Quantitative evaluation method for autonomous controllability of aircraft software architecture

Abstract

The invention discloses an aircraft software architecture autonomous controllability quantitative evaluation method, and relates to the technical field of aircraft software. The method comprises the following steps: determining a first-level evaluation index according to a core influence factor influencing the autonomous controllability of the aircraft software architecture; grading and quantizing the first-level evaluation indexes to obtain second-level indexes and index data of the second-level indexes; processing the second-level indexes and the second-level index data by adopting an analytic hierarchy process introducing a dynamic adjustment mechanism to obtain subjective weights of the second-level indexes; processing the secondary index data by adopting an entropy weight method to obtain an objective weight of a secondary index; processing the secondary indexes by adopting a decision-making laboratory analysis method to obtain importance weights of the secondary indexes; and combining the subjective weight, the objective weight and the importance weight of the secondary indexes to obtain a comprehensive index weight for evaluating the autonomous controllability of the aircraft software architecture. The method can cover the whole life cycle of the software, and improves the accuracy and adaptability of the evaluation system.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft control software, and particularly to a method for quantitatively evaluating the autonomy and controllability of an aircraft software architecture. Background Art

[0002] With the intensification of international technological competition, the autonomy and controllability of aircraft software architectures have become the core requirements for national security and industrial development. However, traditional evaluation methods mainly focus on functional performance, while neglecting the comprehensive autonomy analysis from code, supply chain to ecosystem. At the same time, with the continuous improvement of the requirements for software autonomy and controllability in the aerospace field, the domestic substitution and autonomy and controllability evaluation of aircraft software architectures have become key issues. Traditional software evaluation methods mainly focus on functional performance testing and lack systematic quantitative evaluation of autonomy and controllability. Summary of the Invention

[0003] The present invention provides a method for quantitatively evaluating the autonomy and controllability of an aircraft software architecture, which is used to solve the problem that traditional software evaluation methods mainly focus on functional performance testing and lack systematic quantitative evaluation of autonomy and controllability. In view of this, the present invention is realized through the following solutions.

[0004] The present invention provides a method for quantitatively evaluating the autonomy and controllability of an aircraft software architecture, including: Determine multi-dimensional and quantifiable primary evaluation indicators according to the core influencing factors affecting the autonomy and controllability of the aircraft software architecture; Classify the primary evaluation indicators to obtain secondary indicators, and quantify the secondary indicators to obtain secondary indicator data; Use the analytic hierarchy process with a dynamic adjustment mechanism to process the secondary indicators and the secondary indicator data to obtain the subjective weights of the secondary indicators; use the entropy weight method to process the secondary indicator data to obtain the objective weights of the secondary indicators; use the decision-making trial and evaluation laboratory method to process the secondary indicators to obtain the importance weights of the secondary indicators; Combine the subjective weights, objective weights and importance weights of the secondary indicators to obtain a comprehensive indicator weight for evaluating the autonomy and controllability of the aircraft software architecture.

[0005] As a further description of the present invention, the primary evaluation indicators include: Code autonomy indicator, which is used to evaluate the independent R & D ability of the software and the degree of dependence on foreign technologies; Supply chain security indicator, which is used to measure the domestic production rate and risk level of the supply chain of hardware and software components; Technical controllability indicator, which is used to evaluate the control ability and maintainability of core technologies; Ecological compatibility indicators, which are used to quantify the adaptation degree and performance of software with domestic operating systems and hardware; Security compliance indicators, which are used to check whether the software meets national information security standards and its vulnerability repair capabilities.

[0006] As a further description of the present invention, the code autonomy indicators include the following secondary indicators: The proportion of self-developed code, and the corresponding secondary indicator data is the proportion of the number of self-developed code lines in the total number of code lines; The citation rate of domestic open-source code, and the corresponding secondary indicator data is the proportion of the number of domestic open-source code files in the total number of cited code files; The proportion of key modules relying on external technologies, and the corresponding secondary indicator data is the proportion of the number of core modules relying on foreign technologies in the total number of core modules.

