An aircraft navigation evaluation method and system based on a combination weight evaluation method

Abstract

The application provides an aircraft navigation evaluation method and system based on a combined weight evaluation method, and belongs to the technical field of navigation evaluation. The method comprises the following steps: obtaining original flight parameter data of an aircraft and performing preprocessing, and reconstructing a standard flight path by using a filtering algorithm; calculating the value of a navigation system index by using environmental data, navigation equipment state data, the preprocessed original flight parameter data and the reconstructed standard flight path, generating an index value sequence changing with time; judging a matrix and calculating a subjective weight vector of the index; calculating an objective weight vector of the index; linearly weighting and fusing the subjective weight vector and the objective weight vector according to a dynamic combination factor to generate a final combined weight vector; calculating the arithmetic mean value of the index sequence, and then weighting and synthesizing the combined weight vector to calculate the comprehensive performance score of the navigation system; and generating an evaluation report. The application uses the combined weight evaluation method to comprehensively evaluate the navigation performance, and improves flight safety and task success rate.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of navigation evaluation, and particularly relates to an aircraft navigation evaluation method and system based on a combined weight evaluation method. BACKGROUND

[0002] The aircraft navigation system is a core system of the aircraft, and the performance of the aircraft navigation system is directly related to flight safety, task efficiency and economic benefits. Therefore, reasonable and accurate performance evaluation of the navigation system is an important basis for ensuring safe flight of the aircraft. The traditional navigation evaluation method depends on fixed weight or single weight assignment method, such as pure subjective AHP method or pure customer entropy weight method, which has obvious limitations in actual application.

[0003] Firstly, there is subjective bias. The AHP method depending on expert experience is easily affected by individual cognition in weight distribution, and it is difficult to ensure the objectivity and fairness of the evaluation results. Secondly, there is data dependency and short-sightedness. The entropy weight method depending on pure objective data volatility to determine the weight ignores the inherent importance of the index, which may lead to disconnection between the weight and the actual demand. Thirdly, the adaptability is poor. The fixed weight cannot adapt to the differentiated demand of index weight in different aircraft models and different task stages.

[0004] Therefore, there is an urgent need for a weight distribution method that can integrate subjective and objective information and dynamically adapt to different evaluation scenarios to realize more reasonable and more practical environment-compliant comprehensive evaluation of the navigation system performance. SUMMARY

[0005] In a first aspect, the application provides an aircraft navigation evaluation method based on a combined weight evaluation method, comprising the following steps:

[0006] S1. Obtain the original flight parameter data, environmental data and navigation equipment state data of the aircraft, perform preprocessing of the original flight parameter data by detecting and cleaning the abnormal values, and reconstruct the standard flight path by using a filtering algorithm;

[0007] S2. Calculate the values of each single index of the navigation system according to the environmental data, the navigation equipment state data, the preprocessed original flight parameter data and the reconstructed standard flight path, and generate an index value sequence changing with time for each single index; the single index includes positioning accuracy, stability, reliability, real-time performance, anti-interference performance and availability;

[0008] S3. Construct a judgment matrix and calculate the subjective weight vector of each single index according to expert experience knowledge by using the analytic hierarchy process ;

[0009] S4. Calculate the objective weight vector of each single index by using the entropy weight method based on the index value sequence of each single index in the current evaluation period ;

[0010] S5. Set the dynamic combination factor , the subjective weight vector is linearly weighted and fused with the objective weight vector to generate the final combination weight vector :

[0011] ;

[0012] S6. Calculate the arithmetic mean of each single indicator sequence, and then perform weighted synthesis through the combination weight vector to calculate the comprehensive performance score of the navigation system :

[0013]

[0014] wherein, is the vector composed of the arithmetic mean of each single indicator sequence, is the arithmetic mean of the i-th single indicator sequence, and n is the number of single indicators;

[0015] S7. Output the comprehensive performance score, each single indicator score, and the combination weight distribution scheme to generate an evaluation report.

[0016] Further, the positioning accuracy single indicator in step S2 is calculated through the following steps:

[0017] S211. Calculate the difference between the pre-processed original flight parameter data and the reconstructed standard track in the latitude and longitude of each sampling point i , ), and calculate the horizontal distance deviation according to the following formula:

[0018]

[0019]

[0020]

[0021] wherein, is the measured longitude of sampling point i in the original flight parameter data, is the planned longitude of sampling point i in the standard track, is the measured latitude of sampling point i in the original flight parameter data, is the planned latitude of sampling point i in the standard track;

[0022] S212. Take the arithmetic mean of the horizontal distance deviation of all sampling points i as the positioning accuracy of the navigation system​ :

[0023]

[0024] wherein n is the number of sampling points;

[0025] S213. mapping the maximum value and the minimum value of all positioning accuracy values in the evaluation period to a percentage value based on the linear scoring model :

[0026]

[0027] wherein is the average value of the positioning accuracy ΔP in the evaluation period, is the score of the preset maximum value of the positioning accuracy is the score of the preset minimum value of the positioning accuracy

[0028] Further, the stability index in step S2 is calculated by the following steps:

[0029] S221. forming a deviation sequence from the horizontal distance deviations of each sampling point i in time sequence and performing zero value processing on the deviation sequence to obtain a zero value sequence ;

[0030] S222. calculating the zero mean sequence using the following autocorrelation function based stability evaluation algorithm :

[0031]

[0032] wherein N is the length of the zero value sequence, is the mean value of the zero value sequence;

[0033] S223. calculating the sum of absolute values of the autocorrelation coefficient in the preset maximum lag order m range :

[0034] ;

[0035] S224. setting the stability value in inverse proportion to the sum of absolute values , and then passing the sum of absolute values through a preset monotonically decreasing function ​​​​​​Mapping to a percentage value.

