include the algorithm model for data processing using the analysis operator;

[0032] Index system storage module, used to store the problem analysis index system.

[0033] Compared with the prior art, the advantages of this application are:

[0034] 1. The present invention can directly locate the root causes of poor on-orbit flight effects, including factors such as aircraft performance, payload performance, and human decision-making.

[0035] 2. The present invention can quickly change the problem analysis and evaluation model through interface calls or page configuration, including associated evaluation data and data processing algorithm models, without the user having to recompile and generate executable files, so that the problem analysis and evaluation results can be obtained quickly, and it is universal for all on-orbit flight missions.

[0036] 3. The present invention can quickly change the scope of problem analysis and evaluation for in-orbit flight through interface calls or page configuration, and can support the division of in-orbit flight tasks into major test categories, test items, and test phases. Users do not need to recompile and generate executable files, and can arbitrarily specify the scope of problem analysis for in-orbit flight.

[0037] 4. The present invention can improve the speed of problem analysis and evaluation for in-orbit flight tasks under GB-level data. Through three tests, the average single-problem analysis time is 315.5 seconds, which is far superior to the problem analysis and evaluation time of the prior art. Description of the Drawings

[0038] Figure 1 Shown is the technical evaluation roadmap for problem analysis of in-orbit flight tasks;

[0039] Figure 2 Shown is the analysis index system for task completion degree in the test phase;

[0040] Figure 3 Shown is the analysis index system for key technical capabilities;

[0041] Figure 4 Shown is the overall performance analysis index system;

[0042] Figure 5 Shown is the schematic diagram of the storage structure of analysis index items;

[0043] Figure 6 Shown is the schematic diagram of the storage structure of analysis criteria;

[0044] Figure 7 Shown is the schematic diagram of telemetry data association;

[0045] Figure 8 Shown is the schematic diagram of telemetry data calculation;

[0046] Figure 9 Shown is the schematic diagram of the analysis index system for task completion degree;

[0047] Figure 10 Shown is the schematic diagram of monthly database building for time dimension normalization; some of the text in the figure involves specific project content, so it is covered in black;

[0048] Figure 11 Shown is the schematic diagram of daily database building for time dimension normalization; some of the text in the figure involves specific project content, so it is covered in black;

[0049] Figure 12 Shown is the schematic diagram of the database table structure for task dimension normalization; some of the text in the figure involves specific project content, so it is covered in black;

[0050] Figure 13The figure shows a schematic diagram of the reconstruction of the scoring model;

[0051] Figure 14 The figure shows a schematic diagram of the configuration of the weight evaluation method;

[0052] Figure 15 The figure shows a schematic diagram of the result display of the weight evaluation method;

[0053] Figure 16 The figure shows a schematic diagram of the configuration of the expert scoring method;

[0054] Figure 17 The figure shows a schematic diagram of the result display of the expert scoring method;

[0055] Figure 18 The figure shows a schematic diagram of the configuration of the analytic hierarchy process;

[0056] Figure 19 The figure shows a schematic diagram of the weight calculation result of the analytic hierarchy process;

[0057] Figure 20 The figure shows a schematic diagram of the result display of the analytic hierarchy process;

[0058] Figure 21 The figure shows a schematic diagram of the configuration of the chain relative ratio method;

[0059] Figure 22 The figure shows a schematic diagram of the result display of the chain relative ratio method;

[0060] Figure 23 The figure shows a schematic diagram of the configuration of the fuzzy comprehensive evaluation method;

[0061] Figure 24 The figure shows a schematic diagram of the result display of the fuzzy comprehensive evaluation method;

[0062] Figure 25 The figure shows a schematic diagram of the configuration evaluation index items of the ideal point method;

[0063] Figure 26 The figure shows a schematic diagram of the configuration evaluation matrix of the ideal point method;

[0064] Figure 27 The figure shows a schematic diagram of the calculation of the relative closeness degree of the ideal point method;

[0065] Figure 28 The figure shows a schematic diagram of the result display of the ideal point method. Detailed implementation manners

[0066] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings.