[0007] As a further description of the present invention, the supply chain security indicators include the following secondary indicators: The usage rate of domestic chips, and the corresponding secondary indicator data is the proportion of the number of domestic chips in the total number of chips; The geopolitical risk score of suppliers, and the corresponding secondary indicator data is: , where the supplier risk level is obtained based on the sanction list and political stability score; The multi-source backup rate of key components, and the corresponding secondary indicator data is the proportion of the number of key components with multi-source backup in the total number of key components.

[0008] As a further description of the present invention, the technology controllability indicators include the following secondary indicators: The self-owned rate of core patents, and the corresponding secondary indicator data is the proportion of the number of self-applied core patents in the total number of core patents; The code maintainability index, and the corresponding secondary indicator data is: 100 - average cyclomatic complexity * 0.5 - module coupling degree * 0.3; The integrity of technical documents, and the corresponding secondary indicator data is the proportion of the number of archived document modules in the total number of modules.

[0009] As a further description of the present invention, the ecological compatibility indicators include the following secondary indicators: The suitability score of domestic operating systems, and the corresponding secondary indicator data is: ; The support rate of domestic hardware drivers, and the corresponding secondary indicator data is the proportion of the number of domestic hardware with adapted drivers in the total number of domestic hardware; The performance attenuation in the hybrid environment, and the corresponding secondary indicator data is the proportion of the difference between the performance in the domestic environment and the original environment in the original environment performance.

[0010] As a further description of the present invention, the security compliance indicators include the following secondary indicators: The coverage rate of equal protection 2.0 compliance items, and the corresponding secondary indicator data is the proportion of the number of satisfied equal protection clauses in the total applicable clauses; The vulnerability repair response time, and the corresponding secondary indicator data is the average time from vulnerability discovery to repair; The application rate of domestic cryptographic algorithms, and the corresponding secondary indicator data is the proportion of the total number of modules using SM2 algorithm or SM3 algorithm or SM4 algorithm in the total number of encryption modules.

[0011] As a further description of the present invention, the analytic hierarchy process with a dynamic adjustment mechanism is used to process the secondary indicators and the secondary indicator data to obtain the subjective weights of the secondary indicators, which specifically includes the following process: Construct an initial judgment matrix of experts according to expert scoring, and the judgment matrix is composed of n*n elements representing the relative importance ratio between secondary indicators; When the secondary indicator data changes, perform a consistency test on the judgment matrix. If the consistency test is not passed, dynamically adjust the elements in the judgment matrix to obtain an updated judgment matrix until the consistency test is satisfied. Among them, the dynamic adjustment amount of the elements is calculated according to the change amount of the secondary indicator data; Vectorize and normalize the judgment matrix, and finally obtain the subjective weights of the secondary indicators.

[0012] As a further description of the present invention, the decision-making trial and evaluation laboratory (DEMATEL) method is used to process the secondary indicators to obtain the importance weights of the secondary indicators, which specifically includes: Normalize the updated judgment matrix to obtain a direct influence matrix; Calculate the influence degree and the influenced degree of each secondary indicator according to the direct influence matrix, and calculate the centrality of each secondary indicator from the influence degree and the influenced degree; Normalize the centrality of each secondary indicator and output the importance weights of the secondary indicators.

[0013] As a further description of the present invention, the subjective weights, objective weights and importance weights of the secondary indicators are combined to obtain a comprehensive index weight, which specifically includes: Combine the objective weights and importance weights of the secondary indicators to obtain a first comprehensive index weight, and the specific calculation formula is: ; Wherein, is the first comprehensive index weight, is the objective weight of the secondary indicator, is the importance weight of the secondary index, is the proportion of the objective weight of the secondary index, is the proportion of the importance weight of the secondary index, means adding the objective weights and importance weights of all secondary indexes proportionally; Combining the weight of the first comprehensive index and the subjective weight of the secondary index to obtain the comprehensive index weight. The specific calculation formula is: ; wherein, is the comprehensive index weight, is the subjective weight of the secondary index, is the balance coefficient between expert experience and data-driven.

