Method and system for evaluating anti-unmanned aerial vehicle interference effect

Abstract

The invention discloses an anti-unmanned aerial vehicle interference effect evaluation method and system, and belongs to the technical field of anti-unmanned aerial vehicle interference effect evaluation, and the method comprises the following steps: collecting two types of anti-unmanned aerial vehicle scheme cases, carrying out the standardization and classification processing of cases which are good in evaluation but failing in off-site simulation, and building a failure case library; cases which are both good in evaluation and off-site simulation are classified, then the economic benefit range of each type of evaluation method is determined, and a successful case library is established. According to the method, a failure case library containing structured data and a success case library with a clear economic benefit range are established, a to-be-evaluated scheme is evaluated, then three-dimensional data are extracted from the failure case library to construct a multi-dimensional association model, and an evaluation blind area is positioned; and finally, three evaluation methods are generated in combination with a success case library, economic benefits are calculated and sorted, the difference between evaluation and off-site simulation can be effectively reduced, the evaluation accuracy is improved, and off-site simulation failure conditions are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of anti-UAV interference effect evaluation, in particular to an anti-UAV interference effect evaluation method and system. BACKGROUND

[0002] In the field of anti-UAV technology application, the evaluation of anti-UAV scheme is a key link to ensure the off-site simulation effectiveness. At present, the industry mainly carries out evaluation work through the combination of laboratory evaluation and off-site simulation, but there is often a significant disconnection between the two. A large number of schemes that perform well in laboratory evaluation fail in off-site simulation scenarios due to insufficient environmental adaptability, lack of tactical feasibility, and other problems, resulting in the frequent occurrence of the situation that evaluation is good but off-site simulation fails. The root cause of this disconnection problem is that the existing evaluation system lacks systematic analysis of historical experience. It neither structurally integrates the failure reasons, application environment, and evaluation methods of past cases that evaluation is good but off-site simulation fails, nor provides risk warnings for current evaluation. It also fails to clearly define the economic benefit range of various successful cases, making it difficult to balance the off-site simulation adaptability and economic rationality of the evaluation method.

[0003] In view of the problems in the related art, an effective solution has not been proposed yet. SUMMARY

[0004] In view of the problems in the related art, the present application proposes an anti-UAV interference effect evaluation method and system to overcome the technical problems of disconnection between laboratory and off-site simulation in existing anti-UAV scheme evaluation, and evaluation is good but off-site simulation fails.

[0005] To this end, the specific technical solutions adopted by the present application are as follows:

[0006] An anti-UAV interference effect evaluation method and system, the method comprising the following steps:

[0007] S1, collect two types of anti-UAV scheme cases, standardize and classify the cases that evaluation is good but off-site simulation fails, and establish a failure case library; after classification, clearly define the economic benefit range of the evaluation method of each category for the cases that both evaluation and off-site simulation are good, and establish a successful case library;

[0008] S2, for the anti-UAV scheme to be evaluated, set the evaluation method to evaluate, and perform root cause analysis and iteration processing on the anti-UAV scheme that fails to pass the evaluation, and further optimize the evaluation method of the passed scheme;

[0009] S3, identify the category of the anti-UAV scheme that passes the evaluation, filter out historical failure cases of the same category from the case library, and extract the content, application environment, and evaluation method of the cases, generate correlation analysis of the three dimensions and construct a multi-dimensional correlation model;

[0010] S4, using a multi-dimensional correlation model to evaluate the current evaluation method to identify multi-aspect blind area, and combining the economic benefit range of the successful cases in this category to generate three evaluation methods, and finally calculating the economic benefit of the evaluation method and sorting output.

[0011] As a preferred embodiment, the two types of anti-UAV scheme cases are collected, the cases with good evaluation but off-site simulation failure are standardized and classified, and a failure case library is established; the cases with good evaluation and off-site simulation are classified to determine the economic benefit range of the evaluation method in each category, and a successful case library is established including the following steps:

[0012] S11, collect two types of historical anti-UAV scheme cases, and strictly divide them into two categories according to the evaluation prediction effect and the actual application effect, one category is the case with good evaluation result but actual failure, and the other category is the case with excellent evaluation and actual performance;

[0013] The subsequent analysis needs to be based on these two completely different data samples to summarize the lessons and best practices respectively, thereby providing positive and negative references for the to-be-evaluated scheme;

[0014] S12, standardize the case set with good evaluation but off-site simulation failure, including unified data format, standardized description language and structured failure reason, the failure reason includes environmental misjudgment, cost overrun and tactical misuse; then classify according to the core technology principle of the scheme, the categories include electromagnetic interference suppression, navigation signal deception, radio protocol hijacking, kinetic energy physical destruction and comprehensive multi-level defense, after classification, establish a failure case library for storage;

[0015] S13, standardize the successful case set with good evaluation and actual performance, after processing, classify according to the core technology principle of the scheme, the categories include electromagnetic interference suppression, navigation signal deception, radio protocol hijacking, kinetic energy physical destruction and comprehensive multi-level defense; statistical analysis of the actual economic benefit required by each category case implementation, and determine the economic benefit range of the evaluation method in each category by statistical method, and establish a successful case library with economic quantitative index;

[0016] This classification method is to facilitate rapid horizontal comparison within the same technology track.

