Unmanned system interoperability weight evaluation method based on combined weighting method

Abstract

The invention relates to the technical field of computer information, and provides an unmanned system interoperability weight evaluation method based on a combined weighting method, which comprises the following steps: constructing an unmanned system availability calculation model to obtain an unmanned system availability vector; constructing an unmanned system reliability calculation model, and calculating an unmanned system reliability matrix; constructing an unmanned system combat capability model, and obtaining an unmanned system combat capability vector; and constructing an unmanned system interoperation efficiency model, and calculating an unmanned system interoperation efficiency value. According to the method, a combined weighting method is introduced, a first weight coefficient is obtained based on an analytic hierarchy process, a second weight coefficient is calculated by means of a relative entropy minimization model based on measured data and multiple evaluations, and key indexes of interoperability of the unmanned system are identified. Through game theory optimization of the second weight, double balance is realized, the problem of complex weight distribution of an unmanned system interoperation index system is effectively solved, geometric mean fusion can be realized, and the limitation of a single weighting method is effectively overcome.

Description

Technical Field

[0001] This invention relates to the field of computer information technology, and more particularly to the field of combat capability evaluation technology, specifically a weighted evaluation method for unmanned system interoperability based on a combined weighting method. Background Technology

[0002] The ADC (Availability-Dependability-Capability) performance evaluation method is used to comprehensively assess the impact of availability, reliability, and capability on the combat effectiveness of weapon systems. It can comprehensively reflect the mission reliability of various weapon systems and the combined effect of multiple tactical and technical indicators on equipment effectiveness. By constructing a three-dimensional evaluation framework, the ADC method deconstructs system interoperability into an organic whole of initial availability (A), mission process reliability (D), and technical implementation capability (C). It clarifies the intrinsic relationship between equipment system composition, operational reliability, and tactical and technical indicators, and has considerable applicability, applicable to the performance evaluation of most weapon systems. This model is particularly well-suited to the unique characteristics of unmanned system interoperability in battlefield environments—requiring not only technical protocol compatibility and data exchange efficiency but also attention to functional maintenance in high-confrontation scenarios.

[0003] However, the interoperability evaluation of unmanned systems involves multiple levels of indicators, including node interconnectivity, information interoperability, and equipment interoperability. Relying solely on subjective expert weighting is susceptible to cognitive biases, while purely objective weighting may overlook tactical value. Therefore, to balance the multidimensional weights of the unmanned system interoperability indicator system, it is urgent and necessary to seek a weighting evaluation method for unmanned system interoperability based on a combined weighting approach. This method should achieve geometric mean fusion through a relative entropy minimization model, effectively overcoming the limitations of a single weighting method. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by proposing a weighted evaluation method for unmanned system interoperability based on a combined weighting approach. This method includes constructing an unmanned system availability calculation model to obtain an unmanned system availability vector; constructing an unmanned system reliability calculation model to calculate an unmanned system reliability matrix; constructing an unmanned system combat capability model to obtain an unmanned system combat capability vector; and constructing an unmanned system interoperability effectiveness model to calculate unmanned system interoperability effectiveness values. This invention introduces a combined weighting approach, leveraging the analytic hierarchy process (AHP) and a relative entropy minimization model to effectively overcome the limitations of single weighting methods, resulting in higher accuracy and greater practicality.

[0005] This invention provides a weighted evaluation method for interoperability of unmanned systems based on a combined weighting method, which includes the following steps: S1. Construct an unmanned system availability calculation model to obtain the unmanned system availability vector. Construct an unmanned system availability vector and calculate the unmanned system state probability corresponding to each element of the availability vector. The unmanned system includes four subsystems: platform and control station data link, Joint Integrated Data Link (JIDS), integrated data link, and branch links. Each subsystem is in one of two states before executing a task: either a first state or a fault state. The unmanned system availability vector... for: ; in, Let i represent the probability of the i-th combination state occurring when the unmanned system starts executing the task, where i = 1~16; S2. Construct a reliability calculation model for the unmanned system and calculate the reliability matrix of the unmanned system. Based on historical failure data, calculate the subsystem state transition probabilities of the unmanned system; each element of the unmanned system reliability matrix is ​​represented as the product of the corresponding subsystem state transition probabilities; unmanned system reliability matrix for: ; in, Let i represent the probability of the unmanned system transitioning from state i to state j, where i = 1~16 and j = 1~16. S3. Construct an unmanned system combat capability model and obtain unmanned system combat capability vectors. The first weighting coefficients of the unmanned system capability indicators are determined based on the analytic hierarchy process (AHP); the second weighting coefficients of the unmanned system capability indicators are constructed; the combined weighting coefficients of the unmanned system capability indicators are constructed; and the combat capability vector of the unmanned system is obtained. ; S4. Construct an unmanned system interoperability performance model and calculate the unmanned system interoperability performance value. : ; in, This represents the interoperability performance value of unmanned systems.

