Aircraft navigation evaluation method and system based on combined weight evaluation method

Abstract

The invention 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 steps that original flight parameter data of an aircraft are obtained and preprocessed, and a standard flight path is reconstructed through a filtering algorithm; calculating a quantity value of a navigation system index according to the environment data, the navigation equipment state data, the preprocessed original flight parameter data and the reconstructed standard flight path, and generating an index value sequence changing along with time for the index; judging the matrix and calculating subjective weight vectors of the indexes; calculating objective weight vectors of the indexes; performing linear weighted fusion on the subjective weight vector and the objective weight vector according to the dynamic combination factor to generate a final combined weight vector; calculating an arithmetic average value of the index sequence, carrying out weighted synthesis through a combined weight vector, and calculating a comprehensive effectiveness score of the navigation system; and generating an evaluation report. The navigation efficiency is comprehensively evaluated by adopting a combined weight evaluation method, and the flight safety and the task success rate are improved.

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. Firstly, there is subjective bias. The AHP method depending on pure expert experience is easily affected by individual cognition in weight distribution, and it is difficult to ensure that the evaluation result is objective and fair. Secondly, there is data dependence and short-sightedness. The entropy weight method depending on pure objectivity completely determines the weight according to current data volatility, ignores the inherent importance of the index, and may cause the weight to deviate from 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.

[0003] 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 actual environment-compliant comprehensive evaluation of the navigation system performance. SUMMARY

[0004] In a first aspect, the embodiments of the present application provide an aircraft navigation evaluation method based on a combined weight evaluation method, comprising the following steps: S1. Obtain the original flight parameter data, environment 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; S2. Calculate the value of each single index of the navigation system according to the environment 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; 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 method ; S4. Based on the index value sequence 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 a dynamic combination factor , combine the subjective weight vector and the objective weight vector Linearly weighted fusion is performed to generate the final combined weight vector : ; S6. Calculate the arithmetic mean of each single indicator sequence, and then perform weighted synthesis through the combined weight vector to calculate the comprehensive performance score of the navigation system :

[0005] wherein, is a 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; S7. Output the comprehensive performance score , the score of each single indicator, and the combined weight distribution scheme to generate an evaluation report.

[0006] Further, the positioning accuracy single indicator in step S2 is calculated through the following steps: S211. Calculate the difference between the longitude and latitude of the preprocessed original flight parameter data and the reconstructed standard track at each sampling point i (ΔL i, ΔB i) , and calculate the horizontal distance deviation according to the following formula:

[0007]

[0008]

[0009] 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; S212. Calculate the arithmetic mean of the horizontal distance deviation of all sampling points i as the positioning accuracy of the navigation system:

[0010] wherein, n is the number of sampling points; S213. Based on the linear scoring model, the maximum value and the minimum value of all positioning accuracy values in the evaluation period are calculated.​Mapping to percent values :

[0011] in, is the average value of positioning accuracy ΔP during the evaluation period, The preset maximum positioning accuracy The score, The preset minimum positioning accuracy score.

[0012] Furthermore, the stability index in step S2 is calculated by the following steps: S221. The horizontal distance deviation of each sampling point i Construct deviation sequence in chronological order , and the deviation sequence Perform zero-value processing to obtain a zero-value sequence ; S222. Calculate the zero-mean sequence using the following autocorrelation function stability evaluation algorithm: Autocorrelation coefficient at different lag orders k :

[0013] Where N is the length of the zero-valued sequence, is the mean of the zero-valued series; S223. Calculate the autocorrelation coefficient The sum of the absolute values ​​within the preset maximum lag order m : ; S224. Set stability value and the sum of absolute values Inversely proportional, and then the sum of the absolute values ​​is calculated by a preset monotonically decreasing function. Mapped to percent values.

