A hierarchical evaluation method for laser inertial system performance considering unequal priority of indicators

Abstract

The present invention discloses a method for hierarchical performance evaluation of a laser inertial navigation system (IRU) that considers non-equal priority indicators. This method belongs to the field of monitoring and evaluating laser IRUs in aerospace inertial systems, and includes the following steps: obtaining a predetermined initial IRU performance hierarchical evaluation model; constructing a hierarchical evaluation model by analyzing indicators related to the IRU's performance, and addressing the issue of inconsistent indicator priorities by establishing a BRB-NEP model that considers non-equal priority indicators; obtaining training samples of the IRU to be evaluated; optimizing the parameters in the initial hierarchical performance evaluation model using the training samples of the IRU to be evaluated and a preset optimization algorithm to obtain an optimized hierarchical performance evaluation model for the IRU to be evaluated; and evaluating the performance of the IRU to be evaluated using the optimized hierarchical performance evaluation model. The present invention can improve the accuracy of IRU performance evaluation and address issues such as rule explosion caused by numerous indicators and difficulty embedding expert knowledge.

Description

Technical Field

[0001] The present invention belongs to the field of monitoring and prediction of laser inertial group (LIG) in aerospace inertial systems, and in particular relates to a hierarchical performance evaluation method for LIG taking into account unequal priority of indicators. Background Art

[0002] Laser inertial navigation systems (LIRSs) offer numerous advantages, including complete autonomy, comprehensive and continuous navigation information, high accuracy, and compact size. They are an indispensable fundamental navigation method for rockets and missiles. Because these systems perform extremely critical missions, such as manned spaceflight and target strikes, they place extremely high demands on their reliability. Therefore, monitoring and evaluating the performance of LISs is crucial.

[0003] Laser inertial system performance evaluation has the following characteristics: 1) The value of individual components is extremely high, and their operating life is extremely limited. Therefore, test data is limited, making data-driven evaluation methods impractical. 2) Laser inertial systems are composed of multiple types of components, with signals in multiple, complex, and coupled formats, making mechanistic modeling-based evaluation approaches impractical. 3) Due to the system's complexity, experts' understanding of the system is also fuzzy. Therefore, a highly accurate evaluation model requires the fusion of limited test data and fuzzy expert knowledge. The Belief Rule Base (BRB) is a combination of if-then rules, expert knowledge, and evidential reasoning algorithms. It effectively integrates limited test data and expert knowledge, fully utilizing limited multi-source information.

[0004] When using BRB to solve the performance evaluation problem of laser inertial guidance system, the following problems need to be further solved: (1) There are many indicators of laser inertial guidance system. In the BRB model, the numerous indicators will lead to the explosion of rule combinations and the difficulty of embedding expert knowledge; (2) There is the problem of inconsistent priorities of indicators of laser inertial guidance system. The traditional BRB model is only applicable to the case where indicators have the same priority.

[0005] Therefore, overcoming these shortcomings and accurately assessing the performance of the laser inertial system (LIMU) is an urgent problem. Accurately assessing the performance of the LIMMU would provide a basis for routine maintenance and optimization during critical missions, which would be of significant practical value. Summary of the Invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a hierarchical evaluation method for the performance of a laser inertial navigation system (LIRU) that considers the unequal priority of indicators. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] Obtaining a predetermined initial performance hierarchical evaluation model for the laser inertial system; wherein the initial performance hierarchical evaluation model is constructed by analyzing indicators related to the laser inertial system performance, and establishing a hierarchical evaluation model by considering the inconsistent priority of the indicators through a BRB-NEP model; the BRB-NEP model is implemented based on a confidence rule base method;

[0008] Obtaining a training sample of the laser inertial system to be evaluated; wherein the training sample contains data of each indicator in the initial performance hierarchical evaluation model, and the training sample is marked with a performance label, and the performance label contains the performance level of the laser inertial system and the confidence level corresponding to the performance level;

[0009] Optimizing the parameters of the initial hierarchical performance evaluation model using the training samples of the laser inertial system to be evaluated and a preset optimization algorithm to obtain an optimized hierarchical performance evaluation model for the laser inertial system to be evaluated;

[0010] The performance of the laser inertial system to be evaluated is evaluated using the optimized performance hierarchical evaluation model.

[0011] Beneficial effects of the present invention:

[0012] Aiming at the problem of laser inertial navigation system performance evaluation, based on the confidence rule base theory, the present invention establishes a laser inertial navigation system performance hierarchical evaluation model considering the non-equal priority of indicators, and then realizes a laser inertial navigation system performance hierarchical evaluation method considering the non-equal priority of indicators. The method first obtains a predetermined initial performance hierarchical evaluation model of a laser inertial group; wherein, the initial performance hierarchical evaluation model is obtained by constructing a hierarchical evaluation model by analyzing indicators related to the performance of the laser inertial group, and establishing a BRB-NEP model that takes into account the unequal priority of the indicators to solve the problem of inconsistent indicator priorities; the BRB-NEP model is implemented based on the confidence rule base method; secondly, a training sample of the laser inertial group to be evaluated is obtained; wherein, the training sample contains data of each indicator in the initial performance hierarchical evaluation model, and the training sample is marked with a performance label, and the performance label contains the performance level of the laser inertial group and the confidence corresponding to the performance level; then, since the initial performance hierarchical evaluation model is given by expert knowledge, the fuzziness of expert knowledge causes a certain error in the initial performance hierarchical evaluation model, so the training sample of the laser inertial group to be evaluated is used, and a preset optimization algorithm is adopted to optimize the parameters in the initial performance hierarchical evaluation model to obtain an optimized performance hierarchical evaluation model for the laser inertial group to be evaluated; finally, the performance of the laser inertial group to be evaluated is evaluated using the optimized performance hierarchical evaluation model.

[0013] The hierarchical performance evaluation model for a laser inertial system (LIS) system, which considers indicators with unequal priorities, is essentially a hybrid-driven model that integrates fuzzy expert knowledge and limited test data. This model fully utilizes limited test data and fuzzy expert knowledge, effectively improving the performance evaluation accuracy of the LIS. Based on the hierarchical evaluation concept, the present invention designs a performance evaluation method for a LIS system. Numerous indicators are assigned to different sub-models, each with limited input indicators. This method not only addresses issues such as rule explosion and difficulty embedding expert knowledge due to the large number of indicators, but also provides evaluation results for each component, enabling component performance results to support system performance results, making the LIS evaluation results more interpretable. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic flow chart of a method for hierarchical evaluation of LINS performance considering unequal priority of indicators provided by an embodiment of the present invention;

[0015] Figure 2 A schematic diagram of the structure of a hierarchical indicator system provided by an embodiment of the present invention;

[0016] Figure 3 Schematic diagram of the parameter optimization process of the hierarchical evaluation model based on the artificial bee colony algorithm according to an embodiment of the present invention;

[0017] Figure 4 This is a result diagram of the zero bias stability of the X-axis laser gyroscope of a certain laser strapdown inertial system in an embodiment of the present invention;

[0018] Figure 5 This is a result diagram of the zero bias stability of the Y-axis laser gyroscope of a certain laser strapdown inertial system in an embodiment of the present invention;

[0019] Figure 6 This is a result diagram of the zero bias stability of a Z-axis laser gyroscope of a laser strapdown inertial system in an embodiment of the present invention;

[0020] Figure 7 The changes in the utility RMSE values ​​during the training of the two models in the embodiment of the present invention are shown in the figure.

[0021] Figure 8 This is the evaluation result of 800 test samples by the optimized hierarchical evaluation model in the embodiment case of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0023] The embodiment of the present invention provides a method for hierarchical evaluation of the performance of a laser inertial system considering the non-equal priority of indicators, such as Figure 1 As shown, the method may include the following steps:

[0024] S1, obtain the predetermined initial performance hierarchical evaluation model of the laser inertial group;

[0025] The present invention conducts preliminary performance analysis and research on laser inertial systems (LIRSs) to construct an initial hierarchical performance evaluation model suitable for all LIRRSs. This initial hierarchical performance evaluation model is constructed by analyzing indicators related to LIRRS performance and, to address the issue of inconsistent indicator priorities, establishing a BRB-NEP model that considers unequal indicator priorities. The BRB-NEP model is implemented using a confidence rule base approach.

