Multi-attribute quantitative evaluation method and system for inertial satellite integrated navigation equipment

Abstract

The application relates to the technical field of navigation, and particularly provides a multi-attribute quantitative evaluation method and system for an inertial satellite combined navigation device, which comprises the following steps: collecting corresponding observation sample data of the inertial combined satellite according to multiple evaluation indexes, and extracting characteristic values and characteristic vectors corresponding to the evaluation indexes from the observation sample data; screening multiple core indexes from the multiple evaluation indexes according to the characteristic values of the evaluation indexes by using a cumulative contribution rate inequality; generating evaluation data of the core indexes according to the characteristic vectors corresponding to the core indexes and the observation sample data; determining the weights of the core indexes based on the Euclidean distances between the evaluation data of the core indexes and corresponding preset standard values; calculating a grey correlation coefficient matrix of the multiple core indexes based on the evaluation data, the weights and the preset standard values of the core indexes, and calculating quantitative evaluation values of the core indexes based on the grey correlation coefficient matrix. The application significantly improves the accuracy and scientificity of the evaluation results.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of navigation, and particularly relates to a multi-attribute quantitative evaluation method and system for an inertial satellite integrated navigation device. BACKGROUND

[0002] The inertial satellite integrated navigation device is a navigation device combining an inertial navigation system and a satellite navigation system, and realizes complementation through data fusion technology to improve navigation accuracy, reliability and environmental adaptability. The navigation state of the inertial satellite integrated navigation device directly affects the flight safety of a spacecraft, and whether high-precision, high-reliability and uninterrupted navigation can be achieved in a complex flight environment is crucial for completing the navigation task of the spacecraft.

[0003] The navigation state evaluation of the inertial satellite integrated navigation device involves accuracy, convergence, reliability, maintainability and other aspects, and evaluation indexes include velocity accuracy, position accuracy, attitude accuracy, accelerometer bias estimation stability, convergence accuracy, minimum detectable gross error, consistency of variance matrix, mean time between failures, continuous working time and the like. The navigation state evaluation involves many evaluation indexes and data types, and how to scientifically and accurately evaluate the navigation state of the inertial satellite integrated navigation device based on a large number of evaluation indexes is a difficult problem in the navigation field. In the prior art, the navigation state of the navigation device is usually evaluated by qualitative evaluation, and such a method is greatly affected by subjective human factors and is closely related to the knowledge, experience and cognition of the evaluation subject, which greatly affects the credibility and accuracy of the evaluation results. Further, in the prior art, the existing navigation state evaluation method adopts qualitative evaluation, and such a method cannot determine the development trend of the navigation state and cannot give guidance to the completion of the navigation task according to the navigation state evaluation results.

[0004] Therefore, how to quantitatively evaluate the navigation state of the inertial satellite integrated navigation device in multiple attributes and improve the accuracy and scientificity of the evaluation results is a difficult problem to be solved by those skilled in the art. SUMMARY

[0005] In view of the above problems in the prior art, the present application provides a multi-attribute quantitative evaluation method and system for an inertial satellite integrated navigation device to solve the above technical problems.

[0006] In a first aspect, the present application provides a multi-attribute quantitative evaluation method for an inertial satellite integrated navigation device, comprising:

[0007] According to the plurality of evaluation indexes, corresponding observation sample data of the inertial satellite integrated navigation device is collected, and feature values and feature vectors corresponding to each evaluation index are extracted from the observation sample data;

[0008] The cumulative contribution rate inequality is used to screen a plurality of core indexes from a plurality of evaluation indexes according to eigenvalues of the evaluation indexes, and evaluation data of the core indexes is generated according to corresponding eigenvectors of the core indexes and the observation sample data;

[0009] The weight of each core index is determined based on the Euclidean distance between the evaluation data of each core index and a corresponding preset standard value;

[0010] Based on the evaluation data, the weight and the preset standard value of each core index, a grey correlation coefficient matrix of the plurality of core indexes is calculated, and a quantitative evaluation value of each core index is calculated based on the grey correlation coefficient matrix.

[0011] In an optional implementation, the eigenvalues and the eigenvectors corresponding to each evaluation index are extracted from the observation sample data, including:

[0012] The observation sample data is converted into an observation sample matrix, and the observation sample matrix is subjected to standardization processing;

[0013] A covariance matrix of the observation sample matrix subjected to the standardization processing is calculated;

[0014] The eigenvalues and the eigenvectors corresponding to each evaluation index are extracted based on the covariance matrix.