[0014] Furthermore, the reliability index in step S2 is calculated by the following steps: S231. Based on the navigation equipment status data, extract the continuous working time records of all relevant navigation equipment during the evaluation period, each record contains a time value and an event indicator value ; in, Indicates at a point in time The equipment has malfunctioned. Indicates at a point in time The equipment stopped working due to non-fault reasons and was deleted; 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 ; S233. Traverse each time point in the ordered time sequence , calculate the survival probability estimate value of the time point : Calculate the number of devices still in working condition before the time point ; Calculate the survival probability of the navigation device at the time point according to the event indication value ;

[0015] If , it is determined that a failure has occurred, the conditional survival probability at the time point is calculated , and the survival probability of the navigation device at the time point is updated ; wherein is the number of devices that have failed at the time point ; If , it is determined that a deletion has occurred, and the survival probability at the time point is not updated, i.e. ; S234. The survival probabilities of and are connected to form a reliability function within the evaluation period , which represents the probability that the navigation device has not failed at time t; S235. Take the value of the reliability function at the end point T of the evaluation period as the reliability measure : . .

[0016] Further, the real-time 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 result ; S242. Use a real-time evaluation algorithm based on response time analysis to analyze the time delay sequence ​​A distribution fitting is performed to obtain a 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 x according to the probability distribution function F(x) exceeding the maximum allowed delay threshold value , i.e. the real-time risk probability :

[0017] S245. Map the real-time risk probability to a percentage real-time value by means of a monotonically decreasing function : .

[0018] Further, the anti-jamming property in step S2 is calculated by the following steps: S251. Identify jamming periods in the environment data and non-jamming periods ; S252. Calculate the mean value of the horizontal distance deviation for the jamming periods and the mean value of the horizontal distance deviation for the non-jamming periods ;

[0019]

[0020] wherein denotes the number of sampling points within the non-jamming periods, denotes the number of sampling points within the jamming periods, the horizontal distance deviation of sampling point i; S253. Calculate the navigation performance reference value for the non-jamming periods and the navigation performance reference value for the non-jamming periods ;

[0021]

[0022] S254. Calculate the performance degradation ratio of the navigation system: ; S255. Map the performance degradation ratio to an anti-jamming property value by means of an anti-jamming score model combined with a sigmoid function :

[0023] wherein k is a slope coefficient, is a pre-set drop ratio reference; The availability in step S2 is calculated by the following steps: S261. Based on the navigation device status data, statistics the total downtime of the navigation system in the total evaluation time ; S262. Based on the total evaluation time and the total downtime , calculate the inherent availability of the navigation system ;

[0024] S263. Map the inherent availability to a percentage value using a linear scoring model : .

[0025] Further, the specific steps of step S3 are as follows: S31. Obtain the evaluation values of each single indicator based on the 1-9 scale method for pairwise comparison by each expert, and construct a judgment matrix for each expert ; wherein represents the importance of the ith single indicator relative to the jth single indicator, and n is the number of single indicators; S32. Calculate the subjective weight vector of the judgment matrix of each expert using the eigenvalue method: Calculate the maximum eigenvalue and the corresponding eigenvector of the judgment matrix , denoted as the maximum eigenvector; Normalize the maximum eigenvector to obtain the subjective weight vector ; S33. Perform consistency check on the judgment matrix of each expert: Calculate the consistency index CI:

[0026] wherein n is the order of the judgment matrix ; Determine the average random consistency index RI value according to the order n of the matrix; Calculate the consistency ratio CR:

[0027] ​S34. Determine whether the consistency ratio CR is less than a set ratio threshold ; If yes, accept and output the subjective weight vector through inspection ; If no, readjust the judgment matrix , and return to step S31.

[0028] Further, step S4 includes the following specific steps: S41. Normalize each single index sequence to obtain a standardized matrix ; S42. Calculate the information entropy value of the jth single index:

[0029] wherein, is the jth value in the normalized jth index sequence, and n is the length of the index sequence; S43. Calculate the difference coefficient of the jth single index : ; Based on the difference coefficient of the jth single index , calculate the objective weight of the jth single index ; Then generate an objective weight vector according to the objective weights of the single indexes .

