Simulation test design method for remote sensing satellite system demonstration

Abstract

The application relates to a simulation test design method for a remote sensing satellite system argument, which comprises the following steps: extracting common influence factors of a specified remote sensing satellite in different typical deduction scenes to form an internal test factor list; extracting influence factors according to different task types to construct a test factor list for a specific task, and forming a scene task template; matching the scene task template according to a simulation assumption task type, loading and dynamically adjusting the test factor list, and forming a simulation test scheme; selecting a test design method based on the simulation test scheme, generating test result data and mapping the test result data to the simulation assumption to form a test sample set. The application can realize automatic extraction of factors and assist a user in quickly generating a system simulation test scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of simulation and modeling, and particularly relates to a simulation test design method for remote sensing satellite system demonstration. BACKGROUND

[0002] Simulation tests of remote sensing satellite systems in typical scenarios need to quickly generate a large number of test samples using test design, and based on the test samples, large-sample simulation tests are carried out to provide data for subsequent evaluation and analysis. At present, a large number of statistical analysis software provides powerful test design functions. However, due to the fact that these software are separated from the actual business background, the method is complex to use, and the results are difficult to associate with the simulation test scheme, so these software are difficult to apply in the field of remote sensing satellite simulation.

[0003] In addition, although many test design systems for specific remote sensing satellites have been widely applied, such systems often cannot quickly adapt to changes in satellite types, scenario deduction, and task assignment, and there are significant inconveniences in actual use. SUMMARY

[0004] In view of the above technical problems, the present application provides a simulation test design method for remote sensing satellite system demonstration, which can realize automatic extraction of factors and assist users in quickly generating system simulation test schemes.

[0005] The technical solution for solving the technical problems of the present application is as follows: a simulation test design method for remote sensing satellite system demonstration, comprising the following steps:

[0006] Step S1, common test factor extraction: extracting common influence factors of the specified remote sensing satellite in different typical deduction scenarios to form an internal test factor list;

[0007] Step S2, scene task template construction: extracting influence factors according to different task types to construct a test factor list for a specific task, forming a scene task template;

[0008] Step S3, simulation test scheme generation: matching the scene task template according to the simulation assumption task type, loading and dynamically adjusting the test factor list to form a simulation test scheme;

[0009] Step S4, simulation test sample generation: selecting a test design method based on the simulation test scheme, generating test result data and mapping to the simulation assumption to form a test sample set.

[0010] According to one technical solution of the present application, in step S1, the extraction of common test factors specifically includes:

[0011] Step S101, analyzing the workflow of the designated remote sensing satellite in different typical mission scenarios, extracting common influencing factors, and forming an internal test factor list;

[0012] Step S102, setting the value range, design method and design parameters of each test factor, and generating a discrete factor level value list;

[0013] Step S103, the internal test factor list is described in XML or JSON format and stored in the database.

[0014] According to one technical solution of the present application, the design parameters of the test factor include binomial distribution, exponential distribution, uniform distribution or manual design method.

[0015] According to one technical solution of the present application, in step S2, specifically comprising:

[0016] Step S201, analyzing the workflow of the satellite under the specified mission, extracting the influencing factors, adding them to the test factor list of the corresponding task type, and setting the test factors using the same operation as step S102;

[0017] Step S202, the internal test factor list is described in XML or JSON format, forming a scene task template, and stored in the database.

[0018] According to one technical solution of the present application, in step S3, specifically comprising:

[0019] Step S301, after the user selects a simulation scenario to be designed, according to the type of the task corresponding to the selected simulation scenario, a suitable scene task template is found and matched from the database, and the test factor list information contained therein is loaded;

[0020] Step S302, load the internal test factor list generated in step S1, and combine it with the test factor list generated in step S301;

[0021] Step S303, based on the test factor list generated in step S302, according to the characteristics of the simulation scenario selected by the user, adding or deleting test factors, completing the construction of the test factor list for this simulation scenario, and forming a simulation test scheme.

[0022] According to one technical solution of the present application, the algorithm of the matching process is a keyword matching algorithm.

[0023] According to one technical solution of the present application, in step S4, specifically comprising:

[0024] Step S401, loading the simulation test scheme generated in step S3, reading the test factor list therein, and obtaining the factor level value list of each test factor;

[0025] Step S402, based on the preset test design method, generating test design result data based on the test factor information read in step S401;

[0026] Step S403, according to the mapping relationship between the test factors and the influence factors in the simulation scenario, substituting the level value of each test factor in each sample into the simulation scenario to form a simulation scenario corresponding to each sample, which is called a test sample.

[0027] According to one of the technical solutions of the present application, the preset test design method is a full-factor design method, a uniform design method, an orthogonal design method or a Latin hypercube design method.