[0014] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages: This application breaks through the limitation of traditional evaluation methods that focus on software function performance, incorporates five dimensions of code autonomy, supply chain security, technical availability, ecological compatibility, and security compliance into a unified framework, covers the entire software life cycle, gives an evaluation method for the autonomous controllability of aircraft software, introduces a dynamic adjustment mechanism using the improved analytic hierarchy process, and combines the entropy weight-DEMATEL method to reduce the deviation of subjectivity in the single analytic hierarchy process, reveals the influence relationship between indicators, identifies key driving factors, and increases the accuracy and adaptability of the evaluation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is the flowchart of the method for quantitatively evaluating the autonomous controllability of the aircraft software architecture provided by the embodiment of the present invention.

[0016] Figure 2 is the evaluation index system for the autonomous controllability of the aircraft software architecture provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer, the present invention will be further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] With the continuous improvement of the requirements for software autonomy and control in the aerospace field, the domestic substitution and autonomy and control assessment of aircraft software architectures have become key issues. Traditional software assessment methods mainly focus on functional and performance testing, lacking a systematic quantitative assessment of autonomy and control. The method provided by the embodiments of the present invention aims to build a scientific and comprehensive assessment system, combined with domestic operating systems (such as OpenEuler / UniProton) and hardware ecosystems, to quantitatively assess the autonomy and control of aircraft software architectures.

[0019] As Figure 1 shown, the embodiments of the present invention provide a method for quantitatively assessing the autonomy and control of an aircraft software architecture, including the following steps: Step 1: Determine multi-dimensional and quantifiable primary assessment indicators according to the core influencing factors affecting the autonomy and control of the aircraft software architecture.

[0020] Specifically, the design of multi-dimensional and quantifiable assessment indicators needs to cover the core influencing dimensions of the autonomy and control of the aircraft software architecture and meet national policy requirements and industry technical characteristics. It is designed closely around national strategic needs and industry technical pain points: responding to the hard requirements of laws such as the "Regulations on the Security Protection of Critical Information Infrastructure" for the self-developed rate of core code, diversification of the supply chain, etc., while preventing potential risks such as "supply interruption" and "backdoors" caused by the high dependence of aerospace software systems on foreign technologies, covering core controllable elements such as code, patents, and documents, quantifying the migration adaptation cost and performance degradation issues based on the large-scale application of domestic operating systems and hardware, and then verifying through domestic actual industry practices to ensure that the indicators truly reflect risk scenarios. Through multi-dimensional coordination, it provides full-life-cycle quantitative support for the autonomy and control of aircraft software. Therefore, the code autonomy indicator, supply chain security indicator, technology controllability indicator, ecological compatibility indicator, and security compliance indicator are finally selected as the primary assessment indicators. Among them: The code autonomy indicator is used to evaluate the independent R & D ability of the software and the degree of dependence on foreign technologies; it can ensure the controllability of core code and reduce the risks caused by external technology supply interruption or vulnerabilities.

[0021] The supply chain security indicator is used to measure the domestic substitution rate and risk level of the hardware and software component supply chains; it can ensure the stability of the supply chain and avoid chain breaks caused by a single supplier or geopolitical issues.

[0022] The technology controllability indicator is used to evaluate the control ability and maintainability of core technologies (patents, algorithms, documents); it can ensure the traceability and iterability of technologies and reduce the migration costs caused by technology blockade or document loss.

[0023] The ecological compatibility index is used to quantify the adaptation degree and performance of software with domestic operating systems and hardware; it can promote the implementation of the domestic ecosystem and reduce performance losses caused by compatibility issues.