[0017] As a preferred embodiment, the anti-UAV scheme to be evaluated is set to evaluate the evaluation method, and the anti-UAV scheme that does not pass the evaluation is subjected to root cause analysis and iteration, and the passed scheme is further optimized including the following steps:

[0018] S21, set the evaluation method for the anti-UAV scheme to be evaluated to perform evaluation;

[0019] S22, for the scheme that does not pass, root cause analysis and iterative optimization processing are performed; the evaluation method of the passed scheme is further optimized.

[0020] As a preferred embodiment, the step of setting the evaluation method for the anti-UAV scheme to be evaluated to perform evaluation includes the following steps:

[0021] S211, a test field simulating a real electromagnetic environment is established in a laboratory, and core devices such as a spectrum analyzer, a signal generator, a UAV target group, and a to-be-tested interference device are configured, and navigation and communication signals commonly used by target UAVs are simulated and generated by the signal generator;

[0022] S212, start core single-index quantitative testing, sequentially collect and record key performance data; use the spectrum analyzer to accurately measure the effective radiation power and emission spectrum width of the interference device in the target frequency band; through systematic distance test, determine the effective action distance, spatial coverage range, and precision deception success rate of each type of navigation signal under a specific interference power;

[0023] S213, further protocol layer countermeasure performance evaluation is performed, the signal interception time of the data transmission and image transmission link emitted by the UAV target group is recorded, and the whole process processing delay required from interception to successful cracking of the communication protocol and then taking over the control is examined to evaluate the intelligent countermeasure capability at the information level;

[0024] S214, introduce complex electromagnetic background noise and multiple batches of UAV target groups of different types, apply synchronous interference instructions, comprehensively examine the interference resource allocation capability, system response delay, and overall working stability of the system in a multi-target, strong countermeasure scene, thereby completing the comprehensive evaluation of the core tactical indicators;

[0025] S215, compare all the data collected in the quantitative test with the technical qualified threshold preset in the evaluation scheme, if all the core indicators simultaneously reach or exceed the qualified threshold, it is determined that the scheme passes the evaluation; if any one or more core indicators is unqualified, it is determined that the scheme does not pass this evaluation.

[0026] These thresholds are jointly developed based on the technical standards and tactical requirements of historical successful cases, and any core indicator that does not meet the minimum threshold requirement is considered to be unqualified.

[0027] As a preferred embodiment, the step of identifying the anti-UAV scheme category that passes the evaluation, screening historical failed cases of the same category from the case library, and extracting the content, application environment, and evaluation method of the case to generate relevance analysis of the three dimensions includes the following steps:

[0028] S31, extract the core technical principles of the current anti-drone scheme that has passed the evaluation, and classify the current scheme, preliminarily screen all historical failed cases belonging to the same category from the entire failed case library to form a sub-data set for in-depth analysis;

[0029] S32, in this category sub-data set, further set up a similarity matching algorithm according to the key technical parameters and application scene characteristics of the current scheme, including interference frequency band, action distance, target drone type, environment, screen out historical failed cases with high similarity in technical details and application environment to the current scheme, and narrow down the analysis range;

[0030] S33, from these screened high-similarity failed cases, extract three-dimensional structured data, including the detailed content of the case, the specific application environment of the case, and the evaluation method used in the history of the case, and perform correlation analysis on the three-dimensional data of the extracted content, application environment, and historical evaluation method.

[0031] As a preferred embodiment, the correlation analysis of the three-dimensional data of the extracted content, application environment, and historical evaluation method includes the following steps:

[0032] S331, pre-process the data and convert the extracted unstructured data into quantifiable indicators for analysis;

[0033] S332, use statistical analysis methods to explore the association rules between dimensions, calculate the correlation coefficients between different dimensional variables, and use hypothesis testing to determine whether there is a significant statistical dependence between the evaluation method used in the history and the failed case; finally, integrate all analysis results to build a multi-dimensional association model.