[0067] The existing methods for analyzing and evaluating on-orbit flight mission problems do not subdivide the mission from multiple dimensions, and the results obtained often only include the analysis results of the overall execution problems of the mission. At the same time, a fixed analysis and evaluation index system is adopted in the coding stage, and the analysis and evaluation data and the analysis and evaluation data processing algorithms are fixed in the code. Once the problem analysis and evaluation model is modified, the program developers need to recompile and generate the code, which is time-consuming and laborious.

[0068] This application provides a method for analyzing and evaluating problems of on-orbit flight missions, and its technical route is as Figure 1 shown. It includes the following steps:

[0069] 1. Construct a problem analysis and evaluation operator. An operator refers to a computing unit. Here, we call the computing units that are commonly used and not easily divided in the problem analysis data processing algorithm analysis operators. By combining different analysis operators, a problem analysis data processing model can be generated. The analysis operator is embodied in the code in the form of a function. Commonly used analysis operators include basic mathematical operations such as addition, subtraction, multiplication, division, square, cube, square root, cube root, summation, mean, variance, and standard deviation, and also include basic geometric operations such as two-dimensional vector sum / difference, three-dimensional vector sum / difference, vector scalar multiplication, and vector dot product.

[0070] In fact, functions are pre-written for each analysis operator at the bottom layer of the code.

[0071] 2. Construct a problem analysis and evaluation index system. By pre-constructing the problem analysis and evaluation index system, the automatic analysis of on-orbit flight mission problems can be realized. In addition, dividing the problem analysis and evaluation index system into multiple dimensions and hierarchical levels can quickly locate the root causes of on-orbit flight problems.

[0072] In fact, taking the test data obtained during the on-orbit test process as the objective basis for problem analysis and evaluation, and combining the subjective judgment of users, a problem analysis index system is constructed from three perspectives: mission completion analysis, key technical ability analysis, and overall performance analysis. Secondly, for each perspective, the problem analysis index system is further divided into three levels. The scoring information, including the scoring model and scoring criteria, is recorded in the bottom-level analysis criteria. The three-level index items correspond to several analysis criteria, and the two-level and one-level index items adopt the form of user subjective judgment to complete the construction of the subjective and objective combined analysis index system.

[0073] From the perspective of the completion of on-orbit flight missions, to construct an issue analysis and evaluation index system, it is necessary to divide the on-orbit flight missions. From coarser to finer, they are major experiment categories, experiment items, and experiment phases. The corresponding issue analysis system has three levels of indicators. The first-level indicator is the completion degree of major experiment categories, the second-level indicator is the completion degree of experiment items, and the third-level indicator is the completion degree of experiment phases (according to actual needs, the levels of the issue analysis and evaluation index system can be increased or decreased. Here, the three-level example is used). For example, Figure 2 as shown.

[0074] From the perspective of the completion of key technical actions of the aircraft platform, aircraft payloads, and the key technical actions of the aircraft platform with payloads, construct an issue analysis and evaluation index system. Compared with the issue analysis and evaluation index system for mission completion, the granularity of this issue analysis and evaluation index system is finer and can reflect the implementation of each key technology. Among them, the first-level indicator is the main capabilities of on-orbit flight test missions; the second-level indicator is the capabilities in on-orbit flight test phases, and the third-level indicator is the key technical capabilities in each test phase. For example, Figure 3 as shown.

[0075] From the perspective of the performance of the aircraft platform and each payload, construct an issue analysis and evaluation index system. Among them, the analysis indicators are divided into three levels. The first-level indicators are the comprehensive performance of the aircraft platform and the performance of each payload; the second-level indicators are the subsystem performances of the aircraft platform and each payload; the third-level indicators are the component performances in the subsystems. For example, Figure 4 as shown.

