A method and system for evaluating interoperability level of radar seeker

Abstract

The application discloses a kind of interoperability level evaluation methods applied to radar seeker, comprising the following steps: definition radar seeker interoperability level model;Including: carrying out interoperability level division, and the key features of each level are marked based on relevant attribute;Wherein, relevant attribute includes: regulation attribute P, application attribute A, infrastructure attribute I and data attribute D;To the relevant attribute in radar seeker interoperability level model, index tree is constructed;Based on index tree, using principal component analysis algorithm carries out interoperability level evaluation, generates interoperability level evaluation result.The method constructs detailed interoperability level model and attribute index tree, and provides comprehensive and systematic evaluation framework for the interoperability of radar seeker.Further combined with principal component analysis algorithm, the effective dimension reduction and fusion of multidimensional data are realized, and the accuracy and calculation efficiency of evaluation result are improved.

Description

Technical Field

[0001] This invention relates to the field of radar seekers, and more specifically to an interoperability level assessment method and system for radar seekers. Background Technology

[0002] In modern information warfare, radar seekers, as key components of missile systems, are crucial for achieving precision strikes and efficient operations. With the increasing complexity and variability of the battlefield electromagnetic environment, the interoperability requirements for radar seekers are becoming increasingly stringent. Interoperability involves not only the collaborative working ability between the various components within the radar seeker but also effective interaction with other missile systems and external information sources. Highly interoperable radar seekers can significantly improve the missile's autonomy and guidance accuracy, ensuring accurate and efficient mission completion in various complex environments.

[0003] However, existing evaluation methods are often based on the personal experience and intuition of experts, and different experts may give different evaluation results. This subjectivity leads to a lack of consistency and comparability in the evaluation results. In addition, due to the lack of unified standards and quantitative indicators, it is difficult to form an objective and fair evaluation system.

[0004] It typically considers only a few key factors, neglecting the complex interactions between the various components of the radar seeker and their ability to work collaboratively with the entire system. This not only fails to fully reflect the actual interoperability performance of the system but may also lead to the overlooking of certain potential problems.

[0005] Furthermore, with the development of radar technology, the number of influencing factors that need to be considered is increasing, and the amount of data is also increasing dramatically. Existing methods are inefficient when processing large amounts of multi-dimensional data, making it difficult to complete assessments quickly and accurately, which is a significant weakness in the fast-paced modern battlefield. They also neglect the support of quantitative data. Even when some methods attempt to combine quantitative analysis, the lack of effective data processing tools makes it difficult to organically combine qualitative and quantitative information, thus affecting the accuracy and reliability of the assessment results.

[0006] Therefore, designing an interoperability level assessment method for radar seekers that can provide more scientific, accurate, and efficient assessment results, thereby better guiding the optimization of radar seeker systems, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides an interoperability level evaluation method for radar seekers. By constructing a detailed interoperability level model and attribute index tree, and using principal component analysis algorithm for comprehensive evaluation, the accuracy of the evaluation results and the computational efficiency are improved.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides an interoperability level assessment method for radar seekers, comprising the following steps:

[0010] S1. Define the radar seeker interoperability level model; including: classifying interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedural attribute P, application attribute A, infrastructure attribute I, and data attribute D;

[0011] S2. Construct an index tree for the relevant attributes in the radar seeker interoperability level model; the index tree includes: procedure attribute index tree, application attribute index tree, facility attribute index tree and data attribute index tree;

[0012] S3. Based on the index tree, use the principal component analysis algorithm to evaluate the interoperability level and generate the interoperability level evaluation result.

[0013] Preferably, in S1, the interoperability levels, arranged from low to high, include: isolation level, entry-level, functional level, integration level, collaboration level, and adaptation level.

[0014] Preferably, in step S2, each indicator tree includes three primary branches: data structure, system structure, and management structure, as well as multiple secondary indicators under each primary branch; wherein,

[0015] The secondary indicators under the data structure include: basic data standards, data service standards, and data management standards;

[0016] The secondary indicators under the system architecture include: physical layer, data layer, and semantic layer;

[0017] The secondary indicators under the management structure include: regulations, ordinances, standardization and universality, and integration.