[0034] As a preferred embodiment, the correlation analysis of the three-dimensional data of the extracted content, application environment, and historical evaluation method includes the following steps:

[0035] S3321, calculate the Pearson correlation coefficient between different dimensional numerical variables to obtain the quantitative correlation strength and direction of different dimensions;

[0036] S3322, use a contingency table and chi-square test to analyze whether there is a significant statistical dependence between the historical evaluation method and the failed case: first, construct a two-dimensional contingency table, then calculate the chi-square statistic, the specific formula is:

[0037]

[0038] wherein O ij is the observed frequency, E ij is the expected frequency, x 2 is the chi-square statistic, and the chi-square statistic x 2 is compared with the chi-square distribution to obtain a p-value, and when the p-value is less than 0.05, the null hypothesis is rejected, confirming that the missing item of the evaluation method has a significant dependence on the specific failure mode, i.e., there is an evaluation blind area;

[0039] S3323, integrate all the correlation coefficients and the significance results of the hypothesis test to construct a multidimensional correlation model, and the model formula is:

[0040] η = Bη + γξ + ζ;

[0041] wherein η is an endogenous latent variable vector representing the results affected by other variables in the model, B is a path coefficient matrix quantifying the mutual relationship between different endogenous latent variables η, γ is a path coefficient matrix quantifying the direct influence effect of the exogenous latent variable ξ on the endogenous latent variable η, ξ is an exogenous latent variable vector representing the root causes that are not affected by other variables in the model, and ζ is a residual error vector representing the random error part that the model fails to explain.

[0042] As a preferred embodiment, the current evaluation method is evaluated using the multidimensional correlation model, and multiple blind areas are identified, and three new evaluation methods are generated based on the economic benefit range of the historical successful cases in this category, and finally the economic benefits of the evaluation methods are calculated and sorted as follows:

[0043] S41, the current evaluation method is evaluated using the constructed multidimensional correlation model; the test items, index thresholds and environmental coverage range of the current evaluation method are compared with the model to identify multiple blind areas existing in the current method;

[0044] S42, based on the specific blind areas identified, the economic benefit range of the historical successful cases in this category is obtained from the successful case library, and three new optimized evaluation methods are generated, the first method only repairs the identified core blind area within the existing evaluation framework; the second method introduces the evaluation process that has been proven to bring high economic benefits in the successful cases on the basis of repairing the blind area; the third method refers to the best practices in the successful case library to construct an evaluation system covering more environmental dimensions and technical dimensions;

[0045] S43, the economic benefit results of the three new evaluation methods are calculated and sorted from high to low, and a clear decision output is formed.

[0046] The application discloses an anti-UAV interference effect evaluation system, and relates to the technical field of anti-UAV interference effect evaluation.

[0047] The case library construction module is used for collecting two types of anti-UAV scheme cases, and after standardization and classification, a failure case library containing structured failure causes and a success case library containing economic benefit ranges and economic quantitative indexes are established.

[0048] The scheme evaluation module is used for setting an evaluation method to evaluate an anti-UAV scheme to be evaluated, determining whether the anti-UAV scheme to be evaluated passes, performing root cause analysis and iteration optimization on the anti-UAV scheme to be evaluated which does not pass, and performing transmission and further optimization on the anti-UAV scheme to be evaluated which passes.

[0049] The multi-dimensional correlation model construction module is used for identifying a scheme category which passes evaluation, screening a high-similarity historical failure case of the same type, extracting three-dimensional data and pre-processing, and constructing a multi-dimensional correlation model through statistical analysis.

[0050] The evaluation method optimization and economic benefit analysis module is used for identifying a blind area of a current evaluation method through the multi-dimensional correlation model, generating three new evaluation methods in combination with an economic benefit range of a success case, calculating expected economic benefits of the three new evaluation methods and outputting the three new evaluation methods in sequence.

[0051] The application has the following beneficial effects:

[0052] 1. In the evaluation of anti-UAV schemes, there is a disconnection problem that a scheme which is evaluated to be good but fails in field simulation, so that the scheme cannot play a role in actual application. The application can effectively reduce the gap between laboratory evaluation and field simulation, thereby greatly reducing the occurrence of a situation that a scheme which is evaluated to be good but fails in field simulation, and providing more reliable protection for field simulation application of the anti-UAV scheme.

[0053] 2. The application can sort out the loopholes and deficiencies existing in historical evaluation by collecting cases which are evaluated to be good but fail in field simulation, standardizing and classifying the cases, and establishing a failure case library. The failure case library not only converts scattered failure experience into structured data, but also provides data support for a subsequent scheme to be evaluated, so as to accurately locate a risk point possibly existing in current evaluation, avoid repeating the problem of ignoring field simulation environment adaptability and tactical feasibility in historical evaluation, and reduce field simulation failure caused by evaluation blind area from the source.