[0026] The process of determining the initial performance hierarchical evaluation model of the laser inertial system includes steps A1 and A2:

[0027] Step A1: Analyze multiple indicators related to the performance of the laser inertial system and construct a hierarchical indicator system through indicator selection;

[0028] Specifically, step A1 may include the following steps A11 to A13:

[0029] Step A11, for a laser strapdown inertial system composed of a laser gyroscope and a quartz accelerometer, selecting multiple indicators representing historical information and test information through performance analysis, wherein the historical information includes historical transport times and cumulative transport mileage; the test information includes self-calibration test results, self-aiming test results, and self-detection test results; determining an expression form for the self-detection test results based on a 0-1 output format, determining an expression form for the self-aiming test results based on a 0-1 output format, and determining calibration parameters corresponding to the laser gyroscope and the quartz accelerometer, respectively, by determining expressions for output error models corresponding to the laser gyroscope and the quartz accelerometer, respectively, for use in representing the self-calibration test results;

[0030] First, historical information is analyzed, including the historical transportation times and cumulative transportation mileage of the laser inertial group.

[0031] Rocket inertial navigation systems (i.e., laser inertial groups) contain many high-precision instruments, including gyroscopes, accelerometers, high-precision frames, angle sensors, servo motors, etc. These instruments have very high requirements for transportation and storage environments. In particular, random vibrations and sudden turns during transportation can cause certain damage to high-precision instruments. Therefore, grassroots users will record the historical transportation times of each high-precision inertial navigation system. , cumulative transport mileage and other information.

[0032] Secondly, the test information is analyzed, including the self-calibration test results, the self-aiming test results, and the self-detection test results.

[0033] Because rockets and missiles place extremely high demands on system reliability, high-precision inertial navigation system testing is a crucial component of pre-flight testing. This paper uses a laser strapdown inertial navigation system (LIRS) composed of a laser gyroscope and a quartz accelerometer as an example. Tests are performed after the LRS is powered on and stabilized, primarily including self-calibration, self-aiming, and self-detection testing.

[0034] The self-test is a point-by-point check of the laser strapdown inertial system using a pre-set program and multiple checkpoints. If a point passes the test, its identifier output is 0; if it fails, its identifier output is 1. If all identifiers output 0, the self-test has passed; if a particular identifier outputs 1, the self-test has failed. Therefore, the self-test result is a 0-1 output, i.e.:

[0035] (1);

[0036] In the above formula (1), Indicates the self-detection test results of the laser strapdown inertial system.

[0037] The self-aiming test is used to test the laser strapdown inertial system's aiming performance. Generally, three self-aiming tests are performed. If the difference between the three self-aiming results is within the threshold range, the self-aiming performance is normal. Otherwise, the self-aiming performance is abnormal. From the test method, the output of the self-aiming test result can be regarded as a 0-1 output, which is:

[0038] (2);

[0039] In the above formula (2), Indicates the self-aiming test results of the laser strapdown inertial system; 、 、 These are the results of 3 self-aiming; is the self-aiming error threshold; Indicates intersection.

[0040] The purpose of the self-calibration test is to calibrate the error model of the inertial instrument in the laser strapdown inertial system. The laser gyroscope and quartz accelerometer are distinguished and analyzed separately. For the convenience of description, the X, Y, and Z axis laser gyroscopes are respectively recorded as 、 、 , the X, Y, and Z axis quartz accelerometers are respectively 、 、 .

[0041] For the three-axis laser gyroscope, the output error model is:

[0042] (3);

[0043] In the above formula (3), 、 、 Laser gyroscope 、 、 The number of output rotation angle increment pulses; 、 、 Laser gyroscope 、 、 The scaling factor of 、 、 Laser gyroscope 、 、 Zero bias; 、 、 are the angular velocities of the carrier in the X, Y, and Z directions respectively; 、 Laser gyroscope Installation errors around the Y and Z axes; 、 Laser gyroscope Installation errors around the X and Z axes; 、 Laser gyroscope Installation errors around the X and Z axes; Indicates time.

[0044] The output error model of the three-axis quartz accelerometer is:

[0045] (4);

[0046] In the above formula (4), 、 、 Quartz accelerometer 、 、 Output voltage; 、 、 Quartz accelerometer 、 、 The scaling factor of 、 、 Quartz accelerometer 、 、 Zero bias; 、 、 are the acceleration values ​​of the carrier along the X, Y, and Z directions respectively; 、 Quartz accelerometer 、 Installation error with X axis; 、 Quartz accelerometer 、 Installation error with the Y axis, 、 Quartz accelerometer 、 Installation error with the Z axis.

[0047] The laser strapdown inertial system self-calibration uses gravity acceleration and ground speed as excitation, changes the input excitation through multiple position flips of the laser inertial system frame, collects the outputs of the laser gyroscope and quartz accelerometer, and finally calculates the error model parameters. Specifically,

[0048] The calibration parameters of the laser gyroscope include: 、 、 Zero bias 、 、 , laser gyroscope 、 、 The scaling factor 、 、 , laser gyroscope Installation error around Y and Z axes 、 Laser gyroscope Installation error around X and Z axes 、 Laser gyroscope Installation error around X and Z axes 、 .

[0049] The calibration parameters of the quartz accelerometer include: 、 、 Zero bias 、 、 , quartz accelerometer 、 、 The scaling factor 、 、 , quartz accelerometer 、 Installation error with X axis 、 ;Quartz accelerometer 、 Installation error with Y axis 、 , quartz accelerometer 、 Installation error with Z axis 、 .

[0050] Step A12: Based on the information determined by analyzing multiple indicators, indicators are selected to finally determine the 28-dimensional laser strapdown inertial system indicators, which are expressed as:

[0051] (5);

[0052] in, represents the collection of 28-dimensional laser strapdown inertial system indicators; Indicates the self-diagnosis test result; Indicates the self-aiming test result; Indicates the number of historical transports; Indicates the accumulated transport mileage; Indicates the zero bias stability of the X-axis laser gyroscope; Indicates the zero bias stability of the Y-axis laser gyroscope; Indicates the zero bias stability of the Z-axis laser gyroscope; Indicates the installation error of the X-axis laser gyroscope around the Z-axis; Indicates the installation error of the X-axis laser gyroscope around the Y-axis; Indicates the installation error of the Y-axis laser gyroscope around the Z-axis; Indicates the installation error of the Y-axis laser gyroscope around the X-axis; Indicates the installation error of the Z-axis laser gyroscope around the Y-axis; Indicates the installation error of the Z-axis laser gyroscope around the X-axis; Indicates the nonlinearity of the scale factor of the X-axis laser gyroscope; Indicates the nonlinearity of the scale factor of the Y-axis laser gyroscope; Indicates the nonlinearity of the scale factor of the Z-axis laser gyroscope; Indicates the bias stability of the X-axis quartz accelerometer; Indicates the zero bias stability of the Y-axis quartz accelerometer; Indicates the bias stability of the Z-axis quartz accelerometer; Indicates the installation error between the Y-axis quartz accelerometer and the X-axis; Indicates the installation error between the Z-axis quartz accelerometer and the X-axis; Indicates the installation error between the X-axis quartz accelerometer and the Y-axis; Indicates the installation error between the Z-axis quartz accelerometer and the Y-axis; Indicates the installation error between the X-axis quartz accelerometer and the Z-axis; Indicates the installation error between the Y-axis quartz accelerometer and the Z-axis; Indicates the scale factor stability of the X-axis quartz accelerometer; Indicates the scale factor stability of the Y-axis quartz accelerometer; Indicates the scale factor stability of the Z-axis quartz accelerometer;

[0053] Regarding step A12, during the indicator selection process, the following analysis is performed:

[0054] For historical information, the transportation process greatly affects the accuracy and life of high-precision instruments, so the historical transportation times are selected. , cumulative transport mileage As an indicator for evaluating the performance of laser strapdown inertial system.

[0055] For the self-test results in the test information , self-aiming test results , both are 0-1 output indicators. or When the output is 1, the performance of the laser strapdown inertial system can be directly judged as poor; when and When the output is 0, it is necessary to combine other indicators for further judgment. , self-aiming test results As an indicator for evaluating the performance of laser strapdown inertial system.

[0056] In the self-calibration test, for the laser gyroscope, zero bias refers to the output value of the laser gyroscope under no input conditions. If the zero bias value is stable, only constant compensation is required. Therefore, zero bias stability is the key to measuring this indicator. Based on the above analysis, the zero bias stability is selected. 、 、 As an indicator for evaluating the performance of the laser strapdown inertial system. Among them, zero bias stability 、 、 Laser gyroscope 、 、 Zero bias 、 、 The standard deviation is calculated using existing methods and will not be described in detail here.

