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

Abstract

The invention relates to the technical field of navigation, and particularly provides a multi-attribute quantitative evaluation method and system for inertial satellite integrated navigation equipment, and the method comprises the steps: collecting corresponding observation sample data for an inertial integrated satellite according to a plurality of evaluation indexes, and extracting a feature value and a feature vector corresponding to each evaluation index from the observation sample data; screening out a plurality of core indexes from the plurality of evaluation indexes according to the characteristic value of each evaluation index by utilizing an accumulative contribution rate inequality, and generating evaluation data of each core index according to the characteristic vector corresponding to each core index and the observation sample data; determining 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; and calculating a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight and a preset standard value of each core index, and calculating a quantitative evaluation value of each core index based on the grey correlation coefficient matrix. According to the method, the accuracy and scientificity of an evaluation result are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the field of navigation technology, and in particular relates to a multi-attribute quantitative evaluation method and system for an inertial satellite integrated navigation device. Background Art

[0002] An inertial satellite navigation system (INSN) combines an inertial navigation system with a satellite navigation system, achieving complementary capabilities through data fusion technology to enhance navigation accuracy, reliability, and environmental adaptability. The navigation status of an INSN directly impacts aircraft flight safety. High-precision, reliable, and uninterrupted navigation in complex flight environments is crucial to completing aircraft navigation missions.

[0003] Navigation status assessment for inertial satellite navigation systems involves multiple aspects, including accuracy, convergence, reliability, and maintainability. Evaluation metrics include velocity accuracy, position accuracy, attitude accuracy, accelerometer bias estimation stability, convergence accuracy, minimum detectable gross error, variance matrix consistency, mean time between failures, and continuous operating time. Navigation status assessment involves numerous evaluation metrics and data types. Scientifically and accurately evaluating the navigation status of inertial satellite navigation systems based on these numerous evaluation metrics is a challenging issue in the navigation field. Prior art methods typically employ qualitative evaluation methods, which are significantly influenced by subjective human factors and are closely tied to the evaluator's knowledge, experience, and cognition, significantly impacting the credibility and accuracy of the evaluation results. Furthermore, existing navigation status assessment methods, due to their qualitative nature, struggle to determine navigation status trends and provide guidance for completing navigation tasks based on the navigation status evaluation results.

[0004] Therefore, how to perform multi-attribute quantitative evaluation of the navigation status of the inertial satellite integrated navigation device and improve the accuracy and scientificity of the evaluation results is a difficult problem that technical personnel in this field need to solve. Summary of the Invention

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

[0006] In a first aspect, the present invention provides a multi-attribute quantitative evaluation method for an inertial satellite integrated navigation device, comprising: Collecting corresponding observation sample data for the inertial composite satellite according to a plurality of evaluation indicators, and extracting eigenvalues ​​and eigenvectors corresponding to each evaluation indicator from the observation sample data; Using the cumulative contribution rate inequality to select multiple core indicators from multiple evaluation indicators according to the characteristic value of each evaluation indicator, and generating evaluation data for each core indicator according to the characteristic vector corresponding to each core indicator and the observed sample data; Determine the weight of each core indicator based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value; Based on the evaluation data, weights and preset standard values ​​of the core indicators, the grey correlation coefficient matrix of the core indicators is calculated, and the quantitative evaluation value of each core indicator is calculated based on the grey correlation coefficient matrix.

[0007] In an optional embodiment, extracting the eigenvalues ​​and eigenvectors corresponding to the evaluation indicators from the observed sample data includes: Convert the observation sample data into an observation sample matrix and perform standardization on the observation sample matrix; Calculate the covariance matrix of the standardized observation sample matrix; The eigenvalues ​​and eigenvectors corresponding to the evaluation indicators are extracted based on the covariance matrix.

[0008] In an optional embodiment, the cumulative contribution rate inequality is used to screen out multiple core indicators from multiple evaluation indicators according to the characteristic value of each evaluation indicator, including: Sort the eigenvalues ​​from large to small; The number K of core indicators is determined based on the characteristic value of each evaluation indicator 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.

