Full-coverage testing method, device and equipment of aircraft management system and medium

Abstract

The invention discloses a full-coverage testing method, device and equipment for an aircraft management system and a medium, and relates to the technical field of automatic testing, and the method comprises the steps: obtaining an input parameter range and an output parameter range sent by the aircraft management system; generating a test case data set by using an ant colony algorithm based on the input parameter range, solving relevance among variables in the test case data set by using a variable sensitivity association algorithm, and performing parameter dimension reduction on the test case data set to obtain a target test case data set; inputting the target test case data set into an aircraft management system for simulation test, and obtaining a simulation test result which is output by the aircraft management system and comprises a flight state variable value; judging whether the aircraft state exceeds the boundary, and if yes, screening a corresponding target test case; and the simulation test result and the target test case are visualized by using the graphical user interface, so that the full-coverage test of the aircraft management system is completed, and the efficiency and accuracy of the full-coverage test of the aircraft management system are improved.

Description

Technical Field

[0001] The present invention relates to the field of automated testing technology, and in particular to a full coverage testing method, device, equipment and medium for an aircraft management system. Background Art

[0002] As the core control system of aircraft such as drones, the performance and stability of the aircraft management system are directly related to the flight effect and safety of the aircraft. In the current research on the automatic generation of aircraft test cases, the control law logic module is mainly tested, and a random algorithm, such as the Monte Carlo method, is used to generate a large number of test cases to test and analyze a certain flight software. However, since the generated test cases do not consider sufficient constraints, when there are many parameters and the system is complex, a combinatorial explosion problem will occur, and a large number of meaningless test cases will be generated, thereby wasting computing power; or heuristic algorithms such as genetic algorithms are used to find the flight edge performance and evaluate whether there are design defects in the flight control law, but it will be more complicated in the specific implementation. At first, the problem to be solved needs to be encoded, and the problem needs to be decoded after the solution is found. In addition, static testing is mainly performed on the flight control logic, which can only test whether its logic branches are covered, and it is difficult to perform real-time simulation analysis and judgment on a variety of real-life scenarios; and it is difficult to judge the aircraft management system (including flight control logic, cross-linking system, navigation information voting, and electromechanical system signals). At the same time, since previous studies mostly use single-machine operation, the test time is long, which is difficult to meet the requirements of rapid development.

[0003] As can be seen from the above, how to achieve full coverage testing of aircraft management systems and improve the efficiency and accuracy of full coverage testing of aircraft management systems are problems to be solved in this field. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a full coverage test method, device, equipment and medium for an aircraft management system, which can achieve full coverage test of the aircraft management system and improve the efficiency and accuracy of the full coverage test of the aircraft management system. The specific scheme is as follows:

[0005] In a first aspect, the present application discloses a full coverage test method for an aircraft management system, which is applied to a full coverage test system for a third-party system, comprising:

[0006] Obtaining an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft;

[0007] Based on the input parameter range and using an ant colony algorithm, a test case data set is generated, a variable sensitivity association algorithm is used to solve the correlation between the variables in the test case data set, and parameter dimension reduction is performed on the test case data set according to the correlation to obtain a target test case data set;

[0008] Inputting the target test case data set into the aircraft management system for simulation testing to obtain simulation test results output by the aircraft management system including flight state variable values;

[0009] Determining whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, selecting a corresponding target test case;

[0010] The simulation test results and the target test cases are visualized using a graphical user interface to complete a full coverage test of the aircraft management system.

[0011] Optionally, the obtaining of an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft includes:

[0012] Obtain the original documents of input variables, pilot operation behavior information, pilot operation instructions and input parameter range of offset settings based on the navigation information system, engine control crosslinking system, hydraulic control crosslinking system, electromechanical control crosslinking system, fuel control crosslinking system and environmental control crosslinking system sent by the aircraft's aircraft management system;

[0013] Obtain an output parameter range based on aircraft parameter settings sent by the aircraft management system.

[0014] Optionally, the step of generating a test case data set based on the input parameter range and using an ant colony algorithm, and solving the correlation between variables in the test case data set using a variable sensitivity association algorithm, includes:

[0015] Generate a full coverage test case data set using an ant colony algorithm based on the input parameter range and a positive feedback distributed collaboration method;

[0016] A variable sensitivity association algorithm is used to solve the correlation between the variables in the test case data set; the variable sensitivity association algorithm includes a random forest algorithm.

[0017] Optionally, performing parameter dimension reduction on the test case dataset according to the correlation to obtain a target test case dataset includes:

[0018] Performing parameter dimension reduction on the test case data set according to the correlation, and screening out redundant test cases from the test case data set after parameter dimension reduction;

[0019] The redundant test cases in the test case data set after parameter dimensionality reduction are eliminated to obtain a target test case data set.

[0020] Optionally, judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, screening out corresponding target test cases, includes:

[0021] Using a local data transmission interaction module, and judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value;

[0022] If the aircraft state of the aircraft is out of bounds, a corresponding target test case is screened out, and the target test case and the corresponding flight state variable value are saved in a preset folder.