[0057] From the approximate process of the laser gyroscope installation error, we can know that For example, The smaller the value, the closer the process The smaller the error, the better. Therefore, choose the installation error. 、 、 、 、 、 As an indicator for evaluating the performance of a laser strapdown inertial system. Furthermore, the laser gyroscope is a type of rate gyroscope. For rate gyros, the field is more concerned with scale factor nonlinearity than the scale factor itself. Scale factor nonlinearity refers to the ratio of the maximum deviation of the gyroscope output from the least squares fit line to the maximum output within the input angular rate range. Taking the X-axis laser gyroscope as an example, the calculation method is:

[0058] (6);

[0059] In the above formula (6), Indicates the nonlinearity of the scale factor of the X-axis laser gyroscope; Computational Laser Gyroscope The scaling factor Nonlinearity; The first fitted values; For the Laser gyroscope output value; is the maximum output value of the laser gyroscope; Indicates the absolute value; The nonlinearity of the scale factor can represent the magnitude of the relative error of the laser gyroscope measurement, so the nonlinearity of the scale factor is selected. 、 、 As an indicator for evaluating the performance of the laser strapdown inertial system. It is understandable that 、 They represent the scale factor nonlinearity of the Y-axis and Z-axis laser gyroscopes, respectively. The calculation process of the scale factor nonlinearity can be found in related technical understanding and will not be explained in detail here.

[0060] For quartz accelerometers, the performance indicators used to evaluate include: bias stability 、 、 , installation error 、 、 、 、 、 , scale factor stability 、 、 It is important to emphasize that, unlike laser gyros, the focus with quartz accelerometers is on scale factor stability.

[0061] Among them, bias stability 、 、 Quartz accelerometers 、 、 Zero bias 、 、 Mean square error; scale factor stability 、 、 Quartz accelerometers 、 、 The scaling factor 、 、 The standard deviation of .

[0062] About Bias Stability 、 、 , and scale factor stability 、 、 The calculation process of can be understood by referring to the relevant technical knowledge, which will not be described in detail here.

[0063] Therefore, based on the above analysis, the 28-dimensional laser strapdown inertial system performance index is finally selected, as shown in formula (5).

[0064] Step A13, constructing a five-layer hierarchical index system based on the 28-dimensional laser strapdown inertial system index and stratifying it by instrument type;

[0065] Based on the preceding analysis, this paper selects 28 metrics to evaluate the performance of a laser strapdown inertial system (LSUTU). If these 28 metrics were used directly to construct the BRB model, assuming each metric is divided into three levels, this would result in 328 rules, inevitably leading to a rule explosion and placing significant pressure on computing power. Furthermore, assuming that experts develop rules based on only two or three metrics, expert knowledge can be well embedded. However, when the number of metrics reaches 28, expert knowledge is no longer sufficient to determine the output level and corresponding confidence level based on the metrics.

[0066] The above analysis shows that a large number of indicators not only leads to rule explosion but also prevents expert knowledge from being embedded. To address this issue, the present invention constructs a hierarchical indicator system based on the needs of a hierarchical evaluation model. The indicator system for a laser strapdown inertial system can be stratified based on various criteria, such as instrument type, functional subsystem, or sensitivity. Given the relative ease of integrating indicators for instruments of the same type, a stratification method for instruments of the same type is employed.

[0067] The core components of the laser strapdown inertial system are the three-axis laser gyroscope and the quartz accelerometer, and the indicators of the laser gyroscope and the quartz accelerometer are available. Therefore, the laser gyroscope and the quartz accelerometer are respectively regarded as important evaluation objects. The indicator system of the laser strapdown inertial system is constructed as follows: Figure 2 shown. Figure 2 In the figure, the ellipsis under the Y-axis and Z-axis laser gyroscopes indicates that their index system is consistent with that of the X-axis laser gyroscope; the ellipsis under the X-axis and Y-axis quartz accelerometers indicates that their index system is consistent with that of the Z-axis quartz accelerometer. Figure 2 The laser gyroscope is referred to as gyroscope, and the quartz accelerometer is referred to as accelerometer.

[0068] See also Figure 2 In the hierarchical indicator system of the laser strapdown inertial system, the indicators from the fifth layer to the first layer are set from bottom to top;

[0069] The fifth level indicators include: 、 、 、 、 、 ,as well as, 、 、 、 、 、 ;

[0070] The fourth-level indicators include: bias stability, scale factor nonlinearity, and installation error evaluation results of the X-axis laser gyroscope; bias stability, scale factor nonlinearity, and installation error evaluation results of the Y-axis laser gyroscope; bias stability, scale factor nonlinearity, and installation error evaluation results of the Z-axis laser gyroscope; and bias stability, scale factor stability, and installation error evaluation results of the X-axis quartz accelerometer; bias stability, scale factor stability, and installation error evaluation results of the Y-axis quartz accelerometer; bias stability, scale factor stability, and installation error evaluation results of the Z-axis quartz accelerometer; among which, the installation error evaluation result of the X-axis laser gyroscope is based on the indicators of the fifth level 、 Calculated; the installation error evaluation result of the Y-axis laser gyroscope is based on the fifth layer indicator 、 Calculated; the installation error evaluation result of the Z-axis laser gyroscope is based on the fifth layer indicator 、 Calculated; the installation error evaluation results of the X-axis quartz accelerometer are based on the fifth layer indicators 、 Calculated; the installation error evaluation results of the Y-axis quartz accelerometer are based on the fifth layer indicators 、 Calculated; the installation error evaluation results of the Z-axis quartz accelerometer are based on the fifth layer indicators 、 Calculated;

[0071] The third-level indicators include: the evaluation results of the X-axis laser gyroscope, the evaluation results of the Y-axis laser gyroscope, the evaluation results of the Z-axis laser gyroscope, and the evaluation results of the X-axis quartz accelerometer, the evaluation results of the Y-axis quartz accelerometer, and the evaluation results of the Z-axis quartz accelerometer; among them, the evaluation results of the X-axis laser gyroscope are calculated based on the fourth-level indicators of the X-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; the evaluation results of the Y-axis laser gyroscope are calculated based on the fourth-level indicators of the Y-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; the evaluation results of the Z-axis laser gyroscope are calculated based on the fourth-level indicators of the Y-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; The results are calculated based on the fourth-layer indicators of the Z-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; the evaluation results of the X-axis quartz accelerometer are calculated based on the fourth-layer indicators of the X-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation results; the evaluation results of the Y-axis quartz accelerometer are calculated based on the fourth-layer indicators of the Y-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation results; the evaluation results of the Z-axis quartz accelerometer are calculated based on the fourth-layer indicators of the Z-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation results;

[0072] The second-tier indicators include: self-detection test results, self-aiming test results, historical transport times, cumulative transport mileage, laser gyroscope assembly evaluation results, and quartz accelerometer assembly evaluation results. The laser gyroscope assembly evaluation results are calculated based on the third-tier laser gyroscope indicators; the quartz accelerometer assembly evaluation results are calculated based on the third-tier quartz accelerometer indicators.

[0073] The indicators of the first layer include: the evaluation results of the laser strapdown inertial system, which are calculated based on all the indicators of the second layer.

[0074] That is, the bottom-up evaluation process is:

[0075] 1) From the fifth floor to the fourth floor: Based on installation error 、 The installation error evaluation results of the X-axis laser gyroscope are given; based on the installation error 、 The installation error evaluation results of the Y-axis laser gyroscope are given; based on 、 The installation error evaluation results of the Z-axis laser gyroscope are given, and based on the installation error 、 The installation error evaluation results of the X-axis quartz accelerometer are given; based on the installation error 、 The installation error evaluation results of the Y-axis quartz accelerometer are given; based on the installation error 、 Provide the installation error evaluation results of the Z-axis quartz accelerometer;

[0076] 2) From the fourth layer to the third layer: The three indicators of the X-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation result are integrated to obtain the evaluation result of the X-axis laser gyroscope; the three indicators of the Y-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation result are integrated to obtain the evaluation result of the Y-axis laser gyroscope; the three indicators of the Z-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation result are integrated to obtain the evaluation result of the Z-axis laser gyroscope; the three indicators of the X-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation result are integrated to obtain the evaluation result of the X-axis quartz accelerometer; the three indicators of the Y-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation result are integrated to obtain the evaluation result of the Y-axis quartz accelerometer; the three indicators of the Z-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation result are integrated to obtain the evaluation result of the Z-axis quartz accelerometer;

[0077] 3) From the third layer to the second layer: The evaluation results of the three-axis laser gyroscope are combined to obtain the evaluation results of the laser gyroscope assembly. The evaluation results of the three-axis quartz accelerometer are combined to obtain the evaluation results of the quartz accelerometer assembly. At the same time, the self-detection test results, self-aiming test results, historical transportation times, and cumulative transportation mileage can be calculated.