[0009] In an optional embodiment, generating evaluation data for each core indicator based on the characteristic vector corresponding to each core indicator and the observed sample data includes: Construct the eigenvectors corresponding to the first K largest eigenvalues ​​into a feature matrix; An evaluation data matrix of the core indicators is calculated based on the observation sample matrix composed of the feature matrix and the observation sample data, and the elements of the evaluation data matrix are the evaluation data of the core indicators.

[0010] In an optional embodiment, the weight of each core indicator is determined based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value, including:

[0011] in, is the weight of the jth core indicator; is the evaluation data of the i-th observation of the j-th core indicator; is the preset standard value corresponding to the jth core indicator; M is the total number of observations; K is the total number of core indicators.

[0012] In an optional embodiment, based on the evaluation data, weights and preset standard values ​​of each core indicator, a grey correlation coefficient matrix of the core indicators is calculated, including: Grey correlation coefficient matrix , where r ij The calculation formula is:

[0013] in, Represents weighted indicator data to be evaluated and ideal value The first-level minimum difference of the absolute value of the difference, that is, the absolute value of the difference According to the different values ​​of j, select the minimum value; Indicates the minimum difference of the second level, that is, In the equation, select the minimum value according to the value of i; similarly, Represents weighted indicator data to be evaluated and ideal value The maximum difference in the absolute value of the difference, Indicates the maximum difference of the second level; ρ is the discrimination factor, ranging from 0 to 1, with a typical value of 0.5. Its function is to improve the difference of the correlation coefficients between the indicators to be evaluated; , .

[0014] In an optional embodiment, calculating the quantitative evaluation value of each core indicator based on the grey correlation coefficient matrix includes: The quantitative evaluation results of the jth core evaluation index are:

[0015] Among them, M represents the number of observation samples of the evaluation index, .

[0016] In a second aspect, the present invention provides a multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device, comprising: An indicator observation module is used to collect corresponding observation sample data of the inertial composite satellite according to multiple evaluation indicators, and extract eigenvalues ​​and eigenvectors corresponding to each evaluation indicator from the observation sample data; An indicator screening module is used to screen multiple core indicators from multiple evaluation indicators based on the eigenvalue of each evaluation indicator using the cumulative contribution rate inequality, and generate evaluation data for each core indicator based on the eigenvector corresponding to each core indicator and the observed sample data; A weight calculation module is used to determine the weight of each core indicator based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value; The indicator quantification module is used to calculate the grey correlation coefficient matrix of multiple core indicators based on the evaluation data, weights and preset standard values ​​of each core indicator, and calculate the quantitative evaluation value of each core indicator based on the grey correlation coefficient matrix.

[0017] In an optional embodiment, the indicator observation module includes: Convert the observation sample data into an observation sample matrix and perform standardization on the observation sample matrix; Calculate the covariance matrix of the standardized observation sample matrix; The eigenvalues ​​and eigenvectors corresponding to the evaluation indicators are extracted based on the covariance matrix.

[0018] In an optional embodiment, the indicator screening module includes: Sort the eigenvalues ​​from large to small; The number K of core indicators is determined based on the characteristic value of each evaluation indicator 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.

[0019] According to a third aspect, a device is provided, comprising: A memory for storing a multi-attribute quantitative evaluation program for an inertial satellite integrated navigation device; The processor is configured to implement the steps of the inertial satellite integrated navigation device multi-attribute quantitative evaluation method provided in the first aspect when executing the inertial satellite integrated navigation device multi-attribute quantitative evaluation program.

[0020] In a fourth aspect, a computer-readable medium is provided, on which a multi-attribute quantitative evaluation program for an inertial satellite combined navigation device is stored. When the multi-attribute quantitative evaluation program for an inertial satellite combined navigation device is executed by a processor, the steps of the multi-attribute quantitative evaluation method for an inertial satellite combined navigation device provided in the first aspect are implemented.