[0054] 2、The application can filter out the schemes that do not meet the core technical indicators and do not meet the basic performance requirements of the tactics, reduce the waste of resources in subsequent analysis, and reduce the risk of unqualified schemes flowing into the off-site simulation deployment, by setting the evaluation method for the current anti-UAV scheme first.

[0055] 3、The application can quickly identify the blind area of the current evaluation method by using the multi-dimensional association model, and can also generate three complementary evaluation methods by combining the economic benefit range of each type of scheme in the successful case library, which can not only repair the evaluation blind area, but also integrate the excellent experience in successful cases, to ensure that the newly generated evaluation method has both off-site simulation adaptability and economic rationality.

[0056] Therefore, the application can not only quickly locate the defects in the evaluation method, but also make the evaluation of anti-UAV schemes more accurate, greatly reduce the problem of good evaluation effect but poor off-site simulation effect, and further accurately optimize the evaluation scheme by generating three optimized evaluation methods combined with the blind area and the economic benefit range of successful cases. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0058] Figure 1 is a flow of an anti-UAV interference effect evaluation method according to an embodiment of the application Figure One ;

[0059] Figure 2 is a flow of an anti-UAV interference effect evaluation method according to an embodiment of the application Figure Two . DETAILED DESCRIPTION

[0060] To further illustrate the embodiments, the present application provides drawings which are part of the disclosure of the present application, mainly used to illustrate the embodiments, and can be explained in conjunction with the related description of the specification to understand the operating principle of the embodiments. Those skilled in the art should be able to understand other possible implementations and advantages of the present application in conjunction with these contents. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0061] According to the embodiments of the present application, a method and system for evaluating the anti-UAV interference effect are provided.

[0062] The present application will be further described in conjunction with the drawings and specific embodiments, as shown in Figures 1-2 A method for evaluating the anti-UAV interference effect according to the embodiments of the present application includes the following steps:

[0063] S1, collect two types of anti-UAV scheme cases, standardize and classify the cases that are good in evaluation but fail in off-site simulation, and establish a failure case library; classify the cases that are good in evaluation and off-site simulation, and determine the economic benefit range of each type of evaluation method, and establish a successful case library;

[0064] The establishment of the failure case library can provide key support and unified standards for subsequent screening of similar historical failure cases and construction of a multi-dimensional correlation model. On the one hand, by collecting cases in advance and classifying them according to the core technology principle, a failure case library containing structured failure reasons is formed, which provides a data basis for subsequent precise screening of historical failure cases consistent with the type of the to-be-evaluated scheme. On the other hand, the unified classification rules of the pre-case library can enable subsequent rapid and accurate matching of similar cases, extraction of case content, application environment and evaluation method to carry out correlation analysis. At the same time, the construction of a multi-dimensional correlation model can quickly identify the evaluation method blind area, thereby facilitating the provision of suggestions for the evaluation scheme;

[0065] The establishment of the successful case library not only determines the economic benefit range and economic quantitative indicators of each type of evaluation method, but also provides a reference direction for the subsequent three complementary new evaluation methods generated based on the blind area, and provides data support for calculating the expected economic benefit of the new evaluation method and realizing the sorting output, ensuring that the subsequent evaluation method optimization and economic benefit analysis have clear basis;

[0066] Further, collecting two types of anti-UAV scheme cases, standardizing and classifying the cases that are good in evaluation but fail in off-site simulation, and establishing a failure case library; classifying the cases that are good in evaluation and off-site simulation, and determining the economic benefit range of each type of evaluation method, and establishing a successful case library includes the following steps:

[0067] S11. Collect two types of historical anti-drone solution cases, and strictly distinguish them into two categories based on the two dimensions of evaluation and prediction effect and actual application effect. One category is cases with good evaluation results but failure in practice, and the other category is cases with excellent performance in both evaluation and practice.

[0068] S12. Standardize the case set of cases that performed well in the evaluation but failed in the off-site simulation, including unifying the data format, standardizing the descriptive terminology, and structuring the reasons for failure. The reasons for failure include environmental misjudgment, cost overrun, and tactical misuse. Then, classify the cases according to the core technical principles used in the solution. The categories include electromagnetic interference suppression, navigation signal deception, radio protocol hijacking, kinetic physical destruction, and comprehensive multi-layered defense. After classification, establish a failure case library for storage.

[0069] S13. Standardize the set of successful cases that are both evaluated and implemented well. After processing, classify them according to the core technical principles used in the solution. The categories include electromagnetic interference suppression, navigation signal deception, radio protocol hijacking, kinetic physical destruction, and comprehensive multi-layered defense. Statistically analyze the actual economic benefits required for the implementation of each category of cases, and use statistical methods to determine the economic benefit range of each category of evaluation method. Establish a successful case library with quantitative economic indicators.