[0078] 4) From the second layer to the first layer: Comprehensively analyze the self-detection test results, self-aiming test results, historical transport times, cumulative transport mileage, laser gyroscope assembly evaluation results, and quartz accelerometer assembly evaluation results to obtain the evaluation results of the laser strapdown inertial system.

[0079] In step A2, based on the hierarchical indicator system, the traditional BRB model is used for the evaluation below the highest level. To address the problem of inconsistent priority of indicators at the highest level, a BRB-NEP model that considers unequal priority of indicators is established for evaluation, thereby constructing a hierarchical evaluation model.

[0080] The embodiment of the present invention first constructs a hierarchical indicator system, determines the logic of bottom-up hierarchical evaluation calculation, and then constructs a hierarchical evaluation model based on the hierarchical indicator system, and uses the constructed hierarchical evaluation model to specifically implement the calculation processing of the hierarchical indicator system.

[0081] Step A2 may include:

[0082] In the hierarchical indicator system, the traditional BRB model is set as the corresponding confidence rule base model for the process of obtaining the upper-level indicators from the fifth layer to the fourth layer, the fourth layer to the third layer, and the third layer to the second layer. In order to solve the problem of inconsistent priorities of the first-layer indicators in the evaluation process from the second layer to the first layer, the BRB-NEP model that considers the unequal priority of indicators is established as the corresponding confidence rule base model, thereby constructing a hierarchical evaluation model. Among them, in the hierarchical indicator system, the self-detection test results and the self-targeting test results have the same priority, which is higher than the priority of other indicators.

[0083] Specifically, in the embodiment of the present invention, based on the constructed hierarchical index system and the corresponding evaluation process, a hierarchical evaluation model is constructed with the BRB model as a sub-model. The hierarchical evaluation model includes a total of 15 confidence rule base sub-models.

[0084] This hierarchical evaluation model begins with the fifth-tier indicator and continues upwards until the performance evaluation results for the first-tier "laser strapdown inertial system" are obtained. This hierarchical evaluation model not only solves the rule explosion problem but also provides evaluation results for intermediate links, making the evaluation results of the laser strapdown inertial system more interpretable.

[0085] In addition, the traditional BRB model can be used to evaluate the second and subsequent tiers in the aforementioned evaluation process. These models are designated BRB1 through BRB14. However, the evaluation of the "second tier → first tier" suffers from inconsistent indicator priorities. Therefore, the BRB-NEP model, which considers unequal indicator priorities, is used.

[0086] according to Figure 2 The hierarchical indicator system and corresponding evaluation process are constructed to build a hierarchical evaluation model. The hierarchical evaluation model includes 15 confidence rule base models, as shown in Table 1 (to simplify the article, some text in Table 1 is omitted).

[0087] Table 1 Hierarchical assessment model

[0088]

[0089] Next, we need to solve the problem of establishing and reasoning the BRB-NEP model.

[0090] In the comprehensive evaluation of the laser strapdown inertial system, for the convenience of description, the six indicators of self-detection test results, self-aiming test results, historical transportation times, cumulative transportation mileage, laser gyroscope assembly evaluation results, and quartz accelerometer assembly evaluation results are recorded as ,in , , , . and As an intermediate evaluation result, it is given in the form of evaluation grade and confidence.

[0091] In the indicator middle, and It is a 0-1 output indicator. This type of indicator has a "veto power". or When the output is 1, the performance of the laser strapdown inertial system can be directly judged as poor; when or When the output is 0, the performance evaluation results are given by combining other indicators. and should have a higher priority, while other indicators have a lower priority. This problem can be generalized as follows: Assume that indicators , among which the former indicators 0-1 output and has higher priority, the rest indicators It has a lower priority and needs to solve the BRB modeling problem under different indicator priorities.

[0092] The traditional BRB model requires that all indicators have the same priority. In order to solve the modeling problem under different indicator priorities, the present invention adds an "or rule" on the basis of the "and rule" of the BRB model.

[0093] Specifically, in an embodiment of the present invention, the process of establishing a BRB-NEP model that considers unequal priority of indicators may include the following steps B1 to B4:

[0094] Step B1: To address the modeling issues with different indicator priorities at the highest level, add rules to the traditional BRB model. , described as:

[0095] (7);

[0096] Among them, assuming that indicators , among which the former indicators 0-1 output and has higher priority, the rest indicators Has a lower priority; For the rest indicators The complete works of; representation or rule; Representation and Rules; Laser Inertial Group performance levels; To evaluate the results; For rules The rule weight of for The indicator weight of each indicator; 、 and is a natural number greater than 0;

[0097] Step B2, construct the remaining rules by combining them with rules or rules. Rules Expressed as:

[0098] (8);

[0099] in, For the first Rule No. The reference value of each indicator; For the first Rules confidence level for each performance level; For rules The rule weight of is the total number of rules, is a natural number greater than 0;

[0100] because The or rule is used. In order not to affect the model reasoning process, the mandatory In addition, the rules With the rest of the rules are mutually exclusive, so the rule fusion principle can be determined. See step B3 for details.

[0101] Step B3, Injunction , determine the rule fusion principle as follows:

[0102] ;

[0103] Analyzing equations (7) and (8), we can see that:

[0104] 1) For formula (7), for example, the laser strapdown inertial unit is divided into four performance levels: excellent, good, medium, and poor. Then the rule Indicates that if If any indicator is 1, the performance is directly judged as the worst level, and the confidence level of the worst level is 1;

[0105] 2) For formula (8), Under the premise that all are 0, the remaining indicators are combined , and evaluate the performance level and confidence of the laser strapdown inertial system.

[0106] In step B4, the reasoning process of the BRB-NEP model is divided into three steps: indicator matching, rule activation, and rule fusion, and the corresponding processing method for each step is determined.

[0107] ① The indicator matching process includes:

[0108] The indicators of the BRB-NEP model may have different sources, formats, and dimensions, which makes it impossible to directly integrate the indicators. They need to be converted into a unified format and dimension.

[0109] For non-0-1 output indicators, Indicators, The reference value of the index of the rule is recorded as ;for ,when When, confirm For the first The matching degree of the rules Expressed as:

[0110] (9);

[0111] in: For the Indicators at time The test value of for For the first The matching degree of the rules; is the total number of rules; Indicates the Indicators, The reference value of the indicator of the rule;

[0112] For the 0-1 output indicator, that is, In order not to affect the subsequent rule activation and rule fusion, the matching degree of such indicators is directly set to 0.5, which is expressed as:

[0113] (10);

[0114] in, Indicates time hour For the first The matching degree of the rules.

[0115] ②The rule activation process includes:

[0116] Depending on the degree of match of the indicators, some rules will be activated while others will not. And the degree to which rules are activated varies depending on the degree of match.

[0117] Therefore, first, calculate all the indicators for the The matching degree of the rules for:

[0118] (11);

[0119] (12);

[0120] in, Indicates the The indicator weight of each indicator; Indicates the The relative weight of each indicator;

[0121] In the previous article, the weight of the 0-1 output indicator is forced to be set to 0, that is, ; Set the matching degree of the 0-1 output index to 0.5, that is Substituting the above parameters into formula (11) yields:

[0122] (13);

[0123] Formula (13) means that the 0-1 index does not affect the other indexes on the The matching degree of the rules , indicating that the setting of 0-1 output indicator weight and matching degree is reasonable.

[0124] Due to the inconsistent matching of indicators, the degree to which different rules are activated is also inconsistent. The degree of activation of the rule is determined by the activation weight Therefore, the following processing is performed.

[0125] Comprehensive Rule weight of the rule and matching , get the The activation weights corresponding to the rules are:

[0126] (14);

[0127] in, For the moment Time The activation weights corresponding to the rules; Indicates the The rule weight of the rule; Indicates that all indicators are at time Time to the first The matching degree of the rules.