[0023] Optionally, after judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, the method further includes:

[0024] If the aircraft state of the aircraft is within the bounds, then calculating the distance between the flight state variable value and the boundary value;

[0025] The relationship between the distance and the boundary value is calculated to obtain a relationship score, a weighted average is calculated for all the relationship scores to obtain a total score, and the total score is used to filter out the target test case from the target test case data set.

[0026] Optionally, the visualizing the simulation test results and the target test case by using a graphical user interface includes:

[0027] The simulation test results and the target test cases are plotted using a graphical user interface to obtain a result graph, and the result graph is visualized so that the client can analyze the cause of the fault on the result graph.

[0028] In the second aspect, the present application discloses a full coverage test device for an aircraft management system, which is applied to a full coverage test system for a third-party system, including:

[0029] A parameter range acquisition module, used to acquire an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft;

[0030] A target test case data set generation module is used to generate a test case data set based on the input parameter range and using an ant colony algorithm, solve the correlation between the variables in the test case data set using a variable sensitivity association algorithm, and perform parameter dimension reduction on the test case data set according to the correlation to obtain a target test case data set;

[0031] A simulation test result acquisition module, used for inputting the target test case data set into the aircraft management system for simulation testing, so as to obtain the simulation test result including the flight state variable value output by the aircraft management system;

[0032] A judgment module, used for judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, selecting a corresponding target test case;

[0033] A visualization module is used to visualize the simulation test results and the target test cases using a graphical user interface to complete a full coverage test of the aircraft management system.

[0034] In a third aspect, the present application discloses an electronic device, comprising:

[0035] Memory, used to store computer programs;

[0036] The processor is used to execute the computer program to implement the above-mentioned full coverage test method of the aircraft management system.

[0037] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the full coverage testing method of the aircraft management system disclosed above are implemented.

[0038] It can be seen that the present application provides a full coverage test method for an aircraft management system, including obtaining an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by the aircraft management system of the aircraft; generating a test case data set based on the input parameter range and using an ant colony algorithm, solving the correlation between the variables in the test case data set using a variable sensitivity association algorithm, and performing parameter dimension reduction on the test case data set according to the correlation to obtain a target test case data set; inputting the target test case data set into the aircraft management system for simulation testing to obtain a simulation test result output by the aircraft management system including a flight state variable value; judging whether the aircraft state of the aircraft is out of bounds based on the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, filtering out the corresponding target test case; and visualizing the simulation test results and the target test case using a graphical user interface to complete a full coverage test of the aircraft management system. This application is applied to the full coverage test system of the third-party system. Since the aircraft management system contains more and more complex logics than a single control law logic test, the input parameter range and output parameter range of the aircraft management system based on the aircraft input variables and aircraft parameter settings are obtained to generate meaningful test cases. At the same time, dangerous test cases can be found according to the output parameter range to discover hidden defects or dangerous operations in the aircraft management system. A global test case data set is generated by using the ant colony algorithm, and the correlation between variables is solved through the variable sensitivity association algorithm. Then, parameter dimensionality reduction is performed to reduce test case redundancy and avoid the combinatorial explosion problem caused by too many parameters, thereby achieving full coverage with fewer test cases. Coverage testing, obtaining a target test case data set, inputting the target test case data set into the aircraft management system for simulation testing, realizing fast dynamic real-time simulation testing of the aircraft management system through parallel computing of multiple computers, realizing multi-machine parallel computing through multi-process technology, improving the calculation efficiency of the case, and judging whether the aircraft state of the aircraft is out of bounds. If the aircraft state of the aircraft is out of bounds, the corresponding target test case is screened out. Through dynamic simulation testing, not only the internal logic can be tested, but also different operating conditions can be tested. Finally, a graphical user interface is used to visualize the simulation test results and target test cases to complete the full coverage test of the aircraft management system and improve the efficiency and accuracy of the full coverage test of the aircraft management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0040] Figure 1 A flow chart of a full coverage test method for an aircraft management system disclosed in this application;

[0041] Figure 2 This is a structural diagram of an aircraft management system disclosed in this application;

[0042] Figure 3 An algorithm architecture diagram for generating a test case for an aircraft management system disclosed in this application;

[0043] Figure 4 This is a diagram of the execution process of an ant colony algorithm disclosed in this application;

[0044] Figure 5 A flowchart of an ant colony algorithm disclosed in this application;

[0045] Figure 6 A flow chart of a covering algorithm disclosed in this application;

[0046] Figure 7 This is an operational structure diagram of an aircraft management system disclosed in this application;

[0047] Figure 8 A flowchart of a target test case operation disclosed in this application;

[0048] Fig. 9 A graphical user interface diagram disclosed in this application;

[0049] Fig.10 A visual result diagram disclosed in this application;

[0050] Fig.11 A brief flowchart of a full coverage test of an aircraft management system disclosed in this application;

[0051] Fig.12 This is a schematic diagram of the structure of a full coverage test device for an aircraft management system disclosed in this application;