[0036] Further, the reliability index in step S2 is calculated by the following steps:

[0037] S231. Based on the navigation device status data, extract the continuous working time records of all relevant navigation devices in the evaluation period, each record containing a time value and an event indication value ;

[0038] wherein, indicates that at time point the device has failed, indicates that at time point the device has stopped working due to non-failure reasons and is censored;

[0039] S232. Arrange all time values in ascending order to form an ordered sequence , record the corresponding event indication sequence , and set the initial survival probability of the navigation device ;

[0040] S233. Traverse each time point in the ordered time sequence , calculate the survival probability estimate value at this time point :

[0041] Calculate the number of devices still in working state before time point

[0042] Calculate the survival probability of the navigation device at time point according to the event indication value

[0043] If , it is determined that a failure has occurred, the conditional survival probability at time point is calculated , and the survival probability of the navigation device at time point is updated ;

[0044] wherein, is the number of devices that have failed at time point ;

[0045] If , it is determined that a censoring has occurred, and the survival probability at time point is not updated, i.e. ;

[0046] ​​S234. The survival probability of the navigation device is calculated as the product of the survival probabilities of the components . The survival probability of the navigation device is calculated as the product of the survival probabilities of the components .

[0047] S235. The reliability function at the end point of the evaluation period T is taken as the reliability measure .

[0048] .

[0049] Further, the real-time indicator in step S2 is calculated by the following steps:

[0050] S241. Based on the timestamps in the raw flight parameter data, the time delay sequence of the navigation system from data acquisition to output result is calculated .

[0051] S242. The time delay sequence is distributed fitted by a real-time evaluation algorithm based on response time analysis to obtain the probability distribution function F(x);

[0052] S243. According to the real-time requirement of the navigation system, the maximum allowed delay threshold is determined;

[0053] S244. According to the probability distribution function F(x), the probability that the delay exceeds the maximum allowed delay threshold , i.e. the real-time risk probability is calculated:

[0054]

[0055] S245. The real-time risk probability is mapped to a percentage real-time measure by a monotonically decreasing function:

[0056] .

[0057] Further, the anti-interference in step S2 is calculated by the following steps:

[0058] S251. The interference period and the non-interference period in the environmental data are identified;

[0059] S252. The mean value of the horizontal distance deviation of the interference period is calculated ​The mean of the horizontal distance deviation of the non-interference period ; ;

[0060]

[0061]

[0062] wherein, represents the number of sampling points in the non-interference period, represents the number of sampling points in the interference period, the horizontal distance deviation of the sampling point i;

[0063] S253. Calculate the navigation performance benchmark of the non-interference period The navigation performance benchmark value of the non-interference period ;

[0064]

[0065]

[0066] S254. Calculate the performance degradation ratio of the navigation system :

[0067] ;

[0068] S255. Map the performance degradation ratio to the anti-interference value by using the anti-interference score model combined with the S-shaped function :

[0069]

[0070] wherein, k is the slope coefficient, is the pre-set degradation ratio benchmark;

[0071] The availability in step S2 is calculated by the following steps:

[0072] S261. Based on the navigation device state data, count the total downtime of the navigation system in the total evaluation time ;

[0073] S262. Calculate the inherent availability of the navigation system based on the total evaluation time and the total downtime ;

[0074] ​​​

[0075] S263. Use a linear scoring model to assess inherent availability. Mapped to percentage values :

[0076] .

[0077] Furthermore, the specific steps of step S3 are as follows:

[0078] S31. Obtain the expert's evaluation values ​​for each individual indicator based on the 1-9 scale, and construct a judgment matrix for each expert. ;

[0079] in, This indicates the importance of the i-th individual indicator relative to the j-th individual indicator, where n is the number of individual indicators.

[0080] S32. Calculate the subjective weight vector of each expert's judgment matrix using the eigenvalue method:

[0081] Calculate the judgment matrix Maximum eigenvalue And the corresponding eigenvector, denoted as the largest eigenvector;

[0082] The maximum eigenvector is normalized to obtain the subjective weight vector. ;

[0083] S33. Judgment matrix for each expert Perform a consistency check:

[0084] Calculate the consistency index (CI):

[0085] Where n is the judgment matrix The order of;

[0086] The average random consistency index RI value is determined based on the order n of the matrix.