[0006] Furthermore, step S3, which determines the first weight coefficient of the unmanned system capability index based on the analytic hierarchy process, specifically includes the following sub-steps: S311. Construct the judgment matrix of the first-level capability indicators of the unmanned system and calculate the maximum eigenvalue. and eigenvectors ; Calculate the consistency index and consistency ratio ;like Then for the eigenvector Perform normalization to obtain the weighting coefficients; S312. Construct the first judgment matrix of the secondary capability index of anti-interference capability, and calculate the first maximum eigenvalue. and the first eigenvector ; Calculate the first consistency index Consistency ratio with the first ;like Then for Perform normalization to obtain the first weight coefficient; S313. Construct the second judgment matrix of the secondary capability indicators of network access capability, and calculate the second maximum eigenvalue. Second eigenvector ; Calculate the second consistency index Second consistency ratio ;like Then for Perform normalization to obtain the second weighting coefficient; S314. Construct the third judgment matrix of the secondary capability index of communication transmission capability, and calculate the third largest eigenvalue. and the third eigenvector ; Calculate the third consistency index and the third consistency ratio ;like Then for Perform normalization to obtain the third weighting coefficient; S315. Construct the fourth judgment matrix of the link consistency secondary capability index, and calculate the fourth largest eigenvalue. and the fourth eigenvector ; Calculate the fourth consistency index and the fourth consistency ratio ;like Then for Normalization is performed to obtain the fourth weighting coefficient; S316, Combine to obtain the first weight coefficient of the unmanned system capability index.

[0007] Preferably, the second weighting coefficient for constructing the unmanned system capability index in step S3 specifically includes the following sub-steps: S321. For m secondary capability indicators, set n sets of capability evaluation values; S322. Calculate the entropy value of the kth secondary capability indicator. : ; in, This represents the p-th capability evaluation value of the k-th secondary capability indicator; This represents the sum of all capability evaluation values ​​for the k-th secondary capability indicator, i.e.: ; S323. Normalize the entropy value to obtain the entropy weight of the k-th secondary capability index. : ; Entropy weights of all secondary capability indicators This is the second weighting coefficient of the unmanned system capability index.

[0008] Preferably, obtaining the unmanned system combat capability vector in step S3 includes the following sub-steps: S341. Determine the performance data for unmanned system capability indicators. Calculate the combat capability assessment value of unmanned systems : ; in, This represents the combined weight coefficient of the k-th secondary capability indicator of the unmanned system; This represents the performance data of the k-th secondary capability index of the unmanned system. S342. If one, two, three, or four subsystems of an unmanned system fail, the mission capability of the unmanned system will be reduced by 5%, 10%, 20%, or 40%, respectively. S343. Based on steps S341 and S342, and combined with the combined state of the unmanned system, obtain the combat capability vector of the unmanned system.

[0009] Preferably, in step S1, the probability of a subsystem of the unmanned system being in a first state or a fault state is determined. The unmanned system can be represented by the elements of a degree vector as the product of the probabilities of the corresponding subsystem states. The formula for the probability of a subsystem of the unmanned system being in a first state or a fault state is: ; in, and These represent the probabilities of a subsystem of the unmanned system being in a normal or faulty state, respectively; MTBF is the mean time between failures of a subsystem of the unmanned system; and MTTR is the mean time to repair a fault of a subsystem of the unmanned system.

[0010] Preferably, in step S3, the weights of the first weight coefficient u and the second weight coefficient v of the unmanned system capability index are respectively... and The combined weighting coefficient of the unmanned system capability index is: : v.