[0015] In an optional implementation, the cumulative contribution rate inequality is used to screen a plurality of core indexes from a plurality of evaluation indexes according to eigenvalues of the evaluation indexes, including:

[0016] The eigenvalues are sorted from large to small;

[0017] The number K of core indexes is determined according to the eigenvalues of the evaluation indexes by using the cumulative contribution rate inequality;

[0018] The first K largest eigenvalues are selected from the eigenvalue sorting, and the eigenvectors corresponding to the first K largest eigenvalues are obtained based on the corresponding relationship between the eigenvalues and the vectors.

[0019] In an optional implementation, the evaluation data of each core index is generated according to corresponding eigenvectors of the core indexes and the observation sample data, including:

[0020] The eigenvectors corresponding to the first K largest eigenvalues are constructed into an eigenmatrix;

[0021] An evaluation data matrix of the core indexes is calculated according to an observation sample matrix composed of the eigenmatrix and the observation sample data, and elements of the evaluation data matrix are the evaluation data of the core indexes.

[0022] In an optional embodiment, the weight of each core index is determined based on the Euclidean distance between the evaluation data of each core index and the corresponding preset standard value, including:

[0023]

[0024] wherein, is the weight of the jth core index; is the evaluation data of the ith observation of the jth core index; is the preset standard value corresponding to the jth core index; M is the total number of observations; and K is the total number of core indexes.

[0025] In an optional embodiment, a grey correlation coefficient matrix of a plurality of core indexes is calculated based on the evaluation data, the weight, and the preset standard value of each core index, including:

[0026] Grey correlation coefficient matrix wherein r ij is calculated according to the following formula:

[0027]

[0028] wherein, represents the first-level minimum difference of the absolute value of the difference between the weighted evaluation index data and the ideal value , i.e., the minimum value selected from the absolute value of the difference between the weighted evaluation index data and the ideal value according to the value of j; represents the second-level minimum difference, i.e., the minimum value selected from the absolute value of the difference between the weighted evaluation index data and the ideal value according to the value of i; and represents the maximum difference of the absolute value of the difference between the weighted evaluation index data and the ideal value , .

[0029] In an optional embodiment, a quantitative evaluation value of each core index is calculated based on the grey correlation coefficient matrix, including:

[0030] The quantitative evaluation result of the jth core evaluation index is:

[0031]

[0032] wherein M represents the number of observation samples of the evaluation index, .

[0033] In a second aspect, the present application provides a multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device, comprising:

[0034] an index observation module configured to collect observation sample data of the inertial satellite integrated navigation device according to a plurality of evaluation indexes, and extract eigenvalues and eigenvectors corresponding to each evaluation index from the observation sample data;

[0035] an index screening module configured to screen a plurality of core indexes from the plurality of evaluation indexes according to the eigenvalues of each evaluation index using a cumulative contribution rate inequality, and generate evaluation data of each core index according to the eigenvectors corresponding to each core index and the observation sample data;

[0036] a weight calculation module configured to determine the weight of each core index based on the Euclidean distance between the evaluation data of each core index and a corresponding preset standard value;

[0037] an index quantification module configured to calculate a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight and the preset standard value of each core index, and calculate a quantitative evaluation value of each core index based on the grey correlation coefficient matrix.

[0038] In an optional implementation, the index observation module comprises:

[0039] the observation sample data is converted into an observation sample matrix, and the observation sample matrix is subjected to standardization processing;

[0040] a covariance matrix of the observation sample matrix subjected to the standardization processing is calculated;

[0041] the eigenvalues and the eigenvectors corresponding to each evaluation index are extracted based on the covariance matrix.

[0042] In an optional implementation, the index screening module comprises:

[0043] the eigenvalues are sorted from large to small;

[0044] the number K of core indexes is determined according to the eigenvalues of each evaluation index using the cumulative contribution rate inequality;

[0045] the first K largest eigenvalues are selected from the eigenvalue sorting, and the eigenvectors corresponding to the first K largest eigenvalues are obtained based on the corresponding relationship between the eigenvalues and the vectors.

