CN113610376A - System, method and device for identifying dangerous source of test flight scene and electronic equipment - Google Patents
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Abstract
The invention provides a system, a method and a device for identifying a dangerous source of a test flight scene and electronic equipment. The method comprises the following steps: s101, classifying the test flight scene based on a shell model to obtain a plurality of factors; s102, analyzing the factors through a shell model, and determining the credible interval to which the factors belong; and S103, assigning each factor through a multivariate discrete model, and performing principal component regression analysis according to a credible interval to which the factor belongs to identify the hazard source. The identification method solves the problem that the danger source in the test flight scene cannot be reliably identified in the prior art.
Description
Technical Field
The invention relates to the technical field of civil transport aircrafts, in particular to a system, a method and a device for identifying a dangerous source of a test flight scene and electronic equipment.
Background
According to the ICAO standard definition, a hazard refers to a condition or object that may cause an aircraft accident or sign of an accident (Doc 9859). At present, risk management in the civil aviation transportation and operation industry mostly focuses on risk evaluation of risks, and two risk matrix models, namely an operation condition risk evaluation method (LEC) and a risk matrix method (LS), are mainly used. The identification of the hazard source mainly depends on subjective methods such as a brain storm method or an accident tree method, the application scenes and the application range of the methods are based on reverse backward-pushing of results, the evaluation method is an active evaluation process, most of evaluation elements are not quantitative, the intervention of subjective factors is too much, and the reliability of the evaluation result cannot be guaranteed.
For the test flight mission of the domestic civil aircraft, the method is of great importance for accurately and efficiently performing predictive risk management by effectively identifying the core risk source of the test flight scene of the domestic civil aircraft. The universal civil aviation transportation hazard source identification method is not strong in applicability in the field of national civil aircraft test flight, and can not truly, comprehensively and objectively reflect unsafe factors in the national civil aircraft test flight scene.
Disclosure of Invention
The invention aims to provide a system, a method and a device for identifying a dangerous source of a test flight scene and electronic equipment.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for identifying a dangerous source of a test flight scene specifically comprises the following steps:
s101, classifying the test flight scene based on a shell model to obtain a plurality of factors;
s102, analyzing the factors through a shell model, and determining the credible interval to which the factors belong;
and S103, assigning each factor through a multivariate discrete model, performing principal component regression analysis according to the credible interval to which the factor belongs, and identifying the hazard source according to the analysis result.
On the basis of the technical scheme, the invention can be further improved as follows:
further, the test flight scenario in S101 specifically includes at least one of the following: region, temporal, and state.
Further, the hazard source in S103 specifically includes at least one of: physical risk factors, chemical risk factors, biological risk factors, psychological risk factors, behavioral risk factors, other risk factors, unsafe behavior of people, unsafe state of objects, unsafe conditions of the environment, management defects, and external influences.
Further, the S103 specifically includes:
and S1031, identifying the danger source through a danger source evaluation degree formula, namely formula 1:
the risk source evaluation degree formula is as follows:
Z＝β0+Xi*βj*Dq+ ε formula 1;
wherein, beta0Is an independent variable to be evaluated, is a collection consisting of regions, tenses and states of a test flight scene, and can be expressed as beta0A is a region, T is a temporal state, S is a state; xiAn intermediate variable that is a coefficient composition corresponding to the influencing variable; beta is ajAn adjustment variable that is a coefficient composition corresponding to the influencing variable; dqIs the variable level corresponding to the independent variable, the intermediate variable and the regulated variable; ε is a random term, obeying a normal distribution.
An identification system for a dangerous source of a test flight scene comprises:
the shell model is used for classifying the test flight scenes to obtain a plurality of factors, analyzing the factors and determining a credible interval to which the factors belong;
and the multivariate discrete model is connected with the shell model and used for assigning values to each factor and carrying out principal component regression analysis according to the credible interval to which the factor belongs so as to identify the hazard source.
Further, the test flight scene comprises a region, a temporal state and a state.
Further, the hazard source specifically includes: physical risk factors, chemical risk factors, biological risk factors, psychological risk factors, behavioral risk factors, other risk factors, unsafe behavior of people, unsafe state of objects, unsafe conditions of the environment, management defects, and external influences.