[0018] Preferably, S3 includes:

[0019] S31. Based on the scoring data of the secondary index of radar seeker interoperability, construct the observation data matrix and perform standardization processing;

[0020] S32. Construct a correlation coefficient matrix based on the standardized observation data matrix;

[0021] S33. Solve for the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix using the Jacobian algorithm;

[0022] S34. Based on the eigenvalues ​​and corresponding eigenvectors, determine each principal component and the variance contribution rate of each principal component. Compare the cumulative variance contribution rate of the first Z principal components with a preset variance contribution rate threshold to determine the final principal components.

[0023] S35. Calculate the multiple correlation coefficients of the final principal components and each secondary index, as well as the square of the multiple correlation coefficients, to determine the overall support of each related attribute.

[0024] S36. Based on the comprehensive support of each relevant attribute, the scoring data of the secondary indicators are fused together to generate an interoperability level assessment result.

[0025] Preferably, in S31, the standardization process is represented as follows:

[0026]

[0027] Where i = (1,2,……9), j = (1,2,3,4), x ij Represents the elements in the observation data matrix. This represents the elements of the standardized observation data matrix. This represents the average score of the secondary indicator corresponding to the relevant attribute j, var(x) j The variance of the scores of the secondary indicators under the relevant attribute j, where n represents the number of samples.

[0028] Preferably, in step S32, the correlation coefficient matrix is ​​represented as follows:

[0029]

[0030] Among them, s jk s represents the covariance of related attributes j and k after standardization. jj Let s represent the variance of the relevant attribute j after standardization. kk This represents the variance of the relevant attribute k after standardization.

[0031] Preferably, in S34, the principal components are represented as follows:

[0032]

[0033] Where F1, F2, F3, and F4 represent the first principal component, the second principal component, the third principal component, and the fourth principal component, respectively, and are arranged in descending order of variance as F1, F2, F3, and F4; a kl Let a be an eigenvector, and satisfy a k1 2 +a k2 2 +a k32 +a k4 2 =1, k, l = (1,2,3,4); x1, x2, x3, x4 are the values ​​of each coordinate axis, representing the scores of the secondary indicators.

[0034] Preferably, in step S34, the variance contribution rate of the principal components is expressed as:

[0035]

[0036] Where, λ y Let y = (1,2,3,4) represent the variance of the principal components.

[0037] Preferably, S36 includes:

[0038] The scoring data from the secondary indicators are then integrated and processed.

[0039]

[0040] Based on the fused data, the average value is calculated and the interoperability level of the radar seeker is determined.

[0041] Secondly, the present invention provides an interoperability level evaluation system for radar seekers, comprising:

[0042] Interoperability Level Model Definition Module: Used to define the interoperability level model of radar seeker; including: classifying interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D;

[0043] Indicator Tree Construction Module: Used to construct indicator trees for relevant attributes in the radar seeker interoperability level model; the indicator trees include: procedure attribute indicator trees, application attribute indicator trees, facility attribute indicator trees, and data attribute indicator trees;

[0044] Interoperability Level Assessment Module: Based on the index tree, this module uses principal component analysis algorithm to assess the interoperability level and generate interoperability level assessment results.

[0045] As can be seen from the above technical solutions, compared with the prior art, the technical solutions of the present invention have the following advantages:

[0046] Beneficial effects:

[0047] 1. The radar seeker interoperability level model constructed in this method sets clear standards for different levels of interoperability capabilities and describes in detail the requirements to be met at each level from four attributes: Procedure (P), Application (A), Infrastructure (I), and Data (D). This hierarchical and multi-dimensional evaluation system not only helps to accurately judge the current state but also points the way for future system improvements or upgrades.

[0048] 2. By employing principal component analysis, the interoperability between various components of the radar seeker can be comprehensively assessed from multiple perspectives. It not only considers qualitative factors but also incorporates quantitative data for scientific calculations, thus providing a more objective and comprehensive reflection of the system's actual interoperability level.

[0049] 3. Principal component analysis (PCA) can be used to reduce the dimensionality of data, simplifying the data structure while retaining key information. This not only significantly reduces the computational resources required for subsequent analysis but also accelerates the entire evaluation process, enabling decision-makers to obtain the necessary information more quickly to support rapid response needs.

[0050] 4. By scoring the same indicator multiple times from different perspectives and using a reasonable algorithm to fuse the detection information, the final evaluation result is ensured to be infinitely close to the real situation, thus improving the scientific nature and authority of the evaluation work. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 A flowchart of an interoperability level assessment method for radar seekers provided in an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the procedure attribute index tree provided in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of an application attribute index tree provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of a facility attribute index tree provided in an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of a data attribute index tree provided in an embodiment of the present invention;

[0057] Figure 6This is a schematic diagram illustrating the process of interoperability level assessment using principal component analysis algorithm provided in an embodiment of the present invention.