[0046] In a third aspect, a device is provided, comprising:

[0047] a memory configured to store a multi-attribute quantitative evaluation program for an inertial satellite integrated navigation device;

[0048] The processor is configured to execute the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation program to implement the steps of the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation method according to the first aspect.

[0049] In a fourth aspect, a computer readable medium is provided, and the medium stores an inertial satellite integrated navigation equipment multi-attribute quantitative evaluation program. When the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation program is executed by a processor, the steps of the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation method according to the first aspect are implemented.

[0050] The inertial satellite integrated navigation equipment multi-attribute quantitative evaluation method and system provided by the application can determine the number of core evaluation indexes by calculating the covariance matrix of the standardized sample data matrix and the cumulative contribution rate inequality, realize dimension reduction processing of the evaluation indexes, and convert the multi-evaluation index problem into comprehensive processing of fewer evaluation indexes. The weight of each evaluation index is reasonably allocated by measuring the Euclidean distance between the data of the evaluation index to be evaluated and the ideal state vector. The multi-attribute quantitative evaluation problem is solved from the geometric idea by calculating the grey correlation coefficient matrix of the weighted data of the evaluation index to be evaluated and the ideal value, and the grey correlation degree is used as the measurement standard of the navigation state to determine the development trend of the navigation state, and the multi-attribute quantitative evaluation result is obtained. In the prior art, the qualitative evaluation of the navigation state is easily affected by subjective factors, and the credibility and accuracy of the evaluation result are low, and it is difficult to reflect the development trend of the navigation state, and it is impossible to give guidance to the navigation task completion according to the navigation state evaluation result. Therefore, compared with the prior art, the multi-attribute quantitative evaluation of the navigation equipment is completed by the measures of multi-attribute dimension reduction, scientific allocation of the evaluation index weight, and quantitative evaluation result, and the accuracy and scientificity of the evaluation result are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0052] Figure 1 is a schematic flowchart of a method according to an embodiment of the application.

[0053] Figure 2 is a schematic block diagram of a system according to an embodiment of the application.

[0054] Figure 3 is a structural schematic diagram of a device according to an embodiment of the application. DETAILED DESCRIPTION

[0055] In order to make the person skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0057] The multi-attribute quantitative evaluation method of the inertial satellite integrated navigation equipment provided by the embodiments of the present application is executed by a computer equipment, and accordingly, the multi-attribute quantitative evaluation system of the inertial satellite integrated navigation equipment runs in the computer equipment.

[0058] Figure 1 is a schematic flowchart of the method of an embodiment of the present application. Wherein, Figure 1 The execution subject can be a multi-attribute quantitative evaluation system of an inertial satellite integrated navigation equipment. According to different needs, the order of steps in the flowchart can be changed, and some can be omitted.

[0059] As Figure 1 shown, the method comprises:

[0060] S1. According to a plurality of evaluation indexes, corresponding observation sample data of the inertial satellite integrated navigation equipment is collected, and feature values and feature vectors corresponding to each evaluation index are extracted from the observation sample data;

[0061] S2. According to the feature values of each evaluation index, a plurality of core indexes are screened out from a plurality of evaluation indexes by using cumulative contribution rate inequality, and evaluation data of each core index is generated according to the feature vectors corresponding to each core index and the observation sample data;

[0062] S3. The weight of each core index is determined based on the Euclidean distance between the evaluation data of each core index and the corresponding preset standard value;

[0063] S4. Based on the evaluation data, weight and preset standard value of each core index, a grey correlation coefficient matrix of a plurality of core indexes is calculated, and a quantitative evaluation value of each core index is calculated based on the grey correlation coefficient matrix.

[0064] In an embodiment of the present application, based on step S1, a possible embodiment will be given below to non-restrictively describe its specific implementation scheme. In an embodiment of the present application, based on step S1, a possible embodiment will be given below to non-restrictively describe its specific implementation scheme.

[0065] S101. Convert the observed sample data into an observed sample matrix and standardize the observed sample matrix.

[0066] For a matrix X consisting of M observation samples with N evaluation indicators, matrix X:

[0067]

[0068] The matrix X is standardized to form the standardized sample matrix Y, which is represented as follows:

[0069]

[0070] The element y in the i-th row and j-th column of the standardized sample matrix Y ij Represented as:

[0071]

[0072] Where M represents the number of observed samples for the evaluation index.

[0073] S102. Calculate the covariance matrix of the standardized observation sample matrix.