[0052] Fig.13 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] As the core control system of aircraft such as drones, the performance and stability of the aircraft management system are directly related to the flight effect and safety of the aircraft. In the current research on the automatic generation of aircraft test cases, the control law logic module is mainly tested, and a random algorithm, such as the Monte Carlo method, is used to generate a large number of test cases to test and analyze a certain flight software. However, since the generated test cases do not consider sufficient constraints, when there are many parameters and the system is complex, a combinatorial explosion problem will occur, and a large number of meaningless test cases will be generated, thereby wasting computing power; or heuristic algorithms such as genetic algorithms are used to find the flight edge performance and evaluate whether there are design defects in the flight control law, but it will be more complicated in the specific implementation. At first, the problem to be solved needs to be encoded, and the problem needs to be decoded after the solution is found. In addition, static testing is mainly performed on the flight control logic, which can only test whether its logic branches are covered, and it is difficult to perform real-time simulation analysis and judgment on a variety of real-life scenarios; and it is difficult to judge the aircraft management system (including flight control logic, cross-linking system, navigation information voting, and electromechanical system signals). At the same time, since previous studies mostly use single-machine operation, the test time is long, which is difficult to meet the requirements of rapid development. As can be seen from the above, how to achieve full coverage testing of aircraft management systems and improve the efficiency and accuracy of full coverage testing of aircraft management systems are problems to be solved in this field.

[0055] See also Figure 1 As shown, the embodiment of the present invention discloses a full coverage test method of an aircraft management system, which is applied to a full coverage test system of a third-party system, and specifically may include:

[0056] Step S11: obtaining an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft.

[0057] In this embodiment, the input parameter range of the original document of input variables based on the navigation information system, the engine control cross-linking system, the hydraulic control cross-linking system, the electromechanical control cross-linking system, the fuel control cross-linking system, and the environmental control cross-linking system, the pilot operation behavior information, the pilot operation instructions, and the offset setting sent by the aircraft management system of the aircraft is obtained; and the output parameter range based on the aircraft parameter setting sent by the aircraft management system is obtained.

[0058] The structure of the aircraft management system is as follows Figure 2As shown in the figure, since the aircraft management system includes control laws and there are many interconnected systems, there are many input and output variables. At the same time, by modeling the aircraft management system hardware (sensors, actuators), internal variables are also increased. The aircraft management system full coverage test program automatically reads the original document of the input variables provided by the aircraft management system, obtains the type and value range of each input variable, and generates an XML (Extensible Markup Language) text format file; among them, some information about the interconnection between the navigation information system and the aircraft management system is shown in Table 1:

[0059] Table 1

[0060]

[0061] The original document format of the input variables provided by the aircraft management system is given, and according to the given range, the generated test cases are guaranteed to be within the specified range, thus avoiding meaningless test cases.

[0062] Aircraft input variables include cross-linked inputs of the aircraft management system and other systems (including navigation information system, engine control cross-linking system, hydraulic control cross-linking system, electromechanical control cross-linking system, fuel control cross-linking system, and environmental control cross-linking system), as well as possible faults (such as steering gear faults, brake faults, engine faults, attitude measurement faults, overload faults, etc.), pilot operation behaviors, pilot operation instructions, and offsets, etc.; wherein, the offset refers to the degree of deviation of the system in a certain direction, which is widely used in the fields of navigation and control systems in practical applications, and describes the deviation relative to the predetermined instructions or the aerodynamic characteristics of the aircraft. The size of the offset determines the degree to which the system deviates from the ideal value during operation. In the present invention, it is mainly considered to inject a certain offset into the input of the aircraft operator, such as stick operation, throttle operation, etc., to simulate possible faults during the flight of the aircraft. In the process of fully automatic generation of use cases, the offset is set to different offsets of 10%-60%. By setting the input variables, the test case can reflect the real working conditions and detect the system's ability to respond to the offset.

[0063] The aircraft parameters are set according to the important parameters of the aircraft during flight. Some parameters of the output variables are shown in Table 2. The judgment conditions are set according to the given output variables to make the use case test judgment:

[0064] Table 2

[0065]

[0066] Step S12: Generate a test case data set based on the input parameter range and using an ant colony algorithm, solve the correlation between the variables in the test case data set using a variable sensitivity association algorithm, and perform parameter dimension reduction on the test case data set according to the correlation to obtain a target test case data set.

[0067] In this embodiment, an ant colony algorithm is used and based on the input parameter range and the positive feedback distributed collaboration method to generate a fully covered test case data set; a variable sensitivity association algorithm is used to solve the correlation between the variables in the test case data set; the variable sensitivity association algorithm includes a random forest algorithm; then, the test case data set is subjected to parameter dimensionality reduction according to the correlation, and redundant test cases are screened out from the test case data set after the parameter dimensionality reduction; the redundant test cases in the test case data set after the parameter dimensionality reduction are eliminated to obtain a target test case data set.

[0068] Specifically, the test case generation of the aircraft management system extracts information such as input variable names, variable range values, and variable relationships through the input variable table and the output variable table to generate a test case data set. There are two strategies for generating test cases for the aircraft management system, namely, the ant colony algorithm and the sensitivity analysis between variables using the random forest optimization algorithm. In order to reduce the total number of repetitions of test cases while covering them as completely as possible, this problem can be regarded as a combinatorial optimization problem. Specifically, it can be regarded as a coverage design problem or a test case minimization problem.