[0076] 3. Storage issue analysis and evaluation index system. When storing the issue analysis and evaluation index system with large amounts of data from multiple perspectives and multiple levels, adopt the method of hierarchical decomposition and associated storage. This storage method only associates the bottom-level analysis criteria with analysis data, corresponding test phases, analysis data processing models, and analysis standards. When changing the analysis model through a configuration file or front-end page interaction, there is no need to regenerate the entire issue analysis and evaluation index system, which greatly saves the issue analysis time and is still applicable when changing to other on-orbit flight missions.

[0077] In fact, first disassemble the issue analysis and evaluation index system, store it according to the hierarchical structure as analysis index items, and associate its parent nodes and child nodes. The leaf nodes are associated with their corresponding analysis criterion IDs. For example, Figure 5As shown, operations such as retrieval, addition, deletion, and modification of analysis index items will reduce the amount of data interaction, greatly improving efficiency. Secondly, the analysis and evaluation criteria are associated and stored with the parameter codes of in-orbit telemetry, in-orbit data transmission, etc., the corresponding test phases, data processing models, analysis criteria, etc. The analysis and evaluation criteria are the standards for measuring an analysis index item. One analysis index item corresponds to one or more analysis criteria. The analysis and evaluation criteria contain information such as the data parameter codes required for problem analysis, the corresponding test phases, data processing methods, result judgment criteria, etc., as Figure 6 shown

[0078] 4. Collect in-orbit flight data. A large amount of data will be generated during the operation of the spacecraft according to the mission planning scheme. These data are transmitted to the ground station in frames, and each frame contains the time of the current frame and telemetry and data transmission data in the order of magnitude of ten thousand.

[0079] In practice, collect the telemetry, data transmission and other flight data transmitted in real time during the operation of the spacecraft according to the mission planning for problem analysis, that is, including the position information, speed information, attitude information, battery voltage, battery current and the operating state information of each component of the spacecraft and its payload.

[0080] 5. Data normalization processing. By establishing a pyramid model, reduce the time complexity and space complexity of GB-level data retrieval, so that the analysis speed has an exponential increase; in addition, it can associate the in-orbit flight data originally collected in chronological order with the test tasks, which can be specifically divided into major test categories, test items, and test phases.

[0081] In practice, according to the characteristics of the test tasks, normalize the test data, and construct a pyramid-like standardized hierarchical and block structure according to the time dimension and task dimension to optimize the management of massive data. In the data storage of the time dimension, store the data of months, days, and hours separately in the form of database sharding, table sharding, and partition.

[0082] In the task dimension, record the time ranges of different major test categories, test items, and test phases through configuration files or page editing, realize the association of major test categories, test items, test phases with time, and mark the data in the time dimension in the database, so as to facilitate the rapid retrieval of data according to the task dimension.

[0083] 6. Automatic analysis and evaluation of in-orbit flight mission problems. Automatically analyze and evaluate the problems of in-orbit flight missions, and quickly and accurately find the causes of in-orbit flight problems.

[0084] In implementation, through the above steps, it is possible to quickly retrieve and call the on-orbit telemetry and on-orbit data transmission data required for problem analysis and evaluation from the normalized stored data according to the problem analysis and evaluation index system architecture. For the data that can directly participate in the analysis, the direct association and call method is adopted, such as Figure 7 as shown. For the data that cannot directly participate in the analysis, its value is first obtained and then analyzed after calculation according to the data processing model, such as Figure 8 as shown.

[0085] 7. Modify the stage division of the on-orbit flight mission.

[0086] In implementation, through the interface call or page configuration method, reconfigure the association relationship between the test categories, test items, test stages and time, and relabel all the introduced data.

[0087] 8. Modify the problem analysis and evaluation model.

