CN111091254B - Operation risk quantification method, operation risk evaluation method and device - Google Patents
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Abstract
The embodiment of the invention provides an operation risk quantification method, an operation risk evaluation method and an operation risk evaluation device, wherein the operation risk quantification method comprises the following steps: and acquiring QAR data, and obtaining an operation risk value according to the QAR data and a pre-established risk quantization model. By implementing the method and the device, the quantification of the operation risk is realized, so that a foundation is laid for evaluating and analyzing the operation risk, and the overall grasp and accurate supervision of the operation risk by a management department are facilitated.
Description
Technical Field
The invention relates to the technical field of risk evaluation, in particular to an operation risk quantification method, an operation risk evaluation method and an operation risk evaluation device.
Background
Comparing the number of general events, symptoms, and incidents occurring between airlines is currently the most common method for evaluating the performance of airlines. According to the general event, symptom and accident occurrence quantity, the risk evaluation is carried out on the company only from the operation result, the comprehensive influence of the man-machine ring on the civil aircraft operation risk is neglected, the quantification of the operation risk cannot be realized, the operation risk cannot be evaluated and analyzed, and the general grasping and the accurate supervision of the civil aviation management department on the risk of the airline company are not facilitated. Therefore, how to realize the quantification of the risk of aviation operation becomes a problem to be solved.
Disclosure of Invention
In view of the above, the embodiments of the present invention provide an operation risk quantification method, an operation risk evaluation method and an apparatus, which are used for solving the problem that the quantification of the operation risk cannot be achieved in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect of the present invention, there is provided an operation risk quantification method, including: acquiring QAR data; and obtaining an operation risk value according to the QAR data and a pre-established risk quantification model.
With reference to the first aspect, in a first implementation manner of the first aspect, obtaining an operation risk value according to the QAR data and a pre-established risk quantization model includes: determining an operation risk quantization index; selecting second QAR data for quantifying the operation risk from the QAR data according to the operation risk quantification index; and inputting the second QAR data into the risk quantification model to obtain the running risk value.
With reference to the first aspect or the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the risk quantization model is:
wherein R is Total t Represents the running risk value on day t, R Non-warning t A risk value representing a t-th day non-warning type monitoring item, m 1 Representing the risk average of non-warning type monitoring items in a history period, R Warning t Representing the risk value, m, of the warning class monitoring item on day t 2 Representing a risk average value, M, of warning type monitoring items in the one history period t The flight number on the t-th day is represented, q represents the number of non-warning monitoring items, and w j Weight of j-th non-warning class monitoring item for running risk, R ijt Representing the ratio of the offset of the j-th non-warning type monitoring item of the ith flight on the t-th day relative to a preset standard value to the maximum offset of the non-warning type monitoring item of the whole industry relative to the preset standard value in the history period, p represents the number of warning type monitoring items, and w l Representing the weight of the monitoring item of the first warning class for the running risk, y ilt Indicating the number of times the first warning class monitoring item for the ith flight occurs on day t.
With reference to the second embodiment of the first aspect, in a third embodiment of the first aspect, the R is obtained by the following formula ijt ：
Wherein x is ijt Value of the j-th non-alert class monitoring item representing the ith flight on the t-th day, x j0 A preset standard value x representing the j-th non-warning monitoring item j Representing the maximum value of the j-th non-warning class monitoring item in the history period in the whole industry.
With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, or the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, before obtaining the running risk value according to the QAR data and the pre-established risk quantization model, the method further includes: and cleaning the QAR data.
In a second aspect of the present invention, there is provided an operation risk evaluation method, including: according to the first aspect of the invention or the running risk quantification method of any one of the embodiments of the first aspect, a running risk value in one history period of each object to be evaluated is obtained; determining the mean value, variance and probability density function of the running risk of each object to be evaluated according to the running risk value of each object to be evaluated in one history period; and determining the risk comprehensive evaluation index of each object to be evaluated according to the mean value, variance and probability density function of the running risk of each object to be evaluated.