[0011] Preferably, in step S2, the subsystem state transition probabilities include the probability of maintaining the first state, the probability of transitioning to a fault state, and the probability of autonomous repair; in step S3, the unmanned system's primary capability indicators include anti-interference capability, network access capability, communication transmission capability, and link consistency; the secondary capability indicators of anti-interference capability include processing gain and interference tolerance; the secondary capability indicators of network access capability include network setup time and network access time; the secondary capability indicators of communication transmission capability include transmission rate, transmission bandwidth, transmission distance, transmission delay, and bit error rate; and the secondary capability indicators of link consistency include physical interface, electromagnetic waveform, encryption system, time reference, and spatial reference.

[0012] Preferably, in step S1 satisfy In step S2, the sum of the elements in any row of the unmanned system reliability matrix D is 1, which satisfies the condition that... .

[0013] Compared with the prior art, the technical effects of the present invention are as follows: 1. The unmanned system interoperability weight evaluation method proposed in this invention, on the one hand, obtains the first weight coefficient based on the analytic hierarchy process, which reflects the priority judgment of combat data such as command and control and electromagnetic countermeasures on the unmanned system capability indicators; on the other hand, based on measured data and multiple evaluations, the second weight coefficient is calculated with the help of the relative entropy minimization model to identify the key indicators of unmanned system interoperability.

[0014] 2. The unmanned system interoperability weight evaluation method proposed in this invention introduces a combined weighting method and achieves dual balance through game theory optimization of the second weight. This effectively solves the complex weight allocation problem of the unmanned system interoperability index system, can achieve geometric average fusion, effectively overcomes the limitations of single weighting methods, and has higher accuracy and stronger practicality. Attached Figure Description

[0015] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of the interoperability weight evaluation method for unmanned systems based on the combined weighting method of the present invention; Figure 2 This is a weight distribution diagram of the second weight coefficient and the combined weight coefficient in a specific embodiment of the present invention; Figure 3 This is a trend diagram of the changes in the second weighting coefficient and the combined weighting coefficient in a specific embodiment of the present invention; Figure 4This is a diagram of the interoperability level evaluation index system for unmanned systems in a specific embodiment of the present invention. Detailed Implementation

[0017] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] Figure 1 The present invention provides a weighted evaluation method for interoperability of unmanned systems based on a combined weighting method, comprising the following steps: S1. Construct an unmanned system availability calculation model to obtain the unmanned system availability vector. .

[0019] S11. Construct the availability vector of the unmanned system.

[0020] Unmanned system availability is a measure of the unmanned system's state when it begins mission execution, expressed as the probability of the weapon system's state at that time. Weapon system availability requires a clear understanding of the weapon system's composition and operational states.

[0021] The unmanned system comprises four subsystems: a platform and control station data link, a Joint Integrated Data Link (JIDS), a comprehensive data link (VU chain), and branch links. Each subsystem is in one of two states before executing a task: a first state or a fault state. There are a total of 16 possible combinations of states for the four subsystems before task execution. In a preferred embodiment, the first state refers to a normal functional state, while the fault state refers to a non-critical functional failure. The unmanned system still possesses the ability to execute tasks, but its effectiveness is reduced. The unmanned system can be represented by a degree vector as follows: ; (1) in, Let i represent the probability of the i-th combination state occurring when the unmanned system starts executing the task, where i = 1~16, satisfying... .

[0022] The four subsystems of the unmanned system have 16 possible combinations of states, as shown in Table 1.

[0023] Table 1 S12. Calculate the unmanned system state probability corresponding to each element of the unmanned system availability vector.

[0024] S121. The probability formula for a subsystem of an unmanned system being in a first state or a fault state is expressed as follows: In a preferred embodiment, the first state is a normal state: ; (2) in, and These represent the probabilities of a subsystem of an unmanned system being in a normal state and a fault state, respectively; MTBF is the mean time between failures of a subsystem of an unmanned system, which refers to the average working time of a repairable system between two consecutive failures; MTTR is the mean time to repair a subsystem of an unmanned system, which refers to the average time required for the system to return to normal after a failure.