[0021] The beneficial effects of the present invention are as follows: the method and system for quantitatively evaluating a multi-attribute inertial satellite integrated navigation device provided by the present invention determines the number of core evaluation indicators by calculating the covariance matrix and cumulative contribution rate inequality of the standardized sample data matrix, thereby achieving dimensionality reduction of the evaluation indicators and converting the problem of multiple evaluation indicators into a comprehensive problem with fewer evaluation indicators; the weights of each evaluation indicator are reasonably assigned by measuring the Euclidean distance between the evaluation indicator data and the ideal state vector; and the gray correlation coefficient matrix of the weighted evaluation indicator data and the ideal value is calculated to solve the multi-attribute evaluation quantitative problem from a geometric perspective. The gray correlation coefficient is used as a metric for the navigation state to discern the development trend of the navigation state and obtain a multi-attribute quantitative evaluation result. 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 results are low, making it difficult to reflect the development trend of the navigation state and providing guidance for the completion of the navigation task based on the navigation state evaluation results. Therefore, compared with the prior art, the present invention completes the multi-attribute quantitative evaluation of the navigation device through measures such as multi-attribute dimensionality reduction, scientifically assigning evaluation indicator weights, and quantifying the evaluation results, significantly improving the accuracy and scientific nature of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0024] Figure 2 FIG. 4 is a schematic block diagram of a system according to an embodiment of the present invention.

[0025] Figure 3 A schematic structural diagram of a device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0028] The multi-attribute quantitative evaluation method for an inertial satellite integrated navigation device provided by an embodiment of the present invention is executed by a computer device. Accordingly, the multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device runs in the computer device.

[0029] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject can be a multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0030] like Figure 1 As shown, the method includes: S1. Collecting corresponding observation sample data of the inertial composite satellite according to a plurality of evaluation indicators, and extracting eigenvalues ​​and eigenvectors corresponding to each evaluation indicator from the observation sample data; S2. Filtering multiple core indicators from multiple evaluation indicators based on the eigenvalues ​​of each evaluation indicator using the cumulative contribution rate inequality, and generating evaluation data for each core indicator based on the eigenvector corresponding to each core indicator and the observed sample data; S3. Determine the weight of each core indicator based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value; S4. Based on the evaluation data, weights and preset standard values ​​of each core indicator, a grey correlation coefficient matrix of the core indicators is calculated, and a quantitative evaluation value of each core indicator is calculated based on the grey correlation coefficient matrix.

[0031] In an embodiment of the present invention, based on step S1, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.

[0032] S101. Convert the observed sample data into an observed sample matrix and perform standardization on the observed sample matrix.

[0033] For the matrix X consisting of M observation sample data of N evaluation indicators, the matrix X is:

[0034] Normalize the matrix X to form a standardized sample matrix Y, which is expressed as:

[0035] The element y in the i-th row and j-th column of the standardized sample matrix Y ij Expressed as:

[0036] Among them, M represents the number of observation samples of the evaluation index.

[0037] S102. Calculate the covariance matrix of the standardized observation sample matrix.

[0038] The covariance matrix S of the standardized sample matrix Y is expressed as:

[0039] , , where the element s in the i-th row and j-th column of the covariance matrix S is ij Expressed as:

[0040] 、 are the sample means of the i-th and j-th evaluation indicators, respectively, expressed as: , .

[0041] S103. Extract the eigenvalues ​​and eigenvectors corresponding to each evaluation indicator based on the covariance matrix.

[0042] From the equation , calculate the N eigenvalues ​​of S (λ1,λ2,…,λ i ,…,λ N ) and the corresponding N eigenvectors (Q1, Q2, ..., Q i ,…,Q N ), where S is the covariance matrix of the standardized sample matrix Y, and E is the unit matrix.

[0043] In an embodiment of the present invention, based on step S2, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.

[0044] S201. Sort the eigenvalues ​​from large to small; S202. Determine the number of core indicators K based on the eigenvalues ​​of each evaluation indicator using the cumulative contribution rate inequality: According to the cumulative contribution rate inequality: , Among them, C is a constant with a typical value of 0.85. The minimum value of K that satisfies the above relationship is calculated. At this time, the value of K is the number of core evaluation indicators.

[0045] 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 correspondence between the eigenvalues ​​and the vectors.

[0046] S204. Construct the eigenvectors corresponding to the first K largest eigenvalues ​​into a characteristic matrix; calculate the evaluation data matrix of the core indicators based on 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 indicators.

