Quantitative evaluation method and device for health state of airborne task electronic system

Abstract

The invention provides an airborne task electronic system health state quantitative evaluation method and device, and belongs to the field of health state evaluation, and the method comprises the steps: obtaining the working state information of an airborne task electronic system; determining multiple groups of state index information of the airborne task electronic system according to the working state information; determining the degradation degree of the airborne task electronic system according to the state index information and a preset standard index; determining a target weight of the state index information according to an order relation analysis method and an entropy weight method; health state grading of the airborne task electronic system is constructed, a membership degree vector of the deterioration degree to each level of health state is determined through a normal cloud model, and then a membership degree matrix of each group of state index information is determined according to the membership degree vector; and determining the health state of the airborne task electronic system according to the membership matrix of each group of state index information and the target weight. The evaluation result gives consideration to fuzziness and randomness, and the credibility of the health state evaluation result of the airborne task electronic system is improved.

Description

Technical Field

[0001] The present invention belongs to the field of health status assessment, and in particular relates to a method and device for quantitatively assessing the health status of an airborne mission electronic system. Background Art

[0002] Airborne mission electronic systems are necessary systems for military aircraft to cope with complex electromagnetic environments and carry out diversified military missions. They play a vital role in the current war situation, and their health status directly affects the combat capability of aircraft. However, there is currently a lack of scientific and reasonable evaluation methods for the health status of airborne mission electronic systems, making it difficult to accurately grasp their health status, resulting in a sharp increase in the pressure of use and maintenance support. Therefore, it is necessary to conduct research on the health status evaluation method of airborne mission electronic systems to provide strong support for their combat use and support decisions.

[0003] Existing technologies mainly use ranking methods, membership functions, gray clustering and other methods to aggregate indicator information. For example, the MARCOS method and TOPSIS method are used to obtain the relative order of advantages and disadvantages of the objects to be evaluated, but individual evaluation cannot be performed, and it is difficult to achieve the randomness of health assessment; or different types of membership functions are used to describe the aggregated indicator information, and fuzzy gray clustering is used to conduct a comprehensive health status assessment of various indicators. These two methods are similar in principle, but some membership functions and whitening weight functions will have mutation points when describing fuzziness, which does not conform to the gradual characteristics of the health status and makes it difficult to achieve the fuzziness of health assessment.

[0004] However, the description of the health status of complex equipment must not only be vague, but the underlying indicators that characterize its health status are also random. Current existing technologies make it difficult to achieve the above requirements. Summary of the invention

[0005] In order to solve the problem that the above health assessment method is difficult to take into account both fuzziness and randomness, the present invention provides a method and device for quantitatively assessing the health status of an airborne mission electronic system.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] First, a method for quantitatively evaluating the health status of an airborne mission electronic system is provided, the method comprising:

[0008] Obtain working status information of onboard mission electronic systems;

[0009] Determine a plurality of groups of status indicator information of the onboard mission electronic system respectively according to the working status information;

[0010] Determining the degradation degree of the onboard mission electronic system according to the status indicator information and the preset standard indicator;

[0011] Determine the target weight of the state indicator information according to the order relationship analysis method and the entropy weight method;

[0012] The health status of the airborne mission electronic system is graded, and the membership vectors corresponding to the health status of each level of degradation are determined by a normal cloud model, and then the membership matrix of each group of status indicator information is determined according to the membership vectors;

[0013] The health status of the airborne mission electronic system is determined according to the membership matrix and target weight of each group of status indicator information.

[0014] Optionally, the working status information includes working time, number of failures, number of serious failures, number of identifications and number of identification errors; each group of status indicator information includes a one-sided confidence lower limit value of the mean failure interval time, a one-sided confidence lower limit value of the mean serious failure interval time, a one-sided confidence lower limit value of the task reliability and a one-sided confidence lower limit value of the identification accuracy;

[0015] Determine the one-sided confidence lower limit MFBF of the mean time between failures L The formula is:

[0016]

[0017] Among them, T f is the working time, r is the number of failures; α represents the significance level; is the upper quantile of the chi-square distribution with 2r+2 degrees of freedom;

[0018] Determine the one-sided confidence lower limit MFBCF of the mean time between serious failures L The formula is:

[0019]

[0020] Among them, r c is the number of serious faults;

[0021] Determine the one-sided confidence lower limit MR of task reliability L The formula is:

[0022]

[0023] Where, e is the base of natural logarithm;

[0024] Determine the one-sided confidence lower limit RI of the recognition accuracy L The formula is:

[0025]

[0026] Among them, N F is the number of recognition errors; N is the number of recognitions.