[0088] In implementation, modify the content of the library table through the configuration file or front-end page interaction, including the analysis data parameter codes associated with the analysis criteria and the corresponding stages, and reconstruct the data processing algorithm model by recombining the analysis operators, so as to re-analyze the problems in on-orbit flight.

[0089] 9. Re-analyze and evaluate the problems of the on-orbit flight mission

[0090] Carry out multi-dimensional automatic problem analysis on the on-orbit flight mission again according to the modified problem analysis index system architecture, analysis time range, etc.

[0091] Example 1

[0092] The implementation steps of the problem analysis method for the on-orbit flight mission in the embodiment of the present invention are as follows:

[0093] The first step: Construct analysis and evaluation operators. Write more than a dozen basic functions in the form of code, including addition, subtraction, multiplication, division, square, cube, square root, cube root, summation, mean, variance, standard deviation, two-dimensional vector sum / difference, three-dimensional vector sum / difference, vector scalar multiplication, vector dot product.

[0094] The second step: Construct an analysis and evaluation index system. According to the analysis requirements, analyze the stage completion degree in a certain test category and a certain test item. The first-level analysis index item is the test item task completion degree, the second-level analysis index item is the stage task completion degree, and the third-level analysis index item is the completion degree of each key action task. Analysis criteria are associated below the analysis index items. The score of the third-level analysis index item adopts the direct judgment method. The problem analysis index system is as Figure 9 shown.

[0095] Step 3: Decompose and store the problem analysis and evaluation index system. Decompose it according to the first-level index "Task Completion Analysis", the second-level index "Phase 1", and the third-level index "XX Key Actions". When storing the second-level index "Phase 1", associate its parent node as the first-level index "Task Completion Analysis" through the id. When storing the third-level index "XX Key Actions", associate its parent node as the second-level index "Phase 1" through the id. When storing the analysis criteria, associate its parent node as the third-level index "XX Key Actions" through the id, and include the analysis data code number, data processing algorithm model, scoring criteria, and corresponding phase for analyzing this criterion.

[0096] Take the analysis index item of position control accuracy as an example. The analysis criteria for this analysis index item are position deviation and speed deviation. The data processing methods include the calculation of position control deviation and speed control deviation. The formulas are as follows:

[0097]

[0098] Among them, N represents calculating the relative position control deviation using the values of N points, and μ represents the average value of the relative position control deviation of N points.

[0099] The calculation of the relative speed control deviation in hovering accuracy is as follows:

[0100]

[0101] Among them, N represents calculating the relative speed control deviation using the values of N points, and μ2 represents the average value of the relative speed control deviation of N points.

[0102] The analysis criteria can be quickly changed through interface calls or page configurations without recompiling to generate an executable file. As Figure 13 shown.

[0103] Step 4: Fetch on-orbit data. Through the downlink of telemetry and data transmission data, complete the fetching of on-orbit flight data throughout the life cycle. The fetched data is divided into data packets according to time frames. First, perform data packet parsing, and then extract telemetry and data transmission data.

[0104] Step 5: Normalize processing in the time dimension. First, divide the database by month, as Figure 10 shown, and then store the data in sub-tables in units of days, as Figure 11 shown. Finally, partition the database tables by hour. The amount of data stored in each partition is only in the tens of millions level, greatly improving the retrieval efficiency. In addition, a separate index relationship table between weeks and days is established, for example, the storage location of the data corresponding to the first week of March, to facilitate users to retrieve data in units of weeks.

[0105] Step 6: Task dimension normalization. Configure the time range to which the major test categories, test items, and test phases belong according to the actual situation. During data storage, automatically mark the data according to the major test categories, test items, and test phases. The form of the database table is as Figure 12 shown.