[0028] Compared with the prior art, the present application has the following beneficial effects:

[0029] According to the concept of the present application, a simulation test design method for remote sensing satellite system demonstration is proposed. In view of the demand for rapid generation of large samples of simulation tests, based on the research and analysis of remote sensing satellites, common factors that may affect the effectiveness of the satellite in typical deduction scenarios are extracted to form built-in factors, and other influence factors are extracted according to the characteristics of specific task types to construct a test factor list for specific tasks and form a scene task template, thereby realizing automatic extraction of factors and assisting users in rapidly generating simulation test schemes.

[0030] The present application extracts common influence factors to form a built-in factor list according to the characteristics of a specified remote sensing satellite in different typical task scenarios, avoids repeated operations, and improves test design efficiency.

[0031] The present application establishes a scene task template for each specific task type, stores the test factor list of the specified remote sensing satellite under this type of task, and automatically matches the combat task template based on the task type of the simulation scenario, thereby improving the test design efficiency.

[0032] The present application supports adding or deleting test factors based on the characteristics of the simulation scenario, thereby ensuring the flexibility of simulation test design.

[0033] The present application generates a large number of test samples rapidly through test design for simulation tests in typical deduction scenarios, and conducts large-sample simulation tests based on the test samples, thereby providing data for subsequent evaluation and analysis, and having good application prospects for remote sensing satellite test design. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1The schematic diagram illustrates a simulation test design method for remote sensing satellite system demonstration according to one embodiment of the present invention. Detailed Implementation

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0036] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The embodiments cannot be described in detail here, but the embodiments of the present invention are not limited to the following embodiments.

[0037] like Figure 1 As shown, the present invention provides a simulation experiment design method for remote sensing satellite system demonstration, comprising the following steps:

[0038] Step S1: Extraction of common test factors. Extract common influencing factors of the specified remote sensing satellite in different typical scenarios to form a list of common test factors for the weapon satellite, which serves as the built-in factor list for each simulation test scheme.

[0039] First, we analyze the factors that may affect the combat effectiveness of the remote sensing satellite under study in different typical mission scenarios, and extract the common influencing factors as built-in test factors for all simulation test schemes of the satellite.

[0040] The specific steps of step S1 include:

[0041] Step S101: Analyze the workflow of the specified remote sensing satellite in different typical mission scenarios, extract common influencing factors, and form a list of built-in experimental factors.

[0042] Step S102: For each experimental factor in the built-in factor list generated in step S101, set its value range, design method, and design parameters, and generate a discrete list of factor level values. The design method can be a statistical distribution-based method such as binomial distribution, exponential distribution, or uniform distribution, or a manual design method can be used.

[0043] Step S103: The list of built-in experimental factors is described in XML or JSON format and stored in the database.

[0044] Step S2: Scenario Task Template Construction. Based on the characteristics of different task types, other factors that may affect satellite performance are extracted as experimental factors to construct a list of experimental factors for specific tasks, thus forming a scenario task template.

[0045] According to the characteristics of different task types, other factors that can affect the performance of the specified satellite task are extracted to form test factors, and a test factor list for a specific task is generated, i.e., a scenario task template.

[0046] The specific steps of step S2 include:

[0047] Step S201, analyze the workflow of the satellite under the specified task, extract the influencing factors, add them to the test factor list of this task type, and complete the setting of the test factors by the same operation as step 102.

[0048] Step S202, the built-in test factor list is described in XML or JSON format, forming a scenario task template, and stored in the database.

[0049] Step S3, simulation test scheme generation. According to the task type corresponding to the simulation scenario, automatically match a suitable scenario task template, load the test factor list, and according to the characteristics of the simulation scenario, add or delete test factors, complete the construction of the test factor list of this scenario, and form a simulation test scheme.

[0050] According to the task type corresponding to the simulation scenario selected by the user to carry out the test design, find the suitable scenario task template from the database, load the test factor list information contained therein, and on this basis, according to the characteristics of the scenario, add or delete test factors, complete the construction of the test factor list for the simulation scenario, and form a simulation test scheme.

[0051] Step S3 specifically includes:

[0052] Step S301, after the user selects a simulation scenario to carry out the test design, according to the type of the task corresponding to the selected simulation scenario, find and match a suitable scenario task template from the database, and load the test factor list information contained therein. The matching process can be realized by using a keyword matching algorithm.

[0053] Step S302, load the built-in test factor list generated in step S1, and merge it with the test factor list generated in step S301.

[0054] Step S303, on the basis of the test factor list generated by merging in step S302, according to the characteristics of the simulation scenario selected by the user, add or delete test factors, complete the construction of the test factor list for this simulation scenario, and form a simulation test scheme.

[0055] Step S4, simulation test sample generation. Based on the simulation test scheme, select the appropriate test design method, generate test design result data, and map the test design result data to the corresponding factors in the simulation scenario, and then generate simulation test samples to provide input for large sample simulation tests.

[0056] Based on the simulation test scheme generated in step S3, select the appropriate test design method, generate the test design result, and map the test design result data to the corresponding influencing factors in the simulation scenario, and then generate the test sample as the input of the large sample simulation test.