[0069] In software testing, the coverage design problem involves generating a minimal set of tests that covers all possible combinations of interactions. For example, for each possible value of an input variable, the generated test cases should cover all possible combinations of all variable values. Usually such problems take into account the interactions between variables, that is, not just the values ​​of a single variable, but the values ​​of the variables in combination. The test case minimization problem involves reducing the number of test cases while maintaining the same test coverage. This usually involves identifying and removing redundant test cases, that is, test cases that do not add new coverage.

[0070] Generally speaking, the coverage design problem is a combinatorial mathematics problem, whose core goal is to design a test set that can cover all possible combinations of all variables with as few test cases as possible given a set of variables, each with multiple possible values. More specifically, it involves identifying the smallest test set that ensures that all variable combinations appear at least once, which can be described mathematically as follows:

[0071] Assume there are k parameters, each parameter Can be taken possible values, the coverage design problem requires building an array with as few rows as possible (each row represents a test case) so that all possible value combinations of each parameter appear at least once in a row of the array. In actual software testing, this design is usually limited to two-dimensional arrays and often focuses on t-wise combinations (i.e., considering combinations of up to t parameters at a time), among which pair-wise (2-wise) combinations are the most common. Let C be an N×k array, where N is the number of test cases and k is the number of parameters. Each parameter From a finite set ,in The goal of array C is:

[0072] Each t combination covers: For each set of parameter indexes of size t , parameter combination Every possible combination of values ​​of appears in at least one row of C.

[0073] Minimize N: The row N of array C should be as small as possible to reduce the total number of test cases.

[0074] For the simplified pair-wise covering problem, all possible value pairs of each two parameters ( should find a match in a row of C. From a mathematical point of view, the problem can be transformed into a given k-dimensional space and the possibility of each dimension taking a value. In this case, find a minimum set so that every possible combination of all dimensions appears in at least one element (test case) of the set. Because the perfect minimum covering array is often difficult to accurately calculate in the case of multiple parameters and multiple values. In the present invention, it will be considered to use the ant colony algorithm to first globally generate a sufficient number of data sets, solve the correlation between the variables through sensitivity association algorithms such as random forests, and finally consider parameter dimensionality reduction, so as to achieve full coverage testing with fewer test cases. The algorithm architecture for generating test cases for the aircraft management system is as follows: Figure 3 As shown in the figure: (1) Ant Colony Algorithm. The main idea of ​​the Ant Colony Algorithm is to find the optimal path through positive feedback distributed collaboration. This is a heuristic search algorithm based on population optimization. It shows that in the Ant Colony Algorithm, ants with blind individual behavior can produce unexpected self-organizing behavior after forming group behavior. In the process of searching for food, they can always find the shortest path from the ant nest to the food. The execution process of the Ant Colony Algorithm is as follows: Figure 4 As shown, the basic ant colony algorithm model can be described by the following formula:

[0075] ;

[0076] in, The meaning of is the transition probability of a state, which refers to the probability that the kth ant individual moves from node i to node j at time t; is a set, which represents the set of nodes that the k-th ant individual can choose next, that is, the next set of nodes that can be selected; It is a factor with important reference value for individual ants when choosing the next node path. This is mainly because the more ants pass through a path, the higher the pheromone level on the path will be, and the more inspiration it will provide to the ants when choosing the path. It refers to the influence of the heuristic factor on the ant individual when selecting nodes. The larger the value, the more importance it will receive. It refers to the heuristic function that is inversely proportional to the distance between two nodes. Refers to the pheromone content value from node i to node j.

[0077] ;

[0078] Among them, m is the number of ants, n is the number of iterations, i is the location of the ant, and j is the location that the ant can reach. represents the path from i to j that the kth ant passes through, and M is the pheromone quality coefficient. Let the initial information amount , const is a constant.

[0079] The program flow of the ant colony algorithm is as follows Figure 5 As shown in the figure, the advantages of the ant colony algorithm are listed as follows: First, the ant colony algorithm has good robustness. Unlike the previous genetic algorithm, the ant colony algorithm has no special requirements for the route at the beginning of the solution. Secondly, the inherent parallelism of the ant colony algorithm is easy to implement in parallel, so when solving large-scale problems, it can significantly reduce the time consumption. Finally, the ant colony algorithm can be easily mixed with other methods to avoid the disadvantages of the corresponding methods and make the advantages more obvious, so as to improve the performance of the algorithm. The ant colony algorithm requires a long search time, which can be alleviated by computer performance and parallelism.

[0080] (2) Inter-variable sensitivity analysis method. Inter-variable sensitivity analysis helps to focus on key use cases in the generation logic. In this analysis stage, the methods used include: expert system and random forest algorithm. Random forest is a popular and powerful machine learning algorithm, which belongs to the Bagging (Bootstrap aggregating) strategy in ensemble learning methods. It consists of multiple decision trees and improves the accuracy and robustness of the overall prediction by combining the prediction results of multiple decision trees. Random forest is widely used in various classification and regression tasks because of its easy implementation, strong processing power and reliable results. The following are the main features and working principles of random forest:

[0081] Random forests work by building multiple decision trees and integrating their predictions. This approach can significantly reduce the variance of the model, prevent overfitting, and thus improve the model's generalization ability on unknown data. It introduces randomness when building decision trees, including selecting the best split feature from a random subset of features at each split. And for each tree, randomly sample from the original dataset through bootstrap sampling. Random forests can effectively handle large datasets with high-dimensional features without the need for dimensionality reduction. Random forests can provide an assessment of the importance of each feature in the prediction task, which is of great value for feature selection and model interpretation. Because the model consists of multiple decision trees, random forests are insensitive to noise and outliers. Its working order is: (1) Building decision trees: randomly select a subset of data (with replacement sampling), and use this data for each decision tree; (2) Random feature selection: at each split node of the decision tree, instead of looking at all features, a subset of features is randomly selected and the node is split based on the best split among these features. The tree is built until a certain condition is met, that is, the tree will continue to grow until it reaches a certain size or can no longer reduce the error further, and no pruning is performed; (3) Voting or averaging: In classification tasks, each tree in the forest predicts the sample, and the final output is the category predicted by the majority of trees (majority voting method). In regression tasks, each tree gives a numerical prediction, and the final result is the average of all tree predictions. According to the importance of the decision tree, it is determined whether the global test cases generated by the ant colony algorithm are repeated or out of range, and after elimination, fewer test cases are obtained to achieve full coverage testing.

[0082] According to the test requirements of the aircraft management system, use cases can be generated for static testing of the aircraft management system logic, and use cases can also be generated for real-time simulation of multiple real-life scenarios. Since real-time simulation of all stages is difficult, the present invention mainly tests the entire flight cycle of the aircraft. Flight includes multiple mission stages, such as take-off, climb, cruise, return, approach, descent, and landing stages. In different stages, control quantities can be input to the aircraft management system multiple times to achieve a test case file containing input quantity combinations at multiple times. In each iteration cycle, the test case generation strategy will generate a large number of input variable value combinations. Each input variable combination generated is stored in a container, which can be a text file, an array structure or a list structure of a program, etc. First, set the simulation time of a flight stage, randomly take out a combination from the container at a fixed time interval, and combine the selected combinations in order to form a test case file. According to the total number of interactive inputs and internal variables of the aircraft management system and other systems, a corresponding number of test case files are generated. After that, the file content of each test case is sent to each aircraft management system software according to the transmission protocol using multiple processes, thereby driving the operation of multiple aircraft simulation systems. The test case file generation method is shown in the figure below. A test case is generated for each flight phase. When testing different flight phases, different test case files are sent to the aircraft management system to drive the aircraft to complete a flight simulation test cycle, covering the algorithm process such as Figure 6 shown.

[0083] Step S13: inputting the target test case data set into the aircraft management system for simulation testing to obtain simulation test results output by the aircraft management system including flight state variable values.

[0084] In this embodiment, the operation structure of the aircraft management system is as follows: Figure 7As shown, each computing simulation server is connected to the data storage management server through a network protocol, wherein the network protocols include FC (Fibre Channel), iSCSI (Internet Small Computer System Interface), NFS (Network File System), CIFS (Common Internet File System, a sharing protocol for network connection), HTTP (Hypertext Transfer Protocol), and FTP (File Transfer Protocol). Each computing simulation server deploys an aircraft management system test case running program and aircraft management system software. The aircraft management system test case running program uses multi-process technology to enable or disable multiple aircraft management system software, and transmits data with each aircraft management system software.

[0085] Step S14: judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value; if the aircraft state of the aircraft is out of bounds, selecting corresponding target test cases.

[0086] In this embodiment, a local data transmission interaction module is used, and whether the aircraft state of the aircraft is out of bounds is determined based on the output parameter range and the flight state variable value; if the aircraft state of the aircraft is out of bounds, the corresponding target test case is screened out, and the target test case and the corresponding flight state variable value are saved in a preset folder; if the aircraft state of the aircraft is not out of bounds, the distance between the flight state variable value and the boundary value is calculated; the relationship between the distance and the boundary value is calculated to obtain a relationship score, and a weighted average calculation is performed on all the relationship scores to obtain a total score, and the total score is used to screen out the target test case from the target test case data set.

[0087] Each test case generated by this application will drive the operation of an aircraft management system, which outputs the flight status of the aircraft in real time. The data transmission interaction module is responsible for reading and storing the flight status of the aircraft in real time. The aircraft management system requires the test environment to read the flight status variable value stored in the data transmission interaction module, and judge whether the flight status exceeds the limit based on the reasonable range of the set output variable. If it exceeds the limit, the test case and flight status data are stored in the specified folder.

[0088] In addition to directly judging whether the flight status of the aircraft is out of bounds, the aircraft management system test case running program also judges the distance between the current flight status and the boundary value. The flight status consists of multiple output variables, and the distance between each output variable and the set boundary value is counted to get a score. When the output variable does not exceed the bounds, the closer the output value is to the boundary threshold, the higher the score is. This score is used for iterative generation in the test case generation strategy. Multiple iterations will make the output value continue to approach the output boundary value, thereby finding the input variable set value that causes the aircraft to lose control, that is, the target test case.

[0089] The target test case operation process is as follows: Figure 8 As shown, in order to more comprehensively handle the out-of-bounds situation and ensure that the score can reflect the relationship between the output value and the boundary value, the algorithm is expressed as follows: For each output variable , define the upper bound of its range and lower bound This example requires calculating the scores for each test case's output variable Value , the score is calculated based on its relationship with the boundary value: when When the value exceeds the upper bound or falls below the lower bound (out-of-bounds situation): Scorei = 0. Out-of-bounds output variables are directly assigned the lowest score (0 points) because they represent potential system risks or failures, and the task fails directly at this time.