[0024] The security compliance index is used to examine whether the software meets national information security standards and its vulnerability repair capabilities; it can prevent data leakage and cyber attacks and meet regulatory requirements.

[0025] Step 2: Classify the above first-level evaluation indicators to obtain second-level indicators, and quantify the second-level indicators to obtain second-level indicator data.

[0026] Specifically, the code autonomy index includes the following second-level indicators: the proportion of self-developed code, the citation rate of domestic open-source code, and the proportion of key modules relying on external technologies, where: The proportion of self-developed code can measure the proportion of code independently developed by an enterprise and reflect technological autonomy. The corresponding second-level indicator data is the proportion of the number of self-developed code lines in the total number of code lines. The specific calculation formula is: the number of self-developed code lines / the total number of code lines * 100%.

[0027] The citation rate of domestic open-source code can evaluate the utilization degree of domestic open-source technologies and reduce the dependence on foreign open-source communities. The corresponding second-level indicator data is the proportion of the number of domestic open-source code files in the total number of cited code files. The specific calculation formula is: the number of domestic open-source code files / the total number of cited code files * 100%.

[0028] The proportion of key modules relying on external technologies can analyze whether the core functional modules rely on foreign technologies (such as algorithm libraries, drivers). The corresponding second-level indicator data is the proportion of the number of core modules relying on foreign technologies in the total number of core modules. The specific calculation formula is: the number of core modules relying on foreign technologies / the total number of core modules * 100%.

[0029] Specifically, the supply chain security index includes the following second-level indicators: the usage rate of domestic chips, the geopolitical risk score of suppliers, and the multi-source backup rate of key components, where: The usage rate of domestic chips can count the proportion of domestic chips in the hardware and reflect the level of domesticization of the supply chain. The corresponding second-level indicator data is the proportion of the number of domestic chips in the total number of chips. The specific calculation formula is: the number of domestic chips / the total number of chips * 100%.

[0030] The geopolitical risk score of suppliers can quantify the political stability and sanction risks of the countries / regions where the suppliers are located. The corresponding second-level indicator data is: , where the supplier risk level is obtained based on the sanction list and political stability score.

[0031] The multi-source backup rate of key components can evaluate whether key components have the ability of multi-vendor backup to avoid single dependence. The corresponding secondary index data is the proportion of the number of key components with multi-source backup in the total number of key components. The specific calculation formula is: the number of key components with multi-source backup / the total number of key components * 100%.

[0032] Specifically, the technical controllability index includes the following secondary indexes: the self-owned rate of core patents, the code maintainability index, and the technical document integrity, where: The self-owned rate of core patents can measure the proportion of core technology patents independently mastered by an enterprise to prevent patent blockades. The corresponding secondary index data is the proportion of the number of core patents applied for independently in the total number of core patents. The specific calculation formula is: the number of core patents applied for independently / the total number of core patents * 100%.

[0033] The code maintainability index can comprehensively consider code complexity and module coupling degree to evaluate the readability and modifiability of the code. The corresponding secondary index data is: 100 - average cyclomatic complexity * 0.5 - module coupling degree * 0.3.

[0034] The technical document integrity can test the integrity of technical documents (design documents, interface documents) to ensure the inheritance of knowledge. The corresponding secondary index data is the proportion of the number of archived document modules in the total number of modules. The specific calculation formula is: the number of archived document modules / the total number of modules * 100%.

[0035] Specifically, the ecological compatibility index includes the following secondary indexes: the suitability score of domestic operating systems, the support rate of domestic hardware drivers, and the performance attenuation in a hybrid environment, where: The suitability score of domestic operating systems (such as OpenEuler or Uniproton) can evaluate the functional compatibility and performance stability of software on domestic operating systems. The corresponding secondary index data is: 。

[0036] The support rate of domestic hardware drivers can measure the driver adaptation ability of software to domestic hardware. The corresponding secondary index data is the proportion of the number of domestic hardware with adapted drivers in the total number of domestic hardware. The specific calculation formula is: the number of domestic hardware with adapted drivers / the total number of domestic hardware * 100%.