[0074] The covariance matrix S of the standardized sample matrix Y is expressed as:

[0075]

[0076] , The element s in the i-th row and j-th column of the covariance matrix S ij Represented as:

[0077]

[0078] , Let be the sample means of the i-th and j-th evaluation indicators, respectively, and be expressed as:

[0079] , .

[0080] S103. Extract the eigenvalues ​​and eigenvectors corresponding to each evaluation index based on the covariance matrix.

[0081] From the equation The N eigenvalues ​​(λ1, λ2, ..., λ) of S are calculated. i ,…,λ N ) and the corresponding N feature vectors (Q1, Q2, ..., Q i ,…,Q N), where S is the covariance matrix of the standardized sample matrix Y, and E is an identity matrix.

[0082] In an embodiment of the present application, based on step S2, a possible embodiment will be given below to specifically and non-restrictively illustrate the implementation thereof.

[0083] S201. Sort the eigenvalues from large to small;

[0084] S202. Determine the number K of core indicators according to the eigenvalues of each evaluation indicator by using the cumulative contribution rate inequality:

[0085] According to the cumulative contribution rate inequality:

[0086] ,

[0087] where C is a constant, and a typical value is 0.85, and the minimum value satisfying the above relationship K is calculated, and the value of K is the number of core evaluation indicators.

[0088] S203. Select the first K largest eigenvalues from the eigenvalue sorting, and obtain the eigenvectors corresponding to the first K largest eigenvalues based on the corresponding relationship between the eigenvalues and the vectors.

[0089] S204. Construct the eigenvectors corresponding to the first K largest eigenvalues into an eigenmatrix; calculate an evaluation data matrix of the core indicators according to an observation sample matrix composed of the eigenmatrix and the observation sample data, and an element of the evaluation data matrix is evaluation data of the core indicators.

[0090] Take the eigenvectors corresponding to the K eigenvalues to construct an eigenmatrix Q K , which is expressed as:

[0091]

[0092] Construct a core evaluation indicator matrix A from the eigenmatrix Q K and the sample data matrix X, which is expressed as:

[0093] .

[0094] In an embodiment of the present application, based on step S3, a possible embodiment will be given below to specifically and non-restrictively illustrate the implementation thereof.

[0095] In the prior art, the evaluation index weight distribution mainly includes subjective weight method and objective weight method. The subjective weight method mainly relies on the experience, knowledge and personal values of experts to determine the weight, the weight determined by the method has great dependence on the subjectivity of experts and has certain subjective randomness. The objective weight method is mainly derived from the evaluation of data, only considers the structural characteristics of the data itself and is greatly affected by abnormal data, and it is difficult to establish the complex nonlinear mapping relationship between the data and the navigation state evaluation result. The present application adopts the method of measuring the Euclidean distance between the to-be-evaluated index data and the ideal state vector , determines the weight of each evaluation index; the greater the Euclidean distance, the smaller the influence of the evaluation index on the navigation state evaluation result, and the smaller the weight should be; on the contrary, the greater the influence, the greater the weight should be.

[0096] The ideal state vector of the K core evaluation indexes is , which is expressed as:

[0097]

[0098] The ideal state vector The values of the elements in the ideal state vector can be composed of the optimal values of the navigation equipment performance indexes, or can be determined by experts in the field according to working experience.

[0099] The weighted Euclidean distance between the core evaluation index matrix A and is expressed as:

[0100] ,

[0101] wherein, represents the element value of the i-th row and the j-th column of the core evaluation index matrix A. The weight ω j is obtained by minimizing the sum of all h i , which is the weight of the K core evaluation indexes. The problem can be converted into the following optimization problem:

[0102]

[0103] The above optimization problem can be solved by using the Lagrange function.

[0104] According to the Euclidean distance between the K core evaluation indexes and the ideal state vector , the evaluation index weight ω j is calculated, and the j-th evaluation index weight ω is expressed as:

[0105]

[0106] wherein, is the weight of the jth core index; is the evaluation data of the ith observation of the jth core index; is the preset standard value corresponding to the jth core index; M is the total number of observations; and K is the total number of core indexes.

[0107] In an embodiment of the present application, based on step S4, a possible embodiment will be given below to specifically and non-limitingly illustrate the specific implementation thereof.