[0090] when When in bounds (not out of bounds):

[0091] ;

[0092] Final output variable Score calculation:

[0093] ;

[0094] Select the higher score that is close to the boundary and finally integrate the scores: take a weighted average of the scores of all output variables to get the total score for this test case:

[0095] ;

[0096] Where n represents the number of output variables. Represents output variable The score, Indicates the total score of the test case.

[0097] Through this improved algorithm, the test case generation strategy can not only effectively approach the boundary conditions, but also prevent the system from entering a potentially dangerous state, ensuring the operational safety of the aircraft case test and the stability of the system. The use of scores can help the ant colony algorithm find dangerous cases, thereby testing the hidden defects of the aircraft management system.

[0098] Step S15: Visualize the simulation test results and the target test cases using a graphical user interface to complete a full coverage test of the aircraft management system.

[0099] In this embodiment, a graphical user interface is used to draw a graph of the simulation test results and the target test case to obtain a result graph, and the result graph is visualized so that the client can analyze the cause of the fault on the result graph and complete the full coverage test of the aircraft management system.

[0100] In this embodiment, the value sampling distribution of each input variable is displayed on the GUI (Graphical User Interface) interface of the aircraft management system requirement test environment software, such as Fig. 9 The software GUI contains a data bar for the value of each input variable. If a value of the variable has appeared in the test case and drives the aircraft management system to complete the simulation test, the line color in the variable value data bar will be darkened. The darker the line color, the more times the value is selected, so as to visualize the value coverage of the input variable.

[0101] The aircraft management system test case running program will count the number of cases that have been run, the number of cases that have not been run, and the number of cases that have failed in real time, and display the progress of the test case completion in the software GUI human-computer interaction interface. By visualizing the results after the simulation test is completed, including aircraft trajectory diagrams, attitude diagrams, etc., the designer can analyze the failure factors of the aircraft. By selecting the graphics category to be drawn, currently there are "trajectory diagrams", "altitude diagrams", and "attitude diagrams", you can view the corresponding output results, such as Fig.10 shown.

[0102] The brief process of this application to achieve full coverage testing of the aircraft management system is as follows Fig.11 As shown, it can be divided into: aircraft management system parameter input and output management, aircraft management system full coverage test case generation, aircraft management system model scheduling, multi-machine collaborative calculation and judgment, aircraft management system test case statistics and restoration.

[0103] The innovation of this application lies in: firstly, the input and output parameter processing method of the aircraft management system is studied. Since the aircraft management system contains more and more complex logics, it is different from the single control law logic test. According to the input parameters of the aircraft management system cross-link, a reasonable range is given, so that the aircraft management system test case generation software can generate meaningful test cases. At the same time, dangerous test cases can be found according to the output parameter range of concern, so as to discover hidden defects or dangerous operations in the aircraft management system; secondly, the generation of full coverage test cases for the aircraft management system based on the ant colony algorithm and the sensitivity algorithm between variables is studied. By using the ant colony algorithm, a sufficient number of use case data sets are first generated globally, and the correlation between the variables is solved by the sensitivity association algorithms such as random forests. Finally, the parameter dimensionality reduction is considered to reduce the redundancy of test cases and avoid Since too many parameters lead to the problem of combinatorial explosion, full coverage testing can be achieved with fewer test cases. Afterwards, the aircraft management system is quickly and dynamically simulated in real time through parallel computing of multiple computers. At the same time, the test cases are classified according to the proposed judgment conditions. Multi-process technology is used to achieve multi-machine parallel computing to improve the calculation efficiency of the test cases. At the same time, the output variables are used to determine whether the test case is out of bounds. The output value is judged by scoring the variable boundary threshold. After multiple iterations, the output value is constantly approaching the output boundary value, so as to find the input variable set value that causes the aircraft to lose control, that is, the target test case. At the same time, dynamic simulation testing can not only test the internal logic, but also test different operating conditions. Finally, according to the statistics and restoration results of the aircraft management system test cases, the design defects of the aircraft management system are analyzed from the visual view.

[0104] The present application can automatically generate test cases to reduce human errors, and optimize the generation and execution of test cases through intelligent algorithms to ensure more comprehensive test coverage; secondly, the present invention is a fast dynamic real-time simulation test for aircraft management systems, which is different from a single control law logic test; in addition, by utilizing an ant colony algorithm and a sensitivity association algorithm, test case redundancy is reduced to avoid the combinatorial explosion problem caused by too many parameters, thereby achieving full coverage testing with fewer test cases; finally, by implementing a fast dynamic real-time simulation test of an aircraft management system through parallel computing of multiple computers, the test case test results can be quickly obtained, and at the same time, not only the internal logic can be tested, but also different operating conditions can be tested. Combined with the use case statistics and restoration results, the design defects and dangerous operations of the aircraft management system can be effectively discovered, thereby locating the direction of change for designers.