[0027] Optionally, the degradation degree includes a benefit-type index, a cost-type index, an intermediate-value-type degradation degree, and an interval-type degradation degree;

[0028] The formula for determining the benefit-based indicator is:

[0029]

[0030] Among them, x is the status indicator information; X0 is the preset standard indicator; X L is the minimum acceptable value;

[0031] The formula for determining cost indicators is:

[0032]

[0033] Among them, X U is the highest acceptable value;

[0034] The formula for determining the intermediate value type degradation degree is:

[0035]

[0036] Among them, X m is the midpoint value of the specified interval; L is the length of the specified interval;

[0037] The formula for determining the interval degradation degree is:

[0038]

[0039] Among them, x U 、x L are the upper and lower limits of the interval of the state indicator information respectively; x c It is the length of the overlap between the status indicator information interval and the preset standard indicator interval.

[0040] Optionally, determining the target weight of the state indicator information according to the order relationship analysis method and the entropy weight method includes:

[0041] Determine the subjective weight of the state indicator information according to the order relationship analysis method, and determine the objective weight of the state indicator information according to the entropy weight method;

[0042] The objective function and constraints based on subjective weights and objective weights are constructed through the minimum discriminant information principle, and then the target weight is obtained.

[0043] Optionally, the objective function is:

[0044]

[0045] The constraints are:

[0046]

[0047] Among them, w′ is the subjective weight, w″ j is the objective weight, w j is the target weight, j is the current state indicator information, and n is the total number of state indicator information;

[0048] The target indicators are:

[0049]

[0050] Optionally, determining the membership vector of the degradation degree to each level of health state through a normal cloud model, and then determining the membership matrix of each group of state indicator information according to the membership vector includes:

[0051] Dividing the degradation degree into a plurality of degradation degree value intervals corresponding to a plurality of health states;

[0052] Determining interval boundary values ​​of a plurality of degradation degree value intervals respectively, and constructing a normal cloud model according to the interval boundary values;

[0053] Inputting the degradation degree into a normal cloud model to obtain its corresponding membership vector;

[0054] The membership vector is normalized to obtain a membership matrix corresponding to each group of status indicator information.

[0055] Optionally, determining the health status of the onboard mission electronic system according to the membership matrix and target weight of each group of status indicator information includes:

[0056] Determine the state evaluation vector of the comprehensive state information according to the membership matrix and target weight of each group of state indicator information;

[0057] The health status of the onboard mission electronic system is determined based on a status evaluation vector of the comprehensive status information.

[0058] Secondly, a device for quantitatively evaluating the health status of an airborne mission electronic system is provided, the device comprising:

[0059] An acquisition module, used to obtain the working status information of the onboard mission electronic system;

[0060] A construction module is used to determine multiple groups of status indicator information of the airborne mission electronic system according to the working status information; determine the degradation degree of the airborne mission electronic system according to the status indicator information and preset standard indicators; determine the target weight of the status indicator information according to the order relationship analysis method and the entropy weight method; classify the health status of the airborne mission electronic system, determine the membership vector of the degradation degree corresponding to each level of health status through a normal cloud model, and then determine the membership matrix of each group of status indicator information according to the membership vector;

[0061] The determination module is used to determine the health status of the airborne mission electronic system according to the membership matrix and weight of the status indicator information.

[0062] Then, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method for quantitatively evaluating the health status of an airborne mission electronic system is implemented.

[0063] Finally, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for quantitatively evaluating the health status of an airborne mission electronic system when executing the program.