[0030] Further, the value of the dynamic combination factor in step S5 is determined according to the application scenario of the navigation evaluation: When the application scenario is to compare the inherent performance of different types of navigation systems, set the dynamic combination factor to be greater than a preset factor threshold . When the application scenario is to analyze the performance of an aircraft in an actual operating environment or to analyze the consistency between pilot operation and system response, set the dynamic combination factor to be less than a preset factor threshold .

[0031] In a second aspect, the embodiments of the present application further provide an aircraft navigation evaluation system based on a combined weight evaluation method, comprising: a navigation data acquisition and processing module, configured to acquire original flight parameter data, environmental 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; A single indicator quantification calculation module is used to calculate the value of each single indicator of the navigation system based on environmental data, navigation equipment status data, pre-processed raw flight parameter data, and the reconstructed standard track, and generate a time-varying indicator value sequence for each single indicator; the single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference and availability; The subjective weight vector calculation module is used to construct a judgment matrix and calculate the subjective weight vector of each individual indicator based on the expert experience knowledge using the hierarchical analysis method ; The objective weight vector calculation module is used to calculate the objective weight vector of each individual indicator based on the indicator value sequence of each individual indicator in the current evaluation period using the entropy weight method. ; Combination weight vector generation module, used to set dynamic combination factors , the subjective weight vector With the objective weight vector Perform linear weighted fusion to generate the final combined weight vector : ; The performance score calculation module is used to calculate the arithmetic mean of each single indicator sequence, and then combine the weight vector Perform weighted synthesis to calculate the overall performance score of the navigation system :

[0032] in, is a vector composed of the arithmetic mean of each individual indicator sequence, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators; Evaluation report generation module, used to output comprehensive performance scores , scores of each individual indicator and combined weight distribution plan, and generate an evaluation report.

[0033] It can be seen from the above technical solutions that this application has the following advantages: In the aircraft navigation evaluation method based on the combined weight evaluation method provided in this application, by obtaining the aircraft's original flight parameter data, environmental data and navigation equipment status data, combining subjective and objective weight evaluations, accurately calculating the individual indicators of the navigation system's positioning accuracy, stability, reliability, real-time performance, anti-interference and availability, and generating a comprehensive performance score, it can provide a reasonable and comprehensive navigation performance evaluation for flight missions, thereby improving flight safety and mission success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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 all other drawings obtained by those skilled in the art without creative efforts based on these drawings also fall within the scope of the present application.

[0035] Figure 1 The flowchart of the aircraft navigation evaluation method based on the combined weight evaluation method of the present application.

[0036] Figure 2 The schematic diagram of the aircraft navigation evaluation system based on the combined weight evaluation method of the present application. DETAILED DESCRIPTION

[0037] 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.

[0038] The present embodiment provides an aircraft navigation evaluation method based on a combined weight evaluation method, which adopts a combined weight evaluation method to accurately quantify various indicators of the navigation system, and provides real-time feedback of the evaluation results to improve flight safety and mission success rate.

[0039] 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. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of the present application.

[0040] 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: 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 the standard flight path by using a filtering algorithm; 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 results is ensured; S2. Calculate the value of each single index of the navigation system according to the environmental data, the navigation device state data, the pre-processed original flight parameter data and the reconstructed standard track, and generate the 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; It should be noted that by calculating the value of each single index of the navigation system, the index value sequence changing with time is generated, which provides comprehensive and dynamic data support for navigation evaluation; S3. Construct a judgment matrix and calculate the subjective weight vector of each single index by using the analytic hierarchy process according to expert experience knowledge ; It should be noted that by constructing a judgment matrix and calculating a subjective weight vector by using the analytic hierarchy process, expert experience knowledge is introduced, so that the evaluation index weight is reasonable; 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 using the entropy weight method ; 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; S5. Set a dynamic combination factor , linearly weight and fuse the subjective weight vector and the objective weight vector to generate the final combination weight vector : ; 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; S6. Calculate the arithmetic mean of each single index sequence, and then weight and synthesize by using the combination weight vector to calculate the comprehensive performance score of the navigation system :

[0041] 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; It should be noted that by calculating the arithmetic mean of each single index and weight synthesizing by using the combination weight vector, the comprehensive performance score is obtained, which provides a quantitative basis for the evaluation result; S7. Output the comprehensive performance score , the score of each single index and the combination weight distribution scheme, and generate an evaluation report; It should be noted that the evaluation report is generated by outputting the comprehensive performance score, each single indicator score and the combined weight distribution scheme, thereby providing comprehensive and intuitive data support for flight decision.