With reference to the second aspect, in a first implementation manner of the second aspect, the probability density function is determined by a kernel density estimation method according to an operation risk value in one history period of each object to be evaluated.
With reference to the second aspect or the first implementation manner of the second aspect, in a second implementation manner of the second aspect, determining a risk comprehensive evaluation index of each object to be evaluated according to a mean, a variance and a probability density function of an operation risk of each object to be evaluated includes: for each object to be evaluated, executing: according to the mean value and variance of the motion risk, n deviation lines are determined, and n is an integer greater than or equal to 1; determining n deviation evaluation indexes according to the n deviation lines and the probability density function; and determining the risk comprehensive evaluation index according to each deviation evaluation index.
In a third aspect of the present invention, there is provided an operation risk quantifying apparatus comprising: the data acquisition module is used for acquiring QAR data; and the risk quantification module is used for obtaining an operation risk value according to the QAR data and a pre-established risk quantification model.
In a fourth aspect of the present invention, there is provided an operation risk evaluation device including: the risk value acquisition module is used for acquiring an operation risk value in one history period of each object to be evaluated according to the operation risk quantification method in the first aspect or any implementation mode of the first aspect; the risk data determining module is used for determining the mean value, the variance and the probability density function of the running risk of each object to be evaluated according to the running risk value in one history period of each object to be evaluated; the risk evaluation index determining module is used for determining the risk comprehensive evaluation index of each object to be evaluated according to the mean value, the variance and the probability density function of the running risk of each object to be evaluated.
In a fifth aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a computer to execute the method for quantifying running risk according to the first aspect or any embodiment of the first aspect of the present invention, or causing the computer to execute the method for evaluating running risk according to the second aspect or any embodiment of the second aspect of the present invention.
In a sixth aspect of the present invention, there is provided an electronic apparatus including: the device comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so as to execute the running risk quantification method according to the first aspect or any implementation mode of the first aspect of the invention or execute the running risk evaluation method according to the second aspect or any implementation mode of the second aspect of the invention.
Compared with the prior art, the technical scheme of the invention has at least the following advantages:
the embodiment of the invention provides an operation risk quantification method, an operation risk evaluation method and an operation risk evaluation device, wherein the operation risk quantification method comprises the following steps: the QAR data is acquired, the running risk value is obtained according to the QAR data and a pre-established risk quantification model, and the quantification of the running risk is realized, so that a foundation is laid for evaluating and analyzing the running risk, and the overall grasp and the accurate supervision of the running risk by a management department are facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a specific example of a method for quantifying operational risk in an embodiment of the present invention;
FIG. 2 is a flowchart of another specific example of a method for quantifying operational risk in an embodiment of the present invention;
FIG. 3 is a flowchart of a specific example of a method for evaluating operational risk in an embodiment of the present invention;
FIG. 4 is a flowchart of another specific example of a method for risk assessment in an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a specific example of an operation risk quantification apparatus in an embodiment of the present invention;
FIG. 6 is a schematic block diagram of a specific example of an operation risk assessment apparatus in an embodiment of the present invention;
fig. 7 is a schematic diagram of a specific example of an electronic device in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that technical features of different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The embodiment of the invention provides an operation risk quantification method, which is shown in fig. 1 and comprises the following steps:
step S1: acquiring QAR data;
step S2: and obtaining an operation risk value according to the QAR data and a pre-established risk quantification model.
Through the step S1 and the step S2, the running risk quantification method provided by the embodiment of the invention realizes quantification of the running risk, thereby laying a foundation for evaluation and analysis of the running risk and being beneficial to the overall grasp and accurate supervision of the running risk by a management department.
In a preferred embodiment, as shown in fig. 2, the step S2 of obtaining the running risk value according to the QAR data and the pre-established risk quantization model includes:
step S21: determining an operation risk quantization index; the running risk quantification indicators include, but are not limited to, controlled flight landing (CFIT), runway impact/deviation risk, and runaway risk.
