Interoperability grade evaluation method and system applied to radar seeker

Abstract

The invention discloses an interoperability grade evaluation method applied to a radar seeker. The method comprises the following steps: defining a radar seeker interoperability grade model; comprising the following steps: carrying out interoperability grade division, and marking key features of each grade based on related attributes; wherein the related attributes comprise a regulation attribute P, an application attribute A, an infrastructure attribute I and a data attribute D; constructing an index tree for related attributes in the radar seeker interoperability grade model; and based on the index tree, performing interoperability grade evaluation by using a principal component analysis algorithm to generate an interoperability grade evaluation result. According to the method, a comprehensive and systematic evaluation framework is provided for the interoperability of the radar seeker by constructing a detailed interoperability grade model and an attribute index tree. And further combining with a principal component analysis algorithm, effective dimensionality reduction and fusion of multi-dimensional data are realized, and the accuracy of an evaluation result and the calculation efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of radar seekers, and more particularly to an interoperability level evaluation method and system applied to radar seekers. Background Art

[0002] In modern information warfare, radar seekers, as key components of missile systems, are crucial to achieving precision strikes and efficient operations. As the battlefield electromagnetic environment becomes increasingly complex and changeable, the interoperability requirements for radar seekers are becoming increasingly high. Interoperability not only involves the collaborative working capabilities of the various components within the radar seeker, but also includes effective interaction with other missile systems and external information sources. Highly interoperable radar seekers can significantly improve the autonomy and guidance accuracy of missiles, ensuring that tasks can be completed accurately and efficiently in various complex environments.

[0003] However, existing evaluation methods are often based on the personal experience and intuition of experts. 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] They usually only consider a few key factors, but ignore the complex interactions between the components of the radar seeker and their ability to work together with the entire system. This not only fails to fully reflect the actual interoperability of the system, but may also cause some potential problems to be ignored.

[0005] In addition, with the development of radar technology, more and more influencing factors need to be considered, and the amount of data has also increased dramatically. Existing methods are inefficient in processing large amounts of multi-dimensional data, and it is difficult to complete the evaluation quickly and accurately, which is an obvious shortcoming in the fast-paced modern battlefield. It ignores the support of quantitative data. Even if some methods try to combine quantitative analysis, it is still difficult to organically combine qualitative and quantitative information due to the lack of effective data processing methods, which affects the accuracy and reliability of the evaluation results.

[0006] Therefore, how to design an interoperability level evaluation method for radar seekers that can provide more scientific, accurate and efficient evaluation results, so as to better guide the optimization of radar seeker systems, is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] In view of this, the present invention provides an interoperability level evaluation method applied to radar seekers, which improves the accuracy and computational efficiency of the evaluation results by constructing a detailed interoperability level model and attribute indicator tree and using the principal component analysis algorithm for comprehensive evaluation.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides an interoperability level evaluation method applied to a radar seeker, comprising the following steps:

[0010] S1. Define the radar seeker interoperability level model, including: classifying the interoperability levels and marking 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;

[0011] S2. constructing an index tree for 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;

[0012] S3. Based on the indicator tree, use a principal component analysis algorithm to perform interoperability level assessment to generate an interoperability level assessment result.

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

[0014] Preferably, in S2, each indicator tree includes three first-level branches: data structure, system structure and management structure, and multiple second-level indicators under each first-level 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 structure include: physical layer, data layer and semantic layer;

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

[0018] Preferably, S3 includes:

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

[0020] S32, constructing a correlation coefficient matrix based on the standardized observation data matrix;

[0021] S33, solving the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix by Jacobi algorithm;

[0022] S34, based on the eigenvalue and the corresponding eigenvector, 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, and determine the final principal component;

[0023] S35, calculating the complex correlation coefficient between the final principal component and each secondary index and the square of the complex correlation coefficient, and determining the comprehensive support of each related attribute;

[0024] S36. Based on the comprehensive support of each relevant attribute, the scoring data of the secondary indicators are combined to perform fusion processing to generate an interoperability level evaluation 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, represents the observed data matrix element after standardization, represents the average score of the corresponding secondary index under the relevant attribute j, var(x j ) is the variance value of the secondary indicator score corresponding to the relevant attribute j, and n represents the number of samples.