[0105] In this embodiment, an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft are obtained; a test case data set is generated based on the input parameter range and using an ant colony algorithm, a variable sensitivity association algorithm is used to solve the correlation between the variables in the test case data set, and parameter dimension reduction is performed on the test case data set according to the correlation to obtain a target test case data set; the target test case data set is input into the aircraft management system for simulation testing to obtain a simulation test result output by the aircraft management system including a flight state variable value; whether the aircraft state of the aircraft is out of bounds is determined based on the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, the corresponding target test case is screened out; and the simulation test results and the target test case are visualized using a graphical user interface to complete a full coverage test of the aircraft management system. This application is applied to the full coverage test system of the third-party system. Since the aircraft management system contains more and more complex logics than a single control law logic test, the input parameter range and output parameter range of the aircraft management system based on the aircraft input variables and aircraft parameter settings are obtained to generate meaningful test cases. At the same time, dangerous test cases can be found according to the output parameter range to discover hidden defects or dangerous operations in the aircraft management system. A global test case data set is generated by using the ant colony algorithm, and the correlation between variables is solved through the variable sensitivity association algorithm. Then, parameter dimensionality reduction is performed to reduce test case redundancy and avoid the combinatorial explosion problem caused by too many parameters, thereby achieving full coverage with fewer test cases. Coverage testing, obtaining a target test case data set, inputting the target test case data set into the aircraft management system for simulation testing, realizing fast dynamic real-time simulation testing of the aircraft management system through parallel computing of multiple computers, realizing multi-machine parallel computing through multi-process technology, improving the calculation efficiency of the case, and judging whether the aircraft state of the aircraft is out of bounds. If the aircraft state of the aircraft is out of bounds, the corresponding target test case is screened out. Through dynamic simulation testing, not only the internal logic can be tested, but also different operating conditions can be tested. Finally, a graphical user interface is used to visualize the simulation test results and target test cases to complete the full coverage test of the aircraft management system and improve the efficiency and accuracy of the full coverage test of the aircraft management system.

[0106] See also Fig.12 As shown, the embodiment of the present invention discloses a full coverage test device for an aircraft management system, which is applied to a full coverage test system for a third-party system, and specifically may include:

[0107] A parameter range acquisition module 11 is used to acquire an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft;

[0108] A target test case data set generation module 12 is used to generate a test case data set based on the input parameter range and using an ant colony algorithm, solve the correlation between the variables in the test case data set using a variable sensitivity association algorithm, and perform parameter dimension reduction on the test case data set according to the correlation to obtain a target test case data set;

[0109] A simulation test result acquisition module 13 is used to input the target test case data set into the aircraft management system for simulation testing, so as to obtain the simulation test result including the flight state variable value output by the aircraft management system;

[0110] A judgment module 14, used for judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, selecting a corresponding target test case;

[0111] The visualization module 15 is used to visualize the simulation test results and the target test cases using a graphical user interface to complete a full coverage test of the aircraft management system.

[0112] In some specific embodiments, the parameter range acquisition module 11 may specifically include:

[0113] An input parameter range acquisition module is used to acquire the input parameter range sent by the aircraft management system of the aircraft based on the original documents of input variables of the navigation information system, the engine control cross-linking system, the hydraulic control cross-linking system, the electromechanical control cross-linking system, the fuel control cross-linking system, and the environmental control cross-linking system, the pilot operation behavior information, the pilot operation instructions, and the offset setting;

[0114] The output parameter range acquisition module is used to acquire the output parameter range based on the aircraft parameter setting sent by the aircraft management system.

[0115] In some specific embodiments, the target test case data set generation module 12 may specifically include:

[0116] A test case data set generation module, used to generate a full coverage test case data set based on the input parameter range and the positive feedback distributed collaboration method using an ant colony algorithm;

[0117] The correlation solving module is used to solve the correlation between the variables in the test case data set by using a variable sensitivity correlation algorithm; the variable sensitivity correlation algorithm includes a random forest algorithm.

[0118] In some specific embodiments, the target test case data set generation module 12 may specifically include:

[0119] A parameter dimension reduction and screening module, used to perform parameter dimension reduction on the test case data set according to the correlation, and screen out redundant test cases from the test case data set after parameter dimension reduction;

[0120] The elimination module is used to eliminate the redundant test cases in the test case data set after parameter dimensionality reduction to obtain a target test case data set.

[0121] In some specific embodiments, the determination module 14 may specifically include:

[0122] An aircraft state judgment module, used to use the local data transmission interaction module to judge whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value;

[0123] The flight state variable value saving module is used to filter out the corresponding target test case if the aircraft state of the aircraft exceeds the limit, and save the target test case and the corresponding flight state variable value into a preset folder.

[0124] In some specific embodiments, the determination module 14 may specifically include:

[0125] a distance calculation module, for calculating the distance between the flight state variable value and the boundary value if the aircraft state of the aircraft does not exceed the boundary;

[0126] The total score calculation module is used to calculate the relationship between the distance and the boundary value to obtain a relationship score, perform weighted average calculation on all the relationship scores to obtain a total score, and use the total score to filter out the target test case from the target test case data set.

[0127] In some specific embodiments, the visualization module 15 may specifically include:

[0128] The result graph visualization module is used to draw the simulation test results and the target test case using a graphical user interface to obtain a result graph, and visualize the result graph so that the client can analyze the cause of the fault on the result graph.