[0070] S2. For the anti-drone schemes to be evaluated, set an evaluation method to evaluate them, and conduct root cause analysis and iterative processing for the anti-drone schemes that fail the evaluation, and further optimize the evaluation method for the schemes that pass the evaluation.

[0071] Furthermore, for the anti-drone schemes to be evaluated, an evaluation method is set up for evaluation, and root cause analysis and iterative processing are carried out for anti-drone schemes that fail the evaluation. For schemes that pass the evaluation, the evaluation method is further optimized, including the following steps:

[0072] S21. For the anti-drone scheme to be evaluated, set an evaluation method and conduct the evaluation;

[0073] Furthermore, for the anti-drone scheme to be evaluated, the evaluation method includes the following steps:

[0074] S211. Establish a test field in the laboratory to simulate the real electromagnetic environment, and configure core equipment such as spectrum analyzer, signal generator, UAV target group and interference equipment under test. The signal generator simulates and generates navigation and communication signals commonly used by the target UAV.

[0075] S212, start the core single index quantitative test, collect and record the key performance data in turn; use the spectrum analyzer to accurately measure the effective radiation power and emission spectrum width of the interference equipment in the target frequency band; through systematic distance test, determine the effective action distance, spatial coverage and precision deception success rate of various types of navigation signals under a certain interference power;

[0076] S213, further protocol layer confrontation performance evaluation is carried out, the signal interception time of the data transmission and image transmission link emitted by the UAV target group is recorded, and the whole process processing delay required from interception to successful cracking of the communication protocol and then taking over the control is examined, so as to evaluate the intelligent confrontation ability of the information layer;

[0077] S214, introduce complex electromagnetic background noise and multiple sorties of unmanned aerial vehicle target groups of different types, apply synchronous interference instructions, comprehensively examine the interference resource allocation ability, system response delay and overall working stability of the system in the multi-target, strong confrontation scene, so as to complete the comprehensive evaluation of the core tactical index;

[0078] S215, compare all the data collected in the quantitative test with the technical qualified threshold preset in the evaluation scheme, if all the core indexes meet or exceed the qualified threshold at the same time, it is determined that the scheme passes the evaluation; if any one or more core indexes is unqualified, it is determined that the scheme does not pass this evaluation.

[0079] Using the full index standard as the judgment standard, the deviation of subjective evaluation is completely avoided, and it is ensured that the scheme that passes the evaluation not only meets the laboratory data, but also has the ability to stably play a role in the real electromagnetic confrontation scene, can effectively screen out high-quality schemes that meet the tactical requirements, avoid unqualified equipment entering the subsequent application link, thereby reducing the risk of off-site simulation deployment, reducing resource waste, providing reliable performance guarantee for the off-site simulation application of anti-UAV system;

[0080] S22, for the schemes that do not pass, carry out root cause analysis and iterative optimization processing; further optimize the evaluation method of the schemes that pass;

[0081] S3, identify the anti-UAV scheme category that passes the evaluation, select the historical failed cases of the same category from the case library, extract the content, application environment and evaluation method of the cases, generate the correlation analysis of these three dimensions and build a multi-dimensional correlation model;

[0082] Further, the anti-UAV scheme category that passes the evaluation is identified, the historical failed cases of the same category are selected from the case library, the content, application environment and evaluation method of the cases are extracted, and the correlation analysis of these three dimensions is generated, including the following steps:

[0083] S31, extract the core technology principle adopted by the current anti-drone scheme that has passed the evaluation, and classify the current scheme, preliminarily screen all historical failed cases belonging to the same category from the entire failed case library to form a sub-data set for in-depth analysis;

[0084] S32, in this category sub-data set, further according to the key technical parameters and application scene characteristics of the current scheme, including interference frequency band, action distance, target drone type, environment, set similarity matching algorithm, screen out historical failed cases with high similarity in technical details and application environment with the current scheme, narrow the analysis range;

[0085] S33, from these screened high similarity failed cases, extract three dimensional structured data, including the detailed content of the case, the specific application environment of the case, and the evaluation method used in the history of the case, and carry out correlation analysis on the three dimensions of the extracted content, application environment and historical evaluation method;

[0086] Further, the correlation analysis of the three dimensions of the extracted content, application environment and historical evaluation method includes the following steps:

[0087] S331, pre-process the data, and convert the extracted unstructured data into quantifiable indicators for analysis;

[0088] S332, use statistical analysis method to explore the association rules between dimensions, calculate the correlation coefficients between different dimension variables, and use hypothesis testing to judge whether there is a significant statistical dependence between the evaluation method used in the history and the failed case; Finally, integrate all analysis results to build a multi-dimensional association model.