[0058] Figure 7 This is a system framework diagram for interoperability level assessment using principal component analysis algorithm, provided in an embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1;

[0061] like Figure 1 As shown, this embodiment provides an interoperability level assessment method for radar seekers, including the following steps:

[0062] S1. Define the radar seeker interoperability level model; including: classifying interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedural attribute P, application attribute A, infrastructure attribute I, and data attribute D;

[0063] S2. Construct an index tree for the relevant attributes in the radar seeker interoperability level model; the index tree includes: procedure attribute index tree, application attribute index tree, facility attribute index tree and data attribute index tree;

[0064] S3. Based on the index tree, use the principal component analysis algorithm to evaluate the interoperability level and generate the interoperability level evaluation result.

[0065] This method provides a comprehensive and systematic evaluation framework for the interoperability of radar seekers by constructing a detailed interoperability level model and attribute index tree. Principal component analysis (PCA) is used to effectively reduce and fuse multi-dimensional data, improving the accuracy and computational efficiency of the evaluation results.

[0066] The following provides a further detailed explanation of each of the above steps and related technical features:

[0067] In this embodiment S1, a radar seeker interoperability level model is defined, including: dividing the interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D;

[0068] Interoperability levels, from lowest to highest, are: Isolation, Entry-level, Functional, Integration, Collaboration, and Adaptation.

[0069] As shown in Table 1 below, the key features of each level are labeled based on relevant attributes;

[0070] Table 1

[0071]

[0072] Specifically, the isolation level means that no electronic connection is established between the components of the radar seeker, preventing the computer from directly acquiring the target's position and motion information; the entry-level level can be understood as having electronic connections between components, but only able to exchange homogeneous data types; at the functional level, components can exchange relatively complex heterogeneous information and establish corresponding logical data models; at the integrated level, components are in a wide-area environment, and radar seeker components of the same type can achieve information sharing and database-to-database information exchange for different applications; the collaborative level forms cross-domain joint interoperability, and components of heterogeneous radar seekers can simultaneously access complex data in the information space, achieving cross-domain information sharing; the adaptive level reaches the level of rapid adaptation to change, and each system component can complete higher-level information sharing and collaboration in real time in different scenarios.

[0073] In this embodiment S2, an index tree is constructed for the relevant attributes in the radar seeker interoperability level model; the index tree includes: a procedure attribute index tree, an application attribute index tree, a facility attribute index tree, and a data attribute index tree;

[0074] like Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown, the primary and secondary indicators of the four attributes are the same, indicating that the interoperability level is evaluated from different perspectives for the same indicator.

[0075] Each indicator tree includes three primary branches: data structure, system structure, and management structure, as well as multiple secondary indicators under each primary branch; among them,

[0076] The secondary indicators under the data structure include: basic data standards, data service standards, and data management standards;

[0077] The secondary indicators under the system architecture include: physical layer, data layer, and semantic layer;

[0078] The secondary indicators under the management structure include: regulations, ordinances, standardization and universality, and integration.

[0079] In this embodiment S3, based on the index tree, the interoperability level is evaluated using the principal component analysis algorithm, and the interoperability level evaluation result is generated.

[0080] like Figure 6 As shown, it specifically includes:

[0081] S31. Based on the scoring data of the secondary index of radar seeker interoperability, construct the observation data matrix and perform standardization processing;

[0082] The observation data matrix is ​​represented as follows:

[0083]

[0084] Where, x P1 x A1 x I1 x D1 These represent the basic data standard scores corresponding to the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P2 x A2 x I2 x D2 These represent the data service standard scores corresponding to the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P3 x A3 x I3 x D3 These represent the data management standard scores corresponding to the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P4 x A4 x I4 x D4 These represent the physical layer scores corresponding to the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P5 x A5 x I5 x D5 These represent the data layer scores corresponding to the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P6 x A6 x I6 x D6 These represent the semantic layer scores corresponding to the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P7 x A7 x I7 x D7 These represent the standardization scores of regulations and ordinances corresponding to procedural attributes, application attributes, infrastructure attributes, and data attributes, respectively. P8 x A8 x I8 x D8These represent the generality scores for the procedure attribute, application attribute, infrastructure attribute, and data attribute, respectively. P9 x A9 x I9 x D9 These represent the integration scores corresponding to the procedural attributes, application attributes, infrastructure attributes, and data attributes, respectively.