[0087] Calculate the consistency ratio (CR):

[0088] S34. Determine whether the consistency ratio CR is less than the set ratio threshold. ;

[0089] If so, after verification, accept and output the subjective weight vector. ;

[0090] If not, readjust the judgment matrix. Return to step S31.

[0091] Furthermore, the specific steps of step S4 are as follows:

[0092] S41. Normalize each single index sequence to obtain a standardized matrix ;

[0093] S42. Calculate the information entropy value of the jth single index:

[0094]

[0095] wherein, is the jth value in the jth normalized index sequence, and n is the length of the index sequence.

[0096] S43. Calculate the difference coefficient of the jth single index :

[0097] ;

[0098] Calculate the objective weight of the jth single index based on the difference coefficient of the jth single index .

[0099] Further, the value of the dynamic combination factor in step S5 is determined according to the application scenario of the navigation evaluation:

[0100] When the application scenario is to compare the inherent performance of different types of navigation systems, the dynamic combination factor is set to be greater than a preset factor threshold .

[0101] When the application scenario is to analyze the performance of the aircraft in the actual operating environment or to analyze the consistency of the pilot operation and the system response, the dynamic combination factor is set to be less than a preset factor threshold .

[0102] .

[0103] In a second aspect, the embodiments of the present application also provide a plane navigation evaluation system based on the combined weight evaluation method, comprising: a navigation data acquisition and processing module, configured to acquire original flight parameter data, environment data and navigation equipment state data of an aircraft, perform preprocessing of abnormal value detection and cleaning on the original flight parameter data, and reconstruct a standard flight path by using a filtering algorithm;

[0104] ​​The single-index quantification calculation module is configured to calculate the value of each single index of the navigation system according to the environmental data, the navigation device state data, the preprocessed original flight parameter data and the reconstructed standard flight path, and to generate a time-varying index value sequence for each single index, wherein the single indexes include positioning accuracy, stability, reliability, real-time performance, anti-interference performance and availability.

[0105] The subjective weight vector calculation module is configured to construct a judgment matrix and calculate the subjective weight vector of each single index according to the experience and knowledge of experts by using the analytic hierarchy process.

[0106] The objective weight vector calculation module is configured to calculate the objective weight vector of each single index by using the entropy weight method based on the index value sequence of each single index in the current evaluation period.

[0107] The combined weight vector generation module is configured to set a dynamic combination factor, linearly weight and fuse the subjective weight vector and the objective weight vector, and generate a final combined weight vector.

[0108]

[0109] The performance score calculation module is configured to calculate the arithmetic mean of each single index sequence, and to calculate the comprehensive performance score of the navigation system by weighting and synthesizing the arithmetic mean and the combined weight vector.

[0110]

[0111] wherein, is a vector composed of the arithmetic mean of each single index sequence, is the arithmetic mean of the i-th single index sequence, and n is the number of single indexes.

[0112] The evaluation report generation module is configured to output the comprehensive performance score, the single index score and the combined weight distribution scheme, and to generate an evaluation report.

[0113] As can be seen from the above technical solutions, the present application has the following advantages:

[0114] ​​​​​​​​​​The aircraft navigation evaluation method based on the combined weight evaluation method provided in the application can accurately calculate single indexes of positioning accuracy, stability, reliability, real-time performance, anti-interference performance and usability of the navigation system by acquiring original flight parameter data, environment data and navigation equipment state data, combining subjective and objective weight evaluation, and generating a comprehensive performance score, so as to provide reasonable and comprehensive navigation performance evaluation for a flight task and improve flight safety and task success rate. BRIEF DESCRIPTION OF DRAWINGS

[0115] In order to more clearly illustrate the technical solutions of the present application, the drawings required to be used in the description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0116] Figure 1 The flowchart of the aircraft navigation evaluation method based on the combined weight evaluation method of the present application is shown.

[0117] Figure 2 The schematic diagram of the aircraft navigation evaluation system based on the combined weight evaluation method of the present application is shown. DETAILED DESCRIPTION

[0118] In the following detailed description of the specific steps of the aircraft navigation evaluation method based on the combined weight evaluation method, various embodiments of the present disclosure will be described more fully. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives falling within the spirit and scope of various embodiments of the present disclosure.

[0119] The present embodiment provides an aircraft navigation evaluation method based on a combined weight evaluation method, which uses the combined weight evaluation method to accurately quantify various indexes of the navigation system, and provides real-time feedback of the evaluation results to improve flight safety and task success rate.