[0108] Grey correlation matrix of K core indexes , wherein r ij The calculation formula is:

[0109]

[0110] wherein, denotes the first-level minimum difference of the absolute value of the difference between the weighted evaluation index data and the ideal value ; that is, the minimum value of the absolute value of the difference between the weighted evaluation index data and the ideal value is selected according to the different values of j; denotes the second-level minimum difference, that is, the minimum value of the absolute value of the difference between the weighted evaluation index data and the ideal value is selected according to the different values of i; and denotes the maximum difference of the absolute value of the difference between the weighted evaluation index data and the ideal value ; ρ is a resolution factor, and the value range is 0-1, and the typical value is 0.5, which is used to improve the difference between the correlation coefficients of the evaluation indexes to be evaluated; .

[0111] The quantitative evaluation result of the jth core evaluation index is:

[0112]

[0113] wherein, M represents the number of observation samples of the evaluation index, .

[0114] The navigation state evaluation involves precision, convergence, reliability, maintainability and the like, and evaluation indexes include speed precision, position precision, attitude precision, accelerometer zero offset estimation stability, convergence precision, minimum detectable gross error, consistency of variance matrix, mean time between failures, continuous working time and the like. The traditional navigation state evaluation method needs to analyze and process data of all the above evaluation indexes, and finally gives an evaluation result. Due to the multiple evaluation indexes and various data types, the evaluation process is tedious and time-consuming. In fact, not all the evaluation indexes have the same contribution to the evaluation result, and some evaluation indexes have important influence on the evaluation result, while some evaluation indexes have little influence on the evaluation result. However, how to distinguish which evaluation indexes have great influence on the evaluation result? This relates to the evaluation efficiency and the accuracy of the evaluation result. In order to solve this problem, in the technical scheme disclosed in the embodiment of the application, first, all the evaluation indexes are subjected to standardized data processing to eliminate dimensional differences and form a standardized sample matrix, then a covariance matrix of the standardized sample data matrix is calculated, the eigenvalue and eigenvector of the covariance matrix are solved by using a matrix algorithm, and are arranged in descending order according to the size of the eigenvalue; further, the number of core evaluation indexes is determined by calculating the cumulative contribution rate inequality. Further, the core evaluation index matrix is constructed by matrix transformation of the feature matrix constructed by the eigenvector and the sample data matrix, and a new evaluation index is formed, so that the original evaluation index system is transformed into a new orthogonal system; that is, by the matrix transformation, the original related evaluation indexes are converted into independent evaluation indexes, while most of the evaluation information affecting the evaluation result is retained. Therefore, the technical scheme disclosed in the embodiment of the application realizes dimension reduction processing of the evaluation indexes, converts the multi-attribute evaluation index problem into a comprehensive processing of less attribute evaluation index problem, converts the high-dimensional space problem into a low-dimensional space problem, so that the navigation state evaluation problem becomes simple and intuitive.

[0115] In some embodiments, the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation system can include a plurality of functional modules composed of computer program segments. The computer programs of each program segment in the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation system can be stored in the memory of the computer equipment and executed by at least one processor to perform the functions of the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation (see Figure 1 Description).

[0116] In this embodiment, the inertial satellite integrated navigation equipment multi-attribute quantitative evaluation system can be divided into a plurality of functional modules according to the functions it performs, such as Figure 2The modules referred to in the present application refer to a series of computer program segments capable of being executed by at least one processor and capable of completing fixed functions, which are stored in the memory. In the embodiment, the functions of the modules will be described in detail in subsequent embodiments.

[0117] An index observation module is configured to collect observation sample data corresponding to the inertial combined satellite according to a plurality of evaluation indexes, and extract feature values and feature vectors corresponding to each evaluation index from the observation sample data;

[0118] An index screening module is configured to screen a plurality of core indexes from the plurality of evaluation indexes according to the feature values of each evaluation index by using a cumulative contribution rate inequality, and generate evaluation data of each core index according to the feature vectors corresponding to each core index and the observation sample data;

[0119] A weight calculation module is configured to determine the weight of each core index based on the Euclidean distance between the evaluation data of each core index and a corresponding preset standard value;

[0120] An index quantification module is configured to calculate a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight and the preset standard value of each core index, and calculate a quantified evaluation value of each core index based on the grey correlation coefficient matrix.