[0028] Preferably, in S32, the correlation coefficient matrix is ​​expressed as:

[0029]

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

[0031] Preferably, in S34, the main component is expressed as:

[0032]

[0033] Among them, 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 from large to small based on variance as F1, F2, F3, and F4; a kl represents the eigenvector and satisfies 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, indicating the scores of the secondary indicators.

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

[0035]

[0036] Among them, λ y Represents the variance of the principal component, y=(1,2,3,4).

[0037] Preferably, the S36 includes:

[0038] Combine the scoring data of the secondary indicators for fusion processing:

[0039]

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

[0041] In a second aspect, the present invention provides an interoperability level evaluation system applied to a radar seeker, comprising:

[0042] Interoperability level model definition module: used to define the radar seeker interoperability level model; including: dividing the interoperability level and marking the key features of each level based on relevant attributes; the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D;

[0043] An indicator tree construction module: used to construct an indicator tree for relevant attributes in the radar seeker interoperability level model; the indicator tree includes: a procedure attribute indicator tree, an application attribute indicator tree, a facility attribute indicator tree and a data attribute indicator tree;

[0044] Interoperability level evaluation module: used to perform interoperability level evaluation based on the indicator tree using a principal component analysis algorithm to generate an interoperability level evaluation result.

[0045] It can be seen from the above technical solution that compared with the prior art, the technical solution of the present invention has 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, and describes in detail the requirements that should be achieved at each level from the four attributes of procedures (P), applications (A), infrastructure (I), and data (D). This hierarchical and multi-dimensional evaluation system not only helps to accurately judge the current status, but also points out the direction for future system improvements or upgrades.

[0048] 2. By using the principal component analysis method, the interoperability between the components of the radar seeker can be comprehensively considered from multiple angles. It not only considers qualitative factors, but also combines quantitative data for scientific calculation, thus reflecting the actual interoperability level of the system more objectively and comprehensively.

[0049] 3. The principal component analysis method can be used to reduce the dimension of data and simplify the data structure while retaining the main information. This not only greatly reduces the computing resources required for subsequent analysis, but also speeds up the entire evaluation process, enabling decision makers to obtain the required information more quickly to support rapid response needs.

[0050] 4. By scoring the same indicator multiple times from different angles and using reasonable algorithms to integrate detection information, we ensure that the final evaluation result is as close to the actual situation as possible, thereby improving the scientificity and authority of the evaluation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0052] Figure 1 A flow chart of an interoperability level assessment method for a radar seeker provided in an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a protocol attribute indicator tree provided in an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of an application attribute indicator tree provided by an embodiment of the present invention;

[0055] Figure 4 A schematic diagram of a facility attribute indicator tree provided by an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of a data attribute indicator tree provided by an embodiment of the present invention;

[0057] Figure 6A schematic diagram of an interoperability level evaluation process using a principal component analysis algorithm provided in an embodiment of the present invention;

[0058] Figure 7 A framework diagram of a system for interoperability level assessment using a principal component analysis algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1;

[0061] like Figure 1 As shown, this embodiment provides an interoperability level evaluation method applied to a radar seeker, comprising the following steps:

[0062] S1. Define the radar seeker interoperability level model, including: classifying the interoperability levels and marking 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;

[0063] S2. constructing an index tree for 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;

[0064] S3. Based on the indicator tree, use a principal component analysis algorithm to perform interoperability level assessment to generate an interoperability level assessment result.

[0065] This method provides a comprehensive and systematic evaluation framework for the interoperability of radar seekers by constructing a detailed interoperability grade model and attribute index tree. The principal component analysis algorithm is used to achieve effective dimensionality reduction and fusion of multi-dimensional data, improving the accuracy of the evaluation results and the computational efficiency.