[0064] The quantitative assessment method for the health status of an airborne mission electronic system provided by the present invention has the following beneficial effects:

[0065] By determining the target weights through the order relationship analysis method and the entropy weight method, the expert experience in the order relationship analysis method and the objective data information in the entropy weight method can be combined. The target weights obtained in this way take into account both subjectivity and objectivity, improve the rationality of weighting, and determine the membership vectors of the degradation degree corresponding to each level of health status according to the normal cloud model. In this way, the membership of each degradation degree corresponding to the health status is a probability distribution rather than a fixed value, which makes the evaluation result fuzzy. In addition, the construction of the normal cloud model itself has randomness, so the final health status assessment result has both fuzziness and randomness, which improves the credibility of the health status assessment results of the airborne mission electronic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0067] Figure 1 The present invention is a flowchart of a method for quantitatively evaluating the health status of an airborne mission electronic system provided according to an exemplary embodiment of the present invention.

[0068] Figure 2 The present invention is a block diagram of a device for quantitatively evaluating the health status of an airborne mission electronic system according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0070] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0071] First, the present invention provides a method for quantitatively evaluating the health status of an airborne mission electronic system. Figure 1 As shown, the following steps are included:

[0072] S101. Acquire working status information of an onboard mission electronic system.

[0073] The working status information includes working time, number of faults, number of serious faults, number of identifications and number of identification errors.

[0074] In this step, the user unit of the airborne mission electronic system can assign a dedicated person to collect the field inspection data and usage support information of the mission electronic system, understand its usage support situation, comprehensively record the inspection and maintenance process and time, regularly organize relevant data, check its consistency with various registrations such as resumes, classify it according to different subjects, avoid data confusion, and ensure that data collection is comprehensive, timely, and accurate.

[0075] S102. Determine a plurality of groups of status indicator information of the onboard mission electronic system according to the working status information.

[0076] Among them, each group of status indicator information includes the one-sided confidence lower limit value of the mean failure interval time, the one-sided confidence lower limit value of the mean serious failure interval time, the one-sided confidence lower limit value of the task reliability and the one-sided confidence lower limit value of the recognition accuracy.

[0077] For example, the one-sided confidence lower limit MFBF of the mean time between failures can be determined according to the following formula: L :

[0078]

[0079] Among them, T f is the working time, r is the number of failures; α represents the significance level; is the upper quantile of the chi-square distribution with 2r+2 degrees of freedom;

[0080] The one-sided confidence lower limit of the mean time between serious failures (MFBCF) can be determined according to the following formula: L :

[0081]

[0082] Among them, r c is the number of serious faults;

[0083] The one-sided confidence lower limit MR of task reliability can be determined according to the following formula: L :

[0084]

[0085] Where, e is the base of natural logarithm;

[0086] The one-sided confidence lower limit RI of the recognition accuracy can be determined according to the following formula: L :

[0087]

[0088] Among them, N F is the number of recognition errors; N is the number of recognitions.

[0089] S103: Determine the degradation degree of the airborne mission electronic system according to the status indicator information and a preset standard indicator.

[0090] In this step, since the scale type and dimension of each state indicator information are different, the original data needs to be preprocessed and converted into dimensionless data. The present invention uses relative degradation degree d (hereinafter referred to as "degradation degree") to characterize the degree of degradation of the current state of the system, each function and each indicator relative to the "healthy" state, and its value range is [0,1], where "0" represents a healthy state and "1" represents complete degradation. The evaluation indicators involved in the present invention are extreme value type, intermediate value type and interval type.

[0091] Among them, extreme value indicators can be divided into benefit indicators (such as recognition accuracy) and cost indicators (such as measurement time). The benefit indicators can be determined according to the following formula:

[0092]

[0093] Among them, x is the status indicator information; X0 is the preset standard indicator; X L is the minimum acceptable value;

[0094] The cost-based indicator can be determined according to the following formula:

[0095]

[0096] Among them, X U is the highest acceptable value;

[0097] The intermediate value type degradation degree can be determined according to the following formula:

[0098]

[0099] Among them, X m is the midpoint value of the specified interval; L is the length of the specified interval;

[0100] The interval degradation degree can be determined according to the following formula:

[0101]

[0102] Among them, x U 、x L are the upper and lower limits of the interval of the state indicator information respectively; x c It is the length of the overlap between the status indicator information interval and the preset standard indicator interval.