[0042] 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 weights are dynamically adjusted by combining the analytic hierarchy process and the entropy weight method, thereby ensuring that the evaluation results are comprehensive and accurate and meeting the needs of different application scenarios.

[0043] 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 combined weight evaluation method is provided, and the method comprises the following steps: 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; 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; 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 preprocessed original flight parameter data and the reconstructed standard flight path at each sampling point i (Δ , ), and calculate the horizontal distance deviation according to the following formula:

[0044]

[0045]

[0046] 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. Take the arithmetic mean of the horizontal distance deviation of all sampling points i (Δ ) as the positioning accuracy of the navigation system: ​​

[0047] Where n is the number of sampling points; S213. Based on the linear scoring model, all positioning accuracies within the evaluation period are evaluated. Maximum value and minimum value Mapping to percent values :

[0048] in, is the average value of positioning accuracy ΔP during the evaluation period, The preset maximum positioning accuracy The score, The preset minimum positioning accuracy score; For example, =100, =0; The stability index in step S2 is calculated by the following steps: S221. The horizontal distance deviation of each sampling point i Construct deviation sequence in chronological order , and the deviation sequence Perform zero-value processing to obtain a zero-value sequence ; S222. Calculate the zero-mean sequence using the following autocorrelation function stability evaluation algorithm: Autocorrelation coefficient at different lag orders k :

[0049] Where N is the length of the zero-valued sequence, is the mean of the zero-valued series (to zero); S223. Calculate the autocorrelation coefficient The sum of the absolute values ​​within the preset maximum lag order m (e.g. m=20) : ; S224. Set stability value and the sum of absolute values Inversely proportional, and then the sum of the absolute values ​​is calculated by a preset monotonically decreasing function. Mapped to percentage value; For example, the single decreasing function f(S) may be: ; in, is a scaling factor greater than 0; The reliability index in step S2 is calculated by the following steps: S231. Based on the navigation equipment status data, extract the continuous working time records of all relevant navigation equipment during the evaluation period, each record contains a time value and an event indicator value ; in, Indicates at a point in time The equipment has malfunctioned. Indicates at a point in time The equipment stopped working due to non-fault reasons and was deleted; S232. Set all time values Arrange in ascending order to form an ordered sequence , and record the corresponding event indication sequence , and set the initial survival probability of the navigation equipment ; S233. Traverse ordered time series Every time point in , calculate the time point The estimated survival probability of : Calculated at a point in time The number of devices that were still working

[0050] According to the event indication value Calculate the time of navigation equipment The probability of survival; like , determine the failure and calculate the time point The conditional survival probability , and update the navigation device at the time point The survival probability ; in, For the time point The number of devices that failed; like , it is determined that deletion occurs, then the time point The survival probability of is not updated, that is ; S234. and The survival probability Connect them to form the reliability function within the evaluation period , characterizes the probability that the navigation equipment has not failed at time t; S235. Get reliability function The value at the end of the evaluation period T , as a reliability measure : ; The real-time indicator in step S2 is calculated by the following steps: S241. Calculate the time delay sequence from data collection to output result of the navigation system based on the time stamp in the original flight parameter data ; S242. Perform distribution fitting on the time delay sequence using the real-time evaluation algorithm based on response time analysis to obtain the probability distribution function F(x); S243. Determine the maximum allowable delay threshold value according to the real-time requirement of the navigation system ; S244. Calculate the probability that the delay exceeds the maximum allowable delay threshold value, i.e., the real-time risk probability, according to the probability distribution function F(x) : :