[0128] ③The rule fusion process includes:

[0129] Based on the The activation weight of the rule and confidence , calculate the basic probability mass, expressed as:

[0130] (15);

[0131] in, for Moment The output of the rule is The basic probability mass of represents the empty set; For the first Rule No. confidence level for each performance level; is the complete set of all performance levels; when hour, for The remaining basic probability mass at the moment represents global ignorance; , 、 are the two components of global ignorance;

[0132] Because the matching degree of each input indicator is different, the activated rules are also different. By fusing all activated rules, the final evaluation result of the laser strapdown inertial system can be obtained. Evidential Reasoning (ER) is generally used for rule fusion. ER algorithms include analytical algorithms and recursive algorithms. Considering the better traceability of recursive ER algorithms, the following are the methods used:

[0133] Use the recursive ER algorithm to perform rule fusion and obtain the previous The fusion result of the evidence is expressed as:

[0134] (16);

[0135] in, Before fusion The output after the rules are The normalized basic probability mass of ; Before fusion The output after the rules are The normalized basic probability mass of ; Before fusion Normalization parameters of the rules; Before fusion Normalization parameters of the rules; for abbreviation of; for The abbreviation of Moment The output of the rule is performance levels The basic probability mass of For the The remaining basic probability mass of the rule; Before fusion The remaining basic probability mass after the rules; 、 for The two components of 、 For the The remaining basic probability mass of the rule The two components of

[0136] Based on the normalized basic probability mass, the confidence level of the laser strapdown inertial system at different performance levels is obtained, which is expressed as:

[0137] (17);

[0138] in, yes The abbreviation of for Output results after moment rule fusion confidence level; in the BRB-NEP model reasoning process, since the empty set has no corresponding physical meaning in engineering applications, the basic probability mass and confidence level assigned to the empty set are uniformly set to 0; Indicates the fusion of all The output after the rules are The normalized basic probability mass of ; express One of the components, To integrate all The remaining basic probability mass after the rules;

[0139] According to the fusion results of all rules, the output of the BRB-NEP model is:

[0140] (18);

[0141] Assuming the performance level of the laser strapdown inertial system The utility value is , then the evaluation result The utility value of is:

[0142] (19);

[0143] in, is the utility value of the evaluation result.

[0144] S2, obtain the training samples of the laser inertial group to be evaluated;

[0145] The training samples contain data of each indicator in the initial performance hierarchical evaluation model, and the training samples are marked with performance labels. The performance labels contain the performance level of the laser inertial group and the confidence level corresponding to the performance level.

[0146] Specifically, a training sample contains the specific values ​​of 28-dimensional laser strapdown inertial navigation system indicators. Performance labels can be determined based on expert experience.

[0147] S3, using the training samples of the laser inertial system to be evaluated and a preset optimization algorithm, optimize the parameters in the initial performance hierarchical evaluation model to obtain an optimized performance hierarchical evaluation model for the laser inertial system to be evaluated;

[0148] The initial performance stratification evaluation model is set up based on expert knowledge. Due to the limited and fuzzy nature of expert experience, there are certain errors in the parameters of the initial performance stratification evaluation model, and further optimization is needed to obtain an accurate stratification evaluation model.

[0149] Specifically, S3 may include the following steps:

[0150] S31, taking the minimum root mean square error between the evaluation model utility output value and the actual value as the optimization goal, establish a parameter optimization model for the initial performance hierarchical evaluation model, wherein the parameters to be optimized include: Index weight , rule weight Hedi Rules corresponding to Confidence level of performance ; ;

[0151] The BRB model output is divided into two forms: confidence and utility. The utility value is expressed as a single data output, which is easier to optimize. Therefore, the optimization goal is to minimize the root mean square error between the evaluation model utility output value and the actual value. The objective function of the optimization goal is expressed as:

[0152] (20);

[0153] In formula (20), is the objective function; Number the sample; is the sample size; For the samples; For the The model evaluation utility value of samples; For the The true utility value of samples; For the The indicator weight of each indicator; For the The rule weight of the rule; For the The rule corresponds to The confidence level of each performance level.

[0154] According to the physical meaning of the parameters, the basic constraints that need to be followed in parameter optimization are:

[0155] (twenty one);

[0156] Please refer to the previous description for the meaning of each parameter.

[0157] S32, according to the optimization goal, using the training sample data of the laser inertial system to be evaluated, adopting the artificial bee colony algorithm, optimizing the parameters in the initial performance hierarchical evaluation model, and obtaining the optimized performance hierarchical evaluation model for the laser inertial system to be evaluated.

[0158] The present invention combines the hierarchical evaluation model parameter optimization problem with the Artificial Bee Colony (ABC) algorithm. First, the model parameters are encoded as artificial bee positions, and the parameter optimization problem is converted into an artificial bee optimal position search problem. Second, a fitness function is set according to the parameter optimization goal to achieve the unification of the artificial bee and model parameter evaluation criteria.

[0159] Specifically, according to the artificial bee encoding method, the fitness function setting method, and the principle of the ABC algorithm, a parameter optimization process of the hierarchical evaluation model based on the ABC algorithm is formulated. Please refer to Figure 3 . Specifically including:

[0160] Step a1, encoding the artificial bee position and setting the fitness function;

[0161] The first The position of each artificial bee is encoded as the parameter to be optimized, namely:

[0162] (twenty two);

[0163] in, For the Position coding of each artificial bee; is the location dimension.

[0164] The fitness function is used to evaluate the quality of the artificial bee position. Combined with the background of hierarchical evaluation model parameter optimization, the fitness function is used to evaluate the quality of the model parameters. According to the meaning of the fitness function, the larger the fitness value, the better the model parameters. Based on formula (20), the fitness function is set as:

[0165] (twenty three);

[0166] in, is the fitness function of the artificial bee position;

[0167] Step a2, initializing the position of the artificial bee, i.e. setting the initial position of the artificial bee to;

[0168] (twenty four);

[0169] in, Indicates the Artificial bees in dimension Positional encoding; Indicates that the artificial bee is in dimension The lower limit of the position interval; Indicates that the artificial bee is in dimension The upper limit of the position interval; Represents a random number between (0, 1);

[0170] Step a3: classify the leading bees and the following bees according to the fitness function;

[0171] After all artificial bees have their positions initialized, they are sorted by fitness. The first artificial bee is defined as the leader bee, and the rest of the artificial bees are set as follower bees;

[0172] Step a4: The leader bee transmits the fitness value of its own position to the follower bee through the waggle dance. Based on the fitness function of the leader bee, the probability of the follower bee selecting the leader bee is calculated.

[0173] (25);

[0174] in, represents the probability of the follower bee choosing the leader bee; represents the coding index of the leading bee; Indicates the number of leading bees; Indicates the fitness value of the leader bee;

[0175] Step a5: The follower bee selects the leading bee queen, and the follower bee and the leading bee use the same method to perform location search.

[0176] There are three location search methods, one of which is randomly selected in each iteration:

[0177] (26);

[0178] in, Indicates the Artificial bees in dimension Location after search; Indicates the Artificial bees in dimension location; Represents a random number between (0, 1); Represents a random number between (0, 1); Represents a random number between (0, 1); Represents a random number between (0, 1); Indicates the optimal artificial bee population in dimension location.

[0179] Step a6, determine whether: the number of searches for a certain location reaches the threshold and the fitness does not improve significantly; if so, execute step a7; if not, execute step a5;

[0180] Step a7, the corresponding artificial bee is transformed into a scout bee, and its position is randomly reinitialized, and then transformed into a follower bee;

[0181] (27);

[0182] Step a8, determine whether the maximum number of searches has been reached; if yes, go to step a9, if not, go to step a5;

[0183] The maximum number of searches is generally determined through repeated attempts based on the convergence of the optimization process.

[0184] Step a9: output the optimal position and decode the optimal position into the optimal parameter value. The decoding method is the inverse process of the artificial bee position encoding method.

[0185] Through the processing of S3, the parameters of the initial performance hierarchical evaluation model pre-built for all laser inertial systems can be optimized using the training samples of the known performance levels of the current laser inertial system to be evaluated, thereby obtaining an optimized performance hierarchical evaluation model for the laser inertial system to be evaluated.

[0186] S4, using the optimized performance hierarchical evaluation model, the performance of the laser inertial system to be evaluated is evaluated.

[0187] Specifically, S4 may include:

[0188] S41, collecting indicator data corresponding to the laser inertial sensor group to be evaluated based on the indicators contained in the initial performance hierarchical evaluation model to obtain a sample to be tested;

[0189] It is also understandable that what is collected here is the specific value of the 28-dimensional laser strapdown inertial system indicator corresponding to a moment, thereby obtaining the sample to be tested at that moment.

[0190] S42, using the optimized performance hierarchical evaluation model to perform hierarchical evaluation on the sample to be tested, and obtaining a performance evaluation result of the laser inertial system to be evaluated, wherein the performance evaluation result includes a performance level and a corresponding confidence level.