[0037] The performance attenuation in a hybrid environment can compare the performance differences between the domestic environment and the original environment to reflect the cost of domestic migration. The corresponding secondary index data is the proportion of the difference between the performance of the domestic environment and the performance of the original environment in the performance of the original environment. The specific calculation formula is: (performance of the domestic environment - performance of the original environment) / performance of the original environment * 100%.

[0038] Specifically, the security compliance indicators include the following secondary indicators: the coverage rate of Classified Protection 2.0 compliance items, the vulnerability repair response time, and the application rate of domestic cryptographic algorithms.

[0039] The coverage rate of Classified Protection 2.0 compliance items can examine the proportion of clauses that the system meets the requirements of Classified Protection 2.0 Level 3, ensuring the basic security capabilities. The corresponding secondary indicator data is the proportion of the number of Classified Protection clauses that have been met in the total applicable clauses. The specific calculation formula is: the number of Classified Protection clauses that have been met / the total number of applicable clauses * 100%.

[0040] The vulnerability repair response time can measure the enterprise's repair efficiency of security vulnerabilities and prevent the attack window period. The corresponding secondary indicator data is the average time from vulnerability discovery to repair (unit: hours).

[0041] The application rate of domestic cryptographic algorithms can evaluate whether algorithms certified by the State Cryptography Administration (such as SM2 algorithm or SM4 algorithm) are adopted to ensure data security. The corresponding secondary indicator data is the proportion of the total number of modules using the SM2 algorithm or SM3 algorithm or SM4 algorithm in the total number of encryption modules. The specific calculation formula is: the total number of modules using the SM2 algorithm or SM3 algorithm or SM4 algorithm / the total number of encryption modules * 100%.

[0042] Step 3: Use the analytic hierarchy process (AHP) with a dynamic adjustment mechanism to process the secondary indicators and secondary indicator data to obtain the subjective weights of the secondary indicators; use the entropy weight method to process the secondary indicator data to obtain the objective weights of the secondary indicators; use the decision-making trial and evaluation laboratory (DEMATEL) method to process the secondary indicators to obtain the importance weights of the secondary indicators.

[0043] The traditional analytic hierarchy process relies on expert scoring to construct a judgment matrix, ensuring that the weights meet the actual needs but with a certain degree of subjectivity. The entropy weight method determines the weights based on the degree of quantification of data, based on an objective analysis method, reducing human bias. At the same time, the decision-making trial and evaluation laboratory (DEMATEL) method is used. The DEMATEL method is a method for analyzing system factors using graph theory and matrix tools, which can modify the weights according to the degree of mutual influence between indicators, avoiding the isolation of viewing the importance of indicators and further improving the accuracy of evaluation indicators. The present invention combines the analytic hierarchy process (AHP), the entropy weight method, and the decision-making trial and evaluation laboratory (DEMATEL) method to dynamically adjust the indicator weights, achieving the purpose of complementarity. AHP makes up for the neglect of expert experience by the entropy weight method, the entropy weight method corrects the subjectivity of the AHP method, the DEMATEL method strengthens the correlation analysis between indicators, and at the same time combines the proposed dynamic response mechanism, achieving the three major advantages of "subjective and objective integration", "causal analysis", and "dynamic response".

[0044] The above-mentioned analytic hierarchy process with a dynamic adjustment mechanism is used to process the secondary indicators and their data, and the subjective weights of the secondary indicators are obtained. The specific steps are as follows: Step 301: Construct an initial judgment matrix of experts based on the experts' scores. The judgment matrix consists of n*n elements representing the relative importance ratios between evaluation indicators.