Further, the multivariate discrete model identifies the hazard source through a hazard source evaluation degree formula:
the risk source evaluation degree formula is as follows:
Z＝β0+Xi*βj*Dq+ε
wherein, beta0Is an independent variable to be evaluated, is a collection consisting of regions, tenses and states of a test flight scene, and can be expressed as beta0A is a region, T is a temporal state, S is a state; xiAn intermediate variable that is a coefficient composition corresponding to the influencing variable; beta is ajAn adjustment variable that is a coefficient composition corresponding to the influencing variable; dqIs the variable level corresponding to the independent variable, the intermediate variable and the regulated variable; ε is a random term, obeying a normal distribution.
An identification device for a dangerous source of a test flight scene comprises: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program realizes the steps of the identification method of the dangerous source of the test flight scene when being executed by the processor.
An electronic device is provided, wherein an implementation program for information transfer is stored on the electronic device, and when the implementation program is executed by a processor, the steps of the identification method of the dangerous source of the test flight scene are implemented.
The invention has the following advantages:
the identification method of the dangerous source of the test flight scene comprises the steps of classifying the test flight scene based on a shell model to obtain a plurality of factors; analyzing the factors through a shell model, and determining a credible interval to which the factors belong; and assigning each factor through a multivariate discrete model, and identifying the hazard source by performing principal component regression analysis according to the credible interval to which the factor belongs. A national civil aircraft test flight scene and a hazard source are defined, a method for identifying the national civil aircraft test flight scene hazard source is established, and a tool used for identifying the national civil aircraft test flight scene hazard source is defined. The problem of can't reliably discern the danger source in the scene of trying to fly away among the prior art is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart illustrating a method for identifying a dangerous source in a test flight scenario according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for identifying a hazard source in a test flight scenario according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an identification apparatus for a risk source of a test flight scenario in an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a method for identifying a dangerous source of a test flight scenario specifically includes:
s101, classifying test flight scenes to obtain a plurality of factors;
in the step, classifying the test flight scene based on the shell model 10 to obtain a plurality of factors;
the test flight scene definition: the system is an organized and purposeful system in the test flight process of the domestic civil aircraft, and comprises elements and components which are mutually related and interdependent, and relevant institutional specifications, operation procedures and implementation methods which are established for executing specific activities or solving problems.
S102, determining a credible interval to which the factor belongs;
in the step, the factors are analyzed through the shell model 10, and the credible interval to which the factors belong is determined;
s103, identifying a danger source;
in this step, each factor is assigned through the multivariate discrete model 20, principal component regression analysis is performed according to the credible interval to which the factor belongs, and the hazard source is identified according to the analysis result.
And (3) defining a dangerous source of a test flight scene: the method refers to any existing or potential unsafe factors which can cause personnel injury, diseases or death in a test flight scene of a domestic civil aircraft, or damage or destroy equipment and property related to test flight of a test aircraft, an aircraft system or other test flight environments, or destroy the test flight environments.
The shell model is an existing management model, the logit model is a mathematical model, and the multivariate dispersion is a product formed by combining and iterating the shell model and the logit model.
On the basis of the technical scheme, the invention can be further improved as follows:
further, the test flight scenario in S101 specifically includes: region, temporal, and state.
Further, the hazard source in S103 specifically includes: physical risk factors, chemical risk factors, biological risk factors, psychological risk factors, behavioral risk factors, other risk factors, unsafe behavior of people, unsafe state of objects, unsafe conditions of the environment, management defects, and external influences.
Further, the S103 specifically includes:
and S1031, identifying the danger source according to a danger source evaluation degree formula, namely formula 1:
the risk source evaluation degree formula is as follows:
Z＝β0+Xi*βj*Dq+ ε is equation 1;
wherein, beta0Is an independent variable to be evaluated, is a collection consisting of regions, tenses and states of a test flight scene, and can be expressed as beta0A is a region, T is a temporal state, S is a state; xiAn intermediate variable that is a coefficient composition corresponding to the influencing variable; beta is ajAn adjustment variable that is a coefficient composition corresponding to the influencing variable; dqIs the variable level corresponding to the independent variable, the intermediate variable and the regulated variable; ε is a random term, obeying a normal distribution.
The derived multivariate discrete formula can be expressed as:
Score＝β0+Xi*βj*Dq+ε
the discrete matrix can be represented as a piecewise function:
wherein, C1, C2, C3 and C4 are interval values, and C1, C2, C3 and C4 are calculated according to different data input and sample amounts and are distinguished according to different service ranges or using units.