[0047] Take the eigenvectors corresponding to K eigenvalues ​​to construct the characteristic matrix Q K , expressed as:

[0048] By the characteristic matrix Q K And the sample data matrix X is used to construct the core evaluation index matrix A, which is expressed as: .

[0049] In an embodiment of the present invention, based on step S3, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.

[0050] In the prior art, the weight distribution of evaluation indicators 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 this type of method relies heavily on the subjectivity of experts and has a certain degree of subjective arbitrariness. The objective weight method is mainly derived from the evaluation of data, and only considers the structural characteristics of the data itself. It is greatly affected by abnormal data and it is difficult to establish the complex nonlinear mapping relationship between each data and the navigation state evaluation results. The present invention adopts the method of measuring the index data to be evaluated and the ideal state vector The weight of each evaluation indicator is determined by the Euclidean distance between the evaluation indicators; the larger the Euclidean distance, the smaller the impact of the evaluation indicator on the navigation status evaluation result, and the smaller the assigned weight should be; conversely, the greater the impact, the greater the assigned weight.

[0051] Determine the ideal state vector of K core evaluation indicators , expressed as:

[0052] Ideal state vector The values ​​of each element in the equation can be composed of the optimal values ​​of the navigation equipment performance indicators, or can be determined by experts in this field based on their work experience.

[0053] Core evaluation indicator matrix A and The weighted Euclidean distance between is expressed as: ,

[0054] in, represents the element value of the i-th row and j-th column of the core evaluation index matrix A. Find the value that makes all h j The weight ω when the sum takes the minimum value i is the weight of the K core evaluation indicators. This problem can be transformed into the following optimization problem:

[0055] The above optimization problem can be solved using Lagrangian function.

[0056] According to the K core evaluation indicators and the ideal state vector The Euclidean distance between them is used to calculate the evaluation index weight, and the jth evaluation index weight ω j Expressed as:

[0057] in, is the weight of the jth core indicator; is the evaluation data of the i-th observation of the j-th core indicator; is the preset standard value corresponding to the jth core indicator; M is the total number of observations; K is the total number of core indicators.

[0058] In an embodiment of the present invention, based on step S4, a possible embodiment will be given below to illustrate its specific implementation scheme in a non-limiting manner.

[0059] Grey correlation coefficient matrix of K core indicators , where r ij The calculation formula is:

[0060] in, Represents weighted indicator data to be evaluated and ideal value The first-level minimum difference of the absolute value of the difference, that is, the absolute value of the difference According to the different values ​​of j, select the minimum value; Indicates the minimum difference of the second level, that is, In the equation, select the minimum value according to the value of i; similarly, Represents weighted indicator data to be evaluated and ideal value The maximum difference in the absolute value of the difference, Indicates the maximum difference of the second level; ρ is the discrimination factor, ranging from 0 to 1, with a typical value of 0.5. Its function is to improve the difference of the correlation coefficients between the indicators to be evaluated; , .

[0061] The quantitative evaluation results of the jth core evaluation index are:

[0062] Among them, M represents the number of observation samples of the evaluation index, .

[0063] Navigation state assessment involves multiple aspects, including accuracy, convergence, reliability, and maintainability. Evaluation metrics include velocity accuracy, position accuracy, attitude accuracy, accelerometer bias estimation stability, convergence accuracy, minimum detectable gross error, variance matrix consistency, mean time between failures, and continuous operation time. Traditional navigation state assessment methods require analysis and data processing of all these metrics before providing an evaluation result. The large number of metrics involved and the diverse data types make the evaluation process cumbersome and time-consuming. In reality, not all of these metrics contribute equally to the evaluation results; some have a significant impact, while others have little influence. However, identifying which metrics have the greatest impact is crucial for both evaluation efficiency and accuracy. To solve this problem, the technical solution disclosed in the embodiment of the present invention first performs data standardization on all evaluation indicators to eliminate dimensional differences and form a standardized sample matrix. The covariance matrix of the standardized sample data matrix is ​​then calculated. The eigenvalues ​​and eigenvectors of the covariance matrix are solved using a matrix algorithm and sorted from high to low in order of eigenvalue size. The number of core evaluation indicators is then determined by calculating the cumulative contribution rate inequality. Furthermore, a matrix transformation is performed on the characteristic matrix constructed from the eigenvectors and the sample data matrix to construct a core evaluation indicator matrix, forming new indicators to be evaluated. This transforms the original evaluation indicator system into a new orthogonal system. That is, through the matrix transformation, the originally related evaluation indicators are converted into mutually unrelated indicators to be evaluated, while retaining the vast majority of evaluation information that affects the evaluation results. Therefore, the technical solution disclosed in the embodiment of the present invention achieves dimensionality reduction processing of the evaluation indicators, transforming the multi-attribute evaluation indicator problem into a comprehensive processing of the evaluation indicator problem with fewer attributes, and transforming the high-dimensional space problem into a low-dimensional space problem, making the evaluation problem of the navigation state simpler and more intuitive.