[0121] Figure 3 The multi-attribute quantification evaluation method of the inertial satellite combined navigation device provided in the embodiments of the present application can be applied to the device. Those skilled in the art can understand that the device structure involved in the embodiments of the present application does not constitute a limitation on the device, and the device can include more or fewer components than the illustration, or combine certain components, or different component arrangements. In the embodiments of the present application, the device includes but is not limited to a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0122] The device 300 can include a processor 310, a memory 320, and a communication unit 330. These components communicate through one or more buses, and those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation on the present application, and it can be a bus structure or a star structure, and can include more or fewer components than the illustration, or combine certain components, or different component arrangements.

[0123] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the device 300 can perform part or all of the steps in the following method embodiments.

[0124] The processor 310 is the control center of the storage device, connects various parts of the entire electronic device through various interfaces and lines, and performs various functions of the electronic device and / or processes data by running or executing software programs and / or modules stored in the memory 320 and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, can be composed of a single packaged IC, or can be composed of multiple packaged ICs connected together. For example, the processor 310 can only include a central processing unit (CPU). In the embodiments of the present application, the CPU can be a single operation core or can include multiple operation cores.

[0125] The communication unit 330 is used to establish a communication channel, so that the storage device can communicate with other devices. The communication unit 330 receives user data sent by other devices or sends user data to other devices.

[0126] The present application also provides a computer medium, wherein the computer medium can store a program, and the program can include part or all of the steps in the embodiments provided by the present application when executed. The medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0127] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present application can be embodied in the form of a software product, which is stored in a medium such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a second device, a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0128] In the present specification, the same or similar parts among various embodiments can be referred to each other. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0129] In the several embodiments of the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, another division manner can be used. For example, a plurality of modules or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces. The coupling or communication connection can be electrical, mechanical or in other forms.

[0130] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e. can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0131] In addition, each functional module in the various embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0132] Although the present application has been described in detail with reference to the preferred embodiments, it should be understood that the application is not limited to those preferred embodiments. Various modifications and equivalents can be made by those skilled in the art without departing from the spirit and scope of the application. Any and all modifications and equivalents are intended to be included within the scope of the present application.

Claims

1. A multi-attribute quantitative evaluation method for an inertial satellite integrated navigation device, characterized in that, The method comprises the following steps: According to the evaluation indexes, the corresponding characteristic values and characteristic vectors of the evaluation indexes are extracted from the observation sample data; The cumulative contribution rate inequality is used to screen a plurality of core indexes from the plurality of evaluation indexes according to the characteristic values of the evaluation indexes, and evaluation data of the core indexes is generated according to the characteristic vectors corresponding to the core indexes and the observation sample data; The weight of each core index is determined based on the Euclidean distance between the evaluation data of each core index and the corresponding preset standard value; Based on the evaluation data, the weight and the preset standard value of each core index, a grey correlation coefficient matrix of the plurality of core indexes is calculated, and a quantitative evaluation value of each core index is calculated based on the grey correlation coefficient matrix; The weight of each core index is determined based on the Euclidean distance between the evaluation data of each core index and the corresponding preset standard value, which comprises: wherein, is the weight of the jth core indicator; is the evaluation data of the ith observation of the jth core indicator; is the preset standard value corresponding to the jth core indicator; M is the total number of observations; and K is the total number of core indicators. Based on the evaluation data, the weight and the preset standard value of each core index, a grey correlation coefficient matrix of the plurality of core indexes is calculated, which comprises: grey correlation coefficient matrix where r ij The calculation formula is: in, This indicates the weighted data of the indicators to be evaluated. Compared with ideal value The first-order minimum difference of the absolute value of the difference, that is, at the absolute value of the difference The minimum value is selected based on the different values ​​of j. This represents the minimum difference of the second order, i.e., in In the middle, select the minimum value according to the different values ​​of i; similarly, This indicates the weighted data of the indicators to be evaluated. Compared with ideal value The maximum difference in the absolute values ​​of the differences. This represents the maximum difference at level 2; ρ is the discrimination factor, ranging from 0 to 1, with a typical value of 0.

5. Its function is to improve the difference in the correlation coefficient between the indicators to be evaluated. , ; Based on the grey correlation coefficient matrix, a quantitative evaluation value of each core index is calculated, which comprises: The quantitative evaluation result of the jth core evaluation index is: wherein M represents the number of observation samples of the evaluation index, .