The derivation process is as follows:
Z＝β0+Xi*βj*Dq+ε
P(Score≤k)＝P(Z≤Ck)
＝P(β0+Xi*βj*Dq+≤Ck)
＝P{ε≤(Ck-β0)-Xi*βj*Dq}
＝Fε(αk-Xi*βj*Dq)
wherein k is 1,2,3,4, 5; 1,2,3,4,5,6, 7; j is 1,2,3,4, 5;
P(Score≤k)＝Fε(αk-Xi*βi*Di)
Logit{P(Score≤k)}＝αk-Xi*βi*Di
according to the derivation process, the degree of the hazard source based on the test flight scenario of the domestic civil aircraft can be expressed as follows:
the flight scene change and the task superposition of the domestic civil aircraft test bring great examination to the flight safety and quality. The invention adopts the multivariate discrete model 20 to carry out multivariate analysis, properly, scientifically and comprehensively identifies the hazard source under the influence of the combined factors of the national civil aircraft test flight scene, prevents and controls the risks in the future step by step to prevent the risks in advance, improves the active risk management capability to the predictive risk management capability, and effectively fills the blank in the identification aspect of the hazard source of the national civil aircraft test flight scene.
TABLE 1
As shown in table 1, (1) the test flight scenario is classified in three stages based on the shell model 10, and 5 primary qualitative factors, 30 secondary qualitative factors, and 150 tertiary qualitative factors, such as human factors, management factors, environmental factors, facility and external influences, are determined.
(2) For each minimum unit, the functional deviation which may occur from five aspects through the shell model 10 (human, machine, material, method, ring) and affects safety is analyzed by fully considering 3 tenses (past time, present time, future time), 3 states (normal state, abnormal state, emergency state), 7 aspects of factors (physical risk factor, chemical risk factor, biological risk factor, psychological risk factor, physiological risk factor, behavioral risk factor and other risk factor) and 4 aspects of defects (unsafe behavior of human, unsafe state of object, unsafe condition of environment, management defect). And determining 16 variables which are respectively 1 dependent variable, 3 independent variables, 7 intermediate variables and 5 regulating variables, and analyzing and describing the size and the dispersion degree of the data to a certain extent so as to determine the credible interval to which the factor belongs.
(3) And performing principal component regression analysis on the result, and performing numerical quantification on each individual factor for assignment.
(4) The multivariate discrete model 20 is used to make a degree determination of the source of danger.
(5) The discrete model is verified using historical data.
As shown in fig. 2, a system for identifying a dangerous source of a test flight scenario includes:
the shell model 10 is used for classifying the test flight scenes to obtain a plurality of factors, analyzing the factors and determining a credible interval to which the factors belong;
h, hardware, such as devices, facilities, tools, computers.
S, software, operation rules, hardware driving software, instructions, statutes, programs and files.
E, environment, operating environment, workplace, natural environment.
L, human, performance, ability, limitations of human.
The shell model 10 is a conceptual model describing human factors, proposed by edward in 1972, and modified by hokes in 1987.
The system comprises four elements of software (software), hardware (hard ware), environment (environment) and human (live ware). In this model, the person-to-person relationship has a special status compared to other relationships. The relation has strong uncertainty, short change period, more direct influence on aviation safety and easy improvement.
The relationship between people and equipment, although also an important factor affecting air traffic control, is generally longer in equipment update period. Therefore, the relationship between the person and the equipment is relatively fixed, and the relationship is basically fixed after the person fully understands and grasps the use of the equipment. The relationship between people and environment also has a certain durability. The method has direct relation with the management idea, the management method and the management level of a manager in a control unit. The increase of the manager needs a relatively long process, and the corresponding change of the environment needs a longer process. This means that the human-to-environment relationship is relatively stable over a period of time.
And the multivariate discrete model 20 is connected with the shell model 10 and used for assigning values to each factor and identifying the hazard source by performing principal component regression analysis according to the credible interval to which the factor belongs.
Further, the test flight scene comprises a region, a temporal state and a state.
Further, the hazard source specifically includes: physical risk factors, chemical risk factors, biological risk factors, psychological risk factors, behavioral risk factors, other risk factors, unsafe behavior of people, unsafe state of objects, unsafe conditions of the environment, management defects, and external influences.
Further, the multivariate discrete model 20 identifies the hazard source through a hazard source evaluation degree formula:
the risk source evaluation degree formula is as follows:
Z＝β0+Xi*βj*Dq+ε
wherein, beta0Is an independent variable to be evaluated, is a collection consisting of regions, tenses and states of a test flight scene, and can be expressed as beta0A is a region, T is a temporal state, S is a state; xiIs composed of coefficients corresponding to influencing variablesA medium variable quantity; beta is ajAn adjustment variable that is a coefficient composition corresponding to the influencing variable; dqIs the variable level corresponding to the independent variable, the intermediate variable and the regulated variable; ε is a random term, obeying a normal distribution.