[0191] Using the optimized performance stratification evaluation model for the laser inertial system to be evaluated, a stratified evaluation of the sample to be tested can be performed. Each performance level and corresponding confidence level can be obtained as the performance evaluation result of the laser inertial system to be evaluated. The level with the highest confidence level is the performance level corresponding to the laser inertial system to be evaluated.

[0192] Aiming at the problem of laser inertial navigation system performance evaluation, based on the confidence rule base theory, the present invention establishes a laser inertial navigation system performance hierarchical evaluation model considering the non-equal priority of indicators, and then realizes a laser inertial navigation system performance hierarchical evaluation method considering the non-equal priority of indicators. The method first obtains a predetermined initial performance hierarchical evaluation model of a laser inertial system; wherein, the initial performance hierarchical evaluation model is constructed by analyzing the indicators related to the performance of the laser inertial system, and the BRB-NEP model that takes into account the unequal priority of the indicators is established to solve the problem of inconsistent indicator priorities; the BRB-NEP model is implemented based on the confidence rule base method; secondly, the training samples of the laser inertial system to be evaluated are obtained; wherein, the training samples contain data of each indicator in the initial performance hierarchical evaluation model, and the training samples are marked with performance labels, and the performance labels contain the performance level of the laser inertial system and the confidence level corresponding to the performance level; then, since the initial performance hierarchical evaluation model is given by expert knowledge, the fuzziness of expert knowledge makes the initial performance hierarchical evaluation model have certain errors, so the training samples of the laser inertial system to be evaluated are used, and a preset optimization algorithm is adopted to optimize the parameters in the initial performance hierarchical evaluation model to obtain an optimized performance hierarchical evaluation model for the laser inertial system to be evaluated; finally, the optimized performance hierarchical evaluation model is used to evaluate the performance of the laser inertial system to be evaluated.

[0193] The hierarchical performance evaluation model for a laser inertial system (LIS) system, which considers indicators with unequal priorities, is essentially a hybrid-driven model that integrates fuzzy expert knowledge and limited test data. This model fully utilizes limited test data and fuzzy expert knowledge, effectively improving the performance evaluation accuracy of the LIS. Based on the hierarchical evaluation concept, the present invention designs a performance evaluation method for a LIS system. Numerous indicators are assigned to different sub-models, each with limited input indicators. This method not only addresses issues such as rule explosion and difficulty embedding expert knowledge due to the large number of indicators, but also provides evaluation results for each component, enabling component performance results to support system performance results, making the LIS evaluation results more interpretable.

[0194] Furthermore, this invention is the first to consider how to evaluate the performance of laser inertial system (LIRS) systems when their indicators have inconsistent priorities. Compared to traditional techniques, which focus more on the impact of data reliability on LRS performance evaluation (which can only address performance evaluation when indicators have consistent priorities), this invention can better address the performance evaluation of complex LRS systems and is therefore more practical. Furthermore, after thoroughly analyzing LRS performance-related indicators, this invention selects 28-dimensional LSSRS indicators as evaluation metrics, providing a more comprehensive set of metrics and thus improving evaluation accuracy. Furthermore, this invention implements the evaluation model using a confidence rule base (BRB). By integrating fuzzy logic, probabilistic reasoning, and expert experience, this BRB handles multiple uncertainties (fuzziness, randomness, and incompleteness) in a rule-based manner. It supports mixed quantitative and qualitative inputs and features parameter optimization. Compared to traditional evidential reasoning, this method offers stronger uncertainty modeling capabilities and adaptive optimization features for LRS evaluation, improving evaluation accuracy and interpretability.

[0195] To facilitate understanding of the embodiments of the present invention and verify the performance evaluation accuracy of the hierarchical evaluation model of the present invention for a laser inertial system, a specific experimental case is given below.

[0196] (1) The construction process of the initial performance hierarchical evaluation model;

[0197] 1. Data Collection

[0198] Eight laser strapdown inertial systems (LSIs) from the same batch and model were used as test subjects. These systems are used for navigation on a medium- and long-range rocket. Because the LSIs and rockets undergo only annual inspections and testing during storage, the number of samples collected from each LSI is very limited, at only 30. Since the LSIs have an output frequency of 50 Hz, the data volume of a single sample is very large. Therefore, to expand the sample size to meet the experimental sample size requirements, the single sample data was expanded to 20 using an overlapping sliding window. This expansion yielded 600 samples per LSI, and 4,800 samples for the eight LSIs.

[0199] Each sample consists of 28-dimensional laser strapdown inertial system indicator data, a total of 28 dimensions. Most of the data is calculated from the output of the laser gyroscope and quartz accelerometer. Taking a laser strapdown inertial system as an example, the gyroscope zero bias stability of the three axes of X, Y, and Z (200 samples randomly intercepted) is as follows: Figure 4 、 Figure 5 and Figure 6 shown.

[0200] 2. Construction of initial performance hierarchical evaluation model;

[0201] Based on the hierarchical evaluation model presented in Table 1, three aspects need to be clarified: 1) the BRB model evaluation process; 2) how lower-level evaluation results are applied to higher-level evaluation processes; and 3) the BRB-NEP model evaluation process. Here, while clarifying these three aspects, we present an initial hierarchical evaluation model.

[0202] 1) Evaluation process of the BRB model;

[0203] The hierarchical evaluation model shown in Table 1 includes 14 evaluation models, BRB1 to BRB14. The evaluation process of BRB1 model is introduced as an example. First, the two indicators of BRB1 model are 、 Divided into three reference levels: large (indicated by Q1), medium (indicated by Q2), and small (indicated by Q3). Based on expert experience and gyroscope output, the indicators 、 The indicator weights, reference levels and reference values ​​are shown in Table 2.

[0204] Table 2 Reference levels and values ​​of BRB1 model indicators

[0205]

[0206] The BRB1 model includes two indicator inputs, each with three reference levels. Using rule combination, nine confidence rules can be derived. The fourth-level indicator, "installation error (X-axis gyro)," is also divided into three reference levels: large (denoted by Q1), medium (denoted by Q2), and small (denoted by Q3). Based on expert experience, the initial BRB1 model is derived, as shown in Table 3.

[0207] Table 3 Initial BRB1 model

[0208]

[0209] Referring to the corresponding processing of indicator matching, rule activation, and rule fusion in the previous article, the indicator matching degree is calculated according to Table 2, and then rule activation and rule fusion are performed. Finally, the evaluation result in the form of confidence distribution is obtained, which is:

[0210] (26);

[0211] in, Output for the BRB1 model; 、 、 There are three levels of confidence: Q1, Q2 and Q3;

[0212] The evaluation process of the remaining BRB models is similar to that of the BRB1 model. The only difference lies in the setting of the reference level and reference value of the indicators. Therefore, the evaluation process of the remaining BRB models will not be introduced here.

[0213] 2) the method by which the results of a lower-level assessment are applied to the assessment of a higher level;

[0214] Taking the BRB7 model as an example, this paper introduces the application method of the BRB1 model evaluation results. The BRB7 model has three input indicators, namely, zero bias stability , scale factor nonlinearity , installation error, where 、 By setting the reference level and reference value, and using the relevant formula, the index matching degree can be obtained; the installation error is the evaluation result in the form of formula (26), at this time 、 、 It is considered as the matching degree of three levels: Q1, Q2 and Q3. Then, rule activation and rule fusion can be further performed to obtain the evaluation results of the BRB7 model.

[0215] The application methods of the remaining lower-level evaluation results are consistent with this and will not be introduced here.

[0216] 3) the evaluation process of the BRB-NEP model;

[0217] The output of the BRB-NEP model includes self-detection test results , self-aiming test results , Historical transport times , cumulative transportation distance , laser gyroscope assembly evaluation results, quartz accelerometer assembly evaluation results. 、 For indicators with higher priority, historical transport times , cumulative transportation distance , the laser gyroscope assembly evaluation results, and the quartz accelerometer assembly evaluation results are low priority indicators; in addition, 、 The matching degree is obtained by setting the reference level and reference value. The evaluation results of the laser gyroscope assembly and the quartz accelerometer assembly will use the evaluation confidence as the matching degree. 、 The three reference levels and reference values ​​are set as more (indicated by Q1), medium (indicated by Q2), and less (indicated by Q3), as shown in Table 4.

[0218] Table 4 Reference levels and values ​​of BRB-NEP model indicators

[0219]

[0220] In the BRB-NEP model 、 Set as 3 reference levels, the evaluation results of the gyroscope assembly and accelerometer assembly are excellent (indicated by W1), good (indicated by W2), medium (indicated by W3), and poor (indicated by W4), and the total combination is There are 144 confidence rules in total, plus activation The performance of the laser strapdown inertial system is also divided into four levels: excellent (indicated by W1), good (indicated by W2), medium (indicated by W3), and poor (indicated by W4). In order to save space, only some of the rules are given here, as shown in Table 5. ” indicates any value.