[0120] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0121] Please refer to Figure 1 The flowchart of the aircraft navigation evaluation method based on the combined weight evaluation method in a specific embodiment is shown, and the method comprises the following steps:

[0122] S1. Obtain the original flight parameter data, environmental data and navigation equipment state data of the aircraft, perform abnormal value detection and cleaning preprocessing on the original flight parameter data, and reconstruct a standard flight path by using a filtering algorithm;

[0123] It should be noted that by obtaining and preprocessing the original flight parameter data and reconstructing the standard flight path, data basis is provided for evaluation, and the reliability of the evaluation result is ensured;

[0124] S2. Calculate the values of each single indicator of the navigation system according to the environmental data, the navigation equipment state data, the preprocessed original flight parameter data and the reconstructed standard flight path, and generate an indicator value sequence changing with time for each single indicator; the single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference performance and availability;

[0125] It should be noted that by calculating the values of each single indicator of the navigation system, a sequence of indicator values changing with time is generated, which provides comprehensive and dynamic data support for navigation evaluation;

[0126] S3. Construct a judgment matrix and calculate a subjective weight vector of each single indicator by using the analytic hierarchy process according to expert experience and knowledge ;

[0127] It should be noted that by constructing a judgment matrix and calculating a subjective weight vector by using the analytic hierarchy process, expert experience and knowledge are introduced, so that the evaluation indicator weight is reasonable;

[0128] S4. Calculate an objective weight vector of each single indicator by using the entropy weight method based on the indicator value sequence of each single indicator in the current evaluation period ;

[0129] It should be noted that the objective weight vector is calculated based on the entropy weight method, and the information entropy of the data itself is used to ensure the objectivity and accuracy of the weight distribution;

[0130] S5. Set a dynamic combination factor , linearly weight and fuse the subjective weight vector and the objective weight vector to generate a final combination weight vector :

[0131] ;

[0132] It should be noted that by setting a dynamic combination factor, fusing the subjective and objective weights, and generating a combination weight vector, the evaluation result has comprehensiveness and adaptability;

[0133] S6. Calculate the arithmetic mean of each single indicator sequence, and then pass the combination weight vector The weighted synthesis is performed to calculate the comprehensive performance score of the navigation system :

[0134]

[0135] wherein, is a vector composed of arithmetic mean values of each single index sequence, is the arithmetic mean value of the i-th single index sequence, and n is the number of single indexes;

[0136] It should be noted that the comprehensive performance score is obtained by calculating the arithmetic mean values of each single index and performing weighted synthesis through combination of the weight vector, thereby providing a quantitative basis for the evaluation result;

[0137] S7. Outputting the comprehensive performance score , the single index scores, and the combination weight distribution scheme to generate an evaluation report;

[0138] It should be noted that the evaluation report is generated by outputting the comprehensive performance score, the single index scores, and the combination weight distribution scheme, thereby providing comprehensive and intuitive data support for flight decision.

[0139] The embodiment accurately evaluates the performance of the aircraft navigation system by comprehensively considering subjective and objective factors, thereby providing a reasonable basis for flight safety and task optimization; the weight is dynamically adjusted by combining the analytic hierarchy process and the entropy weight method, thereby ensuring that the evaluation result is comprehensive and accurate and meeting the needs of different application scenarios.

[0140] Further, as a refinement and expansion of the specific implementation manner of the above embodiment, in order to completely describe the specific implementation process in the embodiment, another aircraft navigation evaluation method based on the combination weight evaluation method is provided, and the method comprises the following steps:

[0141] S1. Obtaining original flight parameter data, environmental data, and navigation equipment state data of an aircraft, performing abnormal value detection and cleaning preprocessing on the original flight parameter data, and reconstructing a standard track by using a filtering algorithm;

[0142] S2. Calculating the values of each single index of the navigation system according to the environmental data, the navigation equipment state data, the preprocessed original flight parameter data, and the reconstructed standard track, and generating an index value sequence changing over time for each single index; the single indexes include positioning accuracy, stability, reliability, real-time performance, anti-interference performance, and availability;

[0143] The positioning accuracy single index in step S2 is calculated by the following steps:

[0144] S211. Calculating the difference between the longitude and latitude of the preprocessed original flight parameter data and the reconstructed standard track at each sampling point i (Δx i, Δy i ) , ), and the horizontal distance deviation is calculated as follows :

[0145]

[0146]

[0147]

[0148] wherein, is the measured longitude of the sampling point i in the original flight parameter data, is the planned longitude of the sampling point i in the standard flight path, is the measured latitude of the sampling point i in the original flight parameter data, is the planned latitude of the sampling point i in the standard flight path;

[0149] S212. The arithmetic mean of the horizontal distance deviations of all sampling points i is taken as the positioning accuracy :

[0150]

[0151] wherein n is the number of sampling points;

[0152] S213. The maximum value and the minimum value of all positioning accuracies in the evaluation period are mapped to a percentage value based on a linear scoring model :

[0153]

[0154] wherein, is the average value of the positioning accuracies ΔP in the evaluation period, is the score of the preset maximum value of the positioning accuracy , and is the score of the preset minimum value of the positioning accuracy ;

[0155] Exemplarily, = 100, = 0;

[0156] The stability index in step S2 is calculated by the following steps:

[0157] S221. The horizontal distance deviations of the sampling points i are arranged in time sequence to form a deviation sequence , and the deviation sequence Zero-value processing is performed to obtain a zero-value sequence ;

[0158] S222. The zero-mean sequence is calculated using the following autocorrelation function stability evaluation algorithm Autocorrelation coefficients at different lag orders k :

[0159]

[0160] where N is the length of the zero-value sequence, and the mean of the zero-value sequence is zero (0);

[0161] S223. The autocorrelation coefficient is calculated The sum of absolute values within a preset maximum lag order m (e.g., m = 20) :

[0162] ;

[0163] S224. The stability value is set inversely proportional to the sum of absolute values, and the sum of absolute values is mapped to a percentage value through a preset monotonically decreasing function.