[0064] In some embodiments, the multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the multi-attribute quantitative evaluation system for an inertial satellite integrated navigation device may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 (Description) Function of multi-attribute quantitative evaluation of inertial satellite integrated navigation equipment.

[0065] In this embodiment, the multi-attribute quantitative evaluation system for inertial satellite integrated navigation equipment can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0066] An indicator observation module is used to collect corresponding observation sample data of the inertial composite satellite according to multiple evaluation indicators, and extract eigenvalues ​​and eigenvectors corresponding to each evaluation indicator from the observation sample data; An indicator screening module is used to screen multiple core indicators from multiple evaluation indicators based on the eigenvalue of each evaluation indicator using the cumulative contribution rate inequality, and generate evaluation data for each core indicator based on the eigenvector corresponding to each core indicator and the observed sample data; A weight calculation module is used to determine the weight of each core indicator based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value; The indicator quantification module is used to calculate the grey correlation coefficient matrix of multiple core indicators based on the evaluation data, weights and preset standard values ​​of each core indicator, and calculate the quantitative evaluation value of each core indicator based on the grey correlation coefficient matrix.

[0067] Figure 3 The multi-attribute quantitative evaluation method of the inertial satellite integrated navigation device provided in the embodiment of the present application can be applied to the device. Those skilled in the art will understand that the device structure involved in the embodiment of the present invention does not constitute a limitation of the device, and the device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiment of the present invention, the device includes but is not limited to a laptop computer, a desktop computer, a workbench, 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 assistants, 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 and / or required herein.

[0068] The device 300 may include a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention. The server structure may be a bus structure or a star structure, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0069] The memory 320 can be used to store execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile memory 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 memory, 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 some or all of the steps in the above-described method embodiments.

[0070] The processor 310 is the control center of the storage device, which uses various interfaces and lines to connect various parts of the entire electronic device. It executes 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, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0071] The communication unit 330 is configured to establish a communication channel so that the storage device can communicate with other devices, receive user data sent by other devices, or send user data to other devices.

[0072] The present invention also provides a computer medium, wherein the computer medium may store a program that, when executed, may include some or all of the steps of each embodiment provided by the present invention. The medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0073] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software and a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a medium such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code, and includes 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 various embodiments of the present invention.

[0074] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.

[0075] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, and can be electrical, mechanical or other forms.

[0076] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0077] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0078] Although the present invention has been described in detail with reference to the accompanying drawings and in conjunction with preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, persons of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and such modifications or substitutions shall be within the scope of the present invention. Any changes or substitutions that can be easily conceived by persons skilled in the art within the technical scope disclosed in the present invention shall be within the scope of protection of the present invention.

Claims

1. A multi-attribute quantitative evaluation method for an inertial satellite integrated navigation device, characterized in that: include: Collecting corresponding observation sample data of the inertial composite satellite according to a plurality of evaluation indicators, and extracting eigenvalues ​​and eigenvectors corresponding to each evaluation indicator from the observation sample data; Using the cumulative contribution rate inequality to select multiple core indicators from multiple evaluation indicators according to the characteristic value of each evaluation indicator, and generating evaluation data for each core indicator according to the characteristic vector corresponding to each core indicator and the observed sample data; Determine the weight of each core indicator based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value; Based on the evaluation data, weights and preset standard values ​​of the core indicators, the grey correlation coefficient matrix of the core indicators is calculated, and the quantitative evaluation value of each core indicator is calculated based on the grey correlation coefficient matrix.