[0025] S122. Determine the probability that the subsystems of the unmanned system are in the first state and the fault state.

[0026] S1221. Let m and n be the MTBF and MTTR of the platform-control station data link, respectively. Then the probabilities of the first state and the fault state of the platform-control station data link are respectively... and .

[0027] S1222. Let y and z be the MTBF and MTTR of the Joint Integration Data Link (JIDS), respectively. Then the probabilities of the first state and the fault state of the JIDS are respectively... and .

[0028] S1223. Let the MTBF and MTTR of the synthesized data link be d and f, respectively. Then the probabilities of the first state and the fault state of the synthesized data link are respectively... and .

[0029] S1224. Let r and t be the MTBF and MTTR of the branch link, respectively. Then the probabilities of the first state and the fault state of the branch link are respectively... and .

[0030] S123. The elements of the available degree vector of an unmanned system are represented as the product of the state probabilities of the corresponding subsystems.

[0031] Based on Table 1, ~ For example, they are represented as follows: ; (3) ; (4) ; (5) ; (6) ; (7) S2. Construct a reliability calculation model for the unmanned system and calculate the reliability matrix of the unmanned system. .

[0032] S21. Since the unmanned system is repairable during mission execution, i.e., the subsystem state changes from a fault state to a first state, construct the unmanned system reliability matrix. for: ; (8) in, Let i represent the probability of the unmanned system transitioning from state i to state j, where i = 1~16 and j = 1~16.

[0033] For any given state when the system begins executing a task, the 16 possible transition states during task execution constitute the sample space for that state transition. Therefore, the sum of the elements in any row of matrix D must be 1, i.e., satisfying... .

[0034] S22. Based on historical fault data, calculate the subsystem state transition probabilities of the unmanned system. The subsystem state transition probabilities include the probability of maintaining the first state, the probability of transitioning to a fault state, and the probability of autonomous repair.

[0035] S221. The probabilities of the data link between the computing platform and the control station maintaining the first state, transitioning to a fault state, and autonomously repairing the state are p1, q1, and e1, respectively.

[0036] S222. Calculate the probability of maintaining the first state, the probability of transitioning to a fault state, and the probability of autonomous repair state of the Joint Integrated Data Link (JIDS), which are p2, q2, and e2, respectively.

[0037] S223. Calculate the probability of the integrated data link maintaining the first state, the probability of transitioning to a fault state, and the probability of autonomous repair state, which are p3, q3, and e3, respectively.

[0038] S224. Calculate the probability of the branch link maintaining the first state, the probability of transitioning to a fault state, and the probability of autonomous repair state, which are p4, q4, and e4, respectively.

[0039] S23. Each element of the unmanned system reliability matrix is ​​represented as the product of the state transition probabilities of the corresponding subsystems.

[0040] With d 1,1 ~d 1,5 and d 16,1 ~d 16,5 For example, they are represented as follows: ; (9) ; (10) S3. Construct an unmanned system combat capability model and obtain unmanned system combat capability vectors. The unmanned system combat capability model, as a mission execution capability model, can obtain the mission execution capability vector of the unmanned system.

[0041] S31. Determine the first weight coefficient of the unmanned system capability index based on the analytic hierarchy process.

[0042] The Analytic Hierarchy Process (AHP) combines qualitative and quantitative methods and is a widely used and effective method for determining the weights of indicators. Here, it is used to determine the first weight coefficient of the unmanned system capability indicator. It mainly consists of three steps: first, constructing a hierarchical structure model; second, constructing a judgment matrix; and third, hierarchical ranking and consistency verification.

[0043] The values ​​in the judgment matrix are the comparison results between the two indicators, as shown in Table 2.

[0044] Table 2 S311. Construct a judgment matrix for the primary capability indicators of the unmanned system and obtain the weight coefficients. The primary capability indicators of the unmanned system include anti-interference capability, network access capability, communication transmission capability, and link consistency.

[0045] S3111. Construct the judgment matrix of the first-level capability indicators of the unmanned system and calculate the maximum eigenvalue. and eigenvectors .

[0046] S3112, Calculate the consistency index and consistency ratio : ; (11) in, Indicates the order of the judgment matrix; This represents the average random consistency index.