[0164] For example, the monotonically decreasing function f(S) can be: ;

[0165] wherein is a scaling coefficient greater than 0;

[0166] The reliability index in step S2 is calculated by the following steps:

[0167] S231. Based on the navigation device state data, the continuous working time records of all relevant navigation devices within the evaluation period are extracted, and each record contains a time value and an event indication value ;

[0168] wherein indicates that the device failed at time point , and indicates that the device stopped working due to non-failure reasons at time point ;

[0169] S232. All time values are arranged in ascending order to form an ordered sequence , and the corresponding event indication sequence is recorded, and the initial survival probability of the navigation device is set ;​​

[0170] S233. Traversing an ordered time series Each point in time Calculate this time point Survival probability estimate :

[0171] Calculation at time point Number of devices that were still in operation

[0172] According to the event indicator value Calculate the navigation device at a given time point The probability of survival;

[0173] like Determine if a fault has occurred and calculate the time point. Conditional survival probability And update the navigation device at the specified time. Survival probability ;

[0174] in, For at a certain point in time The number of devices that malfunctioned;

[0175] like If deletion is determined, then the time point... The survival probability is not updated, that is... ;

[0176] S234. Will and Survival probability Connect them to form a reliability function within the evaluation period. The probability that the navigation device has not yet failed at time t is characterized.

[0177] S235. Select the reliability function The value at the end of the evaluation period, T. As a reliability metric :

[0178] ;

[0179] The real-time performance metrics in step S2 are calculated through the following steps:

[0180] S241. Based on the timestamps in the raw flight parameter data, calculate the time delay sequence from data acquisition to output of the navigation system. ;

[0181] S242. A real-time evaluation algorithm based on response time analysis is used to evaluate the time delay sequence. a probability distribution function F(x) is obtained by performing a distribution fitting;

[0182] S243. determining a maximum allowed delay threshold value depending on the real-time requirements of the navigation system ;

[0183] S244. calculating a delay exceeding the maximum allowed delay threshold value , i.e. a real-time risk probability :

[0184]

[0185] S245. mapping the real-time risk probability to a percentage real-time value by means of a monotonically decreasing function :

[0186] ;

[0187] The anti-jamming property in step S2 is calculated by the following steps:

[0188] S251. identifying jamming periods in the environment data and non-jamming periods ;

[0189] S252. calculating the mean of the horizontal distance deviations for the jamming periods and the mean of the horizontal distance deviations for the non-jamming periods ;

[0190]

[0191]

[0192] wherein denotes the number of sampling points in the non-jamming periods, denotes the number of sampling points in the jamming periods, denotes the horizontal distance deviation of sampling point i;

[0193] S253. calculating the navigation performance reference value for the non-jamming periods and the navigation performance reference value for the non-jamming periods ;

[0194]

[0195]

[0196] S254. calculating the performance degradation ratio of the navigation system :

[0197] ;

[0198] S255.Using anti-interference score model combined with S-shaped function to map performance decline ratio to anti-interference value :

[0199]

[0200] wherein k is a slope coefficient, is a pre-set decline ratio benchmark;

[0201] The availability in step S2 is calculated by the following steps:

[0202] S261.Based on the navigation device state data, the total downtime of the navigation system within the total evaluation time is counted ; ;

[0203] It should be noted that the total downtime is the sum of the time when the navigation system cannot work due to failure, maintenance or calibration;

[0204] S262.Based on the total evaluation time and the total downtime , the inherent availability of the navigation system is calculated ;

[0205]

[0206] S263.Using a linear score model to map the inherent availability to a percentage value :

[0207] ;

[0208] S3.Using the analytic hierarchy process to construct a judgment matrix and calculate the subjective weight vector of each single index according to expert experience and knowledge ;

[0209] The specific steps of step S3 are as follows:

[0210] S31.Get the evaluation values of each single index obtained by experts based on 1-9 scale method for pairwise comparison, and construct a judgment matrix for each expert ;

[0211] wherein represents the importance degree of the ith single index relative to the jth single index, and n is the number of single indexes.​

[0212] S32. Calculate the subjective weight vector of the judgment matrix of each expert by eigenvalue method:

[0213] Calculate the maximum eigenvalue of the judgment matrix and the corresponding eigenvector, denoted as the maximum eigenvector;

[0214] Normalize the maximum eigenvector to obtain the subjective weight vector ;

[0215] S33. Perform consistency check on the judgment matrix of each expert :

[0216] Calculate the consistency index CI:

[0217] Wherein, n is the order of the judgment matrix ;

[0218] Determine the average random consistency index RI value according to the order n of the matrix;

[0219] It should be noted that the RI value is a preset constant related to the order n of the matrix, for example: when n=3, RI=0.58; when n=4, RI=0.90; when n=5, RI=1.12; when n=6, RI=1.24;