[0106] Step 7: Use the weight analysis method, expert scoring method, analytic hierarchy process, chain ratio coefficient method, and fuzzy comprehensive evaluation method to analyze the completion degree of the tasks in Phase 1 respectively. The scores of each analysis index item are calculated based on the analysis data. For the weight analysis method, weights need to be manually configured for each index item, such as Figure 14 shown, and the analysis results are as Figure 15 shown. The results show that the analysis and evaluation scores of key action 1 in Phase 1 are 78, those of key action 2 are 68, those of key action 3 are 58, those of key action 4 are 97, those of key action 5 are 94, those of key action 6 are 83, those of key action 7 are 87, those of key action 8 are 78, those of key action 9 are 90, and those of key action 10 are 84 points. The comprehensive analysis and evaluation score of Phase 1 is 82.24 points. Among them, the completion of key action 4 is the best, the completion of key action 3 is the worst, the completion importance of key action 9 is the highest, and the completion importance of key action 1 is the lowest. By splitting the task into each key action and evaluating and analyzing the completion of each key action, it can be known that the reason for the poor completion effect in Phase 1 lies in key action 3.

[0107] For the expert scoring method, weights and scores need to be manually configured for each index item. The configuration interface is as Figure 16 shown, and the analysis results are as Figure 17 shown. The results show that according to expert judgment, the comprehensive analysis and evaluation score of Phase 1 is 83.9 points. Among them, the completion of key action 4 is the best, the completion of key action 3 is the worst, the completion importance of key action 9 is the highest, and the completion importance of key action 1 is the lowest. By splitting the task into each key action and evaluating and analyzing the completion of each key action, it can be known that the reason for the poor completion effect in Phase 1 lies in key action 3.

[0108] For the analytic hierarchy process, an analytic hierarchy matrix needs to be manually configured. The configuration interface is as Figure 18 shown. The weight values of each index item calculated through the analytic hierarchy matrix are as Figure 19 shown, and the analysis results are as Figure 20As shown in the figure. By comparing the importance of each key action, this method obtains a comprehensive analysis and evaluation score of 79.94 points for phase one, among which key action 4 is completed the best, key action 3 is completed the worst, key actions 2 and 9 are completed with the highest importance, and key action 5 is completed with the lowest importance. By splitting the task into key actions and evaluating and analyzing the completion of each key action, it can be known that the reason for the poor completion of phase one is key action 3.

[0109] The month-on-month coefficient method requires configuring the importance coefficients between the analysis indicators. The configuration matrix is ​​as follows: Figure 21 The analysis results are shown in Figure 22 As shown in the figure. By comparing the importance of each key action, this method obtains a comprehensive analysis and evaluation score of 77.47 points for phase one, among which key action 4 is completed the best, key action 3 is completed the worst, key actions 1 and 2 are completed with the highest importance, and key action 9 is completed with the lowest importance. By splitting the task into key actions and evaluating and analyzing the completion of each key action, it can be known that the reason for the poor completion of phase one is key action 3.

[0110] The fuzzy comprehensive evaluation method requires configuring the weight ratio and the proportion of excellent, good, average, and poor for each analysis indicator. The configuration page is as follows: Figure 23 The analysis results are shown in Figure 24 As shown in the figure. By comparing the importance of each key action, this method obtains a comprehensive analysis and evaluation score of 73.21 points for phase one, among which key action 4 is completed the best, key action 3 is completed the worst, key actions 5 and 6 are completed with the highest importance, and key actions 9 and 10 are completed with the lowest importance. By splitting the task into key actions and evaluating and analyzing the completion of each key action, it can be known that the reason for the poor completion of phase one is key action 3.

[0111] Change the test plan, perform ideal point analysis on the two test plans, and determine which test plan is better. First configure the analysis index items, such as Figure 25 As shown in the figure, according to the scores of each analysis indicator item of each test plan, the analysis matrix is ​​filled in, as shown in the figure. Figure 26 shown.