[0103] S104: Determine the target weight of the state indicator information according to the order relationship analysis method and the entropy weight method.

[0104] In this step, in order to effectively evaluate the health status of the airborne mission electronic system, each state indicator information must be reasonably weighted. The methods for determining the target weight of the state indicator information are mainly divided into subjective methods based on expert experience and objective methods based on data statistical analysis. The former can make full use of the engineering experience accumulated over a long period of time, convert it into indicator weights, and use various mathematical tools to reduce or avoid subjective judgment errors. It has certain rationality, but it also inevitably has strong subjectivity; the latter collects data of various state indicator information, mines the objective information contained therein, and uses this as a basis to determine the weight of each state indicator information. It has strong objectivity, but may cause the weighting to completely deviate from the actual engineering. Therefore, in order to improve the credibility of the target weight determination, the present invention adopts a weight determination method that combines subjective and objective factors.

[0105] Specifically, the subjective weight of the state index information is determined according to the order relationship analysis method, and the objective weight of the state index information is determined according to the entropy weight method; the objective function and constraints based on the subjective weight and objective weight are constructed through the minimum discriminant information principle, and then the target weight is obtained.

[0106] Among them, the method for determining subjective weights adopts the ordinal relationship analysis method, which has the advantages of not requiring consistency tests, reducing errors or contradictions in expert subjective judgments, and being easy to operate. Its advantages are more obvious when the evaluation indicators are complex. In order to avoid the subjective weights being determined from a single source and being too arbitrary, multiple experts in the field should be invited to use the ordinal relationship analysis method to perform subjective weighting in layers, and the average value should be taken as the final subjective weight, recorded as w′.

[0107] Entropy is a measure of the amount of information, and the entropy weight method is an objective weighting method that determines the weight of each state indicator information based on the amount of information contained in the state indicator information. It can effectively utilize historical data, significantly reduce the influence of subjective factors, and enhance the objectivity of the evaluation.

[0108] For example, suppose each group has n status indicator information, and after normalizing m groups of historical data, the matrix X=(x ij )m×n. The entropy P of the jth state indicator information j Defined as:

[0109]

[0110] Where K = (lnN) -1 ; p ij is the data proportion of the i-th historical data under the j-th state indicator information, and when p ij = 0, p ij ·lnp ij =0.

[0111] From the above formula, we can see that if the sample data of a certain state indicator information is more discrete, the amount of information contained in the sample data is greater, its entropy is also greater, the role it plays in the evaluation is greater, and the weight should be greater, so the objective weight w″ of the jth indicator can be obtained j for:

[0112]

[0113] In one embodiment, since the technical status of the airborne mission electronic system changes little after being installed and finalized, the subjective weight of the evaluation index based on the order relationship analysis method is determined and will not change unless necessary. As the equipment is used for a longer time, the accumulated use support data will continue to be enriched, so the entropy weight method is used to use historical data to iteratively update the objective weight of the evaluation index.

[0114] In order to reasonably integrate subjective and objective weights, it is necessary to avoid further introducing subjective information and make w j It fully reflects the subjective and objective weights without giving preference to any one item, so the minimum discriminant information principle can be used to obtain w j , construct its objective function as:

[0115]

[0116] The constraints are:

[0117]

[0118] Among them, w′ is the subjective weight, w″ j is the objective weight, w j is the target weight, j is the current state indicator information, and n is the total number of state indicator information.

[0119] Solving the objective function, we can get the objective weight as:

[0120]

[0121] S105: Determine the membership matrix of the state indicator information.

[0122] Specifically, the health status of the airborne mission electronic system is graded, and the membership vectors of the degradation degree corresponding to each level of health status are determined by the normal cloud model, and then the membership matrix of the state indicator information is determined according to the membership vectors. The health status classification includes multiple health states.