[0220] Calculate the consistency ratio CR:

[0221] S34. Determine whether the consistency ratio CR is less than the set ratio threshold (e.g. 0.1);

[0222] If yes, pass the test, accept and output the subjective weight vector ;

[0223] If not, adjust the judgment matrix again, and return to step S31;

[0224] S4. Calculate the objective weight vector of each single index based on the index value sequence of each single index in the current evaluation period by entropy weight method ;

[0225] The specific steps of step S4 are as follows:

[0226] S41. Normalize each single index sequence to obtain the standardized matrix ;

[0227] S42. Calculate the information entropy value of the jth single index:​

[0228]

[0229] wherein, is the jth value in the normalized jth index sequence, n is the length of the index sequence (i.e. the number of time points);

[0230] S43. Calculate the difference coefficient of the jth single index :

[0231] ;

[0232] Based on the difference coefficient of the jth single index , calculate the objective weight of the jth single index ;

[0233] Then generate an objective weight vector according to the customer weight of each single index ;

[0234] S5. Set a dynamic combination factor , linearly weight and fuse the subjective weight vector and the objective weight vector to generate a final combination weight vector :

[0235] ;

[0236] The value of the dynamic combination factor in step S5 is determined according to the application scenario of the navigation evaluation:

[0237] When the application scenario is to compare the inherent performance of different types of navigation systems, the dynamic combination factor is set to be greater than a preset factor threshold ;

[0238] When the application scenario is to analyze the performance of the aircraft in the actual running environment or to analyze the consistency of the pilot operation and the system response, the dynamic combination factor is set to be less than a preset factor threshold ;

[0239] For example, the factor threshold may be set to 0.5. When greater than 0.5, the expert experience and theoretical design index are emphasized, and when less than 0.5, the measured data of this flight is emphasized.

[0240] S6. Calculate the arithmetic mean of each single index sequence, and then weight and synthesize through the combination weight vector to calculate the comprehensive performance score of the navigation system :

[0241]

[0242] wherein, is a vector composed of arithmetic mean values of each single indicator sequence, is the arithmetic mean value of the i-th single indicator sequence, and n is the number of single indicators;

[0243] S7. Outputting the comprehensive performance score, each single indicator score and the combination weight distribution scheme, and generating an evaluation report.

[0244] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0245] As shown in Figure 2 , the following is an embodiment of an aircraft navigation evaluation system based on the combination weight evaluation method provided by the embodiment of the present application. The system and the aircraft navigation evaluation method based on the combination weight evaluation method of each embodiment described above belong to the same inventive concept. Details not described in detail in the embodiment of the aircraft navigation evaluation system based on the combination weight evaluation method can be referred to the embodiment of the aircraft navigation evaluation method based on the combination weight evaluation method.

[0246] The system comprises:

[0247] A navigation data acquisition and processing module is configured to acquire original flight parameter data, environmental data and navigation equipment state data of an aircraft, perform preprocessing of outlier detection and cleaning on the original flight parameter data, and reconstruct a standard flight path by using a filtering algorithm;

[0248] A single indicator quantification calculation module is configured to calculate the values of each single indicator of the navigation system by using the environmental data, the navigation equipment state data, the preprocessed original flight parameter data and the reconstructed standard flight path, and generate a sequence of indicator values changing over time for each single indicator. The single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference performance and availability.

[0249] A subjective weight vector calculation module is configured to construct a judgment matrix and calculate a subjective weight vector of each single indicator according to expert experience and knowledge by using an analytic hierarchy process. ;

[0250] An objective weight vector calculation module is configured to calculate an objective weight vector of each single indicator by using an entropy weight method based on the sequence of indicator values of each single indicator in the current evaluation period. ;

[0251] ​a combination weight vector generation module configured to set a dynamic combination factor a subjective weight vector is linearly weighted and fused with an objective weight vector to generate a final combination weight vector :

[0252] ;

[0253] a performance score calculation module configured to calculate an arithmetic mean of each single-item index sequence, and then perform weighted synthesis on the arithmetic mean through the combination weight vector to calculate a comprehensive performance score of the navigation system :

[0254]

[0255] wherein, is a vector composed of the arithmetic means of the single-item index sequences, is an arithmetic mean of the ith single-item index sequence, and n is the number of single-item indexes;

[0256] an evaluation report generation module configured to output the comprehensive performance score, the single-item index scores, and the combination weight distribution scheme to generate an evaluation report.

[0257] The embodiments disclosed herein realize accurate evaluation of the performance of an aircraft navigation system through the interactive cooperation of the navigation data acquisition and processing module, the single-item index quantification calculation module, the subjective weight vector calculation module, the objective weight vector calculation module, the combination weight vector generation module, the performance score calculation module, and the evaluation report generation module, and provide a basis for flight safety and task optimization.