[0045] Among them represents the secondary indicator The importance ratio of the secondary indicator to the secondary indicator

[0046] (using the 1-9 scale method), n represents the total number of secondary indicators to be compared at the current level. Specifically, 1 means that the secondary indicator i is equally important as the secondary indicator j, 3 means that the secondary indicator i is slightly more important than the secondary indicator j, 5 means that the secondary indicator i is significantly more important than the secondary indicator j, 7 means that the secondary indicator i is strongly more important than the secondary indicator j, 9 means that the secondary indicator i is extremely more important than the secondary indicator j, and other numbers represent the importance levels between the above integers.

[0047] Specifically, the rule for dynamically adjusting the elements in the judgment matrix is as follows: When the data of the secondary indicators changes (such as changes in supplier risks, technological iterations, etc.), update the matrix elements according to the following rules to obtain the updated judgment matrix : ; Among them, represents the element in the updated judgment matrix, represents the element in the judgment matrix before update, represents the secondary indicator The change rate of the secondary indicator data associated with is the adjustment coefficient (usually taking 0.1 - 0.3). For example, if the usage rate of domestic chips increases by 20%, then = 0.2, and the relevant element increases by times.

[0048] Specifically, when conducting the consistency test, the calculation formula for the consistency index CI is as follows:

[0049] Among them, is the maximum eigenvalue of the matrix, n represents the total number of secondary indicators to be compared at the current level. The larger the CI value, the worse the consistency of the judgment matrix.

[0050] Use the consistency index CI and the random index RI for consistency test. When the consistency ratio is satisfied, it means the test passes and the sorting result of the analytic hierarchy process meets the consistency.

[0051] The judgment threshold is generally taken as 0.1, but this application allows the dynamic relaxation of the judgment threshold (such as passing the consistency test when CR < 0.15) to accelerate iteration, but it is necessary to record the adjustment log for subsequent verification. If the consistency test fails, the elements in the matrix are adjusted until the consistency test is satisfied. After passing the consistency test, it indicates that the index weights obtained by the analytic hierarchy process meet the requirements.

[0052] Step 303: Vectorize and normalize the judgment matrix to finally obtain the subjective weights of the secondary indicators.

[0053] Specifically: Multiply each row of the judgment matrix to obtain a new vector :

[0054] Take the nth root of each component of the vector and normalize it to finally obtain the subjective weights of the secondary indicators : .

[0055] The above uses the decision-making trial and evaluation laboratory (DEMATEL) method to process the secondary indicators to obtain the importance weights of the secondary indicators, which specifically includes the following steps: Step 304: Normalize the judgment matrix updated in Step 302 to obtain the direct influence matrix , and the specific calculation formula is: .

[0056] Step 305: Calculate the influence degree and the influenced degree of each secondary indicator based on the direct influence matrix, and calculate the centrality of each secondary indicator from the influence degree and the influenced degree; Specifically, calculate the comprehensive influence matrix from the direct influence matrix :

[0057] where is the identity matrix.

[0058] Obtain the influence degree from the comprehensive influence matrix T and the influenced degree : Influence degree (total influence of secondary index i on other indexes) ; Influenced degree (total influence of secondary index i by other indexes) ; From the influence degree and influenced degree of each secondary index, obtain the centrality of each secondary index , and the centrality represents the importance of secondary index i: .

[0059] Step 306: Normalize the centrality of each secondary index and output the importance weight of the secondary index .

[0060] Specifically, normalize the centrality of each secondary index to obtain the importance weight of the secondary index : ; Output the importance weight vector of the secondary index as .

[0061] The above uses the entropy weight method to process the secondary index data and obtain the objective weight of the secondary index, which specifically includes the following steps: Step 307: Select the data of n secondary indexes in m samples to establish an evaluation matrix , and use the extreme value method to dimensionless the original data, where represents the data of the th secondary index in the th sample.