[0123] For example, the degradation degree is divided into multiple degradation degree value intervals corresponding to multiple health states; the interval boundary values ​​of the multiple degradation degree value intervals are determined respectively, and a normal cloud model is constructed according to the interval boundary values; the degradation degree is input into the normal cloud model to obtain its corresponding membership vector; the membership vector is normalized to obtain the membership matrix corresponding to the state indicator information.

[0124] For example, the present invention divides the health status of the airborne mission electronic system into four levels: healthy, degraded, downgraded, and failed, that is, the comment L = {L1, L2, L3, L4,} = {healthy, degraded, downgraded, failed}.

[0125] Among them, the "healthy (L1)" state means that all performance indicators are within the specified range, which can ensure the realization of various specified functions and the execution of various tasks. The "degraded (L2)" state means that some non-critical performance indicators drift and cannot reach the optimal state, but the main performance indicators are within the specified range, which can basically ensure the realization of various major functions and do not affect the execution of typical tasks. Maintenance can be carried out after the tasks are executed as needed. The "degraded (L3)" state means that a fault has occurred, but some functions are intact, key performance indicators are within the specified range, and specific tasks can be executed. Maintenance can be carried out after the tasks are executed as needed. The "failure (L4)" state means that the fault phenomenon is obvious, the main performance indicators are abnormal, and any specific tasks cannot be completed, and immediate maintenance is required.

[0126] The present invention adopts a forward cloud generator to establish a normal membership cloud model. According to the corresponding relationship between the degradation degree and the comment, the degradation degree value intervals of L1 to L4 are respectively determined as [0, a), [a, b), [b, c), [c, 1].

[0127] Among them, 0, a, b, c, and 1 are the interval boundary values ​​of the degradation degree value interval respectively. According to the interval boundary values, the three characteristic quantities for constructing the normal cloud model are determined: expected Ex, entropy En, and super entropy He. The algorithm for constructing the normal cloud model according to Ex, En, and He is called a forward cloud generator. The ideal state is that the state indicator information has no degradation, so the expected Ex corresponding to L1 is set to 0; to ensure that the Ex interval is reasonable, L4 is set to take the smaller value of 7c / 6 and 1; the elements in the interval [Ex-3En, Ex-2En] and [Ex+2En, Ex+3En] are called "weak peripheral elements", and 3En can be used as the boundary between clouds; He represents the "thickness" of the cloud in terms of cloud shape, and should not be too thick or too thin. The present invention takes He=0.01. The specific calculation method of the three characteristic quantities is shown in Table 1 below:

[0128] Feature quantity <![CDATA[L1]]> <![CDATA[L2]]> <![CDATA[L3]]> <![CDATA[L4]]> Ex 0 (a+b) / 2 (b+c) / 2 min{7c / 6,1} En (a+b) / 6 (a+b) / 6 (ca) / 6 (4c-3b) / 18 He 0.01 0.01 0.01 0.01

[0129] Table 1

[0130] The numerical features of the cloud model are calculated according to Table 1, where a is taken as 0.1, b is taken as 0.3, and c is taken as 0.5, using the normal cloud model generated by the forward cloud generator.

[0131] The degradation degree is input into the normal cloud model to obtain its corresponding membership vector. To improve the credibility, the normal cloud model can be used multiple times to generate membership vectors, and then the average value is taken and normalized to obtain the membership matrix R corresponding to each group of state indicator information. i .

[0132]

[0133] Among them, r mn is the membership degree of the mth state indicator information in the i-th group of state indicator information relative to the health state n.

[0134] S106: Determine the health status of the airborne mission electronic system according to the membership matrix and target weight of each group of status indicator information.

[0135] Specifically, a state evaluation vector of the comprehensive state information is determined according to the membership matrix and target weight of each group of state indicator information; and a health state of the airborne mission electronic system is determined according to the state evaluation vector of the comprehensive state information.

[0136] For example, the R i and its corresponding weight vector W i, calculate the state evaluation vector H of the comprehensive state information i :

[0137] H i =W i ·R i =(h i1 ,h i2 ,…,h in );

[0138] Among them, W i =(w i1 ,w i2 ,…,w im ), w im is the target weight of the mth status indicator information in the i-th group of status indicator information, which is determined by the following formula:

[0139]

[0140] Among them, w j is the weight of the state indicator information, m is the number of state indicator information in each group, X i Indicates the group to which the status indicator information belongs.