[0057] The specific steps of step S4 include:

[0058] Step S401, load the simulation test scheme generated in step S3, read the test factor list therein, and obtain the factor level value list of each test factor.

[0059] Step S402, select a suitable test design method, such as full-factor design, uniform design, orthogonal design, Latin hypercube design, etc., to generate test design result data based on the test factor information read in step S401. The form of test design result is the factor level value of each test factor in each sample.

[0060] Step S403, according to the mapping relationship between the test factor and the influencing factor in the simulation scenario, substitute the level value of each test factor in each sample into the simulation scenario to form the simulation scenario corresponding to each sample, which is called test sample.

[0061] Among them, the test sample set generated in step S4 will be used as the input data of the subsequent large sample simulation test.

[0062] The application discloses a simulation test design method for remote sensing satellite system demonstration, adopts a process-based design idea, forms a remote sensing satellite system demonstration simulation test design process from common test factor extraction, scene task template construction, simulation test scheme generation to test sample generation, quickly forms a test factor list of a specified remote sensing satellite under different deduction scenes and tasks, and thus efficiently generates a simulation test sample set meeting large sample simulation deduction requirements.

[0063] The application, aiming at the remote sensing satellite system demonstration requirement, extracts common key elements of a specified remote sensing satellite in different typical deduction scenes, forms a common test factor list as built-in factors of each simulation test scheme, and on the basis, extracts other test factors related to tasks according to the characteristics of different task types, generates a test factor list associated with the task type, forms a scene task template, and enables a user to quickly form a simulation test scheme based on simulation scenarios of different task types.

[0064] It has to be understood that, in the present document, the terms "comprising", "including", or any other variant thereof, are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise, include or are otherwise characterized as including a list of elements are not limited to only those elements, but can include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0065] Finally, it is to be understood that the above description is a preferred embodiment of the application, and no limitation of the scope of the application is intended to be implied by the description of the preferred embodiment. Accordingly, the scope of the application is intended to be defined only by the appended claims, and even equivalent alterations and modifications thereof.

Claims

1. A simulation experiment design method for remote sensing satellite system demonstration, characterized in that, Includes the following steps: Step S1, Common Experimental Factor Extraction: Extract common influencing factors of the specified remote sensing satellite in different typical simulation scenarios to form a built-in experimental factor list, specifically including: Step S101: Analyze the workflow of the specified remote sensing satellite in different typical mission scenarios, extract common influencing factors, and form a list of built-in experimental factors. Step S102: For each experimental factor, set its value range, design method and design parameters, and generate a discrete list of factor level values; Step S103: The list of built-in experimental factors is described in XML or JSON format and stored in the database; Step S2, Scenario Task Template Construction: Extract influencing factors based on different task types, construct a list of experimental factors for specific tasks, and form a scenario task template, specifically including: Step S201: Analyze the satellite's workflow under the specified mission, extract the influencing factors, add them to the experimental factor list of the corresponding mission type, and complete the setting of the experimental factors using the same operation as in step S102. Step S202: The list of built-in test factors is described in XML or JSON format to form a scenario task template and stored in the database; Step S3: Simulation Test Plan Generation: Match the scenario task template according to the task type defined in the simulation, load and dynamically adjust the list of test factors to form a simulation test plan, which specifically includes: Step S301: After the user selects a simulation scenario for the experimental design to be carried out, according to the type of task corresponding to the selected simulation scenario, a suitable scenario task template is searched and matched from the database, and the experimental factor list information contained therein is loaded. Step S302: Load the built-in test factor list generated in step S1 and merge it with the test factor list generated in step S301; Step S303: Based on the experimental factor list generated in step S302, add or delete experimental factors according to the characteristics of the simulation scenario selected by the user, and complete the construction of the experimental factor list for this simulation scenario to form a simulation test plan. Step S4: Simulation test sample generation: Select the test design method based on the simulation test scheme, generate test result data and map it to the simulation scenario to form a test sample set.

2. The method according to claim 1, characterized in that, The design parameters for the experimental factors include binomial distribution, exponential distribution, uniform distribution, or manual design methods.

3. The method according to claim 1, characterized in that, The matching algorithm is a keyword matching algorithm.

4. The method according to claim 1, characterized in that, Step S4 specifically includes: Step S401: Load the simulation test plan generated in step S3, read the test factor list in it, and obtain the factor level value list for each test factor. Step S402: Based on the preset experimental design method and the experimental factor information read in step S401, generate experimental design result data; Step S403: Based on the mapping relationship between the experimental factors and the influencing factors in the simulation scenario, substitute the level value of each experimental factor in each sample into the simulation scenario to form the simulation scenario corresponding to each sample. This simulation scenario is called the experimental sample.

5. The method according to claim 4, characterized in that, The preset experimental design method is a full factorial design, a uniform design, an orthogonal design, or a Latin hypercube design.

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