[0066] The above steps and related technical features are further described in detail below:

[0067] In this embodiment S1, a radar seeker interoperability level model is defined; including: performing interoperability level classification and marking 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] The interoperability levels are arranged from low to high: isolation level, entry level, functional level, integration level, collaboration level and adaptation level.

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

[0070] Table 1

[0071]

[0072] Specifically, the isolation level means that there is no electronic connection in any form between the components of the radar seeker, which makes it impossible for the computer to directly obtain the target's position and motion information; the entry level can be understood as an electronic connection between the components, but only homogeneous data types can be exchanged; when the functional level is reached, the components can exchange relatively complex heterogeneous information and establish corresponding logical data models; the components at the integration level are in a wide-area environment, and the same type of radar seeker components can realize information sharing and complete database-to-database information exchange for different applications; the collaboration level forms cross-domain joint interoperability, and the components of heterogeneous radar seekers can simultaneously access complex data in the information space to realize cross-domain information sharing; the adaptation level reaches the level of rapid adaptation to changes, and each system component can complete higher-level information sharing and collaboration in real time under 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 first-level and second-level 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 first-level branches: data structure, system structure, and management structure, as well as multiple second-level indicators under each first-level branch;

[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 structure include: physical layer, data layer and semantic layer;

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

[0079] In this embodiment S3, based on the indicator tree, the interoperability level evaluation is performed using the principal component analysis algorithm to generate an interoperability level evaluation result.

[0080] like Figure 6 As shown, specifically including:

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

[0082] The observation data matrix is ​​expressed as:

[0083]

[0084] Among them, x P1 、x A1 、x I1 、x D1 They represent the basic data standard scores corresponding to the procedure attributes, application attributes, infrastructure attributes, and data attributes, respectively. P2 、x A2 、x I2 、x D2 They represent the data service standard scores corresponding to the procedure attributes, application attributes, infrastructure attributes, and data attributes, respectively. P3 、x A3 、x I3 、x D3 They represent the data management standard scores corresponding to the procedure attributes, application attributes, infrastructure attributes, and data attributes, respectively. P4 、x A4 、x I4 、x D4 They represent the physical layer scores corresponding to the protocol attributes, application attributes, infrastructure attributes, and data attributes, respectively. P5 、x A5 、x I5 、x D5 They represent the data layer scores corresponding to the procedure attributes, application attributes, infrastructure attributes, and data attributes, respectively. P6 、x A6 、x I6 、x D6 They 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 Respectively represent the scores of the regulations and directives corresponding to the procedure attributes, application attributes, infrastructure attributes, and data attributes, x P8 、x A8 、x I8 、x D8They represent the universality scores of procedure attributes, application attributes, infrastructure attributes, and data attributes, respectively. P9 、x A9 、x I9 、x D9 They represent the integration scores corresponding to procedure attributes, application attributes, infrastructure attributes, and data attributes respectively.

[0085] The normalization process is expressed as:

[0086]

[0087]

[0088] Where, i = (1, 2, ... 9), j = (1, 2, 3, 4), x ij represents the elements in the observation data matrix, represents the observed data matrix element after standardization, represents the average score of the corresponding secondary index under the relevant attribute j, var(x j ) is the variance value of the secondary indicator score corresponding to the relevant attribute j, and n represents the number of samples.

[0089] S32, constructing a correlation coefficient matrix based on the standardized observation data matrix;

[0090] The correlation coefficient matrix is ​​expressed as:

[0091]

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

[0093] S33, solving the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix by Jacobi algorithm;

[0094] S34, based on the eigenvalue and the corresponding eigenvector, 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, and determine the final principal component;

[0095] The principal components are expressed as:

[0096]

[0097] Among them, 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 from large to small based on variance as F1, F2, F3, and F4; a kl represents the eigenvector and satisfies 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, indicating the scores of the secondary indicators.

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

[0099]

[0100] Among them, λ y Represents the variance of the principal component, y=(1,2,3,4).