[0051] S245. Map the real-time risk probability to a percentage real-time measure through a monotonically decreasing function : ; The anti-interference performance in step S2 is calculated by the following steps: S251. Identify the interference period in the environmental data and the non-interference period ; S252. Calculate the mean of the horizontal distance deviation of the interference period and the mean of the horizontal distance deviation of the non-interference period ;

[0052]

[0053] wherein, denotes the number of sampling points in the non-interference period, denotes the number of sampling points in the interference period, the horizontal distance deviation of sampling point i; S253. Calculate the navigation performance benchmark of the non-interference period and the navigation performance benchmark value of the non-interference period ; ​​​​

[0054]

[0055] S254. Calculate the performance degradation rate of the navigation system : ; S255. Use the anti-interference scoring model combined with the S-type function to reduce the performance degradation ratio Mapping to interference immunity value :

[0056] Where k is the slope coefficient, To pre-set a benchmark for the reduction rate; The availability in step S2 is calculated by the following steps: S261. Based on the navigation device status data, statistics on the navigation system in the total evaluation time Total downtime within ; It should be noted that the total downtime It is the sum of the time that the navigation system is not operational due to malfunction, maintenance or calibration reasons; S262. Based on total evaluation time and total downtime Calculating the inherent availability of a navigation system ;

[0057] S263. Use linear scoring model to convert inherent availability Mapping to percent values : ; S3. Use the hierarchical analysis method to construct a judgment matrix based on expert experience and calculate the subjective weight vector of each individual indicator ; The specific steps of step S3 are as follows: S31. Obtain the expert's judgment value for each individual indicator based on the 1-9 scale method and construct a judgment matrix for each expert. ; in, It indicates the importance of the i-th single indicator relative to the j-th single indicator, and n is the number of single indicators; S32. Use the eigenvalue method to calculate the subjective weight vector of each expert's judgment matrix: Calculate the judgment matrix The maximum eigenvalue of 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 is subjected to consistency check: The consistency index CI is calculated:

[0058] wherein n is the order of the judgment matrix ; The average random consistency index RI value is determined according to the order n of the matrix; 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; The consistency ratio CR is calculated:

[0059] S34. Whether the consistency ratio CR is less than the set ratio threshold (e.g. 0.1); If yes, the test is passed, the subjective weight vector is accepted and output; If not, the judgment matrix is adjusted again, and the step S31 is returned to; S4. Based on the index value sequence 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 ; The specific steps of step S4 are as follows: S41. The single index sequence is subjected to normalization processing to obtain a standardized matrix ; S42. The information entropy value of the jth single index is calculated:

[0060] wherein is the jth value in the jth index sequence after normalization, and n is the length of the index sequence (i.e. the number of time points); S43. The difference coefficient of the jth single index is calculated : ; Based on the difference coefficient of the jth single index, the objective weight of the jth single index is calculated ; ​Then generate the objective weight vector based on the customer weight of each individual indicator ; S5. Setting dynamic combination factors , the subjective weight vector With the objective weight vector Perform linear weighted fusion to generate the final combined weight vector : ; Dynamic combination factor in step S5 The value of is determined by the application scenario of the navigation evaluation: When the application scenario is to compare the inherent performance of different models of navigation systems, set the dynamic combination factor Greater than the preset factor threshold ; When the application scenario is to analyze the performance of the aircraft in the actual operating environment or to analyze the consistency between the pilot operation and the system response, set the dynamic combination factor Less than the preset factor threshold ; For example, the factor threshold It can be set to 0.5. When it is greater than 0.5, it focuses on expert experience and theoretical design indicators. When it is less than 0.5, it focuses on the actual measured data of this flight. S6. Calculate the arithmetic mean of each individual indicator sequence, and then combine the weight vector Perform weighted synthesis to calculate the overall performance score of the navigation system :

[0061] in, is a vector composed of the arithmetic mean of each individual indicator sequence, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators; S7. Output comprehensive performance score , scores of each individual indicator and combined weight distribution plan, and generate an evaluation report.