[0047] The average random consistency index value is obtained by looking up the random consistency index RI value table in Table 3, according to the order of the judgment matrix.

[0048] Table 3 S3113, if This indicates that the weight allocation of each evaluation indicator is statistically reasonable, and the consistency of the judgment matrix is ​​considered acceptable. Normalization is performed to obtain the weighting coefficients.

[0049] In one specific embodiment, a judgment matrix is ​​constructed through pairwise comparisons and expert consultation, as shown in Table 4.

[0050] Table 4 Maximum eigenvalue eigenvectors .

[0051] because Then the consistency index From Table 2, we can see the average random consistency index. Then the consistency index The weighting coefficients are shown in Table 5.

[0052] Table 5 S312. Construct the first judgment matrix of the secondary capability indicators of anti-interference capability and obtain the first weight coefficient. The secondary capability indicators of anti-interference capability include processing gain and interference tolerance.

[0053] S3121. Construct the first judgment matrix of the secondary capability index of anti-interference capability, and calculate the first maximum eigenvalue. and the first eigenvector .

[0054] S3122. Calculate the first consistency index. Consistency ratio with the first : ;(12) in, This indicates the order of the first judgment matrix; This represents the first average random consistency index.

[0055] S3123, if This indicates that the weight allocation of each evaluation indicator is statistically reasonable, and the consistency of the first judgment matrix is ​​considered acceptable. Normalization is performed to obtain the first weight coefficient.

[0056] In one specific embodiment, a first judgment matrix is ​​constructed through pairwise comparisons and expert consultation, as shown in Table 6.

[0057] Table 6 First largest eigenvalue The first eigenvector .

[0058] because Then the first consistency ratio The first weighting coefficients are shown in Table 7.

[0059] Table 7 S313. Construct the second judgment matrix for the secondary capability indicators of network access capability, and obtain the second weight coefficient. The secondary capability indicators of network access capability include network setup time and network access time.

[0060] S3131. Construct the second judgment matrix of the secondary capability indicators for network access capability, and calculate the second maximum eigenvalue. Second eigenvector .

[0061] S3132. Calculate the second consistency index. Second consistency ratio : ; (13) in, This indicates the order of the second judgment matrix; This represents the second average random consistency index.

[0062] S3133, if This indicates that the weighting of each evaluation indicator is statistically reasonable, and the consistency of the second judgment matrix is ​​considered acceptable. Normalization is performed to obtain the second weighting coefficient.

[0063] In one specific embodiment, a second judgment matrix is ​​constructed through pairwise comparisons and expert consultation, as shown in Table 8.

[0064] Table 8 Second largest eigenvalue Second eigenvector .

[0065] because Then the second consistency ratio The second weighting coefficients are shown in Table 9.

[0066] Table 9 S314. Construct the third judgment matrix for the secondary capability indicators of communication transmission capability, and obtain the third weight coefficients. The secondary capability indicators of communication transmission capability include transmission rate, transmission bandwidth, transmission distance, transmission delay, and bit error rate.

[0067] S3141. Construct the third judgment matrix of the secondary capability index of communication transmission capability, and calculate the third largest eigenvalue. and the third eigenvector .

[0068] S3142. Calculate the third consistency index. and the third consistency ratio : ;(14) in, This indicates the order of the third judgment matrix; The third average random consistency index is represented as follows: S3143, if This indicates that the weighting of each evaluation indicator is statistically reasonable, and the consistency of the third judgment matrix is ​​considered acceptable. Normalization is performed to obtain the third weighting coefficient.

[0069] In one specific embodiment, a third judgment matrix is ​​constructed through pairwise comparisons and expert consultation, as shown in Table 10.

[0070] Table 10 Third largest eigenvalue The third eigenvector .

[0071] because Then the third consistency index However, referring to Table 2, we find that the third average random consistency index Then the third consistency index The third weighting coefficients are shown in Table 11.

[0072] Table 11 S315. Construct the fourth judgment matrix for the secondary link consistency capability indicators and obtain the fourth weight coefficient. The secondary link consistency capability indicators include physical interface, electromagnetic waveform, encryption system, time reference, and spatial reference.

[0073] S3151. Construct the fourth judgment matrix of the link consistency secondary capability index, and calculate the fourth largest eigenvalue. and the fourth eigenvector .