[0258] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.​​

Claims

1. An aircraft navigation evaluation method based on a combination weight evaluation method, characterized in that, Comprising the following steps: S1. Obtain the original flight parameter data, environmental data and navigation equipment state data of the aircraft, pre-process the original flight parameter data by detecting and cleaning outliers, and reconstruct the standard flight path by using a filtering algorithm; S2. Calculate the values of each single indicator of the navigation system according to the environmental data, the navigation equipment state data, the pre-processed original flight parameter data and the reconstructed standard flight path, and generate a sequence of indicator values changing over time for each single indicator; the single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference performance and availability; The reliability indicator in step S2 is calculated by the following steps: S231. Based on the navigation device status data, extract a record of the continuous operating time of all relevant navigation devices during the evaluation period, each record containing a time value and an event indication value ; wherein, denotes the time point the device has failed, denotes the time point the device is censored due to non-failure reasons. S232. Arranging all time values in ascending order to form an ordered sequence S232. Arranging all time values in ascending order to form an ordered sequence S232. Arranging all time values in ascending order to form an ordered sequence S232. Arranging all time values in ascending order to form an ordered sequence S232. Arranging all time values in ascending order to form an ordered sequence S233. iterate through the ordered time series for each time point , compute the survival probability estimate for that time point : computing the number of devices that were still in an active state at a point in time the number of devices that were still in an active state before the point in time According to the event indication value The survival probability of the navigation device at the time point is calculated. If , a malfunction is determined, the survival probability of the navigation device at the time point is calculated , and the survival probability of the navigation device at the time point is updated ; wherein, the number of devices that failed at the point in time the number of devices that failed at the point in time If , it is determined that the deletion occurs, the survival probability of the time point is not updated, i.e. ; S234. The and survival probability are connected to form a reliability function over an evaluation period characterizing the probability that the navigation device has not failed at time t; S235. Taking the reliability function at the end of the evaluation period T , as the reliability measure : ； The anti-interference performance in step S2 is calculated by the following steps: S251. Identifying an interference period in environmental data with no interference periods ; S252. Calculate interference period of the horizontal distance deviation of the horizontal distance deviation of the horizontal distance deviation ; wherein, represents the number of sampling points in the non-interference period, represents the number of sampling points in the interference period, horizontal distance deviation of sampling point i; S253. Calculate the navigation performance reference for the interference-free period with the navigation performance reference value for the interference-free period ; S254. The performance degradation ratio of the navigation system is calculated : ； S255. The performance degradation ratio is mapped to an anti-interference value using an anti-interference score model combined with a sigmoid function S255. The performance degradation ratio is mapped to an anti-interference value using an anti-interference score model combined with a sigmoid function ： wherein k is a slope coefficient, is a preset reference for the drop ratio. The availability in step S2 is calculated by the following steps: S261. Based on the navigation device status data, the total downtime of the navigation system within the total evaluation time is counted ​ ； S262. Based on total evaluation time and total downtime Calculate inherent availability of navigation system ； S263. The intrinsic availability is mapped to a percentage value using a linear scoring model :​ ； S3. Construct a judgment matrix and calculate the subjective weight vector of each single index according to the experience and knowledge of experts by using the analytic hierarchy process ; S4. Based on the sequence of the index value of each single index in the current evaluation period, the objective weight vector of each single index is calculated by using the entropy weight method ; S5. Set dynamic combination factor The subjective weight vector is linearly fused with the objective weight vector to generate the final combination weight vector : ； S6. Calculate the arithmetic mean of each single indicator sequence, and combine the weight vector The weighted synthesis is performed to calculate the comprehensive performance score of the navigation system : wherein is a vector of arithmetic means of individual indicator sequences, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators. S7. Output the comprehensive performance score , the individual indicator scores and the combination weight distribution scheme, to generate an evaluation report.

2. The method for aircraft navigation evaluation based on the combined weight evaluation method according to claim 1, characterized in that, The positioning accuracy single indicator in step S2 is calculated by the following steps: S211. Calculate the difference between the longitude and latitude of the pre-processed raw flight parameter data and the reconstructed standard track at each sampling point i , ), and calculate the horizontal distance deviation according to the following formula : wherein, is the measured longitude of the sampling point i in the original flight parameter data, is the planned longitude of the sampling point i in the standard flight path, is the measured latitude of the sampling point i in the original flight parameter data, is the planned latitude of the sampling point i in the standard flight path; S212. The arithmetic mean of the horizontal distance deviations of all sampling points i as the positioning accuracy of the navigation system : Wherein, n is the number of sampling points; S213. Map the maximum value of all positioning accuracy values within the assessment period to a percentage value :​​​ wherein is the average value of the positioning accuracy ΔP in the evaluation period, is the score for a preset maximum value of the positioning accuracy is the score for a preset minimum value of the positioning accuracy is the score for a preset maximum value of the positioning accuracy is the score for a preset minimum value of the positioning accuracy 3. The method for aircraft navigation evaluation based on the combination weight evaluation method according to claim 1, characterized in that, The stability indicator in step S2 is calculated by the following steps: S221. The horizontal distance deviation of each sampling point i is calculated as follows: The deviation sequence is constructed in time sequence , and the zero value processing is performed on the deviation sequence to obtain the zero value sequence ; S222. Calculate the zero-meaned series using the following autocorrelation function stability evaluation algorithm Autocorrelation coefficients at different lag orders k : where N is the length of the zero-out sequence, is the mean of the zero-out sequence; S223. Calculate autocorrelation coefficient Sum of absolute values in the range of preset maximum lag order m : ； S224. Set stability magnitude inversely proportional to the sum of absolute values and map the sum of absolute values to a percentage magnitude via a pre-set monotonically decreasing function. and map the sum of absolute values to a percentage magnitude via a pre-set monotonically decreasing function.