[0101] S35, calculating the complex correlation coefficient between the final principal component and each secondary index and the square of the complex correlation coefficient, and determining the comprehensive support of each related attribute;

[0102] S36: Based on the comprehensive support of each relevant attribute, the scoring data of the secondary indicators are combined to generate an interoperability level evaluation result. Including:

[0103] Combine the scoring data of the secondary indicators for fusion processing:

[0104]

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

[0106] The interoperability level evaluation method applied to the radar seeker in this embodiment realizes an in-depth analysis of the interoperability capabilities of the radar seeker by finely defining the interoperability level model and constructing a multi-dimensional related attribute index tree. The use of the principal component analysis algorithm for quantitative evaluation not only improves the accuracy and objectivity of the evaluation, but also reduces subjective interference in the evaluation process. In addition, the method can clearly distinguish the performance differences of the radar seeker at different interoperability levels, providing an important reference for the integration, upgrade and optimization of the radar system. Through the application of this method, the overall performance and combat effectiveness of the radar system can be significantly improved, and the collaborative combat capability between different systems can be enhanced, thereby meeting complex and changeable combat needs.

[0107] Embodiment 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≤Function Level<3, 3≤Integration Level<4, 4≤Collaboration Level<5, 5≤Adaptation Level;

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

[0110] Table 2

[0111]

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

[0113]

[0114] Perform standardization to obtain the standardized observation data matrix:

[0115]

[0116] Based on the standardized observation data matrix, the correlation coefficient matrix is ​​constructed:

[0117]

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

[0119] The eigenvalue λ = (2.0748, 1.3858, 0.5023, 0.0371), the corresponding eigenvector is:

[0120]

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

[0122] The cumulative variance contribution rates of the first Z principal components are compared with the preset variance contribution rate threshold to determine the final principal component; it can be seen that the cumulative variance contribution rates of the first two principal components are 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 complex correlation coefficient between the final principal component and each secondary index and the square of the complex correlation coefficient are calculated to determine the comprehensive support of each relevant attribute;

[0124] Specifically include:

[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 judged by the threshold value, which is 2;

[0126]

[0127] Further, the complex 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 complex correlation coefficient between the first principal component and attribute P. Here, the real part is 0.8156;

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

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

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

[0133] τ i =(ρ 1i 2 +ρ 2i 2 )

[0134] Comprehensive 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 scoring data of the secondary indicators are fused and processed to generate an interoperability level evaluation result.

[0141] The fusion process is expressed as:

[0142]

[0143] The 9 fused indices finally obtained are 3.3862, 3.1237, 3.8682, 3.7436, 3.6447, 3.7369, 3.5576, 3.4301, and 3.8503. When the weight of each indicator is the same, the average value is calculated to obtain the final fusion result of 3.5935, that is, the interoperability level of the radar seeker in this embodiment is from the integration level to the collaboration level.

[0144] Embodiment 3;

[0145] like Figure 7 As shown, this embodiment provides an interoperability level evaluation system applied to a radar seeker, including:

[0146] Interoperability level model definition module: used to define the radar seeker interoperability level model; including: dividing the interoperability level and marking the key features of each level based on relevant attributes; the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D;

[0147] An indicator tree construction module: used to construct an indicator tree for relevant attributes in the radar seeker interoperability level model; the indicator tree includes: a procedure attribute indicator tree, an application attribute indicator tree, a facility attribute indicator tree and a data attribute indicator tree;

[0148] Interoperability level evaluation module: used to perform interoperability level evaluation based on the indicator tree using a principal component analysis algorithm to generate an interoperability level evaluation result.

[0149] The system consists of three main modules: first, the interoperability level model definition module is responsible for defining and dividing the interoperability level of radar seekers according to key attributes such as procedure attribute P, application attribute A, infrastructure attribute I and data attribute D, and marking the key characteristics of each level; second, the indicator tree construction module is based on the above-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 and subordinate second-level indicators; finally, the interoperability level evaluation module uses the principal component analysis algorithm to process the scoring data in these indicator trees, and after a series of steps such as data standardization, correlation coefficient matrix construction, eigenvalue solution, etc., generates the final interoperability level evaluation results, thereby providing a scientific basis and technical support for the practical application of radar seekers.