[0074] S3152. Calculate the fourth consistency index. and the fourth consistency ratio : ; (15) in, This indicates the order of the fourth judgment matrix; This represents the fourth average random consistency index.

[0075] S3153, if This indicates that the weight allocation of each evaluation indicator is statistically reasonable, and the consistency of the judgment matrix is ​​considered acceptable. Normalization is performed to obtain the fourth weight coefficient.

[0076] In one specific embodiment, a fourth judgment matrix is ​​constructed through pairwise comparisons and expert consultation, as shown in Table 12.

[0077] Table 12 Fourth largest eigenvalue The fourth eigenvector .

[0078] because The fourth consistency index However, referring to Table 2, we find that the fourth average random consistency index The fourth consistency index The fourth weighting coefficients are shown in Table 13.

[0079] Table 13 S316, Combine to obtain the first weight coefficient of the unmanned system capability index.

[0080] In one specific embodiment, the first weighting coefficient of the unmanned system capability index is shown in Table 14.

[0081] Table 14 S32, the second weighting coefficient for the capability index of building unmanned systems.

[0082] S321. For m secondary capability indicators, set n sets of capability evaluation values.

[0083] In physics, entropy is a measure of the degree of disorder in a system. The concept of entropy has gradually been applied to various fields, and in information theory, it is frequently used to describe the reliability and information content of information. To calculate the second weight of each indicator, based on the determinism of each indicator in the evaluated system and its ease of normalization, a probability-based method is used to determine the entropy value of each indicator.

[0084] S322. Calculate the entropy value of the kth secondary capability indicator. : ; (16) in, This represents the p-th capability evaluation value of the k-th secondary capability indicator; This represents the sum of all capability evaluation values ​​for the k-th secondary capability indicator, i.e.: (17).

[0085] According to the meaning of entropy in information theory, the smaller the entropy of a certain indicator, the greater the degree of variation of the indicator value, the more information it provides, and the more effective information it can provide in the evaluation process, and the greater the weight of the indicator.

[0086] S323. Normalize the entropy value to obtain the entropy weight of the k-th secondary capability index. : (18).

[0087] Entropy weights of all secondary capability indicators This is the second weighting coefficient of the unmanned system capability index.

[0088] In one specific embodiment, four sets of capability evaluation values ​​were set, and the results are shown in Table 15. The data of each parameter index are the results after standardization.

[0089] Table 15 by For example, Entropy for: .

[0090] right ~ By using the same calculation steps, the entropy values ​​of each secondary capability indicator can be calculated. for: .

[0091] .

[0092] The entropy values ​​are normalized to obtain the entropy weights of each secondary capability indicator. for: .

[0093] S33. Constructing the combined weighting coefficients of unmanned system capability indicators: Set the weights of the first weighting coefficient u and the second weighting coefficient v of the unmanned system capability indicators as follows: and The combined weighting coefficient of the unmanned system capability index is: : v (19) In one specific embodiment, the combined weighting coefficients of the unmanned system capability indicators are shown in Table 16.

[0094] Table 16 The distributions of the first weighting coefficient, the second weighting coefficient, and the combined weighting coefficient were analyzed, and weight distribution charts and trend charts of the first weighting coefficient, the second weighting coefficient, and the combined weighting coefficient were plotted, as shown below. Figure 2 and Figure 3 As shown, the distribution of the first weight coefficient is relatively dispersed, while the distribution of the second weight coefficient is relatively concentrated. The combined weight coefficient exhibits the distribution characteristics of both the first and second weight coefficients.

[0095] S34, Obtain the unmanned system combat capability vector.

[0096] S341. Determine the performance data for unmanned system capability indicators. Calculate the combat capability assessment value of unmanned systems : (20).

[0097] in, This represents the combined weight coefficient of the k-th secondary capability indicator of the unmanned system; This represents the performance data of the k-th secondary capability indicator of the unmanned system.

[0098] Based on the interoperability performance and combat experience of unmanned systems, its system components should be able to complete the mission whether they are in good condition or in the event of a failure. However, the combat effectiveness of the unmanned system may be weakened to varying degrees depending on the actual situation of the failure and the specific environmental impact.