[0112] The positive ideal solution distance, negative ideal solution distance and relative proximity of test scheme 1 and test scheme 2 are calculated by the ideal point method, as shown in Figure 27 The analysis results are shown in Figure 28As shown. By splitting the task into individual key actions, evaluating and analyzing the completion of each key action, the evaluation and analysis results of Test Plan 1 and Test Plan 2 are obtained. Then, a comparison and analysis of the two test plans are carried out, and the results show that the effect of Test Plan 1 is better.

[0113] This application proposes a multi-dimensional problem analysis and evaluation method, which solves the problems of coarse granularity and inaccurate positioning in conventional analysis by automatically performing three-level analysis from three dimensions: task completion, key technical capabilities, and overall performance.

[0114] This application also proposes a reconfigurable scoring model integration method. Users can modify the analysis data and analysis algorithms through interface calls or page configurations to quickly reconstruct the analysis model, solving the problems of unchangeable rules and inaccurate analysis results in conventional analysis.

[0115] This application also proposes a configurable evaluation data calling method. Users can re-divide the in-orbit flight phase through interface calls or page configurations to achieve separate problem analysis for different tasks, tests, and phases.

[0116] Embodiment 2

[0117] This application also includes a problem analysis and evaluation system for in-orbit flight tasks, which is implemented based on the above method. The system includes:

[0118] An in-connection data module for in-connecting in-orbit data.

[0119] A stored data module for normalizing and storing in-orbit data according to the time dimension; storing in separate databases at the first set time interval according to the time of the in-orbit data, storing in separate tables within a database at the second set time interval, and partitioning the data within a table at the third set time interval.

[0120] A problem analysis module for processing the in-orbit data according to the set analysis criteria to obtain the problems of the in-orbit task.

[0121] A computing unit construction module for constructing the computing unit of the analysis operator; the computing unit includes computing functions for basic geometric operations.

[0122] An index system construction module for constructing a problem analysis and evaluation index system; the problem analysis and evaluation index system is a multi-layer structure; the bottom layer of the multi-layer structure is the analysis criteria; the analysis criteria include an algorithm model for processing data using the analysis operator.

[0123] An index system storage module for storing the problem analysis and evaluation index system.

[0124] The present application may also provide a computer device, including: at least one processor, a memory, at least one network interface, and a user interface. Each component in the device is coupled together through a bus system. It can be understood that the bus system is used to implement the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0125] Among them, the user interface may include a display, a keyboard, or a pointing device. For example, a mouse, a trackball, a touchpad, or a touch screen, etc.

[0126] It can be understood that the memory in the disclosed embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory described herein is intended to include but not be limited to these and any other suitable types of memory.

[0127] In some embodiments, the memory stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof: an operating system and an application program.

[0128] Among them, the operating system includes various system programs, such as the framework layer, the core library layer, the driver layer, etc., which are used to implement various basic services and handle hardware-based tasks. The application programs include various application programs, such as the Media Player, the Browser, etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure may be included in the application programs.

[0129] In the above-mentioned embodiment, the program or instruction stored in the memory can also be called. Specifically, it can be the program or instruction stored in the application program. The processor is used for:

[0130] Executing the steps of the above method.

[0131] The above method can be applied to the processor or implemented by the processor. The processor may be an integrated circuit chip with signal processing capabilities. During the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instruction in the form of software. The above-mentioned processor can be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed above. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. Combining the steps of the above-disclosed method can be directly embodied as being executed and completed by the hardware decoding processor, or by a combination of the hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0132] It can be understood that these embodiments described in the present application can be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0133] For software implementation, the techniques of the present application can be implemented by executing the functional modules of the present application (such as procedures, functions, etc.). The software code can be stored in a memory and executed by a processor. The memory can be implemented inside or outside the processor.

[0134] The present application can also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, the various steps in the above method embodiments can be implemented.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of the present application does not depart from the spirit and scope of the technical solutions of the present application, and they should all be covered within the scope of the claims of the present application.