2. The method of claim 1, wherein, The characteristic values and the characteristic vectors corresponding to the evaluation indexes are extracted from the observation sample data, which comprises: The observation sample data is converted into an observation sample matrix, and the observation sample matrix is standardized; The covariance matrix of the standardized observation sample matrix is calculated; The characteristic values and the characteristic vectors corresponding to the evaluation indexes are extracted based on the covariance matrix.

3. The method of claim 1, wherein, The cumulative contribution rate inequality is used to screen a plurality of core indexes from the plurality of evaluation indexes according to the characteristic values of the evaluation indexes, which comprises: The characteristic values are sorted from large to small; The number K of core indexes is determined according to the characteristic values of the evaluation indexes by using the cumulative contribution rate inequality; The first K largest characteristic values are selected from the sorted characteristic values, and the characteristic vectors corresponding to the first K largest characteristic values are obtained based on the corresponding relationship between the characteristic values and the vectors.

4. The method of claim 3, wherein, The evaluation data of the core indexes is generated according to the characteristic vectors corresponding to the core indexes and the observation sample data, which comprises: The characteristic vectors corresponding to the first K largest characteristic values are constructed into a characteristic matrix; The evaluation data matrix of the core indexes is calculated according to the observation sample matrix composed of the characteristic matrix and the observation sample data, and the elements of the evaluation data matrix are the evaluation data of the core indexes.

5. A multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device, characterized in that, The method comprises the following steps: The index observation module is configured to collect the observation sample data corresponding to the plurality of evaluation indexes from the inertial combination satellite, and extract the characteristic values and the characteristic vectors corresponding to the evaluation indexes from the observation sample data; The index screening module is configured to screen a plurality of core indexes from the plurality of evaluation indexes according to the characteristic values of the evaluation indexes by using the cumulative contribution rate inequality, and generate the evaluation data of the core indexes according to the characteristic vectors corresponding to the core indexes and the observation sample data; The weight calculation module is configured to determine the weight of each core index based on the Euclidean distance between the evaluation data of each core index and the corresponding preset standard value; The weight calculation module is configured to determine the weight of each core index based on the Euclidean distance between the evaluation data of each core index and the corresponding preset standard value; The index quantification module is configured to calculate a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight, and the preset standard value of each core index, and calculate a quantified evaluation value of each core index based on the grey correlation coefficient matrix. The weight of each core index is determined based on the Euclidean distance between the evaluation data and the corresponding preset standard value of each core index, including: wherein, is the weight of the jth core indicator; is the evaluation data of the ith observation of the jth core indicator; is the preset standard value corresponding to the jth core indicator; M is the total number of observations; and K is the total number of core indicators. The index quantification module is configured to calculate a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight, and the preset standard value of each core index, and calculate a quantified evaluation value of each core index based on the grey correlation coefficient matrix. grey correlation coefficient matrix where r ij The calculation formula is: wherein, denotes the first level minimum difference of the absolute value of the difference between the weighted to-be-evaluated index data and the ideal value ; and denotes the minimum value of the absolute value of the difference between the weighted to-be-evaluated index data and the ideal value for different values of j; and denotes the second level minimum difference of the absolute value of the difference between the weighted to-be-evaluated index data and the ideal value for different values of i; and denotes the maximum value of the absolute value of the difference between the weighted to-be-evaluated index data and the ideal value ; and The index quantification module is configured to calculate a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight, and the preset standard value of each core index, and calculate a quantified evaluation value of each core index based on the grey correlation coefficient matrix. The quantified evaluation result of the jth core evaluation index is: wherein M represents the number of observation samples of the evaluation index, .

6. The system of claim 5, wherein, The index observation module includes: The observation sample data is converted into an observation sample matrix, and the observation sample matrix is standardized. The covariance matrix of the standardized observation sample matrix is calculated. The eigenvalue and eigenvector corresponding to each evaluation index are extracted based on the covariance matrix.

7. The system of claim 5, wherein, The index screening module includes: The eigenvalues are sorted from large to small. The number K of core indexes is determined according to the eigenvalues of each evaluation index by using the cumulative contribution rate inequality. The first K largest eigenvalues are selected from the eigenvalue sorting, and the eigenvectors corresponding to the first K largest eigenvalues are obtained based on the corresponding relationship between the eigenvalues and the vectors.

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