[0062] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0063] like Figure 2As shown, the following is an embodiment of the aircraft navigation evaluation system based on the combined weighted evaluation method provided by the embodiment of the present disclosure. This system and the aircraft navigation evaluation methods based on the combined weighted evaluation method of the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the aircraft navigation evaluation system based on the combined weighted evaluation method, please refer to the embodiment of the above-mentioned aircraft navigation evaluation method based on the combined weighted evaluation method.

[0064] The system includes: The navigation data acquisition and processing module is used to obtain the aircraft's original flight parameter data, environmental data, and navigation equipment status data, perform pre-processing on the original flight parameter data to detect and clean outliers, and use filtering algorithms to reconstruct the standard track; A single indicator quantification calculation module is used to calculate the values ​​of each single indicator of the navigation system using environmental data, navigation equipment status data, pre-processed raw flight parameter data, and the reconstructed standard track, and generate a time-varying indicator value sequence for each single indicator; the single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference, and availability; The subjective weight vector calculation module is used to construct a judgment matrix and calculate the subjective weight vector of each individual indicator based on the expert experience knowledge using the hierarchical analysis method ; The objective weight vector calculation module is used to calculate the objective weight vector of each individual indicator based on the indicator value sequence of each individual indicator in the current evaluation period using the entropy weight method. ; Combination weight vector generation module, used to set dynamic combination factors , the subjective weight vector With the objective weight vector Perform linear weighted fusion to generate the final combined weight vector : ; The performance score calculation module is used to calculate the arithmetic mean of each single indicator sequence, and then combine the weight vector Perform weighted synthesis to calculate the overall performance score of the navigation system :

[0065] in, is a vector composed of the arithmetic mean of each individual indicator sequence, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators; Evaluation report generation module, used to output comprehensive performance scores , scores of each individual indicator and combined weight distribution plan, and generate an evaluation report.

[0066] The embodiment realizes accurate evaluation of the efficiency of the aircraft navigation system by the interaction and cooperation of the navigation data acquisition and processing module, the single index quantization calculation module, the subjective weight vector calculation module, the objective weight vector calculation module, the combined weight vector generation module, the efficiency score calculation module and the evaluation report generation module, and provides a basis for flight safety and task optimization.

[0067] The above description of disclosed embodiments enables those skilled in the art to carry out 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 these embodiments shown herein, but will accord with the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An aircraft navigation evaluation method based on a combined weight evaluation method, characterized in that: The steps include: S1. Obtain the aircraft's original flight parameter data, environmental data, and navigation equipment status data, perform preprocessing on the original flight parameter data to detect and clean outliers, and use a filtering algorithm to reconstruct the standard track; S2. Calculate the values ​​of individual indicators of the navigation system based on environmental data, navigation equipment status data, preprocessed raw flight parameter data, and the reconstructed standard track, and generate a time-varying indicator value sequence for each individual indicator; the individual indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference, and availability; S3. Use the hierarchical analysis method to construct a judgment matrix based on expert experience and calculate the subjective weight vector of each individual indicator ; S4. Based on the indicator value sequence of each individual indicator in the current evaluation period, the objective weight vector of each individual indicator is calculated using the entropy weight method ; S5. Setting dynamic combination factors , the subjective weight vector With the objective weight vector Perform linear weighted fusion to generate the final combined weight vector : ； S6. Calculate the arithmetic mean of each individual indicator sequence, and then combine the weight vector Perform weighted synthesis to calculate the overall performance score of the navigation system : in, is a vector composed of the arithmetic mean of each individual indicator sequence, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators; S7. Output comprehensive performance score , scores of each individual indicator and combined weight distribution plan, and generate an evaluation report.