2. The method according to claim 1, characterized in that Extracting eigenvalues ​​and eigenvectors corresponding to each evaluation indicator from the observed sample data includes: Convert the observation sample data into an observation sample matrix and perform standardization on the observation sample matrix; Calculate the covariance matrix of the standardized observation sample matrix; The eigenvalues ​​and eigenvectors corresponding to the evaluation indicators are extracted based on the covariance matrix.

3. The method according to claim 1, characterized in that The cumulative contribution rate inequality is used to screen out multiple core indicators from multiple evaluation indicators based on the characteristic values ​​of each evaluation indicator, including: Sort the eigenvalues ​​from large to small; The number K of core indicators is determined based on the characteristic value of each evaluation indicator 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.

4. The method according to claim 3, characterized in that Generating evaluation data for each core indicator based on the characteristic vector corresponding to each core indicator and the observed sample data, including: Construct the eigenvectors corresponding to the first K largest eigenvalues ​​into a feature matrix; An evaluation data matrix of the core indicators is calculated based on the observation sample matrix composed of the feature matrix and the observation sample data, and the elements of the evaluation data matrix are the evaluation data of the core indicators.

5. The method according to claim 1, wherein The weight of each core indicator is determined based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value, including: in, is the weight of the jth core indicator; is the evaluation data of the i-th observation of the j-th core indicator; is the preset standard value corresponding to the jth core indicator; M is the total number of observations; K is the total number of core indicators.

6. The method according to claim 5, characterized in that Based on the evaluation data, weights and preset standard values ​​of each core indicator, the grey correlation coefficient matrix of the core indicators is calculated, including: Grey correlation coefficient matrix , where r ij The calculation formula is: in, Represents weighted indicator data to be evaluated and ideal value The first-level minimum difference of the absolute value of the difference, that is, the absolute value of the difference According to the different values ​​of j, select the minimum value; Indicates the minimum difference of the second level, that is, In the equation, select the minimum value according to the value of i; similarly, Represents weighted indicator data to be evaluated and ideal value The maximum difference in the absolute value of the difference, Indicates the maximum difference of the second level; ρ is the discrimination factor, ranging from 0 to 1, with a typical value of 0.

5. Its function is to improve the difference of the correlation coefficients between the indicators to be evaluated; , .

7. The method according to claim 6, characterized in that Calculating the quantitative evaluation value of each core indicator based on the grey correlation coefficient matrix includes: The quantitative evaluation results of the jth core evaluation index are: Among them, M represents the number of observation samples of the evaluation index, .

8. A multi-attribute quantitative evaluation system for inertial satellite integrated navigation equipment, characterized in that: include: An indicator observation module is used to collect corresponding observation sample data of the inertial composite satellite according to multiple evaluation indicators, and extract the eigenvalue and eigenvector corresponding to each evaluation indicator from the observation sample data; An indicator screening module is used to screen multiple core indicators from multiple evaluation indicators based on the eigenvalue of each evaluation indicator using the cumulative contribution rate inequality, and generate evaluation data for each core indicator based on the eigenvector corresponding to each core indicator and the observed sample data; A weight calculation module is used to determine the weight of each core indicator based on the Euclidean distance between the evaluation data of each core indicator and the corresponding preset standard value; The indicator quantification module is used to calculate the grey correlation coefficient matrix of multiple core indicators based on the evaluation data, weights and preset standard values ​​of each core indicator, and calculate the quantitative evaluation value of each core indicator based on the grey correlation coefficient matrix.

9. The system according to claim 8, characterized in that The indicator observation module includes: Convert the observation sample data into an observation sample matrix and perform standardization on the observation sample matrix; Calculate the covariance matrix of the standardized observation sample matrix; The eigenvalues ​​and eigenvectors corresponding to the evaluation indicators are extracted based on the covariance matrix.

10. The system according to claim 8, wherein: The indicator screening module includes: Sort the eigenvalues ​​from large to small; The number K of core indicators is determined based on the characteristic value of each evaluation indicator 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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