[0089] It should be noted that the model can clearly reveal that under certain application environment conditions, due to certain specific defects in the historical evaluation method, what specific technical failure content is ultimately caused, and also generate an association analysis report that accurately locates the root cause of failure and the evaluation blind area;

[0090] Further, the correlation analysis of the three dimensions of the extracted content, application environment and historical evaluation method includes the following steps:

[0091] S3321, calculate the Pearson product-moment correlation coefficient between different dimension numerical variables to obtain the quantitative correlation strength and direction of different dimensions

[0092] S3322, use the contingency table and chi-square test to analyze whether there is a significant statistical dependence between the historical evaluation method and the failure case: first construct a two-dimensional contingency table, and then calculate the chi-square statistic, the specific formula is:

[0093]

[0094] where O ij is the observed frequency, E ij is the expected frequency, x 2 is the chi-square statistic, and the calculated chi-square statistic x 2 is compared with the chi-square distribution to obtain the p value, when the p value is less than 0.05, the null hypothesis is rejected, confirming that the missing of this evaluation method and the specific failure mode have a significant dependence, that is, there is an evaluation blind area;

[0095] S3323, integrate all the correlation coefficients and the significance of the hypothesis test to construct a multi-dimensional correlation model, the model formula is:

[0096] η=Bη+γξ+ζ;

[0097] where η is the endogenous latent variable vector, representing the results affected by other variables in the model, B is the path coefficient matrix, quantifying the mutual relationship between different endogenous latent variables η, γ is the path coefficient matrix, quantifying the direct influence effect of the exogenous latent variable ξ on the endogenous latent variable η, ξ is the exogenous latent variable vector, representing the fundamental reasons that are not affected by other variables in the model, ζ is the residual vector, representing the random error part that the model cannot explain;

[0098] S4, use the multi-dimensional correlation model to evaluate the current evaluation method to identify multi-aspect blind areas, and generate three evaluation methods combined with the economic benefit range of the historical successful cases in this category, and finally calculate the economic benefit of the evaluation method and sort it out.

[0099] Further, use the multi-dimensional correlation model to evaluate the current evaluation method, and identify multi-aspect blind areas, and generate three evaluation methods combined with the economic benefit range of the historical successful cases in this category, and finally calculate the economic benefit of the evaluation method and sort it out, including the following steps:

[0100] S41, use the multi-dimensional correlation model constructed to evaluate the current evaluation method; compare the test items, index thresholds and environmental coverage range of the current evaluation method with the model, and identify the multi-aspect blind areas of the current method;

[0101] S42, based on the identified specific blind area, the economic benefit range of the historical successful case of this category obtained from the successful case library, three optimized new evaluation methods are generated, the first method only repairs the identified core blind area in the existing evaluation framework; the second method introduces the evaluation process which is proved to bring high economic benefit in the successful case on the basis of repairing the blind area; the third method refers to the best practice in the successful case library, and constructs an evaluation system covering more environmental dimensions and technical dimensions;

[0102] S43, the economic benefit results of the three new evaluation methods are calculated and sorted from high to low, and a clear decision output is formed;

[0103] An anti-UAV interference effect evaluation system, which adopts an anti-UAV interference effect evaluation method according to any one of the above, comprises a case library construction module, a scheme evaluation module, a multi-dimensional correlation model construction module, and an evaluation method optimization and economic benefit analysis module.

[0104] The case library construction module is used to collect two types of anti-UAV scheme cases, and after standardization and classification processing, a failure case library containing structured failure causes and a successful case library containing economic benefit range and economic quantitative indicators are established.

[0105] The scheme evaluation module is used to set the evaluation method for evaluating the anti-UAV scheme to be evaluated, determine whether it passes, analyze and iteratively optimize the root cause of the scheme that does not pass, and further optimize the scheme that passes.

[0106] The multi-dimensional correlation model construction module is used to identify the category of the passed evaluation scheme, filter high-similarity historical failure cases of the same type, extract three-dimensional data and preprocess, and construct a multi-dimensional correlation model through statistical analysis.

[0107] The evaluation method optimization and economic benefit analysis module uses the multi-dimensional correlation model to identify the blind area of the current evaluation method, generates three new evaluation methods in combination with the economic benefit range of the successful case, calculates the expected economic benefit and sorts and outputs.