[0085] The standardized processing is represented as:

[0086]

[0087]

[0088] Where i = (1,2,……9), j = (1,2,3,4), x ij Represents the elements in the observation data matrix. This represents the elements of the standardized observation data matrix. This represents the average score of the secondary indicator corresponding to the relevant attribute j, var(x) j The variance of the scores of the secondary indicators under the relevant attribute j, where n represents the number of samples.

[0089] S32. Construct a correlation coefficient matrix based on the standardized observation data matrix;

[0090] The correlation coefficient matrix is ​​represented as follows:

[0091]

[0092] Among them, s jk s represents the covariance of related attributes j and k after standardization. jj Let s represent the variance of the relevant attribute j after standardization. kk This represents the variance of the relevant attribute k after standardization.

[0093] S33. Solve for the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix using the Jacobian algorithm;

[0094] S34. Based on the eigenvalues ​​and corresponding eigenvectors, determine each principal component and the variance contribution rate of each principal component. Compare the cumulative variance contribution rate of the first Z principal components with a preset variance contribution rate threshold to determine the final principal components.

[0095] Principal components are represented as:

[0096]

[0097] Where F1, F2, F3, and F4 represent the first principal component, the second principal component, the third principal component, and the fourth principal component, respectively, and are arranged in descending order of variance as F1, F2, F3, and F4; a kl Let a be an eigenvector, and satisfy a k1 2 +a k2 2 +a k3 2 +a k4 2 =1, k, l = (1,2,3,4); x1, x2, x3, x4 are the values ​​of each coordinate axis, representing the scores of the secondary indicators.

[0098] The variance contribution rate of the principal components is expressed as:

[0099]

[0100] Where, λ y Let y = (1,2,3,4) represent the variance of the principal components.

[0101] S35. Calculate the multiple correlation coefficients of the final principal components and each secondary index, as well as the square of the multiple correlation coefficients, to determine the overall support of each related attribute.

[0102] S36. Based on the comprehensive support of each relevant attribute, and combined with the scoring data of the secondary indicators, an interoperability level assessment result is generated. This includes:

[0103] The scoring data from the secondary indicators are then integrated and processed.

[0104]

[0105] Based on the fused data, the average value is calculated and the interoperability level of the radar seeker is determined.

[0106] This embodiment describes an interoperability level assessment method for radar seekers. By precisely defining the interoperability level model and constructing a multi-dimensional tree of relevant attribute indicators, it achieves an in-depth analysis of the interoperability capabilities of radar seekers. The use of principal component analysis (PCA) for quantitative assessment not only improves the accuracy and objectivity of the evaluation but also reduces subjective interference during the process. Furthermore, this method can clearly distinguish the performance differences of radar seekers at different interoperability levels, providing an important reference for the integration, upgrading, and optimization of radar systems. The application of this method can significantly improve the overall performance and combat effectiveness of radar systems, enhance the collaborative combat capabilities between different systems, and thus meet complex and ever-changing operational requirements.

[0107] Example 2;

[0108] In this embodiment, the interoperability level is quantified using a range of numbers, where 0≤Isolation level<1, 1≤Entry level<2, 2≤Functional level<3, 3≤Integration level<4, 4≤Cooperation level<5, and 5≤Adaptive level;

[0109] The obtained scoring data for the secondary interoperability indicators of the radar seeker are shown in Table 2 below:

[0110] Table 2

[0111]

[0112] Based on the scoring data of the secondary index of radar seeker interoperability, an observation data matrix is ​​constructed:

[0113]

[0114] The observation data matrix is ​​obtained by standardizing the data.

[0115]

[0116] Based on the standardized observation data matrix, construct the correlation coefficient matrix:

[0117]

[0118] Furthermore, the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix are solved using the Jacobi algorithm;

[0119] The eigenvalues ​​λ = (2.0748, 1.3858, 0.5023, 0.0371) correspond to the following eigenvectors:

[0120]

[0121] Based on the eigenvalues ​​and corresponding eigenvectors, each principal component and its variance contribution rate are determined. The contribution rates of each principal component are 51.8695%, 34.6453%, 12.5573%, and 0.9279%.