2. The aircraft navigation evaluation method 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 pre-processed original flight parameter data and the longitude and latitude of each sampling point i ( , ), and calculate the horizontal distance deviation according to the following formula : in, 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; S212. Horizontal distance deviation for all sampling points i The arithmetic mean of the navigation system is used as the positioning accuracy : Where n is the number of sampling points; S213. Based on the linear scoring model, all positioning accuracies within the evaluation period are evaluated. Maximum value and minimum value Mapping to percent values : in, is the average value of positioning accuracy ΔP during the evaluation period, The preset maximum positioning accuracy The score, The preset minimum positioning accuracy score.

3. The aircraft navigation evaluation method based on the combined weight evaluation method according to claim 1, characterized in that: The stability index in step S2 is calculated by the following steps: S221. The horizontal distance deviation of each sampling point i Construct deviation sequence in chronological order , and the deviation sequence Perform zero-value processing to obtain a zero-value sequence ; S222. Calculate the zero-mean sequence using the following autocorrelation function stability evaluation algorithm: Autocorrelation coefficient at different lag orders k : Where N is the length of the zero-valued sequence, is the mean of the zero-valued series; S223. Calculate the autocorrelation coefficient The sum of the absolute values ​​within the preset maximum lag order m : ； S224. Set stability value and the sum of absolute values Inversely proportional, and then the sum of the absolute values ​​is calculated by a preset monotonically decreasing function. Mapped to percent values.

4. The aircraft navigation evaluation method based on the combined weight evaluation method according to claim 1, characterized in that: The reliability index in step S2 is calculated by the following steps: S231. Based on the navigation equipment status data, extract the continuous working time records of all relevant navigation equipment during the evaluation period, each record contains a time value and an event indicator value ; in, Indicates at a point in time The equipment has malfunctioned. Indicates at a point in time The equipment stopped working due to non-fault reasons and was deleted; S232. Set all time values Arrange in ascending order to form an ordered sequence , and record the corresponding event indication sequence , and set the initial survival probability of the navigation equipment ; S233. Traverse ordered time series Every time point in , calculate the time point The estimated survival probability of : Calculated at a point in time The number of devices that were still working According to the event indication value Calculate the time of navigation equipment The probability of survival; like , determine the failure and calculate the time point The conditional survival probability , and update the navigation device at the time point The survival probability ; in, For the time point The number of devices that failed; like , it is determined that deletion occurs, then the time point The survival probability of is not updated, that is ; S234. and The survival probability Connect them to form the reliability function within the evaluation period , characterizes the probability that the navigation equipment has not failed at time t; S235. Get reliability function The value at the end of the evaluation period T , as a reliability measure : 。 5. The aircraft navigation evaluation method based on the combined 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 timestamp in the original flight parameter data, calculate the time delay sequence from data acquisition to output of the navigation system ; S242. Using real-time evaluation algorithm based on response time analysis to evaluate time delay sequence Perform distribution fitting to obtain the probability distribution function F(x); S243. Determine the maximum allowable delay threshold based on the real-time requirements of the navigation system ; S244. Calculate delay based on probability distribution function F(x) Exceeded the maximum allowed latency threshold The probability of real-time risk : S245.Real-time risk probability Mapped to a real-time value in percentage through a monotonically decreasing function : 。 6. The aircraft navigation evaluation method based on the combined weight evaluation method according to claim 1, characterized in that: In step S2, the anti-interference performance is calculated by the following steps: S251. Identify interference periods in environmental data and undisturbed periods ; S252. Calculate the interference period The mean horizontal distance deviation and undisturbed periods The mean horizontal distance deviation ; in, Indicates the number of sampling points in the non-interference period, Indicates the number of sampling points during the interference period, Horizontal distance deviation of sampling point i; S253. Calculate the navigation performance benchmark during the interference-free period Navigation performance benchmark during the interference-free period ; S254. Calculate the performance degradation rate of the navigation system : ； S255. Use the anti-interference scoring model combined with the S-type function to reduce the performance degradation ratio Mapping to interference immunity value : Where k is the slope coefficient, To pre-set a benchmark for the reduction rate; The availability in step S2 is calculated by the following steps: S261. Based on the navigation device status data, statistics on the navigation system in the total evaluation time Total downtime within ; S262. Based on total evaluation time and total downtime Calculating the inherent availability of a navigation system ; S263. Use linear scoring model to convert inherent availability Mapping to percent values : 。 7. The aircraft navigation evaluation method based on the combined weight evaluation method according to claim 1, characterized in that: The specific steps of step S3 are as follows: S31. Obtain the expert's judgment value for each individual indicator based on the 1-9 scale method and construct a judgment matrix for each expert. ; in, It indicates the importance of the i-th single indicator relative to the j-th single indicator, and n is the number of single indicators; S32. Use the eigenvalue method to calculate the subjective weight vector of each expert's judgment matrix: Calculate the judgment matrix The maximum eigenvalue of And the corresponding eigenvector is recorded as the maximum eigenvector; Normalize the maximum eigenvector to get the subjective weight vector ; S33. Judgment matrix for each expert Perform consistency check: Calculate the consistency index CI: Where n is the judgment matrix The order of Determine the average random consistency index RI value according to the order n of the matrix; Calculate the consistency ratio CR: S34. Determine whether the consistency ratio CR is less than the set ratio threshold ; If so, pass the test, accept and output the subjective weight vector ; If not, readjust the judgment matrix , return to step S31.