[0221] Table 5 Initial BRB-NEP model (display part)

[0222]

[0223] The BRB-NEP model first follows the fusion principle given by formula (15). If the rule If activated, the output rule The evaluation result of is activated, the remaining The fusion result of the rules is finally obtained. The evaluation result of the BRB-NEP model is the performance evaluation result of the laser strapdown inertial system.

[0224] (2) Hierarchical evaluation process of LINS performance considering non-equal priority of indicators

[0225] As described above, no further details will be given here.

[0226] To obtain the globally optimal parameter set for the hierarchical assessment model, all 15 confidence rule base models within the hierarchical assessment model need to be optimized simultaneously. Table 6 shows the statistics of the parameters that require optimization across the 15 confidence rule base models, totaling 2426 parameters.

[0227] Table 6 Parameters to be optimized for the hierarchical evaluation model

[0228]

[0229] 4,000 samples were randomly selected from 4,800 samples as training samples, and the remaining 800 samples were used as testing samples. The performance labels of the samples were determined by voting by multiple experts. The utility values ​​of the four levels of the laser strapdown inertial unit (W1, W2, W3, and W4) were set to (4, 3, 2, and 1), respectively. The parameters of the artificial bee colony algorithm were set as follows: the number of artificial bees was set to 800, the maximum number of algorithm iterations was set to 5,000, the maximum search threshold for a single location was set to 10, and the number of employed bees was set to 50% of the total size. For comparison, a hierarchical evaluation model and a traditional BRB model were trained simultaneously. Both models were optimized 20 times, and the root mean square error (RMSE) between the utility evaluation results and the actual values ​​is shown in Table 7.

[0230] Table 7 20 optimization results of two models

[0231]

[0232] Taking a certain optimization process as an example, the changes in the utility RMSE values ​​during the training of the two models are as follows: Figure 7 Combined with Table 7 and Figure 7 It can be seen that: 1) In the 20 optimizations, the optimal RMSE value of the hierarchical evaluation model was 1.42 times smaller than that of the traditional BRB model, and the average RMSE was 1.20 times smaller, indicating that the hierarchical evaluation model has higher evaluation accuracy; 2) From the search process, it can be seen that the RMSE value of the hierarchical evaluation model decreases rapidly, while the RMSE value of the traditional BRB model decreases slowly, indicating that the hierarchical evaluation model is easier to optimize; 3) The RMSE standard deviation of the hierarchical evaluation model is 3.38 times smaller than that of the traditional BRB model, indicating that the hierarchical evaluation model has better evaluation stability.

[0233] 3. Evaluate using the optimized performance hierarchical evaluation model;

[0234] Taking the BRB1 model and the BRB-NEP model as examples, the parameter optimization results of the hierarchical evaluation model incorporating prior information are shown in Tables 8 and 9. Due to the large number of rules in the BRB-NEP model, only some of them are given here.

[0235] Table 8 Optimized BRB1 model

[0236]

[0237] Table 9 Optimized BRB-NEP model

[0238]

[0239] 800 test samples were input into the optimized hierarchical evaluation model and the traditional BRB model, and the comparison between the evaluation utility value and the true utility value was shown in Table 10.

[0240] Table 10 20 test results of two models

[0241]

[0242] The evaluation results of the optimized hierarchical evaluation model on 800 test samples are Figure 8 As shown. After calculation, Figure 8 The RMSE between the evaluated utility value and the true utility value is 0.1344. Figure 8 It can be seen that the evaluation utility value of the hierarchical evaluation model is very close to the true utility value, indicating that the optimized hierarchical evaluation model has higher evaluation accuracy. Therefore, the hierarchical evaluation method that considers the unequal priority of indicators has higher evaluation accuracy.

[0243] The above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A hierarchical evaluation method for laser inertial navigation system performance considering unequal priority of indicators, characterized in that: include: Obtaining a predetermined initial performance hierarchical evaluation model for the laser inertial system; wherein the initial performance hierarchical evaluation model is constructed by analyzing indicators related to the laser inertial system performance, and establishing a hierarchical evaluation model by considering the inconsistent priority of the indicators through a BRB-NEP model; the BRB-NEP model is implemented based on a confidence rule base method; Obtaining a training sample of the laser inertial system to be evaluated; wherein the training sample contains data of each indicator in the initial performance hierarchical evaluation model, and the training sample is marked with a performance label, and the performance label contains the performance level of the laser inertial system and the confidence level corresponding to the performance level; Optimizing the parameters of the initial hierarchical performance evaluation model using the training samples of the laser inertial system to be evaluated and a preset optimization algorithm to obtain an optimized hierarchical performance evaluation model for the laser inertial system to be evaluated; Using the optimized performance hierarchical evaluation model, the performance of the laser inertial system to be evaluated is evaluated; According to the constructed hierarchical indicator system, the traditional BRB model is used for the evaluation below the highest level, and in response to the problem of inconsistent priorities of the highest level indicators, a BRB-NEP model considering unequal priorities of indicators is established for evaluation, thereby constructing a hierarchical evaluation model. The process includes: for the process of obtaining upper-level indicators from the fifth to the fourth level, the fourth to the third level, and the third to the second level in the hierarchical indicator system, the traditional BRB model is set as the corresponding confidence rule base model; and in response to the problem of inconsistent priorities of the first-level indicators in the evaluation process from the second level to the first level, a BRB-NEP model considering unequal priorities of indicators is established as the corresponding confidence rule base model, thereby constructing a hierarchical evaluation model; wherein, in the hierarchical indicator system, the priorities of the self-detection test results and the self-aiming test results are the same, and are both higher than the priorities of other indicators; The process of establishing a BRB-NEP model that considers the unequal priority of indicators includes: To address the modeling issues with different indicator priorities at the highest level, rules are added to the traditional BRB model. , described as: ； Among them, assuming that indicators , among which the former indicators 0-1 output and has higher priority, the rest indicators Has a lower priority; For the rest indicators The complete works of; representation or rule; Representation and Rules; Laser Inertial Group performance levels; To evaluate the results; For rules The rule weight of for The indicator weight of each indicator; 、 and is a natural number greater than 0; The rest of the rules are constructed by combining the AND rule or the OR rule. Rules Expressed as: ； in, For the first Rule No. The reference value of each indicator; For the first Rules confidence level for each performance level; For rules The rule weight of is the total number of rules, is a natural number greater than 0; injunction , determine the rule fusion principle as follows: ； The reasoning process of the BRB-NEP model is determined to be divided into three steps: indicator matching, rule activation, and rule fusion, and the corresponding processing method for each step is determined.

2. The method according to claim 1, characterized in that The process of determining the initial performance hierarchical evaluation model of the laser inertial system includes: Analyze multiple indicators related to the performance of the laser inertial system, and construct a hierarchical indicator system through indicator selection.