Step S22: and selecting second QAR data for quantifying the operation risk from the QAR data according to the operation risk quantification index.
In particular, different QAR data are used to describe different running risk quantization indexes, and since the running risk quantization indexes contain many aspects of content, the embodiments of the present invention cannot be exhausted, and thus typical events such as a controllable flight strike, a runway collision/deviation risk, and a runaway risk are still described, but the present invention is not limited thereto.
For the controllable flight collision ground, the 'glidepath deviation, heading deviation, large descent rate from 50 feet to ground, large approach speed, small approach speed, late selected landing configuration, landing of non-landing configuration, landing gear landing, small ground elevation angle, large landing gradient, large approach gradient (200 feet to 50 feet) and GPWS topographic warning' in QAR data are adopted for depiction; for the risk of rushing out of the runway, adopting the 'take-off form warning, take-off interruption, large landing speed, large approach speed, course deviation, glidepath deviation, landing of non-landing configuration and grounding distance' in QAR data for depiction; for the risk of runaway, the method is characterized by adopting 'GPWS ground proximity warning alarm, stall warning, small approach speed, course lane deviation, glidepath deviation, overlarge elevation angle, landing of non-landing configuration and large descent rate' in QAR data.
Step S23: and inputting the second QAR data into a risk quantification model to obtain an operation risk value.
Specifically, for the risk quantization index determined in the step S21, the second QAR data for characterizing the risk quantization index selected in the step S22 is input into a pre-established risk quantization model, and in a preferred embodiment of the present invention, the pre-established risk quantization model is:
wherein R is Total t Represents the running risk value on day t, R Non-warning t A risk value representing a t-th day non-warning type monitoring item, m 1 Representing the risk average of non-warning type monitoring items in a history period, R Warning t Representing the risk value, m, of the warning class monitoring item on day t 2 Representing the risk average value, M, of warning type monitoring items in a history period t The flight number on the t-th day is represented, q represents the number of non-warning monitoring items, and w j Weight of j-th non-warning class monitoring item for running risk, R ijt Representing the ratio of the offset of the j-th non-warning monitoring item of the ith flight on the t day to the preset standard value to the maximum offset of the non-warning monitoring item of the whole industry in the history period, wherein p represents the number of warning monitoring items and w l Representing the weight of the monitoring item of the first warning class for the running risk, y ilt Indicating the number of times the first warning class monitoring item for the ith flight occurs on day t.
When the QAR data is continuous data, the non-warning type monitoring item is defined, and when the QAR data is discrete data, the warning type monitoring item is defined.
Weighting w of the j-th non-warning type monitoring item to the running risk j And the weight w of the first warning class monitoring item for the running risk l The method and the device can be obtained by scoring according to professionals in the aviation field, the professionals score each non-warning type monitoring item and the weight of the warning type monitoring item to the running risk according to own knowledge reserves and combined with rich experience accumulated in the field, and the embodiment of the invention determines the weight w of the jth non-warning type monitoring item to the running risk by referring to the scoring of the professionals j And the weight w of the first warning class monitoring item for the running risk l 。
The ratio R of the offset of the j-th non-warning monitoring item of the ith flight on the t-th day relative to the preset standard value to the maximum offset of the non-warning monitoring item of the whole industry relative to the preset standard value in the history period ijt Can be obtained by the following formula:
wherein x is ijt Value of the j-th non-alert class monitoring item representing the ith flight on the t-th day, x j0 A preset standard value x representing the j-th non-warning monitoring item j Representing the maximum value of the j-th non-warning class monitoring item in the history period in the whole industry. For each non-warning monitoring item, a standard value exists, when the value of the non-warning monitoring item exceeds the standard value, the non-warning monitoring item possibly has certain risk, and the standard value is set according to the machine type and the difference exists according to the machine type.