[0108] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating anti-UAV jamming effect, characterized in that, The method comprises the following steps: S1, collecting two types of anti-UAV scheme cases, standardizing and classifying the cases with good evaluation but failed field simulation, and establishing a failure case library; after classification, the economic benefit range of the evaluation method of each category is determined, and a successful case library is established; S2, for the anti-UAV scheme to be evaluated, set the evaluation method to evaluate, and perform root cause analysis and iteration processing on the anti-UAV scheme that fails to pass the evaluation, and further optimize the evaluation method of the scheme that passes the evaluation; S3, identify the anti-UAV scheme category that passes the evaluation, filter out historical failure cases of the same category from the case library, and extract the content, application environment and evaluation method of the case, generate correlation analysis of the three dimensions and build a multi-dimensional correlation model; S4, use the multi-dimensional correlation model to evaluate the current evaluation method to identify multi-aspect blind spots, and generate three evaluation methods combined with the economic benefit range of the historical successful cases of this category, and finally calculate and sort the economic benefits of the evaluation methods.

2. The anti-UAV jamming effect evaluation method according to claim 1, wherein, The collection of two types of anti-UAV scheme cases, the standardization and classification of cases with good evaluation but failed field simulation, and the establishment of a failure case library; after classification, the economic benefit range of the evaluation method of each category is determined, and a successful case library is established, comprising the following steps: S11, collect two types of historical anti-UAV scheme cases, and strictly divide them into two categories according to the evaluation prediction effect and the actual application effect, one category is the case with good evaluation result but actual failure, and the other category is the case with excellent evaluation and actual performance; S12, standardize the case set with good evaluation but failed field simulation, including unified data format, standardized description language and structured failure reason, the failure reason includes environmental misjudgment, cost overrun and tactical misuse; then classify according to the core technology principle adopted by the scheme, the categories include electromagnetic interference suppression, navigation signal deception, radio protocol hijacking, kinetic energy physical destruction and comprehensive multi-level defense, after classification, a failure case library is established for storage; S13, standardize the successful case set with good evaluation and actual performance, after processing, classify according to the core technology principle adopted by the scheme, the categories include electromagnetic interference suppression, navigation signal deception, radio protocol hijacking, kinetic energy physical destruction and comprehensive multi-level defense; statistical analysis of the actual economic benefit required by each category case implementation, and determine the economic benefit range of the evaluation method of each category by statistical method, and establish a successful case library with economic quantitative index.

3. The method of claim 1, wherein, The evaluation method is set for the anti-UAV scheme to be evaluated, and the root cause analysis and iteration processing are performed on the anti-UAV scheme that fails to pass the evaluation, and the evaluation method of the scheme that passes the evaluation is further optimized, comprising the following steps: S21, set the evaluation method for the anti-UAV scheme to be evaluated; S22, for the scheme that fails to pass, perform root cause analysis and iteration optimization processing; further optimize the evaluation method of the scheme that passes the evaluation.

4. The anti-UAV jamming effect evaluation method according to claim 3, characterized in that, The evaluation method for evaluating the anti-UAV scheme to be evaluated comprises the following steps: S211, a test field simulating a real electromagnetic environment is established in a laboratory, and core devices such as a spectrum analyzer, a signal generator, a UAV target group and a to-be-tested interference device are configured, target UAV commonly used navigation and communication signals are simulated and generated by the signal generator; S212, a core single-index quantitative test is started, and key performance data is collected and recorded in sequence; the effective radiation power and emission spectrum width of the interference device in the target frequency band are accurately measured by using the spectrum analyzer; the effective action distance, the space coverage range and the accuracy deception success rate of the interference device on various types of navigation signals under a specific interference power are determined through systematic distance pulling tests; S213, protocol layer countermeasure performance evaluation is further performed, the signal interception time of the data transmission and image transmission link emitted by the UAV target group is recorded, and the whole process processing delay required from signal interception to successful cracking of the communication protocol and then taking over the control is examined, so as to evaluate the intelligent countermeasure capability in the information layer; S214, complex electromagnetic background noise and multiple batches of UAV target groups of different types are introduced, synchronous interference instructions are applied, the interference resource allocation capability, the system response delay and the overall working stability of the system in a multi-target and strong countermeasure scene are comprehensively examined, so as to complete the comprehensive evaluation of the core tactical indexes; S215, all the data collected in the quantitative test are compared with the technical qualified threshold preset in the evaluation scheme, if all the core indexes simultaneously reach or exceed the qualified threshold, it is determined that the scheme passes the evaluation; if any one or more core indexes is unqualified, it is determined that the scheme does not pass the evaluation.