[0129] Fig.13 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the full coverage test method of the aircraft management system performed by the electronic device disclosed in any of the aforementioned embodiments.

[0130] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0131] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0132] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20 to realize the operation and processing of the data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the full coverage test method of the aircraft management system performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can also further include a computer program that can be used to complete other specific tasks. In addition to including data transmitted from external devices received by the full coverage test equipment of the aircraft management system, the data 223 can also include data collected by its own input and output interface 25, etc.

[0133] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0134] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the full coverage test method for the aircraft management system disclosed in any of the aforementioned embodiments are implemented.

[0135] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0136] The above is a detailed introduction to the full coverage test method, device, equipment and storage medium of an aircraft management system provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A full coverage test method for an aircraft management system, characterized in that: Applied to the third-party system full coverage test system, including: Obtaining an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft; Based on the input parameter range and using an ant colony algorithm, a test case data set is generated, a variable sensitivity association algorithm is used to solve the correlation between the variables in the test case data set, and parameter dimension reduction is performed on the test case data set according to the correlation to obtain a target test case data set; Inputting the target test case data set into the aircraft management system for simulation testing to obtain simulation test results output by the aircraft management system including flight state variable values; Determining whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, selecting a corresponding target test case; The simulation test results and the target test cases are visualized using a graphical user interface to complete full coverage testing of the aircraft management system.

2. The full coverage test method of the aircraft management system according to claim 1, characterized in that: The step of obtaining the input parameter range and the output parameter range based on the aircraft input variables and the aircraft parameter settings sent by the aircraft management system of the aircraft includes: Obtain the original documents of input variables, pilot operation behavior information, pilot operation instructions and input parameter range of offset settings based on the navigation information system, engine control crosslinking system, hydraulic control crosslinking system, electromechanical control crosslinking system, fuel control crosslinking system and environmental control crosslinking system sent by the aircraft's aircraft management system; Obtain an output parameter range based on aircraft parameter settings sent by the aircraft management system.

3. The full coverage test method of the aircraft management system according to claim 1, characterized in that: The step of generating a test case data set based on the input parameter range and using an ant colony algorithm, and solving the correlation between the variables in the test case data set using a variable sensitivity association algorithm, includes: Generate a full coverage test case data set using an ant colony algorithm based on the input parameter range and a positive feedback distributed collaboration method; A variable sensitivity association algorithm is used to solve the correlation between the variables in the test case data set; the variable sensitivity association algorithm includes a random forest algorithm.

4. The full coverage test method of the aircraft management system according to claim 1, characterized in that: The performing parameter dimension reduction on the test case data set according to the correlation to obtain a target test case data set includes: Performing parameter dimension reduction on the test case data set according to the correlation, and screening out redundant test cases from the test case data set after parameter dimension reduction; The redundant test cases in the test case data set after parameter dimensionality reduction are eliminated to obtain a target test case data set.

5. The full coverage test method of the aircraft management system according to claim 1, characterized in that: The determining whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, selecting a corresponding target test case includes: Using a local data transmission interaction module, and judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value; If the aircraft state of the aircraft is out of bounds, a corresponding target test case is screened out, and the target test case and the corresponding flight state variable value are saved in a preset folder.

6. The full coverage test method of the aircraft management system according to claim 1, characterized in that: After judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, the method further includes: If the aircraft state of the aircraft is within the bounds, then calculating the distance between the flight state variable value and the boundary value; The relationship between the distance and the boundary value is calculated to obtain a relationship score, a weighted average is calculated for all the relationship scores to obtain a total score, and the total score is used to filter out the target test case from the target test case data set.

7. The full coverage test method of an aircraft management system according to any one of claims 1 to 6, characterized in that: The visualizing the simulation test results and the target test cases by using a graphical user interface includes: The simulation test results and the target test cases are plotted using a graphical user interface to obtain a result graph, and the result graph is visualized so that the client can analyze the cause of the fault on the result graph.

8. A full coverage test device for an aircraft management system, characterized in that: Applied to the third-party system full coverage test system, including: A parameter range acquisition module, used to acquire an input parameter range and an output parameter range based on aircraft input variables and aircraft parameter settings sent by an aircraft management system of the aircraft; A target test case data set generation module is used to generate a test case data set based on the input parameter range and using an ant colony algorithm, solve the correlation between the variables in the test case data set using a variable sensitivity association algorithm, and perform parameter dimension reduction on the test case data set according to the correlation to obtain a target test case data set; A simulation test result acquisition module, used for inputting the target test case data set into the aircraft management system for simulation testing, so as to obtain the simulation test result including the flight state variable value output by the aircraft management system; A judgment module, used for judging whether the aircraft state of the aircraft is out of bounds according to the output parameter range and the flight state variable value, and if the aircraft state of the aircraft is out of bounds, selecting a corresponding target test case; A visualization module is used to visualize the simulation test results and the target test cases using a graphical user interface to complete a full coverage test of the aircraft management system.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the full coverage testing method for an aircraft management system as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the full coverage testing method for an aircraft management system as described in any one of claims 1 to 7 is implemented.

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