[0141] By integrating the state evaluation vector of the state information, the membership matrix R = (H1, H2, H3, H4) of the health state of the airborne mission electronic system can be obtained. T , and then the evaluation vector H of the airborne mission electronic system is obtained as:

[0142] H=W·R=(h1,h2,h3,h4);

[0143] Among them, W = (W1, W2, W3, W4) is the weight vector corresponding to the comprehensive state information, W i is the weight of each group of status indicator information, h1, h2, h3, and h4 are the membership values ​​of the four health status levels.

[0144] By adopting the above method, the target weight is determined by the order relationship analysis method and the entropy weight method, which can combine the expert experience in the order relationship analysis method and the objective data information in the entropy weight method. The target weight obtained in this way takes into account both subjectivity and objectivity, improves the rationality of weighting, and determines the membership vector of the degradation degree corresponding to each level of health status according to the normal cloud model. In this way, the membership of each degradation degree corresponding to the health state is a probability distribution rather than a fixed value, which makes the evaluation result fuzzy. In addition, the construction of the normal cloud model itself has randomness, so the final health status assessment result has both fuzziness and randomness, which improves the credibility of the health status assessment result of the airborne mission electronic system.

[0145] Secondly, the present invention also provides a device for quantitatively evaluating the health status of an airborne mission electronic system, such as Figure 2 As shown, including:

[0146] The acquisition module 201 is used to acquire the working status information of the onboard mission electronic system.

[0147] The construction module 202 is used to determine multiple groups of status indicator information of the airborne mission electronic system according to the working status information; determine the degradation degree of the airborne mission electronic system according to the status indicator information and preset standard indicators; determine the target weight of the status indicator information according to the order relationship analysis method and the entropy weight method; classify the health status of the airborne mission electronic system, determine the membership vector of the degradation degree corresponding to each level of health status through the normal cloud model, and then determine the membership matrix of each group of status indicator information according to the membership vector.

[0148] The determination module 203 is used to determine the health status of the onboard mission electronic system according to the membership matrix and weight of the status indicator information.

[0149] By adopting the above-mentioned device, the target weight is determined by the order relationship analysis method and the entropy weight method, which can combine the expert experience in the order relationship analysis method and the objective data information in the entropy weight method. The target weight obtained in this way takes into account both subjectivity and objectivity, improves the rationality of weighting, and determines the membership vector of the degradation degree corresponding to each level of health status according to the normal cloud model. In this way, the membership of each degradation degree corresponding to the health status is a probability distribution rather than a fixed value, which makes the evaluation result fuzzy. In addition, the construction of the normal cloud model itself has randomness, so the final health status evaluation result has both fuzziness and randomness, which improves the credibility of the health status evaluation result of the airborne mission electronic system.

[0150] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The steps of the task electronic system condition assessment method are provided.

[0151] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The steps of the task electronic system condition assessment method are provided.

[0152] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] It should be noted that the above-described specific implementations can enable those skilled in the art to more fully understand the invention, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for quantitatively evaluating the health status of an airborne mission electronic system, characterized in that: The method comprises: Obtain working status information of onboard mission electronic systems; Determine a plurality of groups of status indicator information of the onboard mission electronic system respectively according to the working status information; Determining the degradation degree of the onboard mission electronic system according to the status indicator information and the preset standard indicator; Determine the target weight of the state indicator information according to the order relationship analysis method and the entropy weight method; The health status of the airborne mission electronic system is graded, and the membership vectors corresponding to the health status of each level of degradation are determined by a normal cloud model, and then the membership matrix of each group of status indicator information is determined according to the membership vectors; The health status of the airborne mission electronic system is determined according to the membership matrix and target weight of each group of status indicator information.