According to the embodiment of the invention, the QAR data corresponding to the determined running risk quantification index is input into the pre-established risk quantification model, so that the running risk value of a certain model of an airline company in a certain day can be obtained, the quantification of the running risk is realized, and a foundation is laid for evaluating and analyzing the running risk.
In a preferred embodiment, as shown in fig. 2, after the QAR data is obtained in the step S1, before the running risk value is obtained according to the QAR data and the pre-established risk quantization model in the step S2, the method further includes:
step S3: cleaning QAR data; and the acquired QAR data is cleaned, so that distortion data in the QAR data is removed, and the data input into the risk quantization model are all available data.
The embodiment of the invention also provides an operation risk evaluation method, as shown in fig. 3, which comprises the following steps:
step S4: obtaining an operation risk value in a history period of each object to be evaluated according to the operation risk quantification method; the object to be evaluated can be an airline company, and for an airline company, the operation risk value of each type of the airline company in each day can be obtained through the operation risk quantification method embodiment, so that the operation risk value of the airline company in a history period is obtained.
Step S5: determining the mean value, variance and probability density function of the running risk of each object to be evaluated according to the running risk value of each object to be evaluated in one history period; according to the running risk value of each object to be evaluated in one history period obtained in the step S4, the average value of running risks of each object to be evaluated in the history period, namely the running risk value of each day is averaged, and further the variance of the running risk can be obtained easily; according to the operation risk value in one history period of each object to be evaluated, the probability density function of each object to be evaluated can be obtained through a kernel density estimation method.
Step S6: determining the risk comprehensive evaluation index of each object to be evaluated according to the mean value, variance and probability density function of the running risk of each object to be evaluated; specifically, as shown in fig. 4, the following steps are performed for each object to be evaluated:
step S61: and determining n deviation lines according to the mean value and the variance of the motion risk, wherein n is an integer greater than or equal to 1. Let the mean value of the running risk be m, the variance be sigma, the deviation line be l i =m+i×σ, i=1, 2, …, n. In the embodiment of the invention, if n=3 is taken, 3 deviation lines can be determined, i is respectively 1 ＝m+1×σ,l 2 ＝m+2×σ，l 3 ＝m+3×σ。
Step S62: according to the n deviation lines and the probability density function, n deviation evaluation indexes are determined; specifically, the alpha-th deviation evaluation index z is obtained by utilizing the tail probability of the probability density function α ：
Where p (x) represents a probability density function.
Step S63: determining a risk comprehensive evaluation index according to each deviation evaluation index; specifically, the formula z=z is adopted 1 +z 2 +z 3 +1, obtaining a risk comprehensive evaluation index z.
According to the operation risk evaluation method provided by the embodiment of the invention, the operation risk of each airline company can be ranked, specifically, the risk comprehensive evaluation indexes obtained by each airline company through the operation risk evaluation method are ranked from large to small, the operation risk of the airline company corresponding to the risk comprehensive evaluation index ranked at the front is relatively high, the operation risk of the airline company corresponding to the risk comprehensive evaluation index ranked at the rear is relatively low, and therefore, the performance evaluation of each airline company is realized.
The embodiment of the invention also provides an operation risk quantification device, as shown in fig. 5, which comprises: the data acquisition module 1 is configured to acquire QAR data, and details of the data acquisition module may be described in the related description of step S1 of the above method embodiment; the risk quantification module 2 is configured to obtain an operation risk value according to the QAR data and a pre-established risk quantification model, and for details, see the description related to step S2 of the above method embodiment.
Through the data acquisition module 1 and the risk quantification module 2, the running risk quantification device provided by the embodiment of the invention realizes quantification of the running risk, thereby laying a foundation for evaluation and analysis of the running risk and being beneficial to the overall grasp and accurate supervision of the running risk by a management department.