Claims

1. A method for problem analysis and evaluation of on-orbit flight missions, including: Step S1: Access on-orbit data; Step S2: Normalize and store the on-orbit data according to the time dimension; According to the time of the on-orbit data, perform sub-library storage at a first set time interval, perform sub-table storage for the data in one library at a second set time interval, and perform partition processing for the data in one table at a third set time interval; Step S3: Process the on-orbit data according to the set analysis and evaluation criteria to obtain the problems of the on-orbit mission.

2. The problem analysis and evaluation method for on-orbit flight missions according to claim 1, characterized in that, The construction process of the analysis and evaluation criteria includes: Step A1: Construct a calculation unit for the analysis operator; the calculation unit includes calculation functions for basic geometric operations; Step A2: Construct a problem analysis and evaluation index system; the problem analysis and evaluation index system is a multi-layer structure; the bottom layer of the multi-layer structure is the analysis criterion; the analysis criterion includes an algorithm model for processing data using the analysis operator; Step A3: Store the problem analysis and evaluation index system.

3. The problem analysis and evaluation method for on-orbit flight missions according to claim 2, characterized in that The problem analysis and evaluation index system is a mission completion degree analysis index system; The mission completion degree analysis index system is divided into three layers. The first layer is the completion degree of major experiments, the second layer is the completion degree of experimental projects, and the third layer is the completion degree of experimental stages.

4. The problem analysis and evaluation method for on-orbit flight missions according to claim 2, characterized in that, The problem analysis and evaluation index system is a key technical ability analysis index system; The key technical ability analysis index system is divided into three layers. The first layer is the on-orbit flight test mission ability, the second layer is the on-orbit flight test stage ability, and the third layer is the key technical ability of each test stage.

5. The problem analysis and evaluation method for on-orbit flight missions according to claim 2, characterized in that The problem analysis and evaluation index system is a general performance analysis index system; The general performance analysis index system is divided into three layers. The first layer is the comprehensive performance of the aircraft platform and the performance of each payload, the second layer is the subsystem performance of the aircraft platform and each payload, and the third layer is the component performance in the subsystem.

6. The problem analysis and evaluation method for on-orbit flight missions according to claim 2, wherein Step S2 further includes: When storing the on-orbit data, associate the time range corresponding to each layer of the problem analysis and evaluation index system with the time point of the on-orbit data.

7. The problem analysis and evaluation method for on-orbit flight missions according to claim 1, wherein The first set time interval is a month; the second set time interval is a day; the third set time interval is an hour.

8. The problem analysis and evaluation method for on-orbit flight missions according to claim 7, characterized in that Step S2 further includes: Establish an index relationship between weekly data and daily data.

9. The problem analysis and evaluation method for on-orbit flight missions according to claim 1, characterized in that The processing of the on-orbit data to obtain the problems of the on-orbit mission includes: Split the mission into individual key actions, and analyze the completion status of each key action using the weight analysis method, expert scoring method, analytic hierarchy process, ring ratio coefficient method, or fuzzy comprehensive evaluation method to find the key action with the lowest score, which is the problem of the on-orbit mission.

10. A problem analysis and evaluation system for on-orbit flight missions, implemented based on the method described in any one of claims 1-9, characterized in that, The system includes: An on-orbit data access module for accessing on-orbit data; A data storage module for normalizing and storing the on-orbit data according to the time dimension; performing sub-library storage according to the time of the on-orbit data at a first set time interval, performing sub-table storage for the data in one library at a second set time interval, and performing partition processing for the data in one table at a third set time interval; A problem analysis module for processing the on-orbit data according to the set analysis criteria to obtain the problems of the on-orbit mission; Build a computing unit module for building the computing unit of the analysis operator; the computing unit includes a computing function for basic geometric operations; Build an index system module for building an issue analysis index system; the issue analysis index system is a multi-layer structure; the bottom layer of the multi-layer structure is an analysis criterion; the analysis criterion includes an algorithm model for processing data using the analysis operator; and Store the index system module for storing the issue analysis index system.

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