2. The method for quantitatively evaluating the health status of an airborne mission electronic system according to claim 1, characterized in that: The working status information includes working time, number of failures, number of serious failures, number of identifications and number of identification errors; each group of status indicator information includes the one-sided confidence lower limit value of the mean failure interval time, the one-sided confidence lower limit value of the mean serious failure interval time, the one-sided confidence lower limit value of the task reliability and the one-sided confidence lower limit value of the identification accuracy; Determine the one-sided confidence lower limit MFBF of the mean time between failures L The formula is: Among them, T f is the working time, r is the number of failures; α represents the significance level; is the upper quantile of the chi-square distribution with 2r+2 degrees of freedom; Determine the one-sided confidence lower limit MFBCF of the mean time between serious failures L The formula is: Among them, r c is the number of serious faults; Determine the one-sided confidence lower limit MR of task reliability L The formula is: Where, e is the base of natural logarithm; Determine the one-sided confidence lower limit RI of the recognition accuracy L The formula is: Among them, N F is the number of recognition errors; N is the number of recognitions.

3. The method for quantitatively evaluating the health status of an airborne mission electronic system according to claim 1, characterized in that: The degradation degree includes benefit-type index, cost-type index, intermediate value-type degradation degree and interval-type degradation degree; The formula for determining the benefit-based indicator is: Among them, x is the status indicator information; X0 is the preset standard indicator; X L is the minimum acceptable value; The formula for determining cost indicators is: Among them, X U is the highest acceptable value; The formula for determining the intermediate value type degradation degree is: Among them, X m is the midpoint value of the specified interval; L is the length of the specified interval; The formula for determining the interval degradation degree is: Among them, x U 、x L are the upper and lower limits of the interval of the state indicator information respectively; x c It is the length of the overlap between the status indicator information interval and the preset standard indicator interval.

4. The method for quantitatively evaluating the health status of an airborne mission electronic system according to claim 1, characterized in that: Determining the target weight of the state indicator information according to the order relationship analysis method and the entropy weight method includes: Determine the subjective weight of the state indicator information according to the order relationship analysis method, and determine the objective weight of the state indicator information according to the entropy weight method; The objective function and constraints based on subjective weights and objective weights are constructed through the minimum discriminant information principle, and then the target weight is obtained.

5. The method for quantitatively evaluating the health status of an airborne mission electronic system according to claim 4, characterized in that: The objective function is: The constraints are: Among them, w′ is the subjective weight, w″ j is the objective weight, w j is the target weight, j is the current state indicator information, and n is the total number of state indicator information; The target indicators are:

6. The method for quantitatively evaluating the health status of an airborne mission electronic system according to claim 1, characterized in that: Determining the membership vector of the degradation degree to each level of health state through the normal cloud model, and then determining the membership matrix of each group of state indicator information according to the membership vector includes: Dividing the degradation degree into a plurality of degradation degree value intervals corresponding to a plurality of health states; Determining interval boundary values ​​of a plurality of degradation degree value intervals respectively, and constructing a normal cloud model according to the interval boundary values; Inputting the degradation degree into a normal cloud model to obtain its corresponding membership vector; The membership vector is normalized to obtain a membership matrix corresponding to each group of status indicator information.

7. The method for quantitatively evaluating the health status of an airborne mission electronic system according to claim 1, characterized in that: Determining the health status of the airborne mission electronic system according to the membership matrix and target weight of each group of status indicator information includes: Determine the state evaluation vector of the comprehensive state information according to the membership matrix and target weight of each group of state indicator information; The health status of the onboard mission electronic system is determined based on a status evaluation vector of the comprehensive status information.

8. A device for quantitatively evaluating the health status of an airborne mission electronic system, characterized in that: The device comprises: An acquisition module, used to obtain the working status information of the onboard mission electronic system; A construction module is used to determine multiple groups of status indicator information of the airborne mission electronic system according to the working status information; determine the degradation degree of the airborne mission electronic system according to the status indicator information and preset standard indicators; determine the target weight of the status indicator information according to the order relationship analysis method and the entropy weight method; classify the health status of the airborne mission electronic system, determine the membership vector of the degradation degree corresponding to each level of health status through a normal cloud model, and then determine the membership matrix of each group of status indicator information according to the membership vector; The determination module is used to determine the health status of the airborne mission electronic system according to the membership matrix and weight of the status indicator information.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

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