[0150] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0151] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the interoperability level of a radar seeker, characterized in that: The following steps are involved: S1. Define the radar seeker interoperability level model, including: classifying the interoperability levels and marking 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; S2. constructing an index tree for 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; S3. Based on the indicator tree, use a principal component analysis algorithm to perform interoperability level assessment to generate an interoperability level assessment result.

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

3. The interoperability level evaluation method for radar seeker according to claim 1, characterized in that: In S2, each indicator tree includes three first-level branches: data structure, system structure and management structure, and multiple second-level indicators under each first-level branch; wherein, The secondary indicators under the data structure include: basic data standards, data service standards and data management standards; The secondary indicators under the system structure include: physical layer, data layer and semantic layer; The secondary indicators under the management structure include: regulations, standardization and universality of regulations and regulations, and integration.

4. The interoperability level evaluation method for radar seeker according to claim 1, characterized in that: The S3 includes: S31. Based on the scoring data of the secondary indicators of radar seeker interoperability, construct an observation data matrix and perform standardization processing; S32, constructing a correlation coefficient matrix based on the standardized observation data matrix; S33, solving the eigenvalues ​​and corresponding eigenvectors of the correlation coefficient matrix by Jacobi algorithm; S34, based on the eigenvalue and the corresponding eigenvector, 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, and determine the final principal component; S35, calculating the complex correlation coefficient between the final principal component and each secondary index and the square of the complex correlation coefficient, and determining the comprehensive support of each related attribute; S36. Based on the comprehensive support of each relevant attribute, the scoring data of the secondary indicators are combined to perform fusion processing to generate an interoperability level evaluation result.

5. The interoperability level evaluation method for radar seeker according to claim 4, characterized in that: In S31, the standardization process is represented as follows: Where, i = (1, 2, ... 9), j = (1, 2, 3, 4), x ij represents the elements in the observation data matrix, represents the observed data matrix element after standardization, represents the average score of the corresponding secondary index under the relevant attribute j, var(x j ) is the variance value of the secondary indicator score corresponding to the relevant attribute j, and n represents the number of samples.

6. The interoperability level evaluation method for radar seeker according to claim 4, characterized in that: In S32, the correlation coefficient matrix is ​​expressed as: Among them, r jk Represents the elements in the correlation coefficient matrix, s jk represents the covariance of related attribute j and related attribute k after normalization, s jj represents the variance of the relevant attribute j after standardization, s kk Represents the variance of the relevant attribute k after normalization.

7. The interoperability level evaluation method for radar seekers according to claim 4, characterized in that: In S34, the main component is expressed as: Among them, 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 from large to small based on variance as F1, F2, F3, and F4; a kl represents the eigenvector and satisfies 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, indicating the scores of the secondary indicators.

8. The interoperability level evaluation method for radar seeker according to claim 4, characterized in that: In S34, the variance contribution rate of the principal component is expressed as: Among them, λ y Represents the variance of the principal component, y=(1,2,3,4).

9. The interoperability level evaluation method for radar seeker according to claim 4, characterized in that: The S36 includes: Combine the scoring data of the secondary indicators for fusion processing: Based on the fused data, the average value is calculated and the radar seeker interoperability level is determined.

10. An interoperability level evaluation system for a radar seeker, characterized in that: include: Interoperability level model definition module: used to define the radar seeker interoperability level model; including: dividing the interoperability level and marking the key features of each level based on relevant attributes; the relevant attributes include: procedure attribute P, application attribute A, infrastructure attribute I and data attribute D; An indicator tree construction module: used to construct an indicator tree for relevant attributes in the radar seeker interoperability level model; the indicator tree includes: a procedure attribute indicator tree, an application attribute indicator tree, a facility attribute indicator tree and a data attribute indicator tree; Interoperability level evaluation module: used to perform interoperability level evaluation based on the indicator tree using a principal component analysis algorithm to generate an interoperability level evaluation result.

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