The embodiment of the invention also provides an operation risk evaluation device, as shown in fig. 6, which comprises: the risk value obtaining module 3 is configured to obtain an operation risk value in a history period of each object to be evaluated according to the operation risk quantification method provided by the embodiment of the present invention, and details can be seen from the related description of step S4 of the above method embodiment; the risk data determining module 4 is configured to determine, according to the running risk value in one history period of each object to be evaluated, a mean value, a variance and a probability density function of the running risk of each object to be evaluated, and details can be found in the related description of step S5 of the above method embodiment; the risk evaluation index determining module 5 is configured to determine a risk comprehensive evaluation index of each object to be evaluated according to the mean, variance and probability density function of the running risk of each object to be evaluated, and details can be found in the related description of step S6 of the above method embodiment.
The details and effects of the operation risk evaluation device may refer to the details of the operation risk evaluation method embodiment, and are not described herein.
The present invention also provides an electronic device, as shown in fig. 7, which may include a processor 71 and a memory 72, where the processor 71 and the memory 72 may be connected by a bus or other means, and in fig. 7, the connection is exemplified by a bus.
The processor 71 may be a central processing unit (Central Processing Unit, CPU). The processor 71 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above.
The memory 72 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as a running risk quantization method or a program instruction/module corresponding to a running risk evaluation method in an embodiment of the present invention (for example, the data acquisition module 1, the risk quantization module 2, or the risk value acquisition module 3, the risk data determination module 4, and the risk evaluation index determination module 5 shown in fig. 5 or 6). The processor 71 executes various functional applications of the processor and data processing by executing non-transitory software programs, instructions, and modules stored in the memory 72, that is, implements the running risk quantization method or the running risk evaluation method in the above-described method embodiment.
The one or more modules are stored in the memory 72, which when executed by the processor 71, perform the method of quantifying operational risk in the embodiment shown in fig. 1 or fig. 2, or perform the method of evaluating operational risk in the embodiment shown in fig. 3 or fig. 4.
The details of the above electronic device may be understood correspondingly with reference to the corresponding related descriptions and effects in the embodiments shown in fig. 1-4, which are not repeated here.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. Wherein the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations are within the scope of the invention as defined by the appended claims.
Claims (11)
1. A method for quantifying operational risk, comprising:
acquiring QAR data;
obtaining an operation risk value according to the QAR data and a pre-established risk quantification model;
the risk quantization model is as follows:
wherein R is Total t Represents the running risk value on day t, R Non-warning t A risk value representing a t-th day non-warning type monitoring item, m 1 Representing the risk average of non-warning type monitoring items in a history period, R Warning t Representing the risk value, m, of the warning class monitoring item on day t 2 Representing a risk average value, M, of warning type monitoring items in the one history period t The flight number on the t-th day is represented, q represents the number of non-warning monitoring items, and w j Weight of j-th non-warning class monitoring item for running risk, R ijt Representing the ratio of the offset of the j-th non-warning type monitoring item of the ith flight on the t-th day relative to a preset standard value to the maximum offset of the non-warning type monitoring item of the whole industry relative to the preset standard value in the history period, p represents the number of warning type monitoring items, and w l Representing the weight of the monitoring item of the first warning class for the running risk, y ilt Indicating the number of times the first warning class monitoring item for the ith flight occurs on day t.
2. The method for quantifying operational risk according to claim 1, wherein obtaining an operational risk value from the QAR data and a pre-established risk quantification model comprises:
determining an operation risk quantization index;
selecting second QAR data for quantifying the operation risk from the QAR data according to the operation risk quantification index;
and inputting the second QAR data into the risk quantification model to obtain the running risk value.
3. The running risk quantification method of claim 1, wherein the R is obtained by the following formula ijt ：
Wherein x is ijt Value of the j-th non-alert class monitoring item representing the ith flight on the t-th day, x j0 A preset standard value x representing the j-th non-warning monitoring item j Representing the maximum value of the j-th non-warning class monitoring item in the history period in the whole industry.
4. The method of claim 1, wherein after acquiring QAR data, before obtaining an operational risk value from the QAR data and a pre-established risk quantization model, further comprising: and cleaning the QAR data.