8. The aircraft navigation evaluation method based on the combined weight evaluation method according to claim 1, characterized in that: The specific steps of step S4 are as follows: S41. Normalize each individual indicator sequence to obtain a standardized matrix ; S42. Calculate the information entropy value of the j-th single indicator: in, is the jth value in the normalized jth indicator sequence, , n is the length of the indicator sequence; S43. Calculate the coefficient of difference of the jth individual indicator : ； The difference coefficient of the jth individual indicator based on the difference Calculate the objective weight of the jth individual indicator ; Then generate the objective weight vector based on the customer weight of each individual indicator .

9. The aircraft navigation evaluation method based on the combined weight evaluation method according to claim 1, characterized in that: Dynamic combination factor in step S5 The value of is determined by the application scenario of the navigation evaluation: When the application scenario is to compare the inherent performance of different models of navigation systems, set the dynamic combination factor Greater than the preset factor threshold ; When the application scenario is to analyze the performance of the aircraft in the actual operating environment or to analyze the consistency between the pilot operation and the system response, set the dynamic combination factor Less than the preset factor threshold .

10. An aircraft navigation evaluation system based on a combined weighted evaluation method, characterized in that: include: The navigation data acquisition and processing module is used to obtain the aircraft's original flight parameter data, environmental data, and navigation equipment status data, perform pre-processing on the original flight parameter data to detect and clean outliers, and use filtering algorithms to reconstruct the standard track; A single indicator quantification calculation module is used to calculate the value of each single indicator of the navigation system based on environmental data, navigation equipment status data, pre-processed raw flight parameter data, and the reconstructed standard track, and generate a time-varying indicator value sequence for each single indicator; the single indicators include positioning accuracy, stability, reliability, real-time performance, anti-interference and availability; The subjective weight vector calculation module is used to construct a judgment matrix and calculate the subjective weight vector of each individual indicator based on the expert experience knowledge using the hierarchical analysis method ; The objective weight vector calculation module is used to calculate the objective weight vector of each individual indicator based on the indicator value sequence of each individual indicator in the current evaluation period using the entropy weight method. ; Combination weight vector generation module, used to set dynamic combination factors , the subjective weight vector With the objective weight vector Perform linear weighted fusion to generate the final combined weight vector : ； The performance score calculation module is used to calculate the arithmetic mean of each single indicator sequence, and then combine the weight vector Perform weighted synthesis to calculate the overall performance score of the navigation system : in, is a vector composed of the arithmetic mean of each individual indicator sequence, is the arithmetic mean of the i-th individual indicator sequence, and n is the number of individual indicators; Evaluation report generation module, used to output comprehensive performance scores , scores of each individual indicator and combined weight distribution plan, and generate an evaluation report.

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