3. The method according to claim 2, characterized in that The analysis of multiple indicators related to the performance of the laser inertial system and the construction of a hierarchical indicator system through indicator selection include: For a laser strapdown inertial system composed of a laser gyroscope and a quartz accelerometer, multiple indicators are selected through performance analysis to represent historical information and test information. The historical information includes the number of historical transports and the cumulative transport mileage; the test information includes the self-calibration test results, the self-aiming test results, and the self-detection test results. The expression form of the self-detection test results is determined based on a 0-1 output form, and the expression form of the self-aiming test results is determined based on a 0-1 output form. In addition, by determining the expressions of the output error models corresponding to the laser gyroscope and the quartz accelerometer, the calibration parameters corresponding to the laser gyroscope and the quartz accelerometer are determined, respectively, to characterize the self-calibration test results. Based on the information determined by analyzing multiple indicators, the indicators are selected and the 28-dimensional laser strapdown inertial system indicators are finally determined, which are expressed as: ； in, represents the collection of 28-dimensional laser strapdown inertial system indicators; Indicates the self-diagnosis test result; Indicates the self-aiming test result; Indicates the number of historical transports; Indicates the accumulated transport mileage; Indicates the zero bias stability of the X-axis laser gyroscope; Indicates the zero bias stability of the Y-axis laser gyroscope; Indicates the zero bias stability of the Z-axis laser gyroscope; Indicates the installation error of the X-axis laser gyroscope around the Z-axis; Indicates the installation error of the X-axis laser gyroscope around the Y-axis; Indicates the installation error of the Y-axis laser gyroscope around the Z-axis; Indicates the installation error of the Y-axis laser gyroscope around the X-axis; Indicates the installation error of the Z-axis laser gyroscope around the Y-axis; Indicates the installation error of the Z-axis laser gyroscope around the X-axis; Indicates the nonlinearity of the scale factor of the X-axis laser gyroscope; Indicates the nonlinearity of the scale factor of the Y-axis laser gyroscope; Indicates the nonlinearity of the scale factor of the Z-axis laser gyroscope; Indicates the bias stability of the X-axis quartz accelerometer; Indicates the zero bias stability of the Y-axis quartz accelerometer; Indicates the bias stability of the Z-axis quartz accelerometer; Indicates the installation error between the Y-axis quartz accelerometer and the X-axis; Indicates the installation error between the Z-axis quartz accelerometer and the X-axis; Indicates the installation error between the X-axis quartz accelerometer and the Y-axis; Indicates the installation error between the Z-axis quartz accelerometer and the Y-axis; Indicates the installation error between the X-axis quartz accelerometer and the Z-axis; Indicates the installation error between the Y-axis quartz accelerometer and the Z-axis; Indicates the scale factor stability of the X-axis quartz accelerometer; Indicates the scale factor stability of the Y-axis quartz accelerometer; Indicates the scale factor stability of the Z-axis quartz accelerometer; Based on the 28-dimensional laser strapdown inertial system indicators, a five-layer hierarchical indicator system is constructed according to the instrument type. The indicators from the fifth layer to the first layer are set from bottom to top. The fifth level indicators include: 、 、 、 、 、 ,as well as, 、 、 、 、 、 ; The fourth-level indicators include: bias stability, scale factor nonlinearity, and installation error evaluation results of the X-axis laser gyroscope; bias stability, scale factor nonlinearity, and installation error evaluation results of the Y-axis laser gyroscope; bias stability, scale factor nonlinearity, and installation error evaluation results of the Z-axis laser gyroscope; and bias stability, scale factor stability, and installation error evaluation results of the X-axis quartz accelerometer; bias stability, scale factor stability, and installation error evaluation results of the Y-axis quartz accelerometer; bias stability, scale factor stability, and installation error evaluation results of the Z-axis quartz accelerometer; among which, the installation error evaluation result of the X-axis laser gyroscope is based on the indicators of the fifth level 、 Calculated; the installation error evaluation result of the Y-axis laser gyroscope is based on the fifth layer indicator 、 Calculated; the installation error evaluation result of the Z-axis laser gyroscope is based on the fifth layer indicator 、 Calculated; the installation error evaluation results of the X-axis quartz accelerometer are based on the fifth layer indicators 、 Calculated; the installation error evaluation results of the Y-axis quartz accelerometer are based on the fifth layer indicators 、 Calculated; the installation error evaluation results of the Z-axis quartz accelerometer are based on the fifth layer indicators 、 Calculated; The third-level indicators include: the evaluation results of the X-axis laser gyroscope, the evaluation results of the Y-axis laser gyroscope, the evaluation results of the Z-axis laser gyroscope, and the evaluation results of the X-axis quartz accelerometer, the evaluation results of the Y-axis quartz accelerometer, and the evaluation results of the Z-axis quartz accelerometer; among them, the evaluation results of the X-axis laser gyroscope are calculated based on the fourth-level indicators of the X-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; the evaluation results of the Y-axis laser gyroscope are calculated based on the fourth-level indicators of the Y-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; the evaluation results of the Z-axis laser gyroscope are calculated based on the fourth-level indicators of the Y-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; The results are calculated based on the fourth-layer indicators of the Z-axis laser gyroscope's zero bias stability, scale factor nonlinearity, and installation error evaluation results; the evaluation results of the X-axis quartz accelerometer are calculated based on the fourth-layer indicators of the X-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation results; the evaluation results of the Y-axis quartz accelerometer are calculated based on the fourth-layer indicators of the Y-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation results; the evaluation results of the Z-axis quartz accelerometer are calculated based on the fourth-layer indicators of the Z-axis quartz accelerometer's zero bias stability, scale factor stability, and installation error evaluation results; The second-tier indicators include: self-detection test results, self-aiming test results, historical transport times, cumulative transport mileage, laser gyroscope assembly evaluation results, and quartz accelerometer assembly evaluation results. The laser gyroscope assembly evaluation results are calculated based on the third-tier laser gyroscope indicators; the quartz accelerometer assembly evaluation results are calculated based on the third-tier quartz accelerometer indicators. The indicators of the first layer include: the evaluation results of the laser strapdown inertial system, which are calculated based on all the indicators of the second layer.

4. The method according to claim 3, characterized in that The indicator matching process includes: For non-0-1 output indicators, Indicators, The reference value of the index of the rule is recorded as ;for ,when When, confirm For the first The matching degree of the rules Expressed as: ； in: For the Indicators at time The test value of for For the first The matching degree of the rules; is the total number of rules; Indicates the Indicators, The reference value of the indicator of the rule; For indicators with 0-1 output, the matching degree of such indicators is directly set to 0.5, which is expressed as: ； in, Indicates time hour For the first The matching degree of the rules.

5. The method according to claim 4, characterized in that The rule activation process includes: Calculate all indicators for the The matching degree of the rules for: ； ； in, Indicates the The indicator weight of each indicator; Indicates the The relative weight of each indicator; Comprehensive Rule weight of the rule and matching , get the The activation weights corresponding to the rules are: ； in, for Moment The activation weights corresponding to the rules; Indicates the The rule weight of the rule; Indicates that all indicators are at time Time to the first The matching degree of the rules.

6. The method according to claim 5, characterized in that The rule fusion process includes: Based on the The activation weight of the rule and confidence , calculate the basic probability mass, expressed as: ； in, for Moment The output of the rule is The basic probability mass of represents the empty set; For the first Rule No. confidence level for each performance level; is the complete set of all performance levels; when hour, for The remaining basic probability mass at the moment represents global ignorance; , 、 are the two components of global ignorance; Use the recursive ER algorithm to perform rule fusion and obtain the previous The fusion result of the evidence is expressed as: ； in, Before fusion The output after the rules are The normalized basic probability mass of ; Before fusion The output after the rules are The normalized basic probability mass of ; Before fusion Normalization parameters of the rules; Before fusion Normalization parameters of the rules; for abbreviation of; for The abbreviation of Moment The output of the rule is performance levels The basic probability mass of For the The remaining basic probability mass of the rule; Before fusion The remaining basic probability mass after the rules; 、 for The two components of 、 For the The remaining basic probability mass of the rule The two components of Based on the normalized basic probability mass, the confidence level of the laser strapdown inertial system at different performance levels is obtained, which is expressed as: ； in, yes The abbreviation of for Output results after moment rule fusion confidence level; Indicates the fusion of all The output after the rules are The normalized basic probability mass of ; express One of the components, To integrate all The remaining basic probability mass after the rules; According to the fusion results of all rules, the output of the BRB-NEP model is: ； Assuming the performance level of the laser strapdown inertial system The utility value is , then the evaluation result The utility value of is: ； in, is the utility value of the evaluation result.

7. The method according to claim 6, characterized in that The parameters in the initial performance hierarchical evaluation model are optimized using the training sample data of the laser inertial system to be evaluated and a preset optimization algorithm to obtain an optimized performance hierarchical evaluation model for the laser inertial system to be evaluated, including: Taking the minimum root mean square error between the output value of the evaluation model and the actual value as the optimization goal, a parameter optimization model of the initial performance hierarchical evaluation model is established, wherein the parameters to be optimized include: Index weight , rule weight Hedi Rules corresponding to Confidence level of performance ; ; According to the optimization target, the parameters in the initial performance hierarchical evaluation model are optimized using the training sample data of the laser inertial system to be evaluated and an artificial bee colony algorithm to obtain an optimized performance hierarchical evaluation model for the laser inertial system to be evaluated.

8. The method according to any one of claims 1 to 7, characterized in that The performance of the laser inertial system to be evaluated is evaluated using the optimized performance hierarchical evaluation model, including: According to the indicators contained in the initial performance hierarchical evaluation model, the indicator data corresponding to the laser inertial group to be evaluated are collected to obtain a sample to be tested; The optimized performance hierarchical evaluation model is used to perform hierarchical evaluation on the sample to be tested to obtain a performance evaluation result of the laser inertial system to be evaluated, wherein the performance evaluation result includes a performance level and a corresponding confidence level.

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