4. The method for aircraft navigation evaluation based on combination weight evaluation method according to claim 1, characterized in that, The real-time performance indicator in step S2 is calculated by the following steps: S241. Based on the time stamp in the original flight parameter data, calculate the time delay sequence of the navigation system from data collection to output results ; S242. Adopting real-time evaluation algorithm based on response time analysis to time delay sequence performing distribution fitting to obtain probability distribution function F(x); S243. Determine a maximum allowed delay threshold value depending on the real-time requirements of the navigation system ; S244. Calculate the delay according to the probability distribution function F(x) exceeding the maximum allowed delay threshold , i.e. the real-time risk probability : S245. The real-time risk probability is mapped to a percentage real-time value by a monotonically decreasing function : 。 5. The method for aircraft navigation evaluation based on combination weight evaluation method according to claim 1, characterized in that, Step S3 has the following specific steps: S31. Obtain the evaluation values of each single index based on the 1-9 scale method for pairwise comparison by experts, and construct a judgment matrix for each expert ; wherein, represents the degree of importance of the i-th single item indicator with respect to the j-th single item indicator, and n is the number of single item indicators; S32. Calculate the subjective weight vector of the judgment matrix of each expert by using the eigenvalue method: computing the maximum eigenvalue of the judgment matrix and the corresponding eigenvector, denoted as the maximum eigenvector ​ The maximum eigenvector is normalized to obtain a subjective weight vector ; S33. The judgment matrix of each expert Consistency check: A consistency index CI is calculated: where n is the order of the judgment matrix . Determine the average random consistency index RI value according to the order n of the matrix; The consistency ratio CR is calculated as follows: S34. determining whether the consistency ratio CR is less than a set ratio threshold ; If so, by inspection, accept and output the subjective weight vector ; If not, the judgment matrix is adjusted again and the process returns to step S31.

6. The method for aircraft navigation evaluation based on combination weight evaluation method according to claim 1, characterized in that, Step S4 has the following specific steps: S41. Normalizing each single index sequence to obtain a standardized matrix ; S42. Calculate the information entropy value of the jth single indicator: wherein, is the jth value in the jth normalized index sequence, n is the length of the index sequence; S43. Calculate the coefficient of variation for the jth single-item indicator : ； a difference coefficient based on the difference of the jth single index calculating an objective weight of the jth single index ; The customer weight vector according to each single index is generated again .

7. The method for aircraft navigation evaluation based on combination weight evaluation method according to claim 1, characterized in that, Dynamic combination factor in step S5 The value of the dynamic combination factor is determined according to the application scenario of the navigation evaluation: When the application scenario is to compare the intrinsic performance of different models of navigation systems, the dynamic combination factor is set greater than a preset factor threshold ; When the application scenario is to analyze the performance of the airplane in the actual running environment or to analyze the consistency of the pilot operation and the system response, the dynamic combination factor is set less than a preset factor threshold .

8. An aircraft navigation evaluation system based on the combined weight evaluation method according to any one of claims 1 to 7, characterized in that Comprise: A navigation data acquisition and processing module for obtaining the original flight parameter data, environmental data and navigation equipment state data of the aircraft, pre-processing the original flight parameter data by detecting and cleaning outliers, and reconstructing the standard flight path by using a filtering algorithm; A single indicator quantification and calculation module for calculating the values of each single indicator of the navigation system according to the environmental data, the navigation equipment state data, the pre-processed original flight parameter data and the reconstructed standard flight path, and generating a sequence of indicator values changing over time for each single indicator; the single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference performance and availability; The subjective weight vector calculation module is configured to construct a judgment matrix and calculate the subjective weight vector of each single index according to the experience knowledge of experts by using the analytic hierarchy process ; The objective weight vector calculation module is configured to calculate the objective weight vector of each single index based on the index value sequence of each single index in the current evaluation period by using an entropy weight method ; a combination weight vector generating module, configured to set a dynamic combination factor linearly fusing the subjective weight vector and the objective weight vector to generate a final combination weight vector : ； The performance score calculation module is configured to calculate an arithmetic mean value of each single-item index sequence, and then calculate a comprehensive performance score of the navigation system by combining a weight vector The performance score calculation module is configured to calculate an arithmetic mean value of each single-item index sequence, and then calculate a comprehensive performance score of the navigation system by combining a weight vector : wherein is a vector of arithmetic means of the individual indicator sequences, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators. The evaluation report generation module is used to output the overall performance score. The evaluation report is generated by assigning scores and weights to each individual indicator.

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