[0099] S342. If one subsystem of the unmanned system fails, the unmanned system's mission capability is reduced by 5%; if two subsystems of the unmanned system fail, the mission capability is reduced by 10%; if three subsystems of the unmanned system fail, the mission capability is reduced by 20%; if four subsystems of the unmanned system fail, the mission capability is reduced by 40%.

[0100] S343. Based on steps S341 and S342, and combined with the 16 possible combinations of the unmanned system's states in step S11, obtain the unmanned system's combat capability vector. .

[0101] In one specific embodiment, the performance data of the unmanned system capability indicators are shown in Table 17.

[0102] Table 17 The combat capability assessment value of the unmanned system is obtained by weighting and calculating the various capability indicators of the unmanned system. .

[0103] Based on Table 1, calculate the corresponding a for the unmanned system. i Combat capability vector in state (i=1,2,⋯,16) : ; (twenty one) S4. Construct an unmanned system interoperability performance model and calculate the unmanned system interoperability performance value. : ; (twenty two) in, This represents the interoperability performance value of the unmanned systems. The interoperability performance E of the unmanned systems represents the actual performance value of the corresponding capability indicators after being affected by availability and reliability; A and D can be obtained analytically given the number of unmanned system states; the elements in C largely depend on the inherent performance of the unmanned system and the requirements of the combat mission.

[0104] In one specific embodiment, the interoperability level evaluation index system for unmanned systems is as follows: Figure 4 As shown.

[0105] This invention proposes a weighted evaluation method for unmanned system interoperability based on a combined weighting method. On the one hand, it obtains the first weight coefficient based on the analytic hierarchy process (AHP), reflecting the priority of combat data such as command and control and electromagnetic countermeasures on the unmanned system's capability indicators. On the other hand, based on measured data and multiple evaluations, it calculates the second weight coefficient using a relative entropy minimization model to identify key indicators of unmanned system interoperability. By introducing a combined weighting method and optimizing the primary and secondary weights through game theory, a dual balance is achieved, effectively solving the complex weight allocation problem of the unmanned system interoperability indicator system. It can achieve geometric mean fusion, effectively overcoming the limitations of single weighting methods, and is more accurate and practical.

[0106] Finally, it should be noted that the above embodiments are for illustration only and not for limiting the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention without departing from the spirit and scope of the present invention. Any modifications or partial substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A weighted evaluation method for interoperability of unmanned systems based on a combined weighting method, characterized in that, It includes the following steps: S1. Construct an unmanned system availability calculation model to obtain the unmanned system availability vector. Construct an unmanned system availability vector and calculate the unmanned system state probability corresponding to each element of the availability vector. The unmanned system includes four subsystems: platform and control station data link, Joint Integrated Data Link (JIDS), integrated data link, and branch links. Each subsystem is in one of two states before executing a task: either a first state or a fault state. The unmanned system availability vector... for: ； in, Let i represent the probability of the i-th combination state occurring when the unmanned system begins to execute the task phase, where i = 1~16; S2. Construct a reliability calculation model for the unmanned system and calculate the reliability matrix of the unmanned system. Based on historical failure data, calculate the subsystem state transition probabilities of the unmanned system; each element of the unmanned system reliability matrix is ​​represented as the product of the corresponding subsystem state transition probabilities; unmanned system reliability matrix for: in, Let i represent the probability of the unmanned system transitioning from state i to state j, where i = 1~16 and j = 1~16. S3. Construct an unmanned system combat capability model and obtain unmanned system combat capability vectors. The first weighting coefficients of the unmanned system capability indicators are determined based on the analytic hierarchy process (AHP); the second weighting coefficients of the unmanned system capability indicators are constructed; the combined weighting coefficients of the unmanned system capability indicators are constructed; and the combat capability vector of the unmanned system is obtained. ; S4. Construct an unmanned system interoperability performance model and calculate the unmanned system interoperability performance value. : ； in, This represents the interoperability performance value of unmanned systems.

2. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, Step S3, which determines the first weight coefficient of the unmanned system capability index based on the analytic hierarchy process, specifically includes the following sub-steps: S311. Construct the judgment matrix of the first-level capability indicators of the unmanned system and calculate the maximum eigenvalue. and eigenvectors ; Calculate the consistency index and consistency ratio ;like Then for the eigenvector Normalization is performed to obtain the weighting coefficients; S312. Construct the first judgment matrix of the secondary capability index of anti-interference capability, and calculate the first maximum eigenvalue. and the first eigenvector ; Calculate the first consistency index Consistency ratio with the first ;like Then for Perform normalization to obtain the first weight coefficient; S313. Construct the second judgment matrix of the secondary capability indicators of network access capability, and calculate the second maximum eigenvalue. Second eigenvector ; Calculate the second consistency index Second consistency ratio ;like Then for Perform normalization to obtain the second weighting coefficient; S314. Construct the third judgment matrix of the secondary capability index of communication transmission capability, and calculate the third largest eigenvalue. and the third eigenvector ; Calculate the third consistency index and the third consistency ratio ;like Then for Perform normalization to obtain the third weighting coefficient; S315. Construct the fourth judgment matrix of the link consistency secondary capability index, and calculate the fourth largest eigenvalue. and the fourth eigenvector ; Calculate the fourth consistency index and the fourth consistency ratio ;like Then for Normalization is performed to obtain the fourth weighting coefficient; S316, Combine to obtain the first weight coefficient of the unmanned system capability index.

3. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, Step S3, which involves constructing the second weighting coefficient for the unmanned system capability index, specifically includes the following sub-steps: S321. For m secondary capability indicators, set n sets of capability evaluation values; S322. Calculate the entropy value of the kth secondary capability indicator. : ； in, This represents the p-th capability evaluation value of the k-th secondary capability indicator; This represents the sum of all capability evaluation values ​​for the k-th secondary capability indicator, i.e.: ； S323. Normalize the entropy value to obtain the entropy weight of the k-th secondary capability index. : ； Entropy weights of all secondary capability indicators This is the second weighting coefficient of the unmanned system capability index.

4. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, Obtaining the unmanned system combat capability vector in step S3 includes the following sub-steps: S341. Determine the performance data for unmanned system capability indicators. Calculate the combat capability assessment value of unmanned systems : ； in, This represents the combined weight coefficient of the k-th secondary capability indicator of the unmanned system; This represents the performance data of the k-th secondary capability index of the unmanned system. S342. If one, two, three, or four subsystems of an unmanned system fail, the mission capability of the unmanned system will be reduced by 5%, 10%, 20%, or 40%, respectively. S343. Based on steps S341 and S342, and combined with the combined state of the unmanned system, obtain the combat capability vector of the unmanned system.

5. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, In step S1, the probabilities of the unmanned system's subsystems being in the first state and the fault state are determined. The unmanned system can be represented by the elements of its degree vector as the product of the probabilities of the corresponding subsystem states. The formula for the probability of a subsystem of the unmanned system being in the first state or the fault state is as follows: ； in, and These represent the probabilities of a subsystem of the unmanned system being in a normal or faulty state, respectively; MTBF is the mean time between failures of a subsystem of the unmanned system; and MTTR is the mean time to repair a fault of a subsystem of the unmanned system.

6. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, In step S3, the weights of the first weight coefficient u and the second weight coefficient v of the unmanned system capability index are respectively set as follows: and The combined weighting coefficient of the unmanned system capability index is: : v。 7. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, In step S2, the subsystem state transition probabilities include the probability of maintaining the first state, the probability of transitioning to a fault state, and the probability of autonomous repair. In step S3, the first-level capability indicators of the unmanned system include anti-interference capability, network access capability, communication transmission capability, and link consistency. The second-level capability indicators of anti-interference capability include processing gain and interference tolerance. The second-level capability indicators of network access capability include network setup time and network access time. The secondary capability indicators of communication transmission capability include transmission rate, transmission bandwidth, transmission distance, transmission delay, and bit error rate; The secondary capability indicators for link consistency include physical interface, electromagnetic waveform, encryption system, time reference, and spatial reference.

8. The unmanned system interoperability weight evaluation method based on the combined weighting method according to claim 1, characterized in that, In step S1 satisfy In step S2, the sum of the elements in any row of the unmanned system reliability matrix D is 1, which satisfies the condition that... .

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