[0122] The cumulative variance contribution rate of the first Z principal components is compared with the preset variance contribution rate threshold to determine the final principal components. It can be seen that the cumulative variance contribution rate of the first two principal components reaches 86.5148%, which is greater than the preset variance contribution rate threshold of 85%. Therefore, the first two principal components are determined as the final principal components.

[0123] Furthermore, the multiple correlation coefficients of the final principal components and each secondary index, as well as the square of the multiple correlation coefficients, are calculated to determine the overall support of each related attribute;

[0124] Specifically, it includes:

[0125] Based on the final principal component F1 = a 11 x1+a 12 x2+a 13 x3+a 14 x4 and F2 = a 21 x1+a 22 x2+a 23 x3+a 24 x4, calculate df = data * V(:, 1: num); where df represents the matrix composed of the final principal components F1 and F2, and num represents the number of principal components determined by the threshold, which is 2;

[0126]

[0127] Further, the multiple correlation coefficient ρ is calculated;

[0128] ρ 1p =corrcoef(df(:,1),data(:,1));

[0129] In the calculation formula, df(:,1) represents the first column in the principal component matrix (the first principal component F1), data(:,1) represents the first column in the original data (attribute P), and the calculated value represents the multiple correlation coefficient between the first principal component and attribute P, which is 0.8156 in real number here;

[0130] Similarly, by changing the number 1 in data(:,1) to 2, 3, 4, we can obtain the multiple correlation coefficients (taking the real part) between the first principal component and other attributes.

[0131] Change 1 to 2 in df(:,1) to obtain the multiple correlation coefficients between the second principal component and each attribute (take the real part of the coefficients);

[0132] Furthermore, the square of the multiple correlation coefficient τ i :

[0133] τ i =(ρ 1i 2 +ρ 2i 2 )

[0134] Overall Support Z i :

[0135]

[0136] The details are shown in Table 3 below:

[0137] Table 3

[0138]

[0139]

[0140] Based on the comprehensive support of each relevant attribute, the interoperability level assessment result is generated by combining the scoring data of the secondary indicators.

[0141] The fusion process is represented as follows:

[0142]

[0143] The final nine fused indicators are 3.3862, 3.1237, 3.8682, 3.7436, 3.6447, 3.7369, 3.5576, 3.4301, and 3.8503. Taking the average of each indicator with equal weight yields a final fusion result of 3.5935, indicating that the radar seeker interoperability level in this embodiment is integrated-level leaning towards cooperative-level.

[0144] Example 3;

[0145] like Figure 7 As shown, this embodiment provides an interoperability level assessment system for radar seekers, including:

[0146] Interoperability Level Model Definition Module: Used to define the interoperability level model of radar seeker; including: classifying interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D;

[0147] Indicator Tree Construction Module: Used to construct indicator trees for relevant attributes in the radar seeker interoperability level model; the indicator trees include: procedure attribute indicator trees, application attribute indicator trees, facility attribute indicator trees, and data attribute indicator trees;

[0148] Interoperability Level Assessment Module: Based on the index tree, this module uses principal component analysis algorithm to assess the interoperability level and generate interoperability level assessment results.

[0149] The system comprises three main modules: First, the interoperability level model definition module is responsible for defining and classifying the interoperability levels of radar seekers based on key attributes such as procedural attribute P, application attribute A, infrastructure attribute I, and data attribute D, and labeling the key characteristics of each level. Second, the indicator tree construction module, based on the defined interoperability level model, further refines the relevant attributes and constructs a detailed indicator tree containing first-level branches such as data structure, system structure, and management structure, as well as subordinate second-level indicators. Finally, the interoperability level evaluation module uses principal component analysis to process the scoring data in these indicator trees. After a series of steps such as data standardization, correlation coefficient matrix construction, and eigenvalue solving, it generates the final interoperability level evaluation results, thereby providing a scientific basis and technical support for the practical application of radar seekers.