Device embodiment II
An embodiment of the present invention provides an identification apparatus for a risk source of a test flight scenario, as shown in fig. 3, including: a memory 30, a processor 40 and a computer program stored on the memory 30 and executable on the processor 40, the computer program being executed by the processor 40 for the steps as described in the method embodiment shown in fig. 1 above.
Device embodiment III
An embodiment of the present invention provides a computer-readable storage medium, on which an implementation program for information transmission is stored, and the program, when executed by the processor 40, implements the steps described in the method embodiment shown in fig. 1.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.
Claims (10)
1. A method for identifying a dangerous source of a test flight scene is characterized by specifically comprising the following steps:
s101, classifying the test flight scene based on a shell model to obtain a plurality of factors;
s102, analyzing the factors through a shell model, and determining the credible interval to which the factors belong;
and S103, assigning each factor through a multivariate discrete model, performing principal component regression analysis according to the credible interval to which the factor belongs, and identifying the hazard source according to the analysis result.
2. The method for identifying a risk source of a test flight scenario according to claim 1, wherein the test flight scenario in S101 specifically includes at least one of: region, temporal, and state.
3. The method for identifying a dangerous source in a test flight scenario according to claim 2, wherein the dangerous source in S103 specifically comprises at least one of: physical risk factors, chemical risk factors, biological risk factors, psychological risk factors, behavioral risk factors, other risk factors, unsafe behavior of people, unsafe state of objects, unsafe conditions of the environment, management defects, and external influences.
4. The method for identifying a dangerous source of a test flight scenario according to claim 3, wherein the step S103 specifically comprises:
and S1031, identifying the danger source through a danger source evaluation degree formula, namely formula 1:
Z＝β0+Xi*βj*Dq+ ε formula 1;
wherein, beta0Is an independent variable to be evaluated, is a collection consisting of regions, tenses and states of a test flight scene, and can be expressed as beta0A is a region, T is a temporal state, S is a state; xiAn intermediate variable that is a coefficient composition corresponding to the influencing variable; beta is ajAn adjustment variable that is a coefficient composition corresponding to the influencing variable; dqIs the variable level corresponding to the independent variable, the intermediate variable and the regulated variable; ε is a random term, obeying a normal distribution.
5. A system for identifying a dangerous source of a test flight scene comprises:
the shell model is used for classifying the test flight scenes to obtain a plurality of factors, analyzing the factors and determining a credible interval to which the factors belong;
and the multivariate discrete model is connected with the shell model and used for assigning values to each factor and carrying out principal component regression analysis according to the credible interval to which the factor belongs so as to identify the hazard source.
6. The system for identifying a threat source in a test flight scenario of claim 5, wherein the test flight scenario includes a region, a tense, and a state.
7. The system for identifying a risk source of a test flight scenario as claimed in claim 6, wherein the risk source specifically comprises: physical risk factors, chemical risk factors, biological risk factors, psychological risk factors, behavioral risk factors, other risk factors, unsafe behavior of people, unsafe state of objects, unsafe conditions of the environment, management defects, and external influences.
8. The system for identifying a dangerous source in a test flight scenario according to claim 7, wherein the multivariate discrete model identifies the dangerous source through a dangerous source evaluation degree formula:
the risk source evaluation degree formula is as follows:
Z＝β0+Xi*βj*Dq+ε
wherein, beta0Is an independent variable to be evaluated, is a collection consisting of regions, tenses and states of a test flight scene, and can be expressed as beta0A is a region, T is a temporal state, S is a state; xiAn intermediate variable that is a coefficient composition corresponding to the influencing variable; beta is ajAn adjustment variable that is a coefficient composition corresponding to the influencing variable; dqIs the variable level corresponding to the independent variable, the intermediate variable and the regulated variable; ε is a random term, obeying a normal distribution.
9. The utility model provides an identification device of scene danger source flies in examination which characterized in that includes: memory, processor and computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method for identifying a source of risk for a test flight scenario as claimed in any one of claims 1 to 4.
10. An electronic device, characterized in that an information transfer implementation program is stored on the electronic device, and when executed by a processor, the program implements the steps of the method for identifying a risk source of a test flight scenario according to any one of claims to 4.
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