5. An operational risk assessment method, comprising:
obtaining an operation risk value in one history period of each object to be evaluated according to the operation risk quantification method of any one of claims 1 to 4;
determining the mean value, variance and probability density function of the running risk of each object to be evaluated according to the running risk value of each object to be evaluated in one history period;
and determining the risk comprehensive evaluation index of each object to be evaluated according to the mean value, variance and probability density function of the running risk of each object to be evaluated.
6. The running risk evaluation method according to claim 5, wherein the probability density function is determined by a kernel density estimation method based on running risk values in one history period of each of the objects to be evaluated.
7. The running risk evaluation method according to claim 5 or 6, wherein determining a risk integrated evaluation index of each of the objects to be evaluated based on a mean, variance, and probability density function of running risk of each of the objects to be evaluated, comprises: for each object to be evaluated, executing:
according to the mean value and the variance of the running risk of each object to be evaluated, n deviation lines are determined, and n is an integer greater than or equal to 1;
determining n deviation evaluation indexes according to the n deviation lines and the probability density function;
and determining the risk comprehensive evaluation index according to each deviation evaluation index.
8. An operation risk quantization apparatus, comprising:
the data acquisition module is used for acquiring QAR data;
the risk quantification module is used for obtaining an operation risk value according to the QAR data and a pre-established risk quantification model; the risk quantization model is as follows:
wherein R is Total t Represents the running risk value on day t, R Non-warning t A risk value representing a t-th day non-warning type monitoring item, m 1 Representing the risk average of non-warning type monitoring items in a history period, R Warning t Representing the risk value, m, of the warning class monitoring item on day t 2 Representing a risk average value, M, of warning type monitoring items in the one history period t The flight number on the t-th day is represented, q represents the number of non-warning monitoring items, and w j Weight of j-th non-warning class monitoring item for running risk, R ijt Representing the ratio of the offset of the j-th non-warning type monitoring item of the ith flight on the t-th day relative to a preset standard value to the maximum offset of the non-warning type monitoring item of the whole industry relative to the preset standard value in the history period, p represents the number of warning type monitoring items, and w l Representing the weight of the monitoring item of the first warning class for the running risk, y ilt Indicating the number of times the first warning class monitoring item for the ith flight occurs on day t.
9. An operation risk evaluation device, comprising:
a risk value obtaining module, configured to obtain an operational risk value in one history period of each object to be evaluated according to the operational risk quantification method according to any one of claims 1 to 4;
the risk data determining module is used for determining the mean value, the variance and the probability density function of the running risk of each object to be evaluated according to the running risk value in one history period of each object to be evaluated;
the risk evaluation index determining module is used for determining the risk comprehensive evaluation index of each object to be evaluated according to the mean value, the variance and the probability density function of the running risk of each object to be evaluated.
10. A computer-readable storage medium storing computer instructions for causing the computer to perform the running risk quantification method of any of claims 1-4 or the running risk assessment method of any of claims 5-7.
11. An electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of risk of operation quantification of any of claims 1-4 or the method of risk of operation assessment of any of claims 5-7.
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| CN106548294A (en) * | 2016-11-11 | 2017-03-29 | 中国民航大学 | A kind of landing maneuver Performance Evaluation Methods and device based on flying quality |
| CN106651088A (en) * | 2016-08-15 | 2017-05-10 | 中国民航科学技术研究院 | Flight quality monitoring method based on temporal GIS |
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| CN106651088A (en) * | 2016-08-15 | 2017-05-10 | 中国民航科学技术研究院 | Flight quality monitoring method based on temporal GIS |
| CN106548294A (en) * | 2016-11-11 | 2017-03-29 | 中国民航大学 | A kind of landing maneuver Performance Evaluation Methods and device based on flying quality |
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| 汪磊 ; 孙瑞山 ; 吴昌旭 ; 崔振新 ; 陆正.基于飞行QAR数据的重着陆风险定量评价模型.中国安全科学学报.2014,24(2),第88-92页. * |
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