[0150] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0151] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An interoperability level assessment method for radar seekers, characterized in that, Includes the following steps: S1. Define the radar seeker interoperability level model; including: classifying interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedural attribute P, application attribute A, infrastructure attribute I, and data attribute D; S2. Construct an index tree for the relevant attributes in the radar seeker interoperability level model; the index tree includes: procedure attribute index tree, application attribute index tree, facility attribute index tree and data attribute index tree; S3. Based on the aforementioned index tree, perform interoperability level assessment using principal component analysis algorithm to generate interoperability level assessment results; including: S31. Based on the scoring data of the secondary index of radar seeker interoperability, construct the observation data matrix and perform standardization processing; S32. Construct a correlation coefficient matrix based on the standardized observation data matrix; S33. Solve for the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix using the Jacobian algorithm; S34. Based on the eigenvalues ​​and corresponding eigenvectors, determine each principal component and the variance contribution rate of each principal component. Compare the cumulative variance contribution rate of the first Z principal components with a preset variance contribution rate threshold to determine the final principal components. S35. Calculate the multiple correlation coefficients of the final principal components and each secondary index, as well as the square of the multiple correlation coefficients, to determine the overall support of each related attribute. S36. Based on the comprehensive support of each relevant attribute, the scoring data of the secondary indicators are fused together to generate an interoperability level assessment result.

2. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, In S1, the interoperability levels, arranged from low to high, include: isolation level, entry-level, functional level, integration level, collaboration level, and adaptation level.

3. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, In S2, each indicator tree includes three primary branches: data structure, system structure, and management structure, as well as multiple secondary indicators under each primary branch; among them, The secondary indicators under the data structure include: basic data standards, data service standards, and data management standards; The secondary indicators under the system architecture include: physical layer, data layer, and semantic layer; The secondary indicators under the management structure include: regulations, ordinances, standardization and universality, and integration.

4. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, In S31, the standardization process is expressed as follows: Where i = (1,2,……9), j = (1,2,3,4), x ij Represents the elements in the observation data matrix. This represents the elements of the standardized observation data matrix. This represents the average score of the secondary indicator corresponding to the relevant attribute j, var(x) j The variance of the scores of the secondary indicators under the relevant attribute j, where n represents the number of samples.

5. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, In S32, the correlation coefficient matrix is ​​represented as follows: Where, r jk s represents an element in the correlation coefficient matrix. jk s represents the covariance of related attributes j and k after standardization. jj Let s represent the variance of the relevant attribute j after standardization. kk This represents the variance of the relevant attribute k after standardization.

6. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, In S34, the principal components are represented as follows: Where F1, F2, F3, and F4 represent the first principal component, the second principal component, the third principal component, and the fourth principal component, respectively, and are arranged in descending order of variance as F1, F2, F3, and F4; a kl Let a be an eigenvector, and satisfy a k1 2 +a k2 2 +a k3 2 +a k4 2 =1, k, l = (1,2,3,4); x1, x2, x3, x4 are the values ​​of each coordinate axis, representing the scores of the secondary indicators.

7. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, In S34, the variance contribution rate of the principal components is expressed as: Where, λ y Let y = (1,2,3,4) represent the variance of the principal components.

8. The interoperability level assessment method for radar seekers according to claim 1, characterized in that, S36 includes: The scoring data from the secondary indicators are then integrated and processed. Based on the fused data, the average value is calculated and the interoperability level of the radar seeker is determined.

9. An interoperability level evaluation system for radar seekers, characterized in that, include: Interoperability Level Model Definition Module: Used to define the interoperability level model of radar seeker; including: classifying interoperability levels and labeling the key features of each level based on relevant attributes; wherein, the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D; Indicator Tree Construction Module: Used to construct indicator trees for relevant attributes in the radar seeker interoperability level model; the indicator trees include: procedure attribute indicator trees, application attribute indicator trees, facility attribute indicator trees, and data attribute indicator trees; Interoperability Level Assessment Module: Used to perform interoperability level assessment based on the aforementioned indicator tree using principal component analysis algorithm, and generate interoperability level assessment results; including: Based on the scoring data of the secondary index of radar seeker interoperability, an observation data matrix is ​​constructed and standardized. Based on the standardized observation data matrix, a correlation coefficient matrix is ​​constructed. The eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix are obtained using the Jacobi algorithm. Based on the eigenvalues ​​and corresponding eigenvectors, each principal component and its variance contribution rate are determined. The cumulative variance contribution rate of the first Z principal components is compared with a preset variance contribution rate threshold to determine the final principal components. Calculate the multiple correlation coefficients of the final principal components and each secondary index, as well as the square of the multiple correlation coefficients, to determine the overall support of each correlated attribute; Based on the comprehensive support of each relevant attribute, the interoperability level assessment result is generated by combining the scoring data of the secondary indicators.

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