[0062] Step 308: Standardize each element in the evaluation matrix to obtain a standardized evaluation matrix : ; where represents the data of the th secondary index in the th sample, represents the minimum value of the data of the th secondary index in all samples, represents the maximum value of the data of the th secondary index in all samples, represents the The standardized value of the data of the j-th secondary indicator in the i-th sample.

[0063] Step 309: Based on the standardized evaluation matrix, use the entropy weight method to determine the objective weights of each secondary indicator.

[0064] Specifically, calculate the proportion of the data of the j-th secondary indicator in each sample to all samples based on the standardized evaluation matrix : ; Calculate the entropy value of the j-th secondary indicator :

[0065] Calculate the variation index of the j-th secondary indicator :

[0066] Calculate the weight of the j-th secondary indicator : , Output the objective weight vector of each secondary indicator: .

[0067] Step 4: Combine the subjective weights, objective weights, and importance weights of the secondary indicators to obtain the comprehensive indicator weights for evaluating the autonomy and controllability of the aircraft software architecture.

[0068] The above Step 4 includes the following process: Step 401: Combine the objective weight and importance weight of the secondary indicator to obtain the first comprehensive indicator weight. The specific calculation formula is: ; where, is the first comprehensive indicator weight, is the objective weight of the secondary indicator, is the importance weight of the secondary indicator, is the proportion of the objective weight of the secondary indicator, is the proportion of the importance weight of the secondary indicator, means adding the objective weights and importance weights of all secondary indicators proportionally.

[0069] Step 402: Combine the first comprehensive indicator weight and the subjective weight of the secondary indicator to obtain the comprehensive indicator weight. The specific calculation formula is: ; where, is the comprehensive indicator weight, is the subjective weight of the secondary index, is the balance coefficient of expert experience and data-driven.

[0070] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0071] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0074] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A quantitative evaluation method for autonomous controllability of aircraft software architecture, characterized in that: include: Determine multi-dimensional, quantifiable first-level evaluation indicators based on the core factors that affect the autonomous controllability of the aircraft software architecture; The first-level evaluation indicators are graded to obtain second-level indicators, and the second-level indicators are quantified to obtain second-level indicator data; The secondary indicators and the secondary indicator data are processed by the hierarchical analysis method with a dynamic adjustment mechanism to obtain the subjective weight of the secondary indicator; the secondary indicator data is processed by the entropy weight method to obtain the objective weight of the secondary indicator; Processing the secondary indicators using a decision laboratory analysis method to obtain the importance weights of the secondary indicators; The subjective weight, objective weight and importance weight of the secondary indicators are combined to obtain the comprehensive indicator weight, which is used to evaluate the autonomous controllability of the aircraft software architecture.

2. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture according to claim 1, characterized in that: The first-level evaluation indicators include: Code autonomy index, which is used to evaluate the software's independent research and development capabilities and its reliance on foreign technology; Supply chain security indicators, which measure the localization rate and risk level of hardware and software component supply chains; Technical controllability indicators, which are used to evaluate the control and maintainability of core technologies; Ecosystem compatibility index, which is used to quantify the compatibility and performance of software with domestic operating systems and hardware; Security compliance indicators are used to verify whether the software complies with national information security standards and its vulnerability repair capabilities.

3. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture as claimed in claim 2, characterized in that: The code autonomy index includes the following secondary indicators: The proportion of self-developed code, the corresponding secondary indicator data is the proportion of self-developed code lines in the total number of code lines; The citation rate of domestic open source code, the corresponding secondary indicator data is the proportion of domestic open source code files in the total number of cited code files; The proportion of key modules that rely on external technology, and its corresponding secondary indicator data is the proportion of core modules that rely on foreign technology in the total number of core modules.

4. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture as claimed in claim 2, characterized in that: The supply chain security indicators include the following secondary indicators: The utilization rate of domestic chips, the corresponding secondary indicator data is the proportion of domestic chips in the total number of chips; Supplier geopolitical risk score, and its corresponding secondary indicator data are: , where the supplier risk level is based on the sanctions list and political stability score; The multi-source backup rate of key components, whose corresponding secondary indicator data is the proportion of the number of multi-source backup key components in the total number of key components.

5. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture as claimed in claim 2, characterized in that: The technical controllability indicators include the following secondary indicators: The core patent autonomy rate, the corresponding secondary indicator data is the proportion of core patents independently applied for in the total number of core patents; Code maintainability index, the corresponding secondary index data is: 100-average cyclomatic complexity*0.5-module coupling*0.3; The completeness of technical documents, its corresponding secondary indicator data is the proportion of the number of modules of archived documents in the total number of modules.

6. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture as claimed in claim 2, characterized in that: The ecological compatibility indicators include the following secondary indicators: The appropriateness score of the domestic operating system, and its corresponding secondary indicator data are: ; Domestic hardware driver support rate, the corresponding secondary indicator data is the proportion of domestic hardware with adapted drivers in the total number of domestic hardware; The mixed environmental performance attenuation, its corresponding secondary indicator data is the proportion of the difference between the domestic environmental performance and the original environmental performance in the original environmental performance.

7. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture as claimed in claim 2, characterized in that: The safety compliance indicators include the following secondary indicators: The coverage rate of MLSP 2.0 compliance items, the corresponding secondary indicator data is the proportion of the number of MLSP clauses that have been met in the total number of applicable clauses; Vulnerability repair response time, the corresponding secondary indicator data is the average time from vulnerability discovery to repair; The application rate of domestic cryptographic algorithms. The corresponding secondary indicator data is the proportion of the total number of modules using SM2 algorithm, SM3 algorithm or SM4 algorithm in the total number of encryption modules.

8. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture as claimed in claim 1, characterized in that: The method of using the hierarchical analysis method with a dynamic adjustment mechanism to process the secondary indicators and the secondary indicator data to obtain the subjective weights of the secondary indicators specifically includes the following process: Constructing an initial expert judgment matrix based on the expert scores, wherein the judgment matrix is ​​composed of n*n elements representing the relative importance ratios between the secondary indicators; When the secondary indicator data changes, the judgment matrix is ​​subjected to consistency check. If it fails the consistency check, the elements in the judgment matrix are dynamically adjusted to obtain an updated judgment matrix until the consistency check is satisfied. The dynamic adjustment amount of the element is calculated based on the change amount of the secondary indicator data. The judgment matrix is ​​vectorized and normalized to finally obtain the subjective weight of the secondary index.

9. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture according to claim 8, characterized in that: The decision laboratory analysis method is used to process the secondary indicators to obtain the importance weights of the secondary indicators, specifically including: Normalizing the updated judgment matrix to obtain a direct influence matrix; The influence and the influenced degree of each secondary indicator are calculated according to the direct influence matrix, and the centrality of each secondary indicator is calculated according to the influence and the influenced degree; After normalizing the centrality of each secondary indicator, the importance weight of the secondary indicator is output.

10. The method for quantitatively evaluating the autonomous controllability of aircraft software architecture according to claim 1, characterized in that: The subjective weight, objective weight and importance weight of the secondary indicators are combined to obtain the comprehensive indicator weight, which specifically includes: The objective weight and importance weight of the secondary indicators are combined to obtain the weight of the first comprehensive indicator. The specific calculation formula is: ； in, is the weight of the first comprehensive indicator, is the objective weight of the secondary indicator, is the importance weight of the secondary indicator, is the ratio of the objective weight of the secondary indicators, is the proportion of the importance weight of the secondary indicators, It means that the objective weight and importance weight of all secondary indicators are added up in proportion; The weight of the first comprehensive indicator and the subjective weight of the secondary indicator are combined to obtain the weight of the comprehensive indicator. The specific calculation formula is: ； in, is the comprehensive indicator weight, is the subjective weight of the secondary indicator, is the balance coefficient between expert experience and data-driven.

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