5. The anti-UAV jamming effect evaluation method according to claim 1, wherein The anti-UAV scheme category that passes the evaluation is identified, historical failed cases of the same category are screened out from a case library, the content, application environment and evaluation method of the cases are extracted, and correlation analysis of the three dimensions is generated, comprising the following steps: S31, the core technical principle adopted by the anti-UAV scheme that has passed the evaluation is extracted, the current scheme is classified, all historical failed cases belonging to the same category are preliminarily screened out from the entire failed case library, and a sub-data set to be deeply analyzed is formed; S32, in the category sub-data set, a similarity matching algorithm is set according to the key technical parameters and application scene characteristics of the current scheme, including the interference frequency band, the action distance, the target UAV type and the environment, historical failed cases with high similarity in technical details and application environment to the current scheme are screened out, and the analysis range is narrowed; S33, structured data of three dimensions, including the detailed content of the case, the specific application environment of the case and the evaluation method used by the case in history, are extracted from the screened high-similarity failed cases, and correlation analysis is performed on the data of the three dimensions of the extracted content, application environment and historical evaluation method.

6. The anti-UAV jamming effect evaluation method according to claim 5, wherein, The correlation analysis of the data of the three dimensions of the extracted content, application environment and historical evaluation method comprises the following steps: S331, the data is preprocessed, and the extracted unstructured data is converted into quantifiable indexes for analysis. S332, adopt statistical analysis method to explore the correlation rule between dimensions, calculate the correlation coefficient between different dimensional variables, and use hypothesis testing to judge whether there is significant statistical dependence between the evaluation method used in history and the failure case; finally, integrate all analysis results to build a multi-dimensional correlation model.

7. The anti-UAV jamming effect evaluation method according to claim 6, wherein, The method comprises the following steps: S3321, calculate the Pearson product-moment correlation coefficient between different dimensional numerical variables to obtain the quantitative correlation strength and direction of different dimensions; S3322, use a contingency table and chi-square test to analyze whether there is significant statistical dependence between the historical evaluation method and the failure case: first, construct a two-dimensional contingency table, and then calculate the chi-square statistic, the specific formula is: Among them, O ij E represents the observation frequency. ij It is the expected frequency, x 2 This is the chi-square statistic. The chi-square statistic x will be calculated. 2 The p-value obtained by comparing with the chi-square distribution, if the p-value is less than 0.05, rejects the null hypothesis, confirming that this deficiency of the evaluation method is significantly dependent on a specific failure mode, i.e., there is an evaluation blind spot; S3323, integrate all correlation coefficients and hypothesis test significance results to build a multi-dimensional correlation model, the model formula is: η=Bη+γξ+ζ; Where, η is an endogenous latent variable vector, representing the results affected by other variables in the model, B is a path coefficient matrix, quantifying the mutual relationship between different endogenous latent variables η, γ is a path coefficient matrix, quantifying the direct influence effect of exogenous latent variables ξ on endogenous latent variables η, ξ is an exogenous latent variable vector, representing the root causes that are not affected by other variables in the model, ζ is a residual error vector, representing the random error part that the model cannot explain.

8. The anti-UAV jamming effect evaluation method according to claim 1, wherein The method comprises the following steps: S41, use the multi-dimensional correlation model to evaluate the current evaluation method; compare the test items, index thresholds and environmental coverage range of the current evaluation method with the model to identify the multi-aspect blind spots of the current method; S42, based on the specific blind spots identified, generate three new evaluation methods optimized from the economic benefit range of the historical successful cases in the same category, the first method only repairs the identified core blind spots within the existing evaluation framework; the second method introduces the evaluation process that has been proven to bring high economic benefits in successful cases based on repairing the blind spots; the third method refers to the best practices in the successful case library to build an evaluation system covering more environmental dimensions and technical dimensions; S43, calculate the economic benefit results of the three new evaluation methods from high to low, and form a clear decision output.

9. An anti-UAV jamming effect evaluation system, characterized in that, The system adopts an anti-UAV jamming effect evaluation method according to any one of claims 1-8, comprising a case library construction module, a scheme evaluation module, a multi-dimensional correlation model construction module, and an evaluation method optimization and economic benefit analysis module. The case library construction module is used for collecting two types of anti-UAV scheme cases, and establishing a failure case library containing structured failure causes and a success case library containing economic benefit ranges and economic quantitative indexes after standardization and classification processing; The scheme evaluation module is used for setting an evaluation method to evaluate the anti-UAV scheme to be evaluated, determining whether the anti-UAV scheme passes, performing root cause analysis and iterative optimization on the anti-UAV scheme that does not pass, and transmitting the anti-UAV scheme that passes and further optimizing the anti-UAV scheme; The multi-dimensional correlation model construction module is used for identifying the scheme category that passes the evaluation, screening the same type of historical failure cases with high similarity, extracting three-dimensional data and preprocessing, and constructing a multi-dimensional correlation model through statistical analysis; The evaluation method optimization and economic benefit analysis module is used for identifying the blind area of the current evaluation method by using the multi-dimensional correlation model, generating three new evaluation methods in combination with the economic benefit range of the success cases, calculating the expected economic benefits of the three new evaluation methods and outputting the